The synaptic hypothesis of schizophrenia version III: a master … – Nature.com

Saha S, Chant D, Welham J, McGrath J. A systematic review of the prevalence of schizophrenia. PLoS Med. 2005;2:e141.

Article PubMed PubMed Central Google Scholar

McCutcheon RA, Reis Marques T, Howes OD. Schizophrenia an overview. JAMA Psychiatry. 2020;77:20110.

Article PubMed Google Scholar

Howes OD, Murray RM. Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet. 2014;383:167787.

Article PubMed Google Scholar

Cannon TD, Cadenhead K, Cornblatt B, Woods SW, Addington J, Walker E, et al. Prediction of psychosis in youth at high clinical risk: a multisite longitudinal study in North America. Arch Gen Psychiatry. 2008;65:2837.

Article PubMed PubMed Central Google Scholar

Yung AR, McGorry PD. The prodromal phase of first-episode psychosis: past and current conceptualizations. Schizophr Bull. 1996;22:35370.

Article CAS PubMed Google Scholar

Siskind D, Siskind V, Kisely S. Clozapine response rates among people with treatment-resistant schizophrenia: data from a systematic review and meta-analysis. Can J Psychiatry. 2017;62:7727.

Article PubMed PubMed Central Google Scholar

Kaar SJ, Natesan S, McCutcheon R, Howes OD. Antipsychotics: mechanisms underlying clinical response and side-effects and novel treatment approaches based on pathophysiology. Neuropharmacology. 2020;172:107704.

Article CAS PubMed Google Scholar

Feinberg I. Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J Psychiatr Res. 1982;17:31934.

Article PubMed Google Scholar

Keshavan MS, Anderson S, Pettergrew JW. Is schizophrenia due to excessive synaptic pruning in the prefrontal cortex? The Feinberg hypothesis revisited. J Psychiatr Res. 1994;28:23965.

Article CAS PubMed Google Scholar

Feinberg I. Efference copy and corollary discharge: implications for thinking and its disorders. Schizophr Bull. 1978;4:63640.

Article CAS PubMed Google Scholar

Kety SS. Human cerebral blood flow and oxygen consumption as related to aging. J Chronic Dis. 1956;3:47886.

Article CAS PubMed Google Scholar

Huttenlocher PR. Synaptic density in human frontal cortex developmental changes and effects of aging. Brain Res. 1979;163:195205.

Article CAS PubMed Google Scholar

Yu Y, Herman P, Rothman DL, Agarwal D, Hyder F. Evaluating the gray and white matter energy budgets of human brain function. J Cereb Blood Flow Metab. 2018;38:133953.

Article PubMed Google Scholar

Rakic P, Bourgeois JP, Eckenhoff MF, Zecevic N, Goldman-Rakic PS. Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex. Science. 1986;232:2325.

Article CAS PubMed Google Scholar

Bourgeois JP, Rakic P. Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage. J Neurosci. 1993;13:280120.

Article CAS PubMed PubMed Central Google Scholar

Zecevic N, Bourgeois J-P, Rakic P. Changes in synaptic density in motor cortex of rhesus monkey during fetal and postnatal life. Brain Res Dev Brain Res. 1989;50:1132.

Article CAS PubMed Google Scholar

Huttenlocher PR, de Courten C. The development of synapses in striate cortex of man. Hum Neurobiol. 1987;6:19.

CAS PubMed Google Scholar

Brown R, Colter N, Corsellis JA, Crow TJ, Frith CD, Jagoe R, et al. Postmortem evidence of structural brain changes in schizophrenia. Differences in brain weight, temporal horn area, and parahippocampal gyrus compared with affective disorder. Arch Gen Psychiatry. 1986;43:3642.

Article CAS PubMed Google Scholar

Pakkenberg B. Post-mortem study of chronic schizophrenic brains. Br J Psychiatry. 1987;151:74452.

Article CAS PubMed Google Scholar

Andreasen N, Nasrallah HA, Dunn V, Olson SC, Grove WM, Ehrhardt JC, et al. Structural abnormalities in the frontal system in schizophrenia. A magnetic resonance imaging study. Arch Gen Psychiatry. 1986;43:13644.

Article CAS PubMed Google Scholar

DeMyer MK, Gilmor RL, Hendrie HC, DeMyer WE, Augustyn GT, Jackson RK. Magnetic resonance brain images in schizophrenic and normal subjects: influence of diagnosis and education. Schizophr Bull. 1988;14:2137.

Article CAS PubMed Google Scholar

Rubin P, Karle A, Moller-Madsen S, Hertel C, Povlsen UJ, Noring U, et al. Computerised tomography in newly diagnosed schizophrenia and schizophreniform disorder. A controlled blind study. Br J Psychiatry. 1993;163:60412.

Article CAS PubMed Google Scholar

Zipursky RB, Lim KO, Sullivan EV, Brown BW, Pfefferbaum A. Widespread cerebral gray matter volume deficits in schizophrenia. Arch Gen Psychiatry. 1992;49:195205.

Article CAS PubMed Google Scholar

Harvey I, Ron MA, Du Boulay G, Wicks D, Lewis SW, Murray RM. Reduction of cortical volume in schizophrenia on magnetic resonance imaging. Psychol Med. 1993;23:591604.

Article CAS PubMed Google Scholar

Andreasen NC, Ehrhardt JC, Swayze VW II, Alliger RJ, Yuh WT, Cohen G, et al. Magnetic resonance imaging of the brain in schizophrenia. The pathophysiologic significance of structural abnormalities. Arch Gen Psychiatry. 1990;47:3544.

Article CAS PubMed Google Scholar

Buchsbaum MS, Haier RJ. Functional and anatomical brain imaging: impact on schizophrenia research. Schizophr Bull. 1987;13:11532.

Article CAS PubMed Google Scholar

Buchsbaum MS. The frontal lobes, basal ganglia, and temporal lobes as sites for schizophrenia. Schizophr Bull. 1990;16:37989.

Article CAS PubMed Google Scholar

Buchsbaum MS, Haier RJ, Potkin SG, Nuechterlein K, Bracha HS, Katz M, et al. Frontostriatal disorder of cerebral metabolism in never-medicated schizophrenics. Arch Gen Psychiatry. 1992;49:93542.

Article CAS PubMed Google Scholar

Cleghorn JM, Garnett ES, Nahmias C, Firnau G, Brown GM, Kaplan R, et al. Increased frontal and reduced parietal glucose metabolism in acute untreated schizophrenia. Psychiatry Res. 1989;28:11933.

Article CAS PubMed Google Scholar

Jernigan TL, Zisook S, Heaton RK, Moranville JT, Hesselink JR, Braff DL. Magnetic resonance imaging abnormalities in lenticular nuclei and cerebral cortex in schizophrenia. Arch Gen Psychiatry. 1991;48:88190.

Article CAS PubMed Google Scholar

Breier A, Buchanan RW, Elkashef A, Munson RC, Kirkpatrick B, Gellad F. Brain morphology and schizophrenia. A magnetic resonance imaging study of limbic, prefrontal cortex, and caudate structures. Arch Gen Psychiatry. 1992;49:9216.

Article CAS PubMed Google Scholar

Brugger SP, Howes OD. Heterogeneity and homogeneity of regional brain structure in schizophrenia: a meta-analysis. JAMA Psychiatry. 2017;74:110411.

Article PubMed PubMed Central Google Scholar

Anderson SA, Classey JD, Conde F, Lund JS, Lewis DA. Synchronous development of pyramidal neuron dendritic spines and parvalbumin-immunoreactive chandelier neuron axon terminals in layer III of monkey prefrontal cortex. Neuroscience. 1995;67:722.

Article CAS PubMed Google Scholar

Petanjek Z, Judas M, Simic G, Rasin MR, Uylings HB, Rakic P, et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc Natl Acad Sci USA. 2011;108:132816.

Article CAS PubMed PubMed Central Google Scholar

Lyall AE, Shi F, Geng X, Woolson S, Li G, Wang L, et al. Dynamic development of regional cortical thickness and surface area in early childhood. Cereb Cortex. 2015;25:220412.

Article PubMed Google Scholar

Tamnes CK, Herting MM, Goddings AL, Meuwese R, Blakemore SJ, Dahl RE, et al. Development of the cerebral cortex across adolescence: a multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J Neurosci. 2017;37:340212.

Article CAS PubMed PubMed Central Google Scholar

Mills KL, Goddings AL, Herting MM, Meuwese R, Blakemore SJ, Crone EA, et al. Structural brain development between childhood and adulthood: convergence across four longitudinal samples. Neuroimage. 2016;141:27381.

Article PubMed Google Scholar

Norbom LB, Ferschmann L, Parker N, Agartz I, Andreassen OA, Paus T, et al. New insights into the dynamic development of the cerebral cortex in childhood and adolescence: integrating macro- and microstructural MRI findings. Prog Neurobiol. 2021;204:102109.

Article PubMed Google Scholar

Bennett MR. Schizophrenia: susceptibility genes, dendritic-spine pathology and gray matter loss. Prog Neurobiol. 2011;95:275300.

Article CAS PubMed Google Scholar

Paolicelli RC, Bolasco G, Pagani F, Maggi L, Scianni M, Panzanelli P, et al. Synaptic pruning by microglia is necessary for normal brain development. Science. 2011;333:14568.

Article CAS PubMed Google Scholar

Schafer DP, Lehrman EK, Kautzman AG, Koyama R, Mardinly AR, Yamasaki R, et al. Microglia sculpt postnatal neural circuits in an activity and complement-dependent manner. Neuron. 2012;74:691705.

Article CAS PubMed PubMed Central Google Scholar

Stevens B, Allen NJ, Vazquez LE, Howell GR, Christopherson KS, Nouri N, et al. The classical complement cascade mediates CNS synapse elimination. Cell. 2007;131:116478.

Article CAS PubMed Google Scholar

Yilmaz M, Yalcin E, Presumey J, Aw E, Ma M, Whelan CW, et al. Overexpression of schizophrenia susceptibility factor human complement C4A promotes excessive synaptic loss and behavioral changes in mice. Nat Neurosci. 2021;24:21424.

Article CAS PubMed Google Scholar

Druart M, Nosten-Bertrand M, Poll S, Crux S, Nebeling F, Delhaye C, et al. Elevated expression of complement C4 in the mouse prefrontal cortex causes schizophrenia-associated phenotypes. Mol Psychiatry. 2021;26:3489501.

Article CAS PubMed Google Scholar

Chung WS, Allen NJ, Eroglu C. Astrocytes control synapse formation, function, and elimination. Cold Spring Harb Perspect Biol. 2015;7:a020370.

Article PubMed PubMed Central Google Scholar

Chung WS, Clarke LE, Wang GX, Stafford BK, Sher A, Chakraborty C, et al. Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways. Nature. 2013;504:394400.

Article CAS PubMed PubMed Central Google Scholar

Caroni P, Chowdhury A, Lahr M. Synapse rearrangements upon learning: from divergent-sparse connectivity to dedicated sub-circuits. Trends Neurosci. 2014;37:60414.

Article CAS PubMed Google Scholar

Stein IS, Zito K. Dendritic spine elimination: molecular mechanisms and implications. Neuroscientist. 2019;25:2747.

Article CAS PubMed Google Scholar

Uesaka N, Kano M. Presynaptic mechanisms mediating retrograde semaphorin signals for climbing fiber synapse elimination during postnatal cerebellar development. Cerebellum. 2018;17:1722.

Article CAS PubMed Google Scholar

Trubetskoy V, Pardinas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:5028.

Article CAS PubMed PubMed Central Google Scholar

Read the original:
The synaptic hypothesis of schizophrenia version III: a master ... - Nature.com

Optimization of Cas9 activity through the addition of cytosine … – Nature.com

Cell culture

We cultured mESCs in t2iL medium containing Dulbeccos modified eagle medium (DMEM, Nacalai Tesque), 2mM Glutamax (Nacalai Tesque), 1 non-essential amino acids (Nacalai Tesque), 1mM sodium pyruvate (Nacalai Tesque), 100Uml1 penicillin, 100gml1 streptomycin (P/S) (Nacalai Tesque), 0.1mM 2-mercaptoethanol (Sigma) and 15% fetal bovine serum (FBS) (Gibco), supplemented with 0.2M PD0325901 (Sigma), 3M CHIR99021 (Cayman) and 1,000Uml1 recombinant mouse leukaemia inhibitory factor (Millipore)54. A higher PD0325901 concentration of 1M was used for the 2iL medium. mESC colonies were dissociated with trypsin (Nacalai Tesque) and plated on gelatin-coated dishes. Y-27632 (10M, Sigma) was added when cells were passaged. hiPSCs were cultured in mTeSR Plus medium (Veritas). hiPSC colonies were dissociated with Accutase (Nacalai Tesque) and plated on Matrigel-coated dishes (Corning, 3/250 dilution with DMEM). Y-27632 and 1% FBS were added when cells were passaged. WT hiPSCs (409B2, HPS0076) were provided by the RIKEN BioResource Research Centre (BRC)55. FOP hiPSCs (HPS0376) were provided by RIKEN BRC through the National BioResource Project of the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the Agency for Medical Research and Development (AMED)43. Experiments using hiPSCs were approved by the Kyushu University Institutional Review Board for Human Genome/Gene Research. HEK293T cells and mouse embryonic fibroblasts were cultured in 10% FBS medium containing DMEM, 2mM l-glutamine (Nacalai Tesque), 100Uml1 penicillin, 100gml1 streptomycin (P/S) (Nacalai Tesque) and 10% FBS. hADSCs (Thermo Fisher) were cultured in MesenPRO RS medium (Thermo Fisher). Culture conditions of a HB-AIMS cell line are described in the Generation of AIMS cell lines and mice and AIMS analysis section. Cells were maintained at 37C and 5% CO2.

In this study, we used C57BL/6 mice (Clea Japan), ICR mice (Clea Japan) and R26RYFP/YFP mice (a gift from Frank Costantini at Columbia University, NY, USA)56. The experiments were approved by the Kyushu University Animal Experiment Committee, and the care and use of the animals were in accordance with institutional guidelines.

All primers, spacer linkers and ssODNs used in the present study are listed in Supplementary Table 3.

Mouse ES B6-5-2 and B6-D2-4 cell lines were established from E3.5 blastocysts of the C57BL/6 strain using 2iL and t2iL medium, respectively; an R26RYFP/+ mESC line was established using t2iL medium. Blastocysts were placed on feeders (mitomycin C-treated mouse embryonic fibroblasts) after removal of the zona pellucida. Inner cell mass outgrowths (passage number 0, p0) were dissociated with trypsin and plated on gelatin-coated plates (p1). After domed colonies formed, they were dissociated and passaged (p2). mESC lines were generated by repeating this procedure.

Knock-in (KI) template plasmids for Cdh1-AIMS were generated by attaching the 5 and 3 arms to plasmids containing P2A1:Venus or P2A1:tdTomato cassettes. P2A1 is identical to a widely used P2A sequence26. The 5 arm was designed such that the coding end was fused in-frame to the P2A sequence to allow independent production of both E-cadherin (CDH1) and fluorescence protein. KI plasmids for Tbx3-AIMS were constructed using the same strategy. The alternative P2A sequence P2A2 was constructed by introducing silent mutations to each codon of the original P2A sequence. The conventional CRISPR-Cas9 system was used to efficiently knock-in the dual-colour plasmids in a pair of alleles. A spacer linker was designed to induce a DSB downstream of the stop codon, then inserted into the BpiI sites of a pSpCas9(BB)-2A-Puro (PX459) V2.0 plasmid (Addgene, 62988; see the Plasmid construction section)57. All sgRNAs used in this study were designed using the CRISPR DESIGN (http://crispr.mit.edu/) or CRISPOR tool (http://crispor.tefor.net).

The constructed all-in-one CRISPR plasmids and dual-coloured KI plasmids were co-transfected into mESCs using Lipofectamine 3000 (Thermo Fisher). Dissociated mESCs were plated on gelatin-coated 24-well plates with 500l of (t)2iL+Y-27632 medium ((t)2iL+Y). Nucleic acidLipofectamine 3000 complexes were prepared in accordance with the standard Lipofectamine 3000 protocol. We added 1l of Lipofectamine 3000 reagent to 25l Opti-MEM medium; simultaneously, 250ng of each plasmid (all-in-one, Cdh1-P2A-tdTomato and Cdh1-P2A-Venus plasmid) plus 1l of P3000 reagent were mixed with 25l of Opti-MEM medium in a different tube. These mixtures were combined and incubated for 5min at room temperature, then added to the 24-well plate immediately after cells were seeded. At 24h after transfection, puromycin (1.5 or 2gml1) was added for 2d and then washed out. The transiently treated puromycin-resistant cells were cultured for several days; dual-colour-positive colonies were picked and passaged. Genotypes for the candidate dual KI clones were confirmed by PCR. In this study, transfection experiments for mouse and human cells were performed using this procedure, with passage steps added for an AIMS assay to avoid mosaicism (Fig. 1d). Fluorescence microscopes (BZ-X800 (Keyence) and IX73 (Olympus)) were used to analyse the AIMS data. To extract genomic DNA for clonal sequence analysis, single mESC and hiPSC colonies were suspended in 510l 50mM NaOH (Nacalai Tesque) and incubated at 99C for 10min. PCR was performed using the template genomic DNA, and the amplicons were sequenced by Sanger sequencing.

For generation of AIMS mice, the established dual KI mESC clone (Cdh1-P2A1-tdTomato/Venus AIMS) was dissociated with trypsin and 58 cells were injected into 8-cell embryos (E2.5) collected from pregnant ICR mice. Injected blastocysts were transferred into the uteri of pseudo-pregnant ICR mice and chimaeras were generated. Male chimaeras were mated with C57BL/6 females, and Cdh1-P2A1-tdTomato and Cdh1-P2A1-Venus KI mouse lines were obtained through germline transmission. After the two genotype mice were mated, homozygous AIMS mice were generated.

HB-AIMS cells were established from the E12.5 dual KI embryos according to the protocol of a previous work58 with some modifications. Briefly, the whole liver was mechanically dissociated and filtrated, and the dissociated cells were seeded onto a type I collagen-coated plate (Iwaki) with the HB medium. The HB medium is composed of a 1:1 mixture of DMEM and F-12 (Nacalai Tesque), supplemented with 10% FBS (Gibco), 1gml1 insulin (Wako), 0.1M dexamethasone (Sigma-Aldrich), 10mM nicotinamide (Sigma-Aldrich), 2mM l-glutamine (Nacalai Tesque), 50M -mercaptoethanol (Nacalai Tesque), 20ngml1 recombinant human hepatocyte growth factor (rhHGF) (PeproTech), 50ngml1 recombinant human epidermal growth factor (rhHGF) (Sigma), penicillin/streptomycin (Nacalai Tesque), and small molecules of 10M Y-27632 (Wako), 0.5M A8301 (Tocris) and 3M CHIR99021 (Tocris). After expansion of HBs, a single-cell-derived HB colony with homogeneous expression of tdTomato and Venus was picked and established as an HB-AIMS cell line.

To generate all-in-one CRISPR plasmids for [5C](3A), [10C](8A), [15C](13A), [20C](18C), [25C](23A) and [30C](28A)sgRNA expression, spacer linkers were inserted into the BpiI sites of a PX459 plasmid (Extended Data Fig. 2b). In the plasmids, the 3rd, 8th, 13th, 18th, 23rd or 28th cytosine was replaced with adenine because the overhang sequence of CACC is required for linker ligation. The standard spacer linkers (20nt) or longer spacer linkers (30nt or 40nt) were inserted into the BpiI sites of the [0C], [5C](3A), [10C](8A), [15C](13A), [20C](18A), [25C](23A) or [30C](28A) PX459 plasmid, leading to generation of [5C][30C]sgRNA-expressing all-in-one Cas9 plasmids applicable for puromycin selection. The same [C] linkers were also inserted into the BpiI sites of a PX458 plasmid (Addgene, 62988)57 for selection of GFP-positive transfected cells.

For the plasmid dilution assay, sgRNA-expressing plasmid was constructed by removing a Cas9-T2A-Puro cassette from a PX459 plasmid using the KpnI and NotI sites. Different amounts of sgRNA-expressing plasmid (0250ng) were co-transfected with an unmodified PX459 plasmid (250ng). In addition, [5C][30C] linkers including BpiI sites were inserted into this sgRNA-expressing plasmid to construct [5C][30C]sgRNA-expressing plasmids, which were used for the experiments of CRISPRa (Extended Data Fig. 4e) described below.

For the CRISPR inhibition experiments, the pCMVAcrIIA4 plasmid was generated from the anti-Cas9 AcrIIA4-expressing pCMV+AcrIIA4 plasmid, pCMV-T7-AcrIIA4-NLS(SV40) (KAC200) (Addgene, plasmid 133801)59, by truncating the AcrIIA4 cassette using the NotI and AgeI sites.

For the CRISPRi experiments, the [5C][30C] linkers including BsmBI sites were inserted into the BsmBI sites of an LV hU6-sgRNA hUbC-dCas9-KRAB-T2a-Puro (sgRNA-KRAB-Puro) plasmid (Addgene, 71236)60 to construct [C]sgRNA-expressing all-in-one CRISPRi plasmids. The sgRNA spacers targeting BRCA1 and CXCR4 used in previous studies61 were inserted into the BsmBI sites of the all-in-one plasmids. A puromycin-selectable all-in-one plasmid for CRISPRa was constructed by replacing a GFP cassette of a pLV hU6-gRNA(anti-sense) hUbC-VP64-dCas9-VP64-T2A-GFP (sgRNA-VP64-GFP) plasmid (Addgene, 66707) with a puromycin N-acetyl transferase (PuroR) cassette. A synthetic gene encoding VP64-T2A-PuroR (AZENTA) (Supplementary Table 3) was inserted into the sgRNA-KRAB-GFP plasmid using NheI and AgeI sites, resulting in an sgRNA-VP64-Puro plasmid. In Fig. 4e, the [1C][10C] spacer linkers for targeting ASCL162 were inserted into the sgRNA-VP64-Puro plasmid. In Extended Data Fig. 4e, spacer linkers for targeting ASCL1 and TTN62 were inserted into the BpiI sites of the [0C][30]sgRNA-expressing plasmids, and then they were co-transfected with the spacerless all-in-one CRISPRa plasmid.

To construct all-in-one AsCpf1 plasmids enabling puromycin selection, a synthetic DNA fragment encoding U6 promoter and two BpiI sites (AZENTA) (Supplementary Table 3) was inserted into a PX459 plasmid while removing a U6-gRNA cassette using PciI and XbaI sites. Next, a CBh-Cas9 region of the crRNA-Cas9-puro plasmid was replaced with a CBh-AsCpf1 fragment digested from a pY036_ATP1A1_G3_Array plasmid (Addgene, 86619)63 using KpnI and FseI, resulting in the construction of an all-in-one crRNA-AsCpf1-puro plasmid (PX459 plasmid backbone). The crRNA linkers (Supplementary Table 3) targeting P2A2 sites of AIMS are composed of 5 hairpin, 20nt-spacer and U4AU4 3-overhang, which is known to increase editing efficiency of AsCpf1 (ref. 64), and they were inserted into the BpiI sites of the crRNA-AsCpf1-puro plasmid.

pSpCas9(BB)-2A-Puro (PX459) V2.0 (Addgene, plasmid 62988; http://n2t.net/addgene:62988; RRID: Addgene_62988) and pSpCas9(BB)-2A-GFP (PX458) (Addgene, plasmid 48138; http://n2t.net/addgene:48138; RRID: Addgene_48138) were gifts from Feng Zhang. The pY036_ATP1A1_G3_Array was a gift from Yannick Doyon (Addgene, plasmid 86619; http://n2t.net/addgene:86619; RRID: Addgene_86619). pLV hU6-sgRNA hUbC-dCas9-KRAB-T2a-Puro was a gift from Charles Gersbach (Addgene, plasmid 71236; http://n2t.net/addgene:71236; RRID: Addgene_71236). pLV hU6-gRNA(anti-sense) hUbC-VP64-dCas9-VP64-T2A-GFP was a gift from Charles Gersbach (Addgene, plasmid 66707; http://n2t.net/addgene:66707; RRID: Addgene_66707). pCMV-T7-AcrIIA4-NLS(SV40) (KAC200) was gifted by Joseph Bondy-Denomy and Benjamin Kleinstiver (Addgene, plasmid 133801; http://n2t.net/addgene:133801; RRID: Addgene_133801)59.

To detect sgRNAs complexed with Cas9, 1l of Cas9 (1M) (Alt-R S.p. Cas9 Nuclease V3, IDT) and 1l of synthetic sgRNAs (3M, 1M or 0.3M; IDT) were mixed with 8l of distilled water (total reaction volume of 10l) and reacted on ice for 30min. Samples were loaded onto Bullet PAGE One Precast gels (6%) (Nacalai Tesque) in Tris-borate-ethylenediaminetetraacetic acid (Tris-Borate-EDTA) buffer. RNA was transferred to a Hybond N+ membrane (GE Healthcare) and cross-linked using CX-2000 (Analytik Jena). An sgRNA tracer probe was labelled with an alkali-labile digoxigenin (DIG)-11-deoxyuridine triphosphate (dUTP) using a PCR DIG Probe Synthesis kit (Roche); DNA fragments were amplified using PCR and primers (Supplementary Table 3). After hybridization, specific bands were visualized with the CDP-Star reagent (Roche) using a luminescent image analyser (LAS-3000, FUJIFILM).

To detect DNA fragments complexed with sgRNA-dCas9, we mixed 1l of dCas9 (1M) (Alt-R S.p. dCas9 Nuclease V3, IDT) and 1l of synthetic sgRNAs (1M; IDT) with distilled water for a final reaction volume of 10l, then reacted the mixture at room temperature for 10min. After the reaction, the RNP complex was mixed with 100ng of DNA fragment and 1l of 10 Cas9 reaction buffer (1M HEPES, 3M NaCl, 1M MgCl2 and 250mM EDTA (pH 6.5)), then reacted at room temperature for 10min. The resulting 10l samples were loaded onto 2% agarose gels in Tris-acetate-EDTA buffer; DNA bands were detected by staining with ethidium bromide. The target DNA fragment (647bp) was prepared by PCR amplification from a Tbx3-P2A1-Venus KI plasmid using primers (Supplementary Table 3).

The sgRNA-Cas9-DNA complex was formed using most of the gel shift assay procedure, although its formation also included Cas9 and 3M of synthetic sgRNA. The samples were reacted at 37C for 90min, denatured at 70C for 10min and loaded onto Bullet PAGE One Precast gels (6%) (Nacalai Tesque).

A 20l sgRNA-Cas9-DNA complex was prepared via the procedure used in the gel shift assay. A cleavage reaction was performed at 37C for 30min; a 10l volume was kept on ice while the other 10l volume was denatured at 70C for 10min. The products were loaded onto 2% agarose gels.

Total RNAs were extracted from mESCs at 68h after transfection with P2A1-[C]sgRNA1-PX459 plasmids. Transfected cells were selected by 2d of treatment with puromycin (1.5gml1), then resuspended with ISOGEN II (NIPPON GENE). The samples were incubated for 10min at room temperature, then heated at 55C for 10min. Total RNA was isolated following the manufacturers protocol. After reaction at 70C for 10min, 30g RNAs were loaded onto Extra PAGE One Precast gels (520%) (Nacalai Tesque) in Tris-borate-EDTA buffer. RNA transfer, DIG-probe hybridization and signal detection were performed following the procedure used in the gel shift assay. The DIG probe was labelled by PCR amplification of the DNA fragment (primers shown in Supplementary Table 3). The mU6 DIG-probe was prepared by amplifying the DNA fragment from mESC complementary DNA using specific primers (Supplementary Table 3). cDNA was synthesized using a specific primer that targeted U6 small nuclear RNA65.

Template DNA fragments required for IVT were amplified from a P2A1-gRNA1-PX459 plasmid by PCR (primers shown in Supplementary Table 3). The T7 promoter sequence and cytosine tails were added to the 5-end of the forward primer. We synthesized [0C], [10C] and [25C]sgRNAs using the T7 RiboMAX Express large-scale RNA production system (Promega) following the manufacturers protocol.

FIJI software was used to quantify band signals for the gel shift, DNA cleavage and northern blot assays.

The PX458-based all-in-one plasmids (250ng) for targeting VEGFA1 gene were transfected into hADSCs using Lipofectamine 3000 upon 80% confluency. Immediately after adding the plasmid:Lipofectamine mixture into the cells, the plates were centrifuged at 700g at 35C for 10min to increase transfection efficiency. The cells were cultured for 7d without passaging to allow continuous expression of the plasmid, and then GFP-positive single cells were picked using a hand-made capillary and transferred to PCR tubes (1 cell per tube). To enable sequence analysis for a pair of alleles from a single cell, whole genomic DNA were amplified using PicoPLEX (TAKARA) according to the manufacturers instructions. The genomic locus targeted by Cas9 was amplified by PCR using primers (Supplementary Table 3) and the PCR amplicons were sequenced.

At 24h after transfection with the all-in-one Cas9 plasmid, mESCs were treated with the Cas9 inhibitor BRD0539 (TOCRIS) during puromycin selection and subsequent culture until analysis.

pCMV+AcrIIA4 plasmid was co-transfected with 250ng of the all-in-one Cas9 plasmid in different amounts (2.52,500ng for 24-well plates). For the BRD0539 and AcrIIA4 experiments, puromycin selection and indel analysis were performed using the same procedure as described above (Generation of AIMS cell lines and mice and AIMS analysis section) and in Fig. 1d.

A day before transfection, 3104 HEK293T cells were seeded onto a 96-well plate. The all-in-one CRISPRa/i plasmids (50ng, 1/5 scale of the 24-well plate version) were transfected and cultured for 24h. Then, puromycin (5.0gml1) was treated for 2d to exclude untransfected cells. After removal of puromycin, the transfected cells were cultured for 1d and 2d for CRISPRa and CRISPRi, respectively, and total RNAs were extracted using ISOGEN II as described above (Northern blotting section).

The cDNAs were synthesized from total RNAs using SuperScript III Reverse Transcriptase (Thermo Fisher) according to the manufacturers instructions. RTqPCR was conducted using a THUNDERBIRD SYBR qPCR Mix (Toyobo) and CFX Connect real-time PCR detection system (BIO RAD) according to the manufacturers instructions. Primers for ASCL1, TTN, BRCA1 and CXCR4 used in previous studies61,62, and for GAPDH are listed in Supplementary Table 3. The values for GAPDH were used as normalization controls.

A Tbx3-P2A1-tdTomato KI plasmid was co-transfected with Tbx3-sgRNA1-expressing PX459 to the mESCs. After transient puromycin selection, colonies were dissociated and passaged; the resulting colonies were analysed. Colonies with mosaic tdTomato expression were excluded from data analysis. After the colonies had been counted, positive tdTomato colonies were selected and genomic DNA was extracted for sequencing.

The neomycin (Neo) KI plasmid was constructed by replacing the tdTomato cassette of the Tbx3-P2A1-tdTomato KI plasmid with a P2A1-Neo cassette. The KI plasmid was co-transfected with P2A1 sgRNA1-expressing PX459 to a Tbx3-P2A1-AIMS clone. When puromycin was removed, geneticin (400gml1, Gibco) was added to select KI clones. All eight clones were confirmed to possess KI genotypes; geneticin-resistant colonies were identified as KI.

PCR reactions to amplify specific on-target or off-target sites were performed using KOD-Plus-ver.2 DNA polymerase (Toyobo) in accordance with the manufacturers protocol. The resulting PCR amplicons were denatured and re-annealed in 1 NEB buffer 2 (NEB) in a total volume of 9l under the following conditions: 95C for 5min, reduction from 95C to 25C at a rate of 0.1Cs1 and indefinite incubation at 4C. After re-annealing had been performed, 1l of T7 endonuclease I (NEB, 10Ul1) was added and the product was incubated at 37C for 15min.

Purified PCR products to amplify specific on-target or off-target sites were inserted into a T-easy vector (Promega) and transformed into DH5- bacterial cells. For rapid and efficient indel detection, plasmids were directly isolated from each white colony after blue/white screening; the inserted DNA fragment was amplified by PCR. The PCR amplicons were mixed with PCR products amplified from a WT DNA template such as KI plasmid or unedited genomic DNA; a T7E1 assay was then performed. Sanger sequencing was also performed for PCR amplicons that were not digested by T7E1 to determine the total number of colonies that harbour indels. The Bac[P] value was calculated as follows: Bac[P]=Indel/Total.

Bac[P] values for both WT and R206H alleles were determined through indel induction experiments using various [C]sgRNAs in the mESC clone of the FOP model. The targeting sites of both WT and R206H alleles were amplified by PCR, then cloned into a T-easy vector. Sanger sequencing was performed for each PCR product that had been derived from single bacterial clones, as described above. Similarly, Bac[P] values for both R206H (pf) and WT (1mm) alleles were determined by inducing indels in FOP hiPSCs; a corrected cell line (WT/Corrected) was used to determine the Bac[P] value of the corrected allele (2mm). Some PCR products did not contain a G/A hallmark because of intermediate-sized deletions (12~50 nucleotides); it was therefore impossible to determine which allele was edited for these PCR products. We observed that the fraction of such products with intermediate-sized deletions was generally constant (~20% in experiments shown in Fig. 6 and 1020% in experiments shown in Fig. 7) and did not decrease with [C] extension, suggesting that such intermediate-sized deletions are byproducts of the short indel induction processes. Therefore, we assigned products with intermediate-sized deletions to two alleles using the ratio of PCR products with convincingly confirmed origins. For the analysis shown in Fig. 7, we calculated the means of Bac[P] for WT (1mm) alleles on the basis of comparisons of R206H (pf) to WT (1mm) alleles and WT (1mm) to corrected (2mm) alleles for subsequent computational analyses.

Using the transfection protocol described above (Generation of AIMS cell lines and mice and AIMS analysis), 2105 WT hiPSCs or 4104 HEK293T cells were seeded onto 48-well plates and transfected with 100ng of all-in-one CRISPR plasmids (2/5 scale of the 24-well plate version). hiPSCs were dissociated and counted using trypan blue at 3 or 4d after transient puromycin treatment (1.5gml1); HEK293T cells were counted at 4d after transient puromycin treatment (3gml1). The data obtained by this procedure are indicated as Cell number in the Figures.

Biochemical assays were also performed using Cell Count Reagent SF reagent according to the manufacturers instructions (Nacalai Tesque). The Cdh1-P2A1-AIMS mESCs (2104 cells) were seeded onto 96-well plates and transfected with 50ng of all-in-one plasmids (1/5 scale of the 24-well plate version). Two days after puromycin selection, absorbance at 450nm was measured by Multiskan FC (Thermo Fisher). The data obtained from the biochemical assay are indicated as Cell viability (%) in Fig. 4d and Extended Data Fig. 4c by setting the data for [0C] and 0mM as a reference value (1.0), respectively.

For the AcrIIA4 experiments (Fig. 4c and Extended Data Fig. 4b), the Cdh1-P2A1-AIMS mESCs (3104 cells) were seeded onto 96-well plates and 50ng of all-in-one plasmids were co-transfected with different amounts of pCMV+AcrIIA4 and/or pCMVAcrIIA4 plasmids (1/5 scale of the 24-well plate version). In Fig. 4c and Extended Data Fig. 4b, we observed cytotoxicity for higher doses of AcrIIA4 expression plasmids. Similar cytotoxicity profiles were obtained in the absence of the Cdh1-P2A1-sgRNA1 target sequence in WT mESCs.

The transfection protocol for the 24-well plate experiment was performed as described above (Generation of AIMS cell lines and mice and AIMS analysis). For HDR induction in mESCs, WT hiPSCs and HEK293T cells, 1l of 10M ssODN (Eurofins) was added to the plasmidLipofectamine complex; for hiPSC transfection, 1l of 3M ssODN was added because a concentration of 10M induced severe toxicity. After transient puromycin selection, colonies were dissociated and plated at low density to avoid mosaicism. Single colonies were selected and genomic DNA was extracted. Sequence analysis was performed to identify G to A replacement with or without indels. To correct the FOP hiPSCs, clones that underwent HDR were screened by digesting the PCR product using the BstUI restriction enzyme (NEB); BstUI-positive PCR products were then sequenced. A silent mutation was inserted into the ssODN to generate the BstUI site and to distinguish an HDR-corrected (Corrected) allele from an original WT allele. Without this hallmark, WT/ clones, in which PCR amplicons from the R206H allele cannot to be obtained because of large deletions or more complex genomic rearrangement, would be misidentified as WT/Corrected clones.

For p53 staining, we performed transfection for HDR induction (1/5 scale of the 24-well plate version), using the protocol described above. In this assay, 6104 hiPSCs were seeded on a Matrigel-coated 96-well plate in triplicate. Puromycin selection was performed to examine p53 activity solely in transfected cells. The surviving cells were fixed with 4% paraformaldehyde at 2d after puromycin removal. For pSmad1/5/8 staining, 5103 cells were plated on a Matrigel-coated 96-well plate without Y-27632 and with 1% FBS. After 2.5h of culture, activin-A (100ngml1) (R & D Systems) was administered for 30min; cells were fixed with 4% paraformaldehyde. Antibody reactions were performed in accordance with standard protocols. Rabbit polyclonal p53 (FL-393, Santa Cruz, 1:200) and rabbit monoclonal pSmad1/5/8 (D5B10, Cell Signaling Technology, 1:1,000) antibodies were reacted overnight at 4C. Donkey anti-rabbit Alexa Fluor 488 secondary antibody (Thermo Fisher, 1:1,000) was reacted at room temperature for 30min. Data analysis was performed using a cell count application associated with a fluorescent microscope to select cells with p53 and pSmad1/5/8 activation by means of fluorescence intensity thresholds (BZ-X800, Keyence).

An mESC clone of an FOP model (R26RYFP/+ mESC line) was dissociated with trypsin and 58 cells were injected into 8-cell embryos (E2.5) collected from pregnant ICR mice. Injected blastocysts were transferred into the uteri of pseudo-pregnant ICR mice. Chimaeric contribution was confirmed by coat colour and YFP fluorescence. YFP was observed using a fluorescence stereo microscope (M165FC, Leica).

In this study, the probability of single-allele editing (P) was determined using AIMS and a Bac[P] assay, on the basis of a T7E1 assay, complemented by sequence validation. AIMS-based P (AIMS[P]) was determined as follows:

$$begin{array}{*{20}{c}} {mathrm{AIMS}left[ {mathrm{P}} right] = frac{{left( {2Fleft( {mathrm{Bi}} right) + Fleft( {mathrm{Mono}} right)} right)}}{2}} end{array}$$

(1)

where F(Bi) and F(Mono) are the experimental frequencies of cells with bi-allelic and mono-allelic genome editing, respectively.

The efficiency of the single-allele editing P (P(pf), where pf denotes perfect match) can be described as follows:

$${{mathrm{P}}left( {pf} right) = frac{S}{{K + S}}}$$

(2)

where the concentration of effective sgRNA-Cas9 complexes and the dissociation constant between the sgRNA and its target site are defined as S and K, respectively. On the basis of high editing efficiency without [C] extension (P=approximately 1), we assumed that the recovery rate from single-site damage was very low; therefore, it was neglected in subsequent analyses. To mechanistically understand the effects of [C] extension and 1mm, we assumed that [C] extension and 1mm decreased S and increased K, respectively. By setting S=1 for each sgRNA sequence without [C] extension, we approximated K values for each of eight sgRNA sequences. When P (AIMS[P] or Bac[P]) was 1, P was set to 0.99. Next, the relative S concentrations were determined using K and AIMS[P] for sgRNAs with [C] extension. Despite variation in the relationships between [C] extension and AIMS[P] among sgRNA sequences (Fig. 2f), we found clear and similar inverse relationships between [C] extension and relative S values for different sgRNA sequences (Extended Data Fig. 3d). Linear regression analysis demonstrated a good fit for the logarithm of the ratio of S to the length of [C] extension for all sgRNA sequences (Fig. 2g). Analysis of covariance (ANCOVA) indicated that the linear regression slopes did not significantly differ among various sgRNA sequences (Fig. 2h). This finding suggests that [C] extension exerts uniform suppression effects on diverse sgRNA sequences.

Since we observed that [C] extension modestly decreased target cleavage (Fig. 3c), we also performed similar analysis by gradually increasing K according to the length of [C] extension and observed that [C] extension gradually decreases S in a similar manner. In this setting, the effects on S became weaker. However, we observed that the dynamic range of suppression in northern blot analysis (Fig. 3f, ~6,000-fold change at [30C]) was more comparable to the range of change in S with constant K (~2,000-fold change at [30C]) relative to the range of change in S with increased K (~400-fold and 200-fold change with 5-fold and 10-fold increases in K at [30C], respectively). Therefore, this suggests that the effects on complex formation may be dominant, allowing determination of the single-allele editing probability in the cells.

In the initial phase of this study, we compared matched AIMS[P] and Bac[P] values for nine sgRNAs (that is, Cdh1-P2A1-sgRNA1 with different [C] extension lengths) and observed that AIMS[P] was strongly correlated with Bac[P] (Extended Data Fig. 5a). In our subsequent analyses, we used AIMS[P] to model indel insertion frequency (Figs. 2 and 5, and Extended Data Fig. 6) and Bac[P] to model HDR frequency (Figs. 6 and 7).

AIMS error was calculated as the difference between raw AIMS[P] and adjusted AIMS[P] (adjusted AIMS[P]AIMS[P]) (Fig. 1h). The raw AIMS[P] is simply based on fluorescence patterns. Therefore, in Fig. 1e, rare tdTomato+/Venusindel and tdTomatoindel/Venus+ heterozygous clones were grouped into mono-allelic clones. To determine the exact number of bi-allelic indel clones, these ostensibly heterozygous clones were analysed for sequencing (Seq-indel data). When sequencing these clones, most (86%) of these ostensibly heterozygous clones turned out to be homozygous. Adjusted AIMS[P] incorporates Seq-indel data together with fluorescence patterns. In most analyses, we used raw AIMS[P].

T7E1 error was calculated as Bac[P]T7E1:Bac[P] (Fig. 1i,j). T7E1:Bac[P] is the indel probability calculated from the rate of T7E1 sensitive clones, while Bac[P] is the indel probability calculated considering the Seq-indel data. The Seq-indel data were the exact numbers of indel clones that were not digested by T7E1, as determined by sequencing PCR products.

We performed extensive analyses using a combination of AIMS and sgRNAs with various types of [C] extensions. When editing efficiency was homogeneous across the cell population, we estimated the frequencies of cells with bi-allelic, mono-allelic or no genome editing (that is, F(Bi), F(Mono) or F(No)) as follows:

$${F(mathrm{Bi}) = mathrm{AIMS}[{mathrm{P}}]^2}$$

(3)

$${F(mathrm{Mono}) = 2mathrm{AIMS}[{mathrm{P}}]left( {1 - mathrm{AIMS}left[ {mathrm{P}} right]} right)}$$

(4)

$${Fleft( {mathrm{No}} right) = left( {1 - mathrm{AIMS}left[{mathrm{P}}right]} right)^2}$$

(5)

Using these equations, we observed that actual F(Mono) was lower than estimated F(Mono), particularly at intermediate AIMS[P] levels (AIMS[P]=~0.5). Therefore, we considered genome editing frequency heterogeneity at the single-cell level, which we modelled using a beta distribution. The probability density functions of P and mean P (E(P)) were calculated as follows:

$${fleft( {{mathrm{P}};alpha ,beta } right) = frac{{{mathrm{P}}^{alpha - 1}left( {1 - {mathrm{P}}} right)^{beta - 1}}}{{Bleft( {alpha ,beta } right)}}}$$

(6)

$${Eleft( {mathrm{P}} right) = frac{alpha }{{alpha + beta }}}$$

(7)

where the mean P corresponds to AIMS[P] (or Bac[P]) and and are exponents of P and its complement to 1. Using the beta distribution, F(Bi), F(Mono) and F(No) were described as follows:

$${F(mathrm{Bi}) = mathop {smallint }limits_0^1 {mathrm{P}}^2fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(8)

$${F(mathrm{Mono}) = mathop {smallint }limits_0^1 2{mathrm{P}}(1 - {mathrm{P}})fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(9)

$${Fleft( {mathrm{No}} right) = mathop {smallint }limits_0^1 (1 - {mathrm{P}})^2fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(10)

Using these equations, we determined values for each experiment that minimized the squared residuals between experimental F(Bi), F(Mono) and F(No), and simulated F(Bi), F(Mono) and F(No) (Extended Data Fig. 5b). As shown in Extended Data Fig. 5b, we observed that optimized values were generally constant for a wide range of AIMS[P] (0.1

As described above, 1mm (or 2mm) increases K in equation (2). The efficiency of the single-gene editing P on the 1mm (or 2mm) target can be described as follows:

$${{mathrm{P}}left( {1mathrm{mm};or;2mathrm{mm}} right) = frac{S}{{mK + S}}}$$

(11)

where m is the ratio of K for the 1mm target to K for the perfect match target. Thus, the single-gene editing P for 1mm (or 2mm) can be expressed as the function of P(pf), as follows:

$${{mathrm{P}}left( {1mathrm{mm};or;2mathrm{mm}} right) = frac{{{mathrm{P}}left( {pf} right)}}{{left( {1 - m} right){mathrm{P}}left( {pf} right) + m}}}$$

(12)

For the results shown in Figs. 6 and 7, we determined values of m that fit P(pf) and P(1mm or 2mm), using SSR as the error function (Fig. 6g). The ratios of P(pf) and P(1mm or 2mm) can also be described as functions of P(pf), as follows:

$${frac{{{mathrm{P}}(1mathrm{mm};or;2mathrm{mm})}}{{{mathrm{P}}(pf)}} = frac{1}{{left( {1 - m} right){mathrm{P}}left( {pf} right) + m}}}$$

(13)

$${frac{{{mathrm{P}}left( {pf} right)}}{{{mathrm{P}}left( {1mathrm{mm};or;2mathrm{mm}} right)}} = left( {1 - m} right){mathrm{P}}left( {pf} right) + m}$$

(14)

As shown in Fig. 6h, decreasing P(pf) contributes to the reduction in relative off-target ratio and enhancement of specificity. Thus, reduction in CRISPR-Cas9 activity through [C] extension is beneficial for reducing the relative off-target activity and enhancing specificity.

Using the beta distribution, the frequencies of the various HDR clones shown in Fig. 6 were determined as follows (Extended Data Fig. 7c,d):

$${F(mathrm{WT}/mathrm{R206H}) = mathop {smallint }limits_0^1 2h{mathrm{P}}(1 - {mathrm{P}})(1 - (1 - h){mathrm{P}}^{prime} )fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(15)

$${F(mathrm{WT}/mathrm{R206H} + mathrm{indel}) = mathop {smallint }limits_0^1 2h(1 - h){mathrm{P}}(1 - {mathrm{P}}){mathrm{P}}^{prime} fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(16)

$${F(mathrm{indel}/mathrm{R206H}) = mathop {smallint }limits_0^1 2h(1 - h){mathrm{P}}^2(1 - (1 - h){mathrm{P}}^{prime})fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(17)

$${F(mathrm{indel}/mathrm{R206H} + mathrm{indel}) = mathop {smallint }limits_0^1 2h(1 - h)^2{mathrm{P}}^2{mathrm{P}}^{prime} fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(18)

$${F(mathrm{R206H}/mathrm{R206H}) = mathop {smallint }limits_0^1 h^2{mathrm{P}}^2fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(19)

$${Fleft( {mathrm{overall};mathrm{HDR}} right) = mathop {smallint }limits_0^1 left( { - h^2{mathrm{P}}^2 + 2hP} right)fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(20)

where the efficiency of HDR on the Cas9-cleaved single allele is defined as h. The probability of single-gene editing on the edited (that is, 1mm) target is P' (Extended Data Fig. 7d), which is described in a manner similar to equation (12), as follows:

$${mathrm{P}^{prime} = frac{{mathrm{P}}}{{left( {1 - m} right){mathrm{P}} + m}}}$$

(21)

where m=1.723. P is decreased according to the [C] extension length (Extended Data Fig. 7e).

For simplicity, we considered h to be constant across the cell population in each experiment. On the basis of the experimental overall HDR frequency results and equation (20), we estimated h for each [C] extension (Fig. 6f). Although h was very low for sgRNAs without [C] extension (2.07%), h for sgRNAs with [C] extension was generally high (~11%). This result suggests that the conventional system without [C] extension suppresses HDR; [C] extension releases this suppression to allow HDR to reach its upper limit. On the basis of these findings, we used the mean estimated h (10.99%) for [C]-extended sgRNAs; we estimated the frequencies of distinct HDR patterns, overall HDR and precise HDR (Fig. 6i,j). For sgRNAs without [C] extension, we used the estimated h (2.07%). The simulated data adequately fit the experimental results (Fig. 6ik). To predict continuous HDR outcomes, we designed a hypothetical function for h for the range of P, such that h=2.07% for P>0.9 and h=10.99% for P<0.9 (Extended Data Fig. 7f); we estimated the frequencies of distinct HDR patterns, overall HDR and precise HDR (Extended Data Fig. 7g). In the simulation, precise HDR reached a maximum at P=0.313 (Extended Data Fig. 7e,g).

Read more:
Optimization of Cas9 activity through the addition of cytosine ... - Nature.com

CGMP-compliant iPSC manufacturing and development of … – Drug Target Review

In this webinar, experts will discuss the current state-of-the-art for iPSC generation and differentiation. The presentation will also highlight the development of advanced CGMP-compatible protocols for converting these cells into several cell types of therapeutic relevance.

Despite the potential benefits of induced pluripotent stem cells (iPSCs) to differentiate into multiple cell lineages and serve in regenerative medicine and immune cell therapy, CGMP-compatible implementation of the necessary methodologies remains challenging. At the technical level, the methodology to generate functional cell types from iPSCs must display a high degree of robustness, as well as scalability, to comply with the strict CGMP requirements.

In this webinar, experts will discuss the current state-of-the-art for iPSC generation and differentiation. The presentation will also highlight the development of advanced CGMP-compatible protocols for converting these cells into several cell types of therapeutic relevance, including retinal pigment epithelium (RPE), mesenchymal stem cells (MSCs), cardiomyocytes (CMs), and immune natural killer (NK) cells. There will also be discussion of the development of meaningful assays to assess purity and impurities in the product and to monitor potency.

Key Takeaways

Boris Greber, Ph.D, Head of Research & Development, iPSC, Catalent Cell & Gene Therapy.

Dr Greber is the Head of Research and Development (R&D), iPSC at Catalent Cell & Gene therapy in Europe. He joined Catalent as part of the RheinCell Therapeutics acquisition. He previously served as an independent research group leader at the Max Planck Institute for Molecular Biomedicine (Germany). Dr Greber is internationally recognized with a 15 year track record in basic and applied human iPSC research.

View original post here:
CGMP-compliant iPSC manufacturing and development of ... - Drug Target Review

Two dads, one baby? Gene technique works in mice – The Mercury News

This photo provided by researcher Katsuhiko Hayashi shows mice derived from stem cells, four weeks after their birth, in Osaka, Japan in September 2021. In a study published Wednesday, March 16, 2023, in the journal Nature, scientists led by Hayashi have created baby mice with two fathers for the first time by turning male mouse stem cells into female cells in a lab. (Katsuhiko Hayashi via AP)

For the first time in history, scientists have created mice with two dads, foretellinga day when same-sex couples may be able to have biological children of their own.

The success, announced by Japanese researchers last month, has not yet been tried on people.

But scientists at two Bay Area startups, as well as a company in New York City and another in Japan, are striving to move the mouse research into humans, and rewrite the rules of reproduction by making sex cells in a lab. If successful in people, the technique would allow the creation of an egg cell from blood or a tiny sliver of a man or womans skin.

So far, the research has focused on making egg cells, which would enable male-male reproduction. Creating sperm for female-female reproduction is a tougher scientific challenge.

Even the remote possibility of same-sex couples creating a baby without a donor is extraordinary and exciting, said Drew Lloyd, board president of the Bay Area Municipal Elections Committee, which advocates for the civil rights of LGBTQpeople. Ive learned not to put limits on whats possible.

Both Bay Area companies are secretive about the progress of their research and would not consent to interviews. The Berkeley-based startup Conception, with 34 employees and at least $20 million in private funding, seeks to create human eggs using stem cells from human blood samples. The other company, San Franciscos Ivy Natal, aims to build eggs with a skin biopsy.

The new research, described by Katsuhiko Hayashi of Osaka University in the March 15 issue of the journal Nature, marks a milestone in reproductive biology.

The Japanese team guided stem cells from a male mouse to form eggs, which were fertilized by another male mouse. The two mice conceived seven pups who were healthy and fertile, eventually conceiving babies of their own.

But the projects success rate was extremely low. About 30% of the male mouses stem cells matured into eggs, and 40% of those eggs were successfully fertilized to create embryos. The embryos were transferred to a surrogate female mouse to gestate, but only 1% 7 out of 630 were born alive.

Experts said it is not yet known whether the strategy would work in humans.

Mice arent people, said Hank Greely, director of the Center for Law and the Biosciences at Stanford University. And its a complicated method.

UC San Francisco developmental biologists Jonathan Bayer and Diana Laird agreed, writing we have much to learn before we use cultured stem cells to make human eggs in a dish, in an article that accompanied the Nature paper.

The technique, called in vitro gametogenesis or IVG, builds on the Nobel Prize-winning work of Dr. Shinya Yamanaka, a biologist at Japans Kyoto University who is also affiliated with San Franciscos Gladstone Institute. In 2007, Yamanaka described how to create stem cells by reprogramming skin cells, turning them into induced pluripotent stem cells, or iPSCs. These iPSCs can be coaxed into becoming nearly any cell type in the human body, from brain to liver and perhaps, someday, human egg or sperm.

The latest work was technically complex and required many steps. First, the team took skin cells from the tail of an adult male mouse. Then it reprogrammed these skin cells to become stem cells.

The biggest challenge was converting these stem cells from male to female. Because the production of mature eggs requires two copies of the X chromosome, the authors devised a way to find rare male stem cells that jettison their Y chromosome and then duplicate their X chromosome.

Once chromosomally female, these cells were biochemically nudged to turn into immature eggs.

The team tried, but failed, to make sperm from female cells. So two female mice could not conceive together. Thats because theres not yet a successful technique for converting a cell with two X chromosomes into a Y chromosome and without a Y chromosome, no sperm can be made.

In 2017, researchers in China created healthy mice with two mothers, but it involved a tremendous amount of gene editing with CRISPR, making it impractical to use for anything other than research. They also made mice with two dads, but the offspring quickly died.

But the breakthrough opens up exciting new avenues in reproductive biology and fertility research.

For instance, it could be used to rapidly produce inbred strains of identical mice, useful for laboratory experiments.It also offers a strategy to propagate endangered mammals from a single male.

It could also make replacement eggs for older women to have children, as well as couples who are infertile due to congenital problems, an accident, disease or treatment such as chemotherapy.

And it would offer same-sex or transgender couples the chance to have their own biological children. Currently such couples must use the eggs or sperm from one person, and the eggs or sperm from a donor.

Adoptive parent Johnny Symons, professor in the School of Cinema at San Francisco State University whose documentary film Daddy & Papa focuses on the experience of gay men raising children through adoption and surrogacy, called family-building an incredibly personal decision.

Having biological families has been least accessible to us, he said. This technology stands to be a real breakthrough in terms of providing options for people who want that.

If the technology advances, it could really change societal perceptions of gay men as parents, and potentially legitimize us in a new way, said Symons. Theres something very powerful and undeniable about physical resemblance. It makes it harder for people who oppose us to deny us political rights or equal social standards.

But theres a darker side to making sex cells in the lab, said Greely, author of the 2016 book, The End of Sex and the Future of Human Reproduction.

If this worked, he said, you could make eggs from sperm from 8-year-olds. You could make eggs from sperm from fetal remains. You could make eggs from sperm from somebody who has been dead for years, but whose cells were frozen. That gets a little weird.

Someday, perhaps, it may be possible to create both eggs and sperm from the same person, creating what Greely calls a unibaby. Youre pregnant by yourself, he said. I cant imagine a good reason to do this.

Even if the technique works in humans, it must first be proven safe, experts agree. Created through genetic manipulation, embryos may have hidden defects. There must be wider societal debate and regulatory oversight.

The next step is to test the technique in monkeys or chimpanzees.

Fertility experts, such as the American Society for Reproductive Medicines Research Institute, think its on the horizon.

If we can do this properly and safely and we can bring the cost down to being something accessible for everyone, said Conception CEO Matt Krisiloffin a company video, I really think theres a possibility that this could become the default way people choose to have children.

Original post:
Two dads, one baby? Gene technique works in mice - The Mercury News

Researchers Created "Embryos" From Monkey Stem Cells For The … – Inverse

The early beginnings of an embryo as it transforms from a clump of cells to a tiny human is a bit of a mystery scientifically speaking. We know many of the broad strokes, but there are a lot of intricate steps involved in the transformation that we arent privy to simply because we cant see inside the uterus as everything unfolds.

Not having front-row seats hasnt stopped scientists from trying to parse together an IKEA instruction manual for embryonic development. Over the last several decades, a variety of studies conducted in animals like zebrafish, mice, and fruit flies have identified some of the genetic switches underpinning embryogenesis a fertilized eggs journey to becoming a multicellular organism. There have also been studies using human and human-animal hybrid embryos to various degrees, of which theres been terse progress due to ethical concerns constraining such research.

The goal, therefore, is to find workarounds and proxies that can mimic a human uterus or resemble human embryonic development. Theres been some effort on that front in recent years when in August 2022, scientists successfully created a synthetic mouse embryo using stem cells instead of the usual mishmash of eggs and sperm and incubated the embryo in a mechanical womb.

Now, in a study published Thursday in the journal Cell Stem Cell, researchers in China have created embryo-like structures from embryonic stem cells taken from the crab-eating macaque. This structure called a blastoid, was similar to a crucial embryonic structure called a blastocyst, and possessed the transformative ability that eventually gives rise to the different cells and tissues in the body. However, when implanted into the uteri of female macaques, the blastoids didnt survive past a week (nearly three weeks in total from creation), although they did develop gestational sacs.

I wouldnt call this a breakthrough study, Jianping Fu, professor of biomedical engineering at the University of Michigan, who wasnt involved in the study, tells Inverse. But it points to an exciting direction to bypass the existing constraints [set] by human and animal models.

Stem cells, particularly embryonic and induced pluripotent stem cells (which are derived from adult cells and rewired to resemble their embryonic counterparts), have become a hotbed of interest simply for one reason: They have the potential to change into any cell type in the body, kind of like a cellular Animorph. This means they can be used to generate a wide range of cell types for research and clinical therapies, such as targeting neurodegenerative diseases like Parkinsons to diabetes and even dental issues.

The embryonic stem cells used in this new study came from crab-eating macaques, a species of long-tailed, brown-gray Old World monkeys native to Southeast Asia. These primates are widely used in medical research due to their physiological and genetic similarities to humans (thus their classification as near-human primates), particularly in areas such as neuroscience, infectious diseases, and reproductive biology.

To get growing and transforming, embryonic stem cells regardless if theyre human or monkey need chemicals called growth factors. This jumpstarts and nudges the cell down a certain career path (think your high school counselor on career day), which prompts certain genes to turn on and off depending on the desired cell type.

For their study, the researchers across various academic research institutions in China bathed their macaque embryonic stem cells in growth factors known from past studies to be involved in embryonic development. (Its important to note, though, we dont have an expansive knowledge of all the growth factors present in an embryo.) After about a week simmering in this chemical cocktail, the embryonic stem cells started to take on the appearance of a blastocyst a hollow sphere-like structure, parts of which will eventually develop into the placenta when viewed under the microscope (hence the name blastoid).

Also, under the microscope, these researchers noticed the blastoids appeared to have reached a stage in embryonic development called gastrulation. This is when three cells, or germ, layers the ectoderm, mesoderm, and endoderm start to form, ultimately giving rise to all the different types of cells and tissues in the body. This seemed to be corroborated by single-cell RNA sequencing, a technique used to photograph gene expression with resolution down to the single cell (around 6,000 in this study). The gene expression snapshots showed that different cells within the blastoid shared a nearly similar gene expression to natural blastocysts or embryos right after implantation, when the fertilized egg attaches to the uterine wall in early pregnancy.

So if it looks like a blastoid, does it act like a blastoid? Specifically, can it become an embryo? Not exactly.

To see how the blastoids fared in a more natural habitat namely inside a surrogate mama the early embryonic structures (about two weeks old at this point) were surgically implanted into eight female macaques. Of the eight, the blastoids appeared to successfully tether in three primates. The cells seemed to trigger pregnancy, indicated by the presence of hormones progesterone and chorionic gonadotropin, both crucial for sustaining pregnancy in monkeys as well as humans. This demonstrated that the blastoids were able to mimic some of the critical functions of a developing embryo, albeit on a limited scale.

While the blastoids did form gestational sacs fluid-filled structures that serve as an early sign of pregnancy and potentially a yolk sac in one (another early embryonic structure that produces blood and germ cells) seven to 10 days after implantation, these structures didnt progress any further. Roughly twenty days after they were first created, the blastoids disappeared without a trace.

Growing these embryo-like structures outside the uterus (at least initially) is among one of many modes of exploration the researchers hope will provide us insight into the molecular mechanisms behind-the-scenes of embryonic development.

[This research] provides new tools and perspectives for the subsequent exploration of primate embryos and reproductive medical health, Qiang Sun, the studys co-author and director of Suzhou Non-human Primate Facility at the Chinese Academy of Sciences, said in a statement.

Especially for reproductive health, this research could lend to a better understanding of why early miscarriages happen, which occur in 10 percent to 20 percent of pregnancies. Theres no exact cause, but there are a variety of reasons, such as random chromosomal abnormalities or structural deficits, whether in the moms uterus or in the baby, that prevent implantation or proper embryonic development.

Such monkey models may be very useful [for] toxicity screening applications to identify chemicals that have potential toxic effects on pregnancy, says Fu of the University of Michigan. Theres also a lot of hope such animal models, especially related to primate development, might guide us to better understand early development so we can [create] better protocols which might be very useful for [in vitro fertilization].

But hes skeptical of exactly how much these findings can contribute to our understanding of early development. Previous studies fusing mouse and human embryonic stem cells show that even these hybrids can develop into blastoids, so demonstrating the same with monkey embryonic stem cells isnt altogether new. Not only that, hes not convinced the monkey blastoids actually achieved gastrulation since, judging from the data provided, they still look very much disorganized.

Fu warns that a monkey model workaround may still toe the line of whats ethically permissible since, evolution-wise, macaques are pretty close to humans. This may make their blastoids nearly equivalent to human cells. Ethical concerns about primate models for embryonic development have been raised in the past.

The researchers acknowledge the potential ethical conundrum but note that the blastoids theyve created are still very different and not functionally on par with human blastocysts.

This research still has a long way to go, and likely many more years before we can completely pick apart the black box that is embryonic development.

The rest is here:
Researchers Created "Embryos" From Monkey Stem Cells For The ... - Inverse

Neurite outgrowth deficits caused by rare PLXNB1 mutation in … – Nature.com

Bebbington P, Ramana R. The epidemiology of bipolar affective disorder. Soc Psychiatry Psychiatr Epidemiol. 1995;30:27992.

Article CAS PubMed Google Scholar

Pini S, de Queiroz V, Pagnin D, Pezawas L, Angst J, Cassano GB, et al. Prevalence and burden of bipolar disorders in European countries. Eur Neuropsychopharmacol. 2005;15:42534.

Article CAS PubMed Google Scholar

Burton CZ, Ryan KA, Kamali M, Marshall DF, Harrington G, McInnis MG, et al. Psychosis in bipolar disorder: does it represent a more severe illness? Bipolar Disord. 2018;20:1826.

Article PubMed Google Scholar

Brus MJ, Solanto MV, Goldberg JF. Adult ADHD vs. bipolar disorder in the DSM-5 era: a challenging differentiation for clinicians. J Psychiatr Pract. 2014;20:42837.

Article PubMed Google Scholar

Schulze TG, Akula N, Breuer R, Steele J, Nalls MA, Singleton AB, et al. Molecular genetic overlap in bipolar disorder, schizophrenia, and major depressive disorder. World J Biol Psychiatry. 2014;15:2008.

Article PubMed Google Scholar

Smeland OB, Bahrami S, Frei O, Shadrin A, OConnell K, Savage J, et al. Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence. Mol Psychiatry. 2020;25:84453.

Article CAS PubMed Google Scholar

Bipolar Disorder and Schizophrenia Working Group of the Psychiatric Genomics Consortium. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173:170515.e16.

Article PubMed Central Google Scholar

Weller EB, Weller RA, Fristad MA. Bipolar disorder in children: misdiagnosis, underdiagnosis, and future directions. J Am Acad Child Adolesc Psychiatry. 1995;34:70914.

Article CAS PubMed Google Scholar

Renk K, White R, Lauer BA, McSwiggan M, Puff J, Lowell A. Bipolar disorder in children. Psychiatry J 2014;2014:119.

Article Google Scholar

Beyer DKE, Freund N. Animal models for bipolar disorder: from bedside to the cage. Int J Bipolar Disord. 2017;5:35.

Article PubMed PubMed Central Google Scholar

Strakowski SM, DelBello MP, Adler CM. The functional neuroanatomy of bipolar disorder: a review of neuroimaging findings. Mol Psychiatry. 2005;10:10516.

Article CAS PubMed Google Scholar

Pavuluri MN, OConnor MM, Harral EM, Sweeney JA. An fMRI study of the interface between affective and cognitive neural circuitry in pediatric bipolar disorder. Psychiatry Res. 2008;162:24455.

Article PubMed PubMed Central Google Scholar

Chen CH, Suckling J, Lennox BR, Ooi C, Bullmore ET. A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disord. 2011;13:115.

Article CAS PubMed Google Scholar

OShea KS, McInnis MG. Neurodevelopmental origins of bipolar disorder: iPSC models. Mol Cell Neurosci. 2016;73:6383.

Article PubMed Google Scholar

Madison JM, Zhou F, Nigam A, Hussain A, Barker DD, Nehme R, et al. Characterization of bipolar disorder patient-specific induced pluripotent stem cells from a family reveals neurodevelopmental and mRNA expression abnormalities. Mol Psychiatry. 2015;20:70317.

Article CAS PubMed PubMed Central Google Scholar

Bavamian S, Mellios N, Lalonde J, Fass DM, Wang J, Sheridan SD, et al. Dysregulation of miR-34a Links neuronal development to genetic risk factors for bipolar disorder. Mol Psychiatry. 2015;20:57384.

Article CAS PubMed PubMed Central Google Scholar

Wang JL, Shamah SM, Sun AX, Waldman ID, Haggarty SJ, Perlis RH. Label-free, live optical imaging of reprogrammed bipolar disorder patient-derived cells reveals a functional correlate of lithium responsiveness. Transl Psychiatry. 2014;4:e4288.

Article CAS PubMed PubMed Central Google Scholar

Mertens J, Wang QW, Kim Y, Yu DX, Pham S, Yang B, et al. Differential responses to lithium in hyperexcitable neurons from patients with bipolar disorder. Nature. 2015;527:959.

Article CAS PubMed PubMed Central Google Scholar

Stern S, Santos R, Marchetto MC, Mendes APD, Rouleau GA, Biesmans S, et al. Neurons derived from patients with bipolar disorder divide into intrinsically different sub-populations of neurons, predicting the patients responsiveness to lithium. Mol Psychiatry. 2018;23:145365.

Article CAS PubMed Google Scholar

Craddock N, Sklar P. Genetics of bipolar disorder. Lancet 2013;381:165462.

Article CAS PubMed Google Scholar

Sullivan PF, Geschwind DH. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell. 2019;177:162.

Article CAS PubMed PubMed Central Google Scholar

Song J, Bergen SE, Kuja-Halkola R, Larsson H, Landn M, Lichtenstein P. Bipolar disorder and its relation to major psychiatric disorders: a family-based study in the Swedish population. Bipolar Disord. 2015;17:18493.

Article PubMed Google Scholar

Hou L, Bergen SE, Akula N, Song J, Hultman CM, Landn M, et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet. 2016;25:338394.

Article CAS PubMed PubMed Central Google Scholar

Mullins N, Forstner AJ, OConnell KS, Coombes B, Coleman JRI, Qiao Z, et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet. 2021;53:81729.

Article CAS PubMed PubMed Central Google Scholar

Sul JH, Service SK, Huang AY, Ramensky V, Hwang SG, Teshiba TM, et al. Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates. Transl Psychiatry. 2020;10:110.

Article Google Scholar

Toma C, Shaw AD, Allcock RJN, Heath A, Pierce KD, Mitchell PB, et al. An examination of multiple classes of rare variants in extended families with bipolar disorder. Transl Psychiatry. 2018;8:112.

Article Google Scholar

Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51:793803.

Article CAS PubMed PubMed Central Google Scholar

Clifton NE, Hannon E, Harwood JC, di Florio A, Thomas KL, Holmans PA, et al. Dynamic expression of genes associated with schizophrenia and bipolar disorder across development. Transl Psychiatry. 2019;9:74.

Zeng B, Bendl J, Kosoy R, Fullard JF, Hoffman GE, Roussos P. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat Genet. 2022;54:1619.

Article CAS PubMed PubMed Central Google Scholar

Ament SA, Szelinger S, Glusman G, Ashworth J, Hou L, Akula N, et al. Rare variants in neuronal excitability genes influence risk for bipolar disorder. Proc Natl Acad Sci USA. 2015;112:357681.

Article CAS PubMed PubMed Central Google Scholar

Kataoka M, Matoba N, Sawada T, Kazuno AA, Ishiwata M, Fujii K, et al. Exome sequencing for bipolar disorder points to roles of de novo loss-of-function and protein-altering mutations. Mol Psychiatry. 2016;21:88593.

Article CAS PubMed PubMed Central Google Scholar

Sullivan PF, Daly MJ, ODonovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13:53751.

Article CAS PubMed PubMed Central Google Scholar

Falk A, Heine VM, Harwood AJ, Sullivan PF, Peitz M, Brstle O, et al. Modeling psychiatric disorders: from genomic findings to cellular phenotypes. Mol Psychiatry. 2016;21:116779.

Article CAS PubMed PubMed Central Google Scholar

Ishii T, Ishikawa M, Fujimori K, Maeda T, Kushima I, Arioka Y, et al. In vitro modeling of the bipolar disorder and schizophrenia using patient-derived induced pluripotent stem cells with copy number variations of PCDH15 and RELN. eNeuro. 2019;6:ENEURO.040318.2019.

Article PubMed Google Scholar

Zoghbi AW, Dhindsa RS, Goldberg TE, Mehralizade A, Motelow JE, Wang X, et al. High-impact rare genetic variants in severe schizophrenia. Proc Natl Acad Sci USA. 2021;118:e2112560118.

Lopez-Larson MP, Shah LM, Weeks HR, King JB, Mallik AK, Yurgelun-Todd DA, et al. Abnormal functional connectivity between default and salience networks in pediatric bipolar disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2017;2:8593.

PubMed Google Scholar

The Mini-International Neuropsychiatric Interview (M.I.N.I.). the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59:2233.

Google Scholar

Achenbach TM, Rescorla LA. Manual for the ASEBA school-age forms & profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families; 2001.

Google Scholar

Wechsler D. Wechsler abbreviated scale of intelligence. New York, NY: The Psychological Corporation: Harcourt Brace & Company; 1999.

Google Scholar

Jo HJ, Gotts SJ, Reynolds RC, Bandettini PA, Martin A, Cox RW, et al. Effective preprocessing procedures virtually eliminate distance-dependent motion artifacts in resting state FMRI. J Appl Math. 2013. https://doi.org/10.1155/2013/935154.

Article PubMed PubMed Central Google Scholar

Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29:16273.

Article CAS PubMed Google Scholar

Anderson JS, Ferguson MA, Lopez-Larson M, Yurgelun-Todd D. Reproducibility of single-subject functional connectivity measurements. Am J Neuroradiol. 2011;32:54855.

Article CAS PubMed PubMed Central Google Scholar

Saad ZS, Gotts SJ, Murphy K, Chen G, jo HJ, Martin A, et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2012;2:2532.

Article PubMed PubMed Central Google Scholar

Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage. 2009;44:893905.

Article PubMed Google Scholar

Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142.

Article PubMed Google Scholar

Yeo BTT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:112565.

Article PubMed Google Scholar

Han DH, Kim SM, Bae S, Renshaw PF, Anderson JS. Brain connectivity and psychiatric comorbidity in adolescents with Internet gaming disorder. Addict Biol. 2017;22:80212.

Article PubMed Google Scholar

Shah LM, Cramer JA, Ferguson MA, Birn RM, Anderson JS. Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state. Brain Behav. 2016;6:e00456.

Article PubMed PubMed Central Google Scholar

Curtis BJ, Williams PG, Jones CR, Anderson JS. Sleep duration and resting fMRI functional connectivity: examination of short sleepers with and without perceived daytime dysfunction. Brain Behav. 2016;6:e00576.

Freed D, Aldana R, Weber J, Edwards J. The sentieon genomics tools - a fast and accurate solution to variant calling from next-generation sequence data. BioRxiv 2017 https://doi.org/10.1101/115717.

Faust GG, Hall IM. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics. 2014;30:25035.

Article CAS PubMed PubMed Central Google Scholar

Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:30478.

Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018;34:i88490.

Here is the original post:
Neurite outgrowth deficits caused by rare PLXNB1 mutation in ... - Nature.com

Softer and more gelatinous: taste testing Australias first lab-grown pork – The Guardian

Food science

Advocates of cultivated meat say its better for animal welfare and the environment, but the jury is out on whether the industry is headed for greener pastures

The liability waiver does not inspire confidence. I am not a natural thrill seeker, but in my limited experience, sitting down in an empty Melbourne cafe to eat a snack is not a typical activity that may cause serious or grievous injuries, including bodily injury, and/or death.

I am here to taste a product that is much hyped but not yet commercially available in Australia (and most other jurisdictions): meat that has been grown in a lab.

The meat in question is a soupon of pork, which has been cultivated from the cells of a pigs ear. The pig, I have been assured by Paul Bevan, the chief executive of cultivated meat startup Magic Valley, is still alive and well, continuing to live its happy, healthy, normal life.

Lab-grown meat or cultivated meat, as it is known in the industry is purported by its proponents to be better for animal welfare and the environment. It exited the realm of science fiction in 2013, when a research team at Maastricht University presented the first prototype, a lab-grown beef burger patty.

Since then, the cellular agriculture industry has yielded just a single commercially available product cell-based chicken launched in Singapore in 2020 by the American firm Eat Just. A cultivated chicken product from Eat Just subsidiary Good Meat is now making its way through US regulatory approval.

Magic Valley is hoping to apply for regulatory approval in Australia by the end of the year, and to sell their cultivated meat products lamb and pork, so far by the end of 2024.

I am a meat eater and dont consider myself particularly squeamish, but as I wait to taste Magic Valleys pork I try to suppress a mental image of muscle fibres growing in a Petri dish, la science experiments from university biology class.

The morsel of lab-grown meat is served in a silky wonton skin, doused with chilli oil, spring onion and black vinegar. The recipe has been cooked up by Wendy Chua, a Magic Valley scientist and their in-house gourmet.

The pork is, in a word, delicious. But then again, what generously seasoned dumpling isnt?

The texture of the meat is perhaps slightly softer and more gelatinous than regular mince. Any differences in taste would have been easier to parse with a more sizeable portion, but I suspect the meagre serving is a deliberate logistical (and economic) choice.

Magic Valley is not yet operating at industrial scale, so all their meat is still cultivated in a lab, rather than in 20,000-litre bioreactors that they hope to eventually use. A facility with two bioreactors of such size would eventually be able to produce 300,000kg of meat annually, Bevan says. (It is unclear whether bioreactors this large are commonly used in the industry, but in 2022 Eat Just announced it was building 250,000-litre vats, set to be operational in 2024.)

The process of making the pork begins with reprogramming cells taken from a pigs ear to create induced pluripotent stem cells. These stem cells, which are not yet specialised, have an essentially unlimited ability to generate other cell types. From there, were able to direct the cells to become muscle, fat, connective tissue, bone whatever we choose, Bevan says.

Cells are brewed in nutrient media a liquid of glucose and amino acids that enables the cells to grow. Currently, muscle and fat are grown separately and combined at the end to form the final product. The process takes about three weeks, Bevan says.

Magic Valley describes its meat as slaughter-free, but other cellular agriculture companies use foetal bovine serum a byproduct of the meatpacking industry which is harvested from the blood of cow foetuses as a growth medium.

In terms of input costs at the moment, for us it costs around $50 a kilo to produce, Bevan says. His hope is that once production is scaled up, the costs may drop to $5 per kilogram.

While the current price tag is steep, it is far cheaper than it once was the first lab-grown patty cost US$330,000 to create. But some critics are sceptical that cultivated meat can achieve cost parity with traditional agriculture.

If your interest is maximising profitability in the early years, you should never start a cultivated meat company, Eat Justs chief executive, Josh Tetrick, told the Financial Times in June last year. The article highlighted that despite Eat Justs 2020 commercial milestone in Singapore, the lossmaking companys products are not in shops.

Though lab-grown meat has high energy requirements, analyses suggest the production process involves less carbon emissions and a smaller land-use footprint per kg of meat than traditional agriculture.

From a greenhouse gas perspective, and from a water use and a land use perspective, were looking at between a 70% and 90% reduction compared to conventional meat, Bevan says.

Nutritionally, our cultivated pork products are identical, Bevan says.

Independent studies are unclear on whether cultivated meat provides the same essential minerals, such as iron and vitamin B12, as regular meat. One 2020 review found that vitamins are necessary in the [growth] media for optimal cell proliferation, but it is not clear whether the uptake from media results in levels of vitamins in cultured meat comparable to traditional meat.

But unlike a pork cutlet, which is what it is, Bevan says with cultivated meat we can remove things like saturated fat, add additional protein content, vitamins, minerals, etcetera.

Critics of alternative proteins such as lab-grown meat have suggested the industry could jeopardise the livelihoods of food producers globally, rather than supporting transformational changes in the way we eat. Nonetheless, the demand for meat is rising worldwide, and cultured meat companies have proliferated accordingly.

In March, another Australian startup, Vow, unveiled a meatball engineered from the tissue of the long-extinct woolly mammoth, which nobody has yet tasted. In addition to traditional livestock animals, the company is making meat from the stem cells of at least 13 other animal species, such as alpaca and water buffalo.

As the technology advances, Bevan says the ultimate goal for Magic Valley is to create structured meat products such as steaks and chops. Last year, one Israeli firm used 3D printing techniques to create a 110g steak, but while the technology is impressive, the end product hardly resembles the real thing.

Replacing the chicken in a nugget or the pork filling in a wonton with cultivated meat is beginning to seem like a real possibility. But whether lab-grown chops wont smack of the uncanny valleys pastures is a question thats yet to be answered.

{{topLeft}}

{{bottomLeft}}

{{topRight}}

{{bottomRight}}

{{.}}

Original post:
Softer and more gelatinous: taste testing Australias first lab-grown pork - The Guardian

SaaS Spend Management Software Market 2023: Exclusive Insights … – Digital Journal

PRESS RELEASE

Published April 11, 2023

SaaS Spend Management Software Market Research Report 2023 | Pages | presents granular analysis on current and future market growth status with industry revenue and CAGR status across all regions with Top Key Players analysis - Flexera, Cledara, Blissfully, Intello, Binadox, G2 Track, License DashboarD

"Final Report will add the analysis of the impact of COVID-19 on this industry."

The SaaS Spend Management Software Market report 2023 offers a comprehensive and precise analysis of the various aspects related to business growth opportunities, challenges, risk factors, and trends across all geographic regions. The report provides up-to-date information on the latest technological advancements, SWOT and PESTLE analysis, and insightful market size information.

In addition, the SaaS Spend Management Software market report covers an in-depth analysis of growth factors, global trending technologies, and key players profiling with company profiles, and supply-demand scope. The report also gives a holistic overview of the industry revenue, demand status, competitive landscape, and regional segments of the global industry. This report is an indispensable value addition to any company looking to develop its future strategies and plan its path forward.

Get a sample PDF of the report at - https://www.marketresearchguru.com/enquiry/request-sample/19910636

The SaaS Spend Management Software market has witnessed growth from USD million to USD million from 2017 to 2022. With the CAGR of %, this market is estimated to reach USD million in 2029.

Key Players covered in the global SaaS Spend Management Software Market are:

The report covers the competitive landscape of various major players, their current market positions, and key business strategies adopted by players. This SaaS Spend Management Software market report includes information about the product launch, expansion of the production facilities or plants, adoption of new technologies, latest merger and acquisition, partnership, and collaboration of the key players. It furthers provides concrete information about the existing market scope for the new entrants and the current competitive levels and scenario for the emerging players in the global market.

Get a sample PDF of the SaaS Spend Management Software Market Report

Most important types of SaaS Spend Management Software products covered in this report are:

Most widely used downstream fields of SaaS Spend Management Software market covered in this report are:

The report combines extensive quantitative analysis and exhaustive qualitative analysis, ranges from a macro overview of the total market size, industry chain, and market dynamics to micro details of segment markets by type, application and region, and, as a result, provides a holistic view of, as well as a deep insight into the SaaS Spend Management Software market covering all its essential aspects.

For the competitive landscape, the report also introduces players in the industry from the perspective of the market share, concentration ratio, etc., and describes the leading companies in detail, with which the readers can get a better idea of their competitors and acquire an in-depth understanding of the competitive situation. Further, mergers and acquisitions, emerging market trends, the impact of COVID-19, and regional conflicts will all be considered.

In a nutshell, this report is a must-read for industry players, investors, researchers, consultants, business strategists, and all those who have any kind of stake or are planning to foray into the market in any manner.

Inquire or Share Your Questions If Any Before the Purchasing This Report - https://www.marketresearchguru.com/enquiry/pre-order-enquiry/19910636

Following Chapter Covered in the SaaS Spend Management Software Market Research:

Chapter 1 mainly defines the market scope and introduces the macro overview of the industry, with an executive summary of different market segments ((by type, application, region, etc.), including the definition, market size, and trend of each market segment.

Chapter 2 provides a qualitative analysis of the current status and future trends of the market. Industry Entry Barriers, market drivers, market challenges, emerging markets, consumer preference analysis, together with the impact of the COVID-19 outbreak will all be thoroughly explained.

Chapter 3 analyzes the current competitive situation of the market by providing data regarding the players, including their sales volume and revenue with corresponding market shares, price and gross margin. In addition, information about market concentration ratio, mergers, acquisitions, and expansion plans will also be covered.

Chapter 4 focuses on the regional market, presenting detailed data (i.e., sales volume, revenue, price, gross margin) of the most representative regions and countries in the world.

Chapter 5 provides the analysis of various market segments according to product types, covering sales volume, revenue along with market share and growth rate, plus the price analysis of each type.

Chapter 6 shows the breakdown data of different applications, including the consumption and revenue with market share and growth rate, with the aim of helping the readers to take a close-up look at the downstream market.Chapter 7 provides a combination of quantitative and qualitative analyses of the market size and development trends in the next five years. The forecast information of the whole, as well as the breakdown market, offers the readers a chance to look into the future of the industry.

Chapter 8 is the analysis of the whole market industrial chain, covering key raw materials suppliers and price analysis, manufacturing cost structure analysis, alternative product analysis, also providing information on major distributors, downstream buyers, and the impact of COVID-19 pandemic.

Chapter 9 shares a list of the key players in the market, together with their basic information, product profiles, market performance (i.e., sales volume, price, revenue, gross margin), recent development, SWOT analysis, etc.

Chapter 10 is the conclusion of the report which helps the readers to sum up the main findings and points.

Chapter 11 introduces the market research methods and data sources.

Geographically, the report includes the research on production, consumption, revenue, market share and growth rate, and forecast (2018 -2029) of the following regions:

To Understand How Covid-19 Impact Is Covered in This Report - https://marketresearchguru.com/enquiry/request-covid19/19910636

The report delivers a comprehensive study of all the segments and shares information regarding the leading regions in the market. This report also states import/export consumption, supply and demand Figures, cost, industry share, policy, price, revenue, and gross margins.

Key Offerings of SaaS Spend Management Software Market:

Trend and forecast analysis: Market trends, forecast, and Analysis to 2023 by segments and regions

Segmentation analysis: Market size by various applications such as product, material, shape, and end use in terms of value and volume shipment.

Regional analysis: Global SaaS Spend Management Software market breakdown by North and South America, Europe, Asia Pacific, Middle East and the Rest of the World.

Growth opportunities: Analysis of growth opportunities in different applications and regions in the Global SaaS Spend Management Software Market

Strategic analysis: This includes new product development and competitive landscape in the Global SaaS Spend Management Software Market

Reasons to purchase the SaaS Spend Management Software market report:

Purchase this Report (Price 2980 USD for a Single-User License) -https://marketresearchguru.com/purchase/19910636

Detailed TOC of SaaS Spend Management Software Market Forecast Report 2023-2029:

1 SaaS Spend Management Software Market Overview

1.1 Product Overview and Scope of SaaS Spend Management Software Market

1.2 SaaS Spend Management Software Market Segment by Type

1.2.1 Global SaaS Spend Management Software Market Sales Volume and CAGR (%) Comparison by Type

1.3 Global SaaS Spend Management Software Market Segment by Application

1.3.1 SaaS Spend Management Software Market Consumption (Sales Volume) Comparison by Application

1.4 Global SaaS Spend Management Software Market, Region Wise

1.5 Global Market Size of SaaS Spend Management Software

1.5.1 Global SaaS Spend Management Software Market Revenue Status and Outlook

1.5.2 Global SaaS Spend Management Software Market Sales Volume Status and Outlook

1.6 Global Macroeconomic Analysis

1.7 The impact of the Russia-Ukraine war on the SaaS Spend Management Software Market

2 Industry Outlook

2.1 SaaS Spend Management Software Industry Technology Status and Trends

2.2 Industry Entry Barriers

2.2.1 Analysis of Financial Barriers

2.2.2 Analysis of Technical Barriers

2.2.3 Analysis of Talent Barriers

2.2.4 Analysis of Brand Barrier

2.3 SaaS Spend Management Software Market Drivers Analysis

2.4 SaaS Spend Management Software Market Challenges Analysis

2.5 Emerging Market Trends

2.6 Consumer Preference Analysis

2.7 SaaS Spend Management Software Industry Development Trends under COVID-19 Outbreak

2.7.1 Global COVID-19 Status Overview

2.7.2 Influence of COVID-19 Outbreak on SaaS Spend Management Software Industry Development

3 Global SaaS Spend Management Software Market Landscape by Player

3.1 Global SaaS Spend Management Software Sales Volume and Share by Player

3.2 Global SaaS Spend Management Software Revenue and Market Share by Player

3.3 Global SaaS Spend Management Software Average Price by Player

3.4 Global SaaS Spend Management Software Gross Margin by Player

3.5 SaaS Spend Management Software Market Competitive Situation and Trends

3.5.1 SaaS Spend Management Software Market Concentration Rate

3.5.2 SaaS Spend Management Software Market Share of Top 3 and Top 6 Players

3.5.3 Mergers and Acquisitions, Expansion

4 Global SaaS Spend Management Software Sales Volume and Revenue Region Wise

4.1 Global SaaS Spend Management Software Sales Volume and Market Share, Region Wise

4.2 Global SaaS Spend Management Software Revenue and Market Share, Region Wise

4.3 Global SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.4 United States SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.4.1 United States SaaS Spend Management Software Market Under COVID-19

4.5 Europe SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.5.1 Europe SaaS Spend Management Software Market Under COVID-19

4.6 China SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.6.1 China SaaS Spend Management Software Market Under COVID-19

4.7 Japan SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.7.1 Japan SaaS Spend Management Software Market Under COVID-19

4.8 India SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.8.1 India SaaS Spend Management Software Market Under COVID-19

4.9 Southeast Asia SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.9.1 Southeast Asia SaaS Spend Management Software Market Under COVID-19

4.10 Latin America SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.10.1 Latin America SaaS Spend Management Software Market Under COVID-19

4.11 Middle East and Africa SaaS Spend Management Software Sales Volume, Revenue, Price and Gross Margin

4.11.1 Middle East and Africa SaaS Spend Management Software Market Under COVID-19

5 Global SaaS Spend Management Software Sales Volume, Revenue, Price Trend by Type

5.1 Global SaaS Spend Management Software Sales Volume and Market Share by Type

5.2 Global SaaS Spend Management Software Revenue and Market Share by Type

5.3 Global SaaS Spend Management Software Price by Type

5.4 Global SaaS Spend Management Software Sales Volume, Revenue and Growth Rate by Type

Here is the original post:
SaaS Spend Management Software Market 2023: Exclusive Insights ... - Digital Journal

Tenaya Therapeutics Has Been An Early Stage Company For Too … – Seeking Alpha

BorisRabtsevich/iStock via Getty Images

Tenaya Therapeutics (NASDAQ:TNYA) is an early stage developer of heart disease therapies and a relatively new IPO that I covered briefly 2 years ago. They have a set of interesting platforms - cellular regeneration, gene therapy and precision medicine - that they are using to develop a number of molecules targeting various heart diseases. The pipeline is at an early stage, with just one molecule in the clinic, but heart disease companies are rare, and the company looks like it is doing interesting science. So we will take a look.

Tenaya was established in 2016 with IP from Gladstone Institute and UT Southwestern. The company was able to raise $50mn in a Series A financing that year. In 2019, they raised another $90mn in a series B. 2 years later, they were able to raise another $106mn in a series C financing after they published preclinical data from two programs. That same year, they launched their IPO.

The company, like I mentioned, has three platforms. The Cellular Regeneration platform delivers genes to cardiac cells using viral vectors to regenerate them. Diseases like myocardial infarction, chemotherapy-related toxicity, and viral infection which result in loss of cardiomyocytes can be targeted through this platform. The Gene Therapy platform uses AAV vectors to deliver genes to correct functional defects in heart cells. These defects could be congenital or non-genetic forms. The precision medicine platform uses "human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) as proprietary disease models and analysis of human genetics for the identification of new targets, validation of known targets, and high-throughput screening for drug discovery."

The company is very early stage and their pipeline looks thus:

TNYA Pipeline (TNYA website)

At the time of this Corporate Presentation, they had 2 INDs approved and a third in the works. The latest stage product candidate seems to be small molecule HDAC6 inhibitor TN-301 for HFpEF or Heart Failure with preserved Ejection Fraction. This is in a phase 1 trial, however there is no listing in the registry. TN-201 also has a phase 1 trial - ongoing? - however, again there's no listing yet. TN-201 is a gene therapy targeting mutations of the MYBPC3 gene in hypertrophic cardiomyopathy (HCM). The third IND-enabled asset is TN-401, another gene therapy targeting PKP2 gene in Arrhythmogenic right ventricular cardiomyopathy.

These gene therapy assets use the AAV9 vector. AAVs have been in use for over 2 decades, and 6 gene therapies using AAVs have been FDA-approved. More than 5500 patients have been treated across 40 countries. In hundreds of trials, they have demonstrated their safety, and their long lasting transgene expression. Other important positives for AAV vectors are their low immunogenicity and ability to penetrate both dividing and nondividing cells, and so on. Some disadvantages include inability to deliver larger molecules, expensive manufacturing and so on.

As to the various diseases, MYBPC3 HCM has some 115k US patients. This genetic mutation is the most common form of inherited cardiomyopathy. There are no treatments for the underlying genetic mutation although the disease can lead to higher risks of sudden heart failures. Tenaya's treatment produces a functional copy of the MYBPC3 gene to the cardiomyocytes. These transgenes produce the MyBP-C protein which carries out normal heart function.

In preclinical trials, TN-201 demonstrated that despite a 5x increase in RNA versus wild type genes, there was no protein overexpression:

In vivo comparison (Company website)

There was also higher selectivity for the heart than other cells elsewhere in the body. Preclinical data also showed signs of efficacy in mice models. A single dose in mice demonstrated reduced hypertrophy, durable improvement in cardiac function and extended survival. The phase 1b study, informed by this preclinical data, will begin dosing in Q3. It is an open label dose escalation and dose expansion study. Initial data is expected in 2024.

The small molecule HDAC6 inhibitor TN-301 targets HFpEF. HDAC6 inhibition is an area of recent research interest in stopping the progression of this disease. In 2021, data published in Nature from a study of CKD-506 showed improvements in exercise capacity, heart function, and quality of life. Standard treatments for HFpEF have not included this modality previously. Tenaya says that in preclinical studies, TN-301 has shown a differentiated mechanism versus SGLT2 inhibitors, which are part of the arsenal against HFpEF. The company will start a randomized, placebo-controlled phase 1 SAD/MAD study with safety and tolerability as primary endpoints and PK/PD as secondary endpoints. The company says that "Dosing Commenced in Multiple-Ascending Dose Stage of First-In-Human Clinical Trial of TN-301; Data Anticipated in Second Half 2023." I still do not see anything on the registry.

TNYA has a market cap of $180mn and a cash balance of $204mn. In November, the company raised $77mn through a secondary offering. R&D expenses were $25.7 million for the fourth quarter and G&A expenses were $8.8 million. At that rate, they have a cash runway extending into 2025.

According to insider data, the company saw heavy insider buying in recent months. I was especially glad to see insiders buying the secondary in the open market.

Insider transactions (openinsider.com)

The company has heavy institutional and PE/VC presence, with over 90% of the shares.

In the two years since I covered it last, TNYA has put together preclinical data for its assets. However, nothing has gone into the clinic, although the company has been in existence for nearly a decade, with hundreds of millions of dollars in funding available. I am afraid there's nothing to see here until we have the first proof of viability from the company. That should happen in 2024. We will take another look at that time.

Thanks for reading. At the Total Pharma Tracker, we offer the following:-

Our Android app and website feature a set of tools for DIY investors, including a work-in-progress software where you can enter any ticker and get extensive curated research material.

For investors requiring hands-on support, our in-house experts go through our tools and find the best investible stocks, complete with buy/sell strategies and alerts.

Sign up now for our free trial, request access to our tools, and find out, at no cost to you, what we can do for you.

See the rest here:
Tenaya Therapeutics Has Been An Early Stage Company For Too ... - Seeking Alpha

Pathology and Astrocytes in Autism | NDT – Dove Medical Press

Introduction

Autism spectrum disorder (ASD) is as a neurodevelopmental disorder that presents with disturbances in social communication and repetitive behaviors.1 One in every 54 children suffers from ASD in the United States with a prevalence 4.3 times higher in males than in females.2 Global prevalence around the world is approximately of 1/100 children varying based on geographic, ethnic, and socioeconomic factors.3 The etiology of ASD is not well understood, however genetic, environmental, and immune factors have been reported to be the cause.4 Many genes linked to ASD have also been associated to other neurodevelopmental disorders, indicating a etiological heterogeneity and genetic pleiotropy in ASD.5 Candidate genes linked to ASD include DISC1, DYX1C1, RELN, AVPR1a, ITGB3, RPL10, and SHANK3, among many others.6 These genes regulate development, metabolism, plasticity, synapsis, and other important functions.7,8 Although numerous genes have been involved in ASD, a genetic diagnosis is not possible in most of the cases because the established genetic causes of ASD account for only a small portion of cases.9 Environmental factors such as hypoxia or trauma at birth, heavy metal exposure, maternal obesity, vitamin D deficiency, and maternal diabetes, have been associated with ASD.10 There is also a significant link between ASD with increase in reactive oxygen species (ROS) and a reduction in antioxidant capacity in the brain of ASD patients. ROS accumulation can directly enhance neuroinflammation and cytokine release.11 Accordingly, immune system impairment with elevated expression of pro-inflammatory cytokines and chemokines and microglia activation have been reported in postmortem ASD brains.12,13 Genetic, environmental, and immune factors are involved in the ASD phenotype, but how exactly is poorly understood.

The pathology of ASD is yet to be determined. However, the anatomy of several brain areas such as cerebellum, amygdala, hippocampus and cerebral cortex have been reported to be affected.1416 Increased brain size and disorganization of white and grey matter have been identified in patients with ASD.17,18 MRI studies showed abnormalities in gyral cortical anatomy, especially in the sylvian fissure, superior temporal sulcus,intraparietal sulcus,and inferior frontal gyrus in ASD patients.19,20 Multiregional dysplasia is present in 92% of ASD cases.21 It has been hypothesized that focal dysplasia in ASD may result from abnormalities in progenitor cell division, and/or migration and maturation of newly generated cells during prenatal brain development.21,22 Cortical dysplasia in ASD could explain high seizure prevalence and sensory disturbance in ASD.23 Mini-columnar abnormalities have also been reported in ASD.24,25 Mini-columns contain oriented arrays of pyramidal cells and GABAergic interneurons that modulate pyramidal cells input and output. Mini-columns are considered the basic functional unit in the neocortex. In ASD, there are more mini-columns but they are smaller in size. There is also less neuropil space resulting in cells more compacted.24,25 The increased number of mini-columns may result from additional division of progenitor cells during prenatal development, while the deficits in peripheral neuropil space may result from lack of inhibitory cells.26 White matter is also affected in ASD. In particular, there is a reduction in the number of long axons that are connected to long distance areas, and an increase in thin axons that communicate neighboring areas. This indicates a disconnection between long distance pathways and short distance over-connection. This is the case for the white matter in the anterior cingulate cortex, an area associated with attention, social interaction and emotion, functions altered in ASD. Moreover, there is also a reduction in axonal myelin thickness in some areas such as the white matter of the orbitofrontal cortex.27

The ASD brain also presents with alterations in the number of specific cell types. However, most of the cell types and regions of the brain have not been studied, and some of the data collected do not agree. Alteration in cerebellar cortex including a decrease in size and number of Purkinje cells and abnormality in functional connectivity between the cerebellum and other areas of the brain was reported in postmortem ASD brains. Decrease in Purkinje cells number was more noticeable in posterior lobe (lobule VIIA) of the cerebellum. Accordingly, a reduction in grey matter volume and a smaller vermis lobules VIVII were present in ASD children.28 Children with ASD had a bigger amygdala than typically developing children.29 A study on non-neuronal cell population numbers in the amygdala, reported no changes in number, however there was a strong microglial activation in two of eight ASD brains. In addition, there was a reduced number of a oligodendrocytes in the amygdala of adult ASD cases aged 20 and older.30 In the fusiform gyrus in seven postmortem ASD subjects, there was a decrease in number of neurons in layers III, V and VI, and in the mean perikaryal neuronal volumes in layers V and VI.31 An increase in the pyramidal cell population32,33 and a reduction in oligodendrocyte and astrocyte numbers (Figure 1AB) have also been reported in the prefrontal cortex of ASD postmortem brains.33,34 Also, a reduction in parvalbumin+ chandelier GABAergic interneurons was found in the dorsolateral and ventral prefrontal cortex.17,35,36 Decreased dendrite numbers in the dorsolateral prefrontal cortex and reduced dendrite branching in the CA4 and CA1 have been reported in individuals with ASD.37 Overall, abnormalities in different cell type populations and their morphology may lead to the disturbed neuronal function characteristic of ASD.

Figure 1 (A and B) GFAP+ astrocytes in prefrontal cortical plate (CP) and the white matter (WM) (A) control (CT) and (B) ASD. (A and B) Reconstruction of an average case depicting GFAP+ astrocyte location in the CP and WM. (A) control (CT) and (B). (CF) Astrocytes activation state. (C) Resting astrocyte with few processes and small cell body, (D) mild reactive astrocyte with slightly enhanced staining of glial processes and minor enlargement of cell body, (E) moderate reactive astrocyte with significant increase of cell body size and glial cell ramifications with dark stained processes and (F) severe reactive astrocyte with gemistocytic cell body and degraded processes that present as dark stained puncta.105 Scale bar in A, A, B, B: 500 m; C, D, E, F: 20 m.

Astrocytes are key elements for neuronal metabolic and structural support in the brain. They control ion concentration, modulate neurotransmitter release, maintain the bloodbrain barrier, and regulate blood flow in the nervous system, among many other functions.38 They also have crucial roles in neurodevelopment including in neurogenesis, neuronal migration, and synaptic plasticity.39,40 In addition, with pre- and postsynaptic neurons, perisynaptic astrocytes form tripartite synapses to modulate synaptic transmission.41 Together with microglia, astrocytes are regulators of the inflammatory responses. Innate immune responses are mediated through activation of microglia and astrocytes that produce cytokines, chemokines, and other immune mediators.38,42,43 Astrocyte activation could be either neurotoxic, by accelerating inflammatory responses and tissue damage, or neuroprotective by promoting neuronal survival and tissue repair, though this classification is not clear cut. Pro-inflammatory astrocytes secrete pro-inflammatory factors, such as tumor necrotic factor (TNF) and nitric oxide (NO), whereas neuroprotective astrocytes upregulate neurotrophic factors and thrombospondins to control neuroinflammation. Excessive neuroinflammation with increased reactive astrocytes and pro-inflammatory cytokines has been reported in ASD. Given the role of astrocytes in higher cognitive functions, any alteration in their number, distribution, morphology, and/or function, could lead to major neuronal dysfunction that could contribute to neurodevelopmental disorders such as ASD.44

Glial fibrillary acidic protein (GFAP) is a type III intermediate filament that is mainly expressed in astrocytes. It is also known as a marker for reactive astrocytes (Figure 1CF).42,45 GFAP is reported to be elevated in the cerebrospinal fluid of ASD subjects.46,47 Increased GFAP is correlated with astrogliosis and reactive damage that might result in immune response and further cytokines release.13,48 Data regarding GFAP gene expression in different regions of the ASD brain is controversial. Some studies reported upregulation of GFAP gene expression in the prefrontal cortex and cerebellum,49,50 whereas others reported no significant changes in GFAP gene expression in anterior cingulate cortex and anterior prefrontal cortex in ASD brains.51,52 Rats treated with propionic acid showed increased GFAP gene expression in the hippocampus, and presented ASD-like behaviors including aggressive behavior during adjacent interactions.53 At the protein level, several studies reported an increase in GFAP protein in superior frontal cortex, parietal cortex, cerebellum, and anterior cingulate cortex white matter.13,48,51 There was also an increase in GFAP protein in the cerebellum of postmortem brains whereas vimentin was decreased in both cerebellum and prefrontal cortex.54 In a valproic acid (VPA) animal model of ASD, there was an increase in the number of astrocytes and GFAP in medial prefrontal cortex and primary somatosensory cortex on postnatal day 30.55 In contrast, some other studies showed no change in GFAP protein in anterior cingulate grey matter, amygdala, and anterior and dorsolateral prefrontal white matter of postmortem ASD brains.30,51,56 Other proteins expressed by astrocytes are also changed in ASD. There was decreased amount of aquaporin 4 (AQP4), a water channel protein located in astrocytes, in the medial prefrontal cortex, but an elevation in the primary somatosensory area in the VPA animal model of ASD. AQP4 is mainly responsible for eliminating water from the cerebral parenchyma as well as supporting potassium buffering.55,55 In addition, there was a reduction in AQP4 protein in the cerebellum and an increase of connexin (cnx) 43, a gap junction protein located in astrocytes, in BA9 of postmortem ASD brains.57 Beside buffering ions and neurotransmitters concentration, cnx43 is responsible for regulating cellular growth and cell-cell adhesion. Increased cnx43 expression in ASD subjects could signify enhancement of glial-neuronal communication in frontal lobe that is in charge of executive functions.57

Data regarding the number of astrocytes in the brain with ASD are scarce (Table 1). We previously reported a decrease in the number of astrocytes, labeled with GFAP and S100, and a mild activation in GFAP+ astrocytes in the prefrontal areas BA9, BA46, and BA47 of postmortem ASD brains compared to control individuals.34 Figure 1 depicts representative images of astrocytes labeled with GFAP antibody and their location in control and ASD prefrontal cortex and astrocytes in different stages of activation. In another study from our laboratory, using Nissl staining, we showed a generalized reduction in astrocytes number with an increase in the neuronal population in layer II in the same areas.33 A reduced number of astrocytes could result from a reduced production and/or increased cell death. Increased overall glial cell densities, including astrocytes, oligodendrocytes and microglial cells, in layer II of olfactory cortex was reported, that may correlate with sensory deficits including damaged olfactory identification observed in patients with ASD. This increased glial cell density was correlated positively with the scores for restricted and repetitive behavior domain in the autism diagnostic interview revised (ADI-R) questionnaire.58 Using clustering nuclear profiles, genetic studies showed upregulated protoplasmic astrocyte gene expression in the prefrontal cortex and anterior cingulate cortex of postmortem ASD brains.59 In addition, upregulation of a gene set that was enriched in astrocytes and microglia was observed in frontal and temporal cortex of 251 postmortem samples from 48 ASD cases and 49 control subjects.60 These data contrast with anatomical studies demonstrating a decreased number of astrocytes in the prefrontal cortex. This may be because anatomical landmarks were not taking into account and the number of astrocytes was quantified using homogenated tissue.

Table 1 Summarizing Astrocyte Abnormalities in ASD Human Studies

Astrocytes play a critical role in neurotransmitter homeostasis, and in regulating the excitation/inhibition balance that is disturbed in the ASD cortex. Disturbance in astrocyte calcium signaling through inositol 1,4,5-trisphosphate 6 receptor 2 (IP3R2), that regulates neurotransmitter release, leads to ASD-like behaviors including repetitive behaviors and abnormal social interaction in mice.6163 Also, elevated level of glutamine synthetase (GS), an adenosine triphosphate-dependent enzyme that maintains glutamate levels located in astrocytes, was reported in the plasma of ASD patients.64 Increased mRNA expression of excitatory amino acid transporter 1 (EAAT1), located in astrocytes and responsible for glutamate uptake, and glutamate receptor AMPA 1, were found in the cerebellum of postmortem ASD brains. However, the density of AMPA glutamate receptor protein was decreased in the cerebellum. These findings reveal abnormalities in glutamatergic system in ASD.50 Some other studies reported a correlation between the glutamate transporter single gene polymorphism and the severity of anxiety and repetitive behaviors in ASD children.65 Furthermore, excessive electrical activity resulting from an abnormal glutamatergic function has been reported in ASD patients that can lead to pathologic behaviors.66 In VPA animal model of ASD, there was a decrease of 40% in glutamate transporter 1 (GLT1) at P15, but an increase of 92% in GLT1 with an increase of 160% in glutamate uptake at P120. The amount of glutathhione (GSH) was also increased 27% at P120 suggesting a disturbance in astrocytic glutamate clearance from the synaptic cleft in an animal model of ASD.67

Some report ASD as a hypo-glutamatergic disorder because of the symptoms produced by glutamate antagonists in ASD.68 Accordingly, a hypo-glutamatergic animal model displayed behavioral phenotypes that overlapped with the features observed in ASD69, indicating an alteration in the glutamatergic function in ASD.

Astrocytes also participate in gamma-aminobutyric acid (GABA) clearance. Some studies have shown a relationship between astrocyte abnormalities and the GABAergic system dysfunction in ASD. Wang et al showed a reduction in astrocyte-derived ATP that impaired GABAergic system and lead to ASD-like behaviors in the PFC of the IP3R2 mutant mice. ATP can modulate GABAergic synaptic transmission via P2X2 receptors located at the GABAergic interneuron terminals.63 In an in vitro study, cultured astrocytes exposed to VPA showed impairment in GABAergic inhibitory synapses but the excitatory synapses remained unchanged. This indicates that VPA can alter E/I balance in neural network by affecting the astrocyte-neuron interaction, highlighting the impact of astrocyte dysfunction in ASD pathology.70 Overall, there is evidence that astrocyte regulating of both glutamate and GABA neurotransmitters is altered in the ASD brain.

Neuroinflammation plays a main role in ASD pathology and many studies reported activation of astrocytes in postmortem ASD brains.13,71,72 Reactive astrocytes are the major source of releasing cytokines. The macrophage chemoattractant protein (MCP-1), that is in charge of monocyte/macrophage recruitment to the areas of inflammation, and pro-inflammatory cytokine interleukin-6 (IL-6), are altered in cortical and subcortical white matter in ASD.13 The expression of the translocator protein 18 kDa (TSPO), that is a marker for brain inflammation, and the amount of activated microglia in the frontal cortex and cerebellum are increased in reactive astrocytes in ASD.73 Monocyte chemoattractant protein-1 (MCP-1/CCL2) is a chemokine that has been reported to be elevated in the brain and blood of ASD cases.13,74 CCL2 is produced by astrocytes and microglia in the brain and is necessary for proliferation, migration and activation of microglia and astrocytes.75,76 Elevated level of CCL2 could also increase bloodbrain barrier (BBB) permeability and allow more T-lymphocytes to enter the brain during neuroinflammation.77 Multifocal perivascular lymphocytic cuffs are associated with astrocytes blebs that represents a cytotoxic reaction to lymphocyte attack, suggesting a dysregulation in cellular immunity that could damage astrocytes in ASD brains.78 Although many studies reported immune system dysfunction in ASD, it is not clear whether it is a cause or a consequence of the pathology.72

Astrocytes perform a critical role in synaptic formation, maturation, function, and elimination. An alteration in astrocyte structure and function alters neuronal activity.79 Astrocytes secrete platelet responsive protein (TSP) that works through its neuronal receptor calcium channel subunit 2-1, to control excitatory synaptogenesis.80 The synaptic signaling protein Rho GTPase Ras-related C3 Botulinum toxin substrate 1 (RAC1), is downstream of the TSP-2-1 pathway and has an important role in regulating synaptic and spinal growth.81 Disturbed RAC1 signaling is strongly associated with ASD and epilepsy pathology.82,83 The fact that astrocytes control the TSP-2-1-RAC1 pathway, is an example of the role of astrocytes on synaptic formation in ASD.84 Astrocytes secrete cytokines, such as transforming growth factor 1 (TGF-1) to regulate synaptogenesis. TGF-1 enhances phosphorylation of calcium/calmodulin dependent protein kinase II (CaMK II), downstream of NMDA receptors, to induce the formation of inhibitory synapses.85 TGF-1, with the NMDA coactivator D-serine, encourages the formation of excitatory synapses through NMDA receptor-dependent mechanisms.86 Supporting a role of TGF-1 in the formation of inhibitory synapses suggest that a relationship between the TGF-1 dysfunction and inhibitory synapse disturbance in ASD.87 Hevin is another protein secreted by astrocytes that is essential for maintaining synaptogenesis. Hevin bridges the presynaptic protein Neurexin-1 (NRX1) and postsynaptic Neuroligin-1B (NL1B) to assemble excitatory synapses.88 Mutations in Hevin, Neurexins and Neuroligins are strongly related to ASD pathology suggesting a critical role of these proteins in normal brain development.89

Astrocyte abnormalities have also been reported in other neurodevelopmental disorders, such as schizophrenia (SZ), bipolar disorder (BD) and major depressive disorder (MDD). A reduction in astrocyte densities was present in some brain areas of postmortem brains with SZ including cingulate and motor cortex, medial and ventrolateral regions of the nucleus accumbens, basal nuclei and substantia nigra.90 In an electron microscopic morphometric study of astrocytes in hippocampal CA3 region of 19 SZ cases, mitochondrial volume fraction and area density was negatively correlated with the duration of disease. However, the volume fraction of lipofuscin granules was positively associated with the duration of illness suggesting progressive astrocyte dysfunction due to the mitochondrial deficit.91 An increased expression of GFAP mRNA with astrogliosis was also observed in SZ patients with neuroinflammation.92 Furthermore, in animal studies of SZ, transgenic mice that expressed a mutant form of the disrupted in schizophrenia 1 (DISC1) gene in astrocytes, showed behavioral abnormalities related to SZ supporting the role of astrocytes in SZ pathology.93,94

In BD, astrocytic density was also reduced supporting astrocyte dysfunction in regulating glutamate homeostasis, calcium signaling, circadian rhythms and metabolism. Beneficial therapeutical effects of many BD drugs such as lithium, valproic acid (VPA) and carbamazepine (CBZ) are partly due to their positive actions on astrocytes by affecting the gene expression in astrocytes and regulating astroglia homeostatic pathways.95,96 There is also an elevation reported in the expression profile of cortical astrocytes in the postmortem BD subjects generated from eight different cohorts of subjects.97 In an in vitro study, astrocytes derived from induced pluripotent stem cells (iPSCs) generated from BD individuals showed alteration in transcriptome and a decrease in neuronal activity when they were co-cultured with neuronal cells. BD astrocytes also increased IL-6 secretion in the blood of BD patients highlighting the role of astrocytes in inflammatory signaling in BD pathology.98

A reduction in the astrocyte density in various regions of the brain including the prefrontal cortex, cingulate cortex and amygdala is an important feature in MDD pathology.99101 Golgi staining showed astrocytic hypertrophy in cell bodies and processes in the white matter of cingulate cortex of depressed patients that died by suicide. The presence of hypertrophic astrocytes could reflect local inflammation supporting the neuro-inflammatory hypothesis in depressed patients.102 In addition, the protein and mRNA level of pro-inflammatory cytokines secreted by reactive astrocytes were increased in the prefrontal cortex of suicide victims.103 However, other astrocytic proteins and markers such as GFAP, AQP4, cnx43, cnx30, glutamate transporters, and glutamine synthetase were reduced in MDD.104

Astrocytes play an important role in neurodevelopment and neuronal function in the brain, including higher cognitive functions. Available data indicates that astrocyte number is decreased in the cerebral cortex, while their state of activation and GFAP expression is increased in the ASD brain. This dysfunction and other astrocytic alterations may contribute to the ASD pathology. More research is needed to help our understanding of the mechanisms involved in astrocytic-related pathophysiology in ASD, and to introduce astrocytes as one of the promising targets for ASD treatment. Future research should answer questions as if the decreased in astrocyte number found in cortex occurs in other brain areas, if there are areas where astrocytic activation is more pronounced that others, what is the role of astrocytes on development, plasticity, and inflammation, and what other astrocytic functions are altered in ASD.

The authors report no conflicts of interest in this work.

1. Baio J. Prevalence of autism spectrum disorders: autism and developmental disabilities monitoring network, 14 sites, United States, 2008. Morbid Mortal Wkly Rep. 2012;61(3):157.

2. Maenner MJ, Shaw KA, Baio J, et al. Prevalence of autism spectrum disorder among children aged 8 years autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveill Summ. 2020;69:112. doi:10.15585/mmwr.ss6904a1

3. Zeidan J, Fombonne E, Scorah J, et al. Global prevalence of autism: a systematic review update. Autism Res. 2022;15:778790. doi:10.1002/aur.2696

4. Mandy W, Lai M-C. Annual research review: the role of the environment in the developmental psychopathology of autism spectrum condition. J Child Psychol Psychiatry. 2016;57:271292. doi:10.1111/jcpp.12501

5. Vorstman JAS, Parr JR, Moreno-De-Luca D, Anney RJL, Nurnberger JI, Hallmayer JF. Autism genetics: opportunities and challenges for clinical translation. Nat Rev Genet. 2017;18:362376. doi:10.1038/nrg.2017.4

6. Cardoso IL, Almeida S. Genes involved in the development of autism. Int Arch Commun Disord. 2019;2:19. doi:10.23937/iacod-2017/1710011

7. Ansel A, Rosenzweig JP, Zisman PD, Melamed M, Gesundheit B. Variation in gene expression in autism spectrum disorders: an extensive review of transcriptomic studies. Front Neurosci. 2017;10. doi:10.3389/fnins.2016.00601

8. Chaste P, Leboyer M. Autism risk factors: genes, environment, and gene-environment interactions. Dialogues Clin Neurosci. 2012;14:281292. doi:10.31887/DCNS.2012.14.3/pchaste

9. Voineagu I. Gene expression studies in autism: moving from the genome to the transcriptome and beyond. Neurobiol Dis Assess Gene Expres Neuropsychiatr Dis. 2012;45:6975. doi:10.1016/j.nbd.2011.07.017

10. Modabbernia A, Velthorst E, Reichenberg A. Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses. Mol Autism. 2017;8:13. doi:10.1186/s13229-017-0121-4

11. Pangrazzi L, Balasco L, Bozzi Y. Oxidative stress and immune system dysfunction in autism spectrum disorders. Int J Mol Sci. 2020;21:3293. doi:10.3390/ijms21093293

12. Morgan JT, Chana G, Pardo CA, et al. Microglial activation and increased microglial density observed in the dorsolateral prefrontal cortex in autism. Biol Psychiatry. 2010;68:368376. doi:10.1016/j.biopsych.2010.05.024

13. Vargas DL, Nascimbene C, Krishnan C, Zimmerman AW, Pardo CA. Neuroglial activation and neuroinflammation in the brain of patients with autism. Ann Neurol. 2005;57:6781. doi:10.1002/ana.20315

14. Bauman ML, Kemper TL. Neuroanatomic observations of the brain in autism: a review and future directions. Int J Dev Neurosci. 2005;23:183187. doi:10.1016/j.ijdevneu.2004.09.006

15. Garbett K, Ebert PJ, Mitchell A, et al. Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol Dis. 2008;30:303311. doi:10.1016/j.nbd.2008.01.012

16. Teffer K, Semendeferi K. Chapter 9 - human prefrontal cortex: evolution, development, and pathology. In: Hofman MA, Falk D, editors. Progress in Brain Research, Evolution of the Primate Brain. Elsevier; 2012:191218. doi:10.1016/B978-0-444-53860-4.00009-X

17. Hashemi E, Ariza J, Rogers H, Noctor SC, Martnez-Cerdeo V. The number of parvalbumin-expressing interneurons is decreased in the prefrontal cortex in autism. Cereb Cortex. 2017;27:19311943. doi:10.1093/cercor/bhw021

18. Scuderi C, Verkhratsky A. Chapter eleven - the role of neuroglia in autism spectrum disorders. In: Ilieva M, Lau -WK-W, editors. Progress in Molecular Biology and Translational Science, Autism. Academic Press; 2020:301330. doi:10.1016/bs.pmbts.2020.04.011

19. Levitt JG, Blanton RE, Smalley S, et al. Cortical sulcal maps in autism. Cereb Cortex. 2003;13:728735. doi:10.1093/cercor/13.7.728

20. Nordahl CW, Dierker D, Mostafavi I, et al. Cortical folding abnormalities in autism revealed by surface-based morphometry. J Neurosci. 2007;27:1172511735. doi:10.1523/JNEUROSCI.0777-07.2007

21. Wegiel J, Kuchna I, Nowicki K, et al. The neuropathology of autism: defects of neurogenesis and neuronal migration, and dysplastic changes. Acta Neuropathol. 2010;119:755770. doi:10.1007/s00401-010-0655-4

22. Casanova MF. Autism as a sequence: from heterochronic germinal cell divisions to abnormalities of cell migration and cortical dysplasias. Med Hypotheses. 2014;83:3238. doi:10.1016/j.mehy.2014.04.014

23. Casanova MF, El-Baz AS, Kamat SS, et al. Focal cortical dysplasias in autism spectrum disorders. Acta Neuropathol Commun. 2013;1:67. doi:10.1186/2051-5960-1-67

24. Casanova MF, Buxhoeveden DP, Switala AE, Roy E. Minicolumnar pathology in autism. Neurology. 2002;58:428432. doi:10.1212/WNL.58.3.428

25. Casanova MF, van Kooten IAJ, Switala AE, et al. Minicolumnar abnormalities in autism. Acta Neuropathol. 2006;112:287303. doi:10.1007/s00401-006-0085-5

26. Casanova MF. The Minicolumnopathy of Autism. In: Casanova MF, Opris I, editors. Recent Advances on the Modular Organization of the Cortex. Netherlands, Dordrecht: Springer; 2015:225237. doi:10.1007/978-94-017-9900-3_13

27. Zikopoulos B, Barbas H. Changes in prefrontal axons may disrupt the network in autism. J Neurosci. 2010;30:1459514609. doi:10.1523/JNEUROSCI.2257-10.2010

28. Palermo S, Morese R. Behavioral Neuroscience. BoD Books on Demand; 2019.

29. Schumann CM, Hamstra J, Goodlin-Jones BL, et al. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J Neurosci. 2004;24:63926401. doi:10.1523/JNEUROSCI.1297-04.2004

30. Morgan JT, Barger N, Amaral DG, Schumann CM. Stereological study of amygdala glial populations in adolescents and adults with autism spectrum disorder. PLoS One. 2014;9. doi:10.1371/journal.pone.0110356

31. van Kooten IAJ, Palmen SJ, von Cappeln P, et al. Neurons in the fusiform gyrus are fewer and smaller in autism. Brain. 2008;131:987999. doi:10.1093/brain/awn033

32. Courchesne E, Mouton PR, Calhoun ME, et al. Neuron number and size in prefrontal cortex of children with autism. JAMA. 2011;306:20012010. doi:10.1001/jama.2011.1638

33. Falcone C, Mevises N-Y, Hong T, et al. Neuronal and glial cell number is altered in a cortical layer-specific manner in autism. Autism Int J Res Pract. 2021;13623613211014408. doi:10.1177/13623613211014408

34. Vakilzadeh G, Falcone C, Dufour B, Hong T, Noctor SC, Martnez-Cerdeo V. Decreased number and increased activation state of astrocytes in gray and white matter of the prefrontal cortex in autism. Cereb Cortex. 2022;32:49024912. doi:10.1093/cercor/bhab523

35. Amina S, Falcone C, Hong T, et al. Chandelier cartridge density is reduced in the prefrontal cortex in autism. Cereb Cortex. 2021;31:29442951. doi:10.1093/cercor/bhaa402

36. Ariza J, Rogers H, Hashemi E, Noctor SC, Martnez-Cerdeo V. The number of chandelier and basket cells are differentially decreased in prefrontal cortex in autism. Cereb Cortex. 2018;28:411420. doi:10.1093/cercor/bhw349

37. Mukaetova-Ladinska EB, Arnold H, Jaros E, Perry R, Perry E. Depletion of MAP2 expression and laminar cytoarchitectonic changes in dorsolateral prefrontal cortex in adult autistic individuals. Neuropathol Appl Neurobiol. 2004;30:615623. doi:10.1111/j.1365-2990.2004.00574.x

38. Kwon HS, Koh S-H. Neuroinflammation in neurodegenerative disorders: the roles of microglia and astrocytes. Transl Neurodegener. 2020;9:42. doi:10.1186/s40035-020-00221-2

39. Farhy-Tselnicker I, Allen NJ. Astrocytes, neurons, synapses: a tripartite view on cortical circuit development. Neural Dev. 2018;13:7. doi:10.1186/s13064-018-0104-y

40. Wegiel J, Brown WT, Fauci GL, et al. The role of reduced expression of fragile X mental retardation protein in neurons and increased expression in astrocytes in idiopathic and syndromic autism (duplications 15q11.2-q13). Autism Res. 2018;11:13161331. doi:10.1002/aur.2003

41. Perea G, Navarrete M, Araque A. Tripartite synapses: astrocytes process and control synaptic information. Trends Neurosci. 2009;32:421431. doi:10.1016/j.tins.2009.05.001

42. Colombo E, Farina C. Astrocytes: key regulators of neuroinflammation. Trends Immunol. 2016;37:608620. doi:10.1016/j.it.2016.06.006

43. Pardo-Villamizar CA. Can neuroinflammation influence the development of autism spectrum disorders? In: Zimmerman AW, editor. Autism: Current Theories and Evidence, Current Clinical Neurology. Totowa, NJ: Humana Press; 2008:329346. doi:10.1007/978-1-60327-489-0_15

44. Jacobs S, Nathwani M, Doering LC. Fragile X astrocytes induce developmental delays in dendrite maturation and synaptic protein expression. BMC Neurosci. 2010;11:132. doi:10.1186/1471-2202-11-132

45. Liedtke W, Edelmann W, Bieri PL, et al. GFAP is necessary for the integrity of CNS white matter architecture and long-term maintenance of myelination. Neuron. 1996;17:607615. doi:10.1016/S0896-6273(00)80194-4

46. Ahlsn G, Rosengren L, Belfrage M, et al. Glial fibrillary acidic protein in the cerebrospinal fluid of children with autism and other neuropsychiatric disorders. Biol Psychiatry. 1993;33:734743. doi:10.1016/0006-3223(93)90124-V

47. Rosengren LE, Wikkels C, Hagberg L. A sensitive ELISA for glial fibrillary acidic protein: application in CSF of adults. J Neurosci Methods. 1994;51:197204. doi:10.1016/0165-0270(94)90011-6

48. Laurence JA, Fatemi SH. Glial fibrillary acidic protein is elevated in superior frontal, parietal and cerebellar cortices of autistic subjects. Cerebellum. 2005;4:206210. doi:10.1080/14734220500208846

49. Edmonson C, Ziats MN, Rennert OM. Altered glial marker expression in autistic post-mortem prefrontal cortex and cerebellum. Mol Autism. 2014;5:3. doi:10.1186/2040-2392-5-3

50. Purcell AE, Jeon OH, Zimmerman AW, Blue ME, Pevsner J. Postmortem brain abnormalities of the glutamate neurotransmitter system in autism. Neurology. 2001;57:16181628. doi:10.1212/wnl.57.9.1618

51. Crawford JD, Chandley MJ, Szebeni K, Szebeni A, Waters B, Ordway GA. Elevated GFAP protein in anterior cingulate cortical white matter in males with autism spectrum disorder. Autism Res. 2015;8:649657. doi:10.1002/aur.1480

52. Sciara AN, Beasley B, Crawford JD, et al. Neuroinflammatory gene expression alterations in anterior cingulate cortical white and gray matter of males with autism spectrum disorder. Autism Res. 2020;13:870884. doi:10.1002/aur.2284

53. Choi J, Lee S, Won J, et al. Pathophysiological and neurobehavioral characteristics of a propionic acid-mediated autism-like rat model. PLoS One. 2018;13:e0192925. doi:10.1371/journal.pone.0192925

54. Broek JA, Guest PC, Rahmoune H, Bahn S. Proteomic analysis of post mortem brain tissue from autism patients: evidence for opposite changes in prefrontal cortex and cerebellum in synaptic connectivity-related proteins. Mol Autism. 2014;5:41. doi:10.1186/2040-2392-5-41

55. Deckmann I, Santos-Terra J, Fontes-Dutra M, et al. Resveratrol prevents brain edema, bloodbrain barrier permeability, and altered aquaporin profile in autism animal model. Int J Dev Neurosci. 2021;81:579604. doi:10.1002/jdn.10137

56. Lee TT, Skafidas E, Dottori M, et al. No preliminary evidence of differences in astrocyte density within the white matter of the dorsolateral prefrontal cortex in autism. Mol Autism. 2017;8:64. doi:10.1186/s13229-017-0181-5

57. Fatemi SH, Folsom TD, Reutiman TJ, Lee S. Expression of astrocytic markers aquaporin 4 and connexin 43 is altered in brains of subjects with autism. Synapse. 2008;62:501507. doi:10.1002/syn.20519

58. Menassa DA, Sloan C, Chance SA. Primary olfactory cortex in autism and epilepsy: increased glial cells in autism. Brain Pathol. 2017;27:437448. doi:10.1111/bpa.12415

59. Velmeshev D, Schirmer L, Jung D, et al. Single-cell genomics identifies cell typespecific molecular changes in autism. Science. 2019;364:685689. doi:10.1126/science.aav8130

60. Parikshak NN, Swarup V, Belgard TG, et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature. 2016;540:423427. doi:10.1038/nature20612

61. Bezzi P, Volterra A. A neuronglia signalling network in the active brain. Curr Opin Neurobiol. 2001;11:387394. doi:10.1016/S0959-4388(00)00223-3

62. Petrelli F, Pucci L, Bezzi P. Astrocytes and microglia and their potential link with autism spectrum disorders. Front Cell Neurosci. 2016;10. doi:10.3389/fncel.2016.00021

63. Wang Q, Kong Y, Wu D-Y, et al. Impaired calcium signaling in astrocytes modulates autism spectrum disorder-like behaviors in mice. Nat Commun. 2021;12:3321. doi:10.1038/s41467-021-23843-0

64. Hamed NO, AlAyadhi L, Osman MA, et al. Understanding the roles of glutamine synthetase, glutaminase, and glutamate decarboxylase autoantibodies in imbalanced excitatory/inhibitory neurotransmission as etiological mechanisms of autism. Psychiatry Clin Neurosci. 2018;72:362373. doi:10.1111/pcn.12639

65. Gadow K, Roohi J, DeVincent C, Kirsch S, Hatchwell E. Brief report: glutamate transporter gene (SLC1A1) single nucleotide polymorphism (rs301430) and repetitive behaviors and anxiety in children with autism spectrum disorder. J Autism Dev Disord. 2010;40:11391145. doi:10.1007/s10803-010-0961-7

66. Choudhury PR, Lahiri S, Rajamma U. Glutamate mediated signaling in the pathophysiology of autism spectrum disorders. Pharmacol Biochem Behav Glutamate Recept. 2012;100:841849. doi:10.1016/j.pbb.2011.06.023

67. Bristot Silvestrin R, Bambini-Junior V, Galland F, et al. Animal model of autism induced by prenatal exposure to valproate: altered glutamate metabolism in the hippocampus. Brain Res. 2013;1495:5260. doi:10.1016/j.brainres.2012.11.048

68. Carlsson ML. Hypothesis: is infantile autism a hypoglutamatergic disorder? Relevance of glutamate serotonin interactions for pharmacotherapy. J Neural Transm. 1998;105:525535. doi:10.1007/s007020050076

69. Nilsson M, Carlsson A, Markinhuhta KR, et al. The dopaminergic stabiliser ACR16 counteracts the behavioural primitivization induced by the NMDA receptor antagonist MK-801 in mice: implications for cognition. Prog Neuropsychopharmacol Biol Psychiatry. 2004;28:677685. doi:10.1016/j.pnpbp.2004.05.004

70. Takeda K, Watanabe T, Oyabu K, et al. Valproic acid-exposed astrocytes impair inhibitory synapse formation and function. Sci Rep. 2021;11:23. doi:10.1038/s41598-020-79520-7

71. Liao X, Liu Y, Fu X, Li Y. Postmortem studies of neuroinflammation in autism spectrum disorder: a systematic review. Mol Neurobiol. 2020;57:34243438. doi:10.1007/s12035-020-01976-5

72. Matta SM, Hill-Yardin EL, Crack PJ. The influence of neuroinflammation in autism spectrum disorder. Brain Behav Immun. 2019;79:7590. doi:10.1016/j.bbi.2019.04.037

73. Fiorentino M, Sapone A, Senger S, et al. Bloodbrain barrier and intestinal epithelial barrier alterations in autism spectrum disorders. Mol Autism. 2016;7:49. doi:10.1186/s13229-016-0110-z

74. Ashwood P, Krakowiak P, Hertz-Picciotto I, Hansen R, Pessah IN, Van de Water J. Associations of impaired behaviors with elevated plasma chemokines in autism spectrum disorders. J Neuroimmunol. 2011;232:196199. doi:10.1016/j.jneuroim.2010.10.025

75. He M, Dong H, Huang Y, et al. Astrocyte-derived CCL2 is associated with M1 activation and recruitment of cultured microglial cells. Cell Physiol Biochem. 2016;38:859870. doi:10.1159/000443040

76. Hinojosa AE, Garcia-Bueno B, Leza JC, Madrigal JL. CCL2/MCP-1 modulation of microglial activation and proliferation. J Neuroinflammation. 2011;8:77. doi:10.1186/1742-2094-8-77

77. Song L, Pachter JS. Monocyte chemoattractant protein-1 alters expression of tight junction-associated proteins in brain microvascular endothelial cells. Microvasc Res. 2004;67:7889. doi:10.1016/j.mvr.2003.07.001

78. DiStasio MM, Nagakura I, Nadler MJ, Anderson MP. T lymphocytes and cytotoxic astrocyte blebs correlate across autism brains. Ann Neurol. 2019;86:885898. doi:10.1002/ana.25610

79. Clarke LE, Barres BA. Emerging roles of astrocytes in neural circuit development. Nat Rev Neurosci. 2013;14:311321. doi:10.1038/nrn3484

80. Eroglu , Allen NJ, Susman MW, et al. Gabapentin receptor 2-1 is a neuronal thrombospondin receptor responsible for excitatory CNS synaptogenesis. Cell. 2009;139:380392. doi:10.1016/j.cell.2009.09.025

81. Risher WC, Kim N, Koh S, et al. Thrombospondin receptor 2-1 promotes synaptogenesis and spinogenesis via postsynaptic Rac1. J Cell Biol. 2018;217:37473765. doi:10.1083/jcb.201802057

Go here to see the original:
Pathology and Astrocytes in Autism | NDT - Dove Medical Press