Dynamic molecular network analysis of iPSC-Purkinje cells differentiation delineates roles of ISG15 in SCA1 at the … – Nature.com

iPSC culture and differentiation to pan-neurons

SCA1-iPSCs and normal iPSCs were differentiated to pan-neurons87. SCA1-iPSCs and normal iPSCs were cultured in TeSR-E8 medium (STEMCELL Technologies, BC, Canada) with 10M Y27632 (253-00513, Wako, Osaka, Japan). After 24h, medium was changed to Stem Fit (AK02N, Ajinomoto, Tokyo, Japan) containing 5M SB431542 (13031, Cayman Chemical, Ann Arbor, MI, USA), 5M CHIR99021(13122, Cayman Chemical, Ann Arbor, MI, USA), and 5M dorsomorphin (044-33751, Wako, Osaka, Japan). After 5 days, iPSCs were dissociated with TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA). Neurospheres were then cultured in KBM medium (16050100, KHOJIN BIO, Saitama, Japan) with 20ng/mL Human-FGF-basic (100-18B, Peprotech, London, UK), 10ng/mL Recombinant Human LIF (NU0013-1, Nacalai, Kyoto, Japan), 10M Y27632 (253-00513, Wako, Osaka, Japan), 3M CHIR99021 (13122, Cayman Chemical, Ann Arbor, MI, USA), and 2M SB431542 (13031, Cayman Chemical, Ann Arbor, MI, USA) for 10 days. Finally, neurospheres were dissociated and seeded onto chambers coated with poly-L-ornithine (P3655, Sigma-Aldrich, St. Louis, MO, USA) and laminin (23016015, Thermo Fisher Scientific, Waltham, MA, USA), and cultured in DMEM/F12 (D6421, Sigma-Aldrich, St. Louis, MO, USA) supplemented with B27 (17504044, Thermo Fisher Scientific, Waltham, MA, USA), Glutamax (35050061, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA) for 14 days.

SCA1-iPSCs and normal iPSCs were differentiated to Purkinje cells88. To form EBs, iPSCs were dissociated with TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA), and 24,000 cells were aggregated by centrifugation at 200g for 2min in 96-well U-bottomed culture plates (650-180, Greiner, Kremsmnster, Austria) coated with Lipidure (CM5206, Nichiyu, Tokyo, Japan). Cells were cultured with gfCDM/insulin medium, 1:1 Iscoves modified Dulbeccos medium (12440053, Thermo Fisher Scientific, Waltham, MA, USA), and Hams F-12 (11765054, Thermo Fisher Scientific, Waltham, MA, USA) with 7g/mL insulin (I5500, Sigma-Aldrich, St. Louis, MO, USA); 1x chemically defined lipid concentrate (11905031, Thermo Fisher Scientific, Waltham, MA, USA); 15g/ml apo-transferrin (T4382, Sigma-Aldrich, St. Louis, MO, USA); 450M monothioglycerol (195-15791, Thermo Fisher Scientific, Waltham, MA, USA); 5mg/mL BSA (A7608, Sigma-Aldrich, St. Louis, MO, USA), Glutamax (35050061, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA); 20M Y-27632 (253-00513, Wako, Osaka, Japan); and 10M SB431542 (13031, Cayman Chemical, Ann Arbor, MI, USA). After 2 days, 50ng/mL recombinant human FGF2 (233-FB-025, R&D systems, MN, USA) was added to culture medium. After 21 days, EBs were transferred to 10cm Petri dishes (1020-100, Iwaki, Shizuoka, Japan) coated with Lipidure (CM5206, Nichiyu, Tokyo, Japan) and cultured for 14 days in Neurobasal/N2 medium, Neurobasal (21103-049, Thermo Fisher Scientific, Waltham, MA, USA) with N2 supplement (17502048, Thermo Fisher Scientific, Waltham, MA, USA), Glutamax (35050061, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA).

EBs were dissociated and cocultured with rhombic lip (RL) cells isolated from cerebellums of E14 Slc:ICR mice to induce differentiation into Purkinje cells. Briefly, RLs and EBs were dissociated with TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA) and cocultured in DMEM/F12 medium (11330032, Sigma-Aldrich, St. Louis, MO, USA) with 10% FBS, N2 supplement (17502048, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA). A total of 1.0106 cells at cell ratio = 1:10 (EB: RL) with 80L medium were seeded on chambers coated with poly-L-lysine (P1524-25MG, Sigma-Aldrich, St. Louis, MO, USA) and laminin (23016015, Thermo Fisher Scientific, Waltham, MA, USA). After incubation for 6h, DMEM/F-12 supplemented with N2 (17502048, Thermo Fisher Scientific, Waltham, MA, USA), 100g/mL BSA (A7608, Sigma-Aldrich, St. Louis, MO, USA), 50ng/mL human BDNF (248-BDB-010/CF, R&D systems, MN, USA), 50ng/mL human NT3 (267-N3-005/CF, R&D systems, MN, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA) medium was added and cultured for 10 days.

EB-derived differentiating cells and RL-derived cells were cultured in cell culture insert dishes (140640, Thermo Fisher, Waltham, MA, USA) in which the two types of cells could be separated to avoid RL contamination of RNA-seq samples. The cell culture insert dish was coated with poly-L-lysine (P1524-25MG, Sigma-Aldrich, St. Louis, MO, USA) and laminin (23016015, Thermo Fisher Scientific, Waltham, MA, USA), embryonic body-derived cells were then seeded on the lower well, and RL-derived cells were seeded on a polycarbonate insert with a 4m pore. The culture medium was the same as described above.

To quantify the cell growth rate of SCA1-iPSCs and normal iPSCs, 30,000 iPSCs were seeded per well on Day 0. After 2, 4, 6, or 8 days, cells were collected, dissociated by 0.5 x TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA) and counted by using Burker-Turk hemocytometer. To quantify the size of iPSC-derived EBs, images of EBs were taken by microscope (IX70, Olympus) on day 1, 7, 14 and 21, and the 2D areas of EBs reflecting their 3D sizes were measured by ImageJ software (version 1.50, NIH, MD, USA).

AAV-HMGB1-EGFP or AAV-EGFP were infected into differentiated pan-neurons (MOI 2000). Twelve days after AAV infection, cells were fixed with 1% paraformaldehyde in PBS for 30min. After blocking in PBS containing 10% FBS for 30min, cells were stained with the following primary and secondary antibodies: mouse anti-III-tubulin 1 (1:2000 for 16h at 4C, #T8660 Sigma-Aldrich, St. Louis, MO, USA), rabbit anti-PSD95 (1:1000 for 16h at 4C, 3409, Cell Signaling Technology, Danvers, MA, USA), donkey anti-mouse IgG Alexa 488-conjugated (1:600 for 1h at room temperature, #715-545-150, Jackson ImmunoResearch Laboratories, West Grove, PA, USA), and donkey anti-rabbit IgG Alexa488-conjugated (1:1000 for 1h at room temperature, A-21206, Thermo Fisher Scientific). The dendritic spine was assessed after acquisition of image by confocal microscopy (FV1200 laser scanning microscope, Olympus, Tokyo, Japan). We used x40 objective lens (UPLSAPO40X2 (NA:0.95)). The dendritic spine density was measured by ImageJ software (version 1.50, NIH, MD, USA). After calibration of scale information, dendrite (Tuj-1-immunostained image) were manually traced and calculate the dendrite length in observing image window. Next, the spine (PSD95-immunostained dots) was counted, and the dendritic spine density was defined as the number of spines in 1m length of a dendrite, calculated by dividing a spine number in one dendrite by the length of it.

Differentiated Purkinje cells were fixed with 1% paraformaldehyde in PBS for 30min. Cells were incubated with 10% FBS followed by incubation with primary and secondary antibodies as follows: mouse anti-Calbindin (1:2000 for 16h at 4C, C9848, Sigma-Aldrich, St. Louis, MO, USA) and donkey anti-mouse IgG Alexa 488-conjugated (1:1000 for 1h at room temperature, A-21202, Thermo Fisher Scientific).

Mice brain tissues at different timepoints (P0, P28, P91, and P392) were fixed with 4% paraformaldehyde in 0.1M phosphate buffer for 12h and embedded in paraffin. Sagittal sections were deparaffinized in xylene and rehydrated in ethanol. For antigen retrieval, sections were incubated in Tris-EDTA solution pH 9.0 (100mM Tris-base and 10mM EDTA) at 121C for 15min. Human brain paraffin sections of SCA1 patients or control sections were also processed. Sections were then incubated with 0.5% triton-X 100 in PBS for 20min to perform permeabilization. Next, sections were incubated with 10% FBS in PBS for 30min and were incubated with primary and secondary antibodies as follows: rabbit anti-ISG15 (1:100 for 16h at 4C, HPA004627, Sigma-Aldrich, St. Louis, MO, USA), mouse anti-Calbindin (1:2000 for 16h at 4C, C9848, Sigma-Aldrich, St. Louis, MO, USA), mouse anti-Atxn1 (1:100 for 16h at 4C, MABN37, Millipore, Burlington, MA, USA), mouse anti-Ub (1:100 for 16h at 4C, #3936, Cell Signaling technology, Danvers, MA, USA), donkey anti-mouse IgG Alexa 488-conjugated (1:600 for 1h, #715-545-150, Jackson ImmunoResearch Laboratories, West Grove, PA, USA), and donkey anti-rabbit IgG Cy3-conjugated (1:600 for 1h, #711-165-152, Jackson ImmunoResearch Laboratories, West Grove, PA, USA). Nuclei were stained with DAPI (0.2g/mL in PBS, D523, DOJINDO Laboratories, Kumamoto, Japan). Z-stacked images (0.5m interval x 5 slices) were acquired from cerebellar cortex (Lobule IV/V) using a confocal microscope (FV1200IXGP44, Olympus, Tokyo, Japan) and a super-resolution microscope (LSM980 with Airyscan 2, Zeiss, Oberkochen, Germany). Signal intensities were measured using ImageJ software.

Male mice brain tissues at different time points (P0, P28, P91, and P392) were homogenized using the BioMasher II (#893062, Nippi, Tokyo, Japan) with RIPA buffer (10mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 1% Triton-X 100, 0.1% SDS, 0.1% DOC, and 1:250 volume Protease Inhibitor Cocktail (#539134, Calbiochem, San Diego, CA, USA)). Homogenates were centrifuged at 12,000g for 10min, and supernatants were added to equal volumes of sample buffer (0.1M Tris-HCl pH 6.8, 4% SDS, 20% glycerol, 0.05% BPB, and 12% -mercaptoethanol) and boiled at 100C for 10min. Samples were subjected to SDS-PAGE and transferred onto PVDF membrane. After blocking the membranes with 5% skim milk in TBST (20mM Tris-HCl pH 7.5, 150mM NaCl, 0.05% Tween-20) for 1h, membranes were incubated with primary and secondary antibodies as follows: rabbit anti-ISG15 (1:1000 for 3h at room temperature, HPA004627, Sigma-Aldrich, St. Louis, MO, USA), mouse anti-GAPDH (1:3000 for 16h at 4C, MAB374, Merck, Darmstadt, Germany), mouse anti-Atxn1 (1:1000 for 3h at room temperature, MABN37, Millipore, Burlington, MA, USA), mouse anti-Ub (1:1000 for 16h at 4C, #3936, Cell Signaling Technology, Danvers, MA, USA), mouse anti-Myc (1:3000 for 1h at room temperature, M047-3, MBL, Aichi, Japan), rabbit anti-FLAG (1:3000 for 1h at room temperature, F7425, Sigma, St. Louis, MO, USA), sheep anti-mouse IgG HRP conjugated (1:3000 for 1h, NA931, Cytiva, Tokyo, Japan), and rabbit anti-IgG HRP conjugated (1:3000 for 1h, NA934, Cytiva, Tokyo, Japan). Proteins were detected using Amersham ECL select (RPN2235, Cytiva, Tokyo, Japan) on an Image-Quant luminescence image analyzer LAS500 (Cytiva, Tokyo, Japan). Signal intensities were measured using ImageJ software.

SCA1-iPSCs and normal iPSCs were collected and homogenized in 350L RNA RLT buffer (Qiagen)/0.01% 2-mercaptoethanol (Wako, Tokyo, Japan). Total RNA was purified with RNeasy mini kit (Qiagen). To eliminate genomic DNA contamination, on-column DNA digestion was conducted for each sample with DNase I (Qiagen). Prepared RNA samples were subjected to a HiSeq-based RNA-seq by TAKARA (700 million bp reads).

Gene expression profiles of each sample were evaluated by the number of short reads that were mapped onto gene coding sequences in the reference human genome assembly hg38. Differential expression genes were analyzed with DESeq234. Log2FC (Fold Change) between SCA1-derived and normal cells was calculated by DESeq2, and the difference of gene expression was determined at | Log2FC|>0.5.

To generate the pathological network based on PPI, UniProt accession numbers were added to genes identified in RNA-seq-based gene expression analysis. The pathological PPI network was constructed by connecting genes using the integrated database of the Genome Network Project (GNP) (https://cell-innovation.nig.ac.jp/GNP/index_e.html), which includes BIND, BioGrid (http://www.thebiogrid.org/), HPRD, IntAct (http://www.ebi.ac.uk/intact/site/index.jsf), and MINT. A database of GNP-collected information was created on the Supercomputer System available at the Human Genome Center of the University of Tokyo.

To create a static molecular network, statistically significantly changed molecules were connected at two neighboring time points based on interactions in the PPI database (an integrated database collected by GNP, which includes BIND, BioGrid (http://www.thebiogrid.org/), HPRD, IntAct (http://www.ebi.ac.uk/intact/site/index.jsf), and MINT), without considering their cause-result relationships. Each network starting from a changed molecule was expanded step-by-step from one-hop (directly linked) to six extra connections, and the degree of significance at each expansion step was evaluated by calculating the z-score of their ratio of changed nodes.

To gain insights into the dynamics of the pathological molecular network, a PPI-based chronological molecular network was constructed by connecting two proteins between the two neighboring time points using the integrated PPI database.

To estimate the impact of a significantly differentially expressed gene to the future time point, a gene in a certain time point was connected to a set of genes in the next time point based on the PPI database (an integrated database collected by GNP, which includes BIND, BioGrid (http://www.thebiogrid.org/), HPRD, IntAct (http://www.ebi.ac.uk/intact/site/index.jsf), and MINT) and defined as downstream genes. The magnitude of impact of a gene at the first time point (defined as r) was calculated by the ratio of downstream genes that were significantly changed in mRNA expression levels to all genes at the second time point. The selection of genes was performed based on comparison of impact of a specific gene (specific impact) and total impact at the second time point. The statistical significance of the impact was examined using two-tailed Fishers exact test with post-hoc Benjamini-Hochberg procedure (adjusted p-value<0.05, red dots). The statistical significance of mRNA expression change was examined using log2FC between SCA1 and normal cells (|Log2FC|> 0.5, blue dots). A digraph was created from a significantly differentially expressed gene at the original time point (iPSCs) to significant genes at the end point (Purkinje cells) via significant genes at the intermediate time point based on the impact analysis. The digraph predicts the original gene whose change at the initial time point leads to molecular changes at the final time point.

To select cytokine-relevant genes, Gene Ontology (GO) enrichment analysis was performed using clusterProfiler89 package in R. A list of genes included in a selected pathway was used as input of enrichGO function of clusterProfiler. From all the enrichment results, GO terms related to cytokine were selected to extract a list of cytokine-relevant genes. The GO terms related to cytokine were searched by keyword cytokine, and thereafter terms that were not related to cytokine such as cytokinesis were excluded.

A search for transcriptomic studies of SCA1 in Homo sapiens and Mus musculus in the NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) was performed on August 7, 2023 using the keywords spinocerebellar ataxia type 1 and SCA1. Eight studies (PRJNA305316, PRJNA422988, PRJNA472147, PRJNA472754, PRJNA503578, PRJNA688073, PRJNA871289, and PRJNA903078) that include RNA-seq data and raw sequence information generated from cerebellar tissues of SCA1 mouse models were found, while no studies with human RNA-seq data were found.

Raw sequence data were downloaded from NCBI SRA using the SRA Toolkit (https://github.com/ncbi/sra-tools) and mapped to the M. musculus genome assembly GRCm38/mm10 using HISAT2. Gene expression was calculated using the featureCounts function from Subread v1.5.2. Gene expression differences between the SCA1 and control groups were tested using Welchs t-test. Gene expression changes with a p-value0.05 were considered significant.

RNA was isolated from iPSCs, EBs and Purkinje cells with TRIzol RNA Isolation Reagents (15596026, Thermo Fisher Scientific, MA, USA). Reverse transcription was performed by using the SuperScript VILO cDNA Synthesis kit (11754-250, Invitrogen, Carlsbad, CA, USA). Quantitative PCR analyses were performed with the 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) by using THUNDERBIRD SYBR qPCR Mix (QPS-201, TOYOBO, Osaka, Japan) and assessed by the standard curve method. The primer sequences were:

ISG15, forward primer: 5-CGCAGATCACCCAGAAGATCG-3 and reverse primer: 5- TTCGTCGCATTTGTCCACCA-3

UbE2L6, forward primer: 5-GTGGCGAAAGAGCTGGAGAG-3 and reverse primer: 5 -ACACTGTCTGCTGGTGGAGTTC- 3

ARIH1, forward primer: 5-CAGGAGGAGGATTACCGCTAC-3 and reverse primer: 5-CTCCCGGATACATTCCACCA-3

GAPDH, forward primer: 5-AGATCATCAGCAATGCCTCCTG-3 and reverse primer: 5-ATGGCATGGACTGTGGTCATG-3

PCR conditions for amplification were 40 cycles of 95C for 1min for enzyme activation, 95C for 15sec for denaturation, and 60C for 1min for extension. The expression levels of ISG15, UbE2L6 and ARIH1 were corrected by GAPDH.

Frozen mouse brains (male, P28) were lysed with TNE buffer (10mM Tris-HCL (pH 7.5), 150mM NaCl, 1mM EDTA, and 1% NP-40) and collected by centrifugation (15,000g10min). Aliquots (100g protein in cerebellar tissue lysate) were then incubated 1h with a 50% slurry of protein G-sepharose beads. After centrifugation (2000g3min), the supernatants were incubated with 1g rabbit anti-ISG15 antibody (aHPA004627, Sigma-Aldrich, St. Louis, MO, USA) overnight at 4C. Reactants were then incubated with Protein G-sepharose beads for 4h, washed with TNE buffer, and eluted by sample buffer. For double-precipitation, samples were incubated with 2g biotin-labeled mouse anti-Ub (#3936, Cell Signaling technology, Danvers, MA, USA) overnight at 4C, followed by incubation with streptavidin beads (TrueBlot(R) Streptavidin Magnetic Beads, S000-18-5, Rockland, Pottstown, PA, USA). The collected samples were further incubated with 2g mouse anti-Atxn1 (MABN37, Millipore, Burlington, MA, USA) overnight at 4C. Then, reactants were incubated with Protein G-sepharose beads for 4h, washed with TNE buffer, and eluted in sample buffer.

HeLa cells were seeded at a density of 4105 cells/well in a 6-well plate (3516, Corning, Glendale, AZ, USA) and transfected with 30 pmol human ISG15-siRNA (sc-43869, Santa Cruz Biotechnology, Dallas, TX, USA) or scrambled siRNA (SR30004, OriGene, Rockville, MD, USA) using 4L Lipofectamine RNAiMAX (13778-075, Thermo Fisher Scientific, Waltham, MA, USA). At 24h after siRNA transfection, 2.5g myc-Ataxin1-33Q, myc-Ataxin1-86Q, or FLAG-Ku70 plasmid was transfected using 5L Lipofectamine 2000 (11668-019, Thermo Fisher Scientific, Waltham, MA, USA). At 48h after siRNA transfection, 100nM Bafilomycin A1 (BVT-0252-C100, AdipoGen, Liestal, Basel-Landschaft, Switzerland) or 5M MG132 (139-18451 Wako, Osaka, Japan) was added to the culture medium in order to inhibit autophagy or proteasome-dependent protein degradation, respectively. At 49h, 100g/mL cycloheximide (033-20993, Wako, Osaka, Japan) was added to the culture medium in order to inhibit protein synthesis. The cells were collected at 0, 6, 12, and 24h after addition of cycloheximide, lysed with RIPA buffer (10mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 1% Triton-X 100, 0.1% SDS, 0.1% DOC, and 1:250 volume Protease Inhibitor Cocktail (#539134, Calbiochem, San Diego, CA, USA)), and centrifuged at 12,000g for 10min. The supernatants were mixed with equal volumes of sample buffer (0.1M Tris-HCl pH6.8, 4% SDS, 20% glycerol, 0.05% BPB, and 12% -mercaptoethanol), boiled at 100C for 10min, and subjected to SDS-PAGE.

Mutant Atxn1-KI mice (Sca1154Q/2Q mice) were generously gifted by Prof. Huda Y. Zoghbi (Baylor College of Medicine, TX, USA)39. The backcrossed strain with C57BL/6 mice were further crossed with C57BL/6 female mice more than 10 times in our laboratory. The number of CAG repeats was checked by fragment analysis using the following primers: forward (5CACCAGTGCAGTAGCCTCAG3, labeled with 6-carboxyfluorescein) and reverse (5AGCTCTGTGGAGAGCTGGAA3). Mice were maintained under suitable humidity (around 50%) at 22C with a 12h light-dark cycle. We have complied with all relevant ethical regulations for animal use.

Cerebellar specimens collected at autopsy from three SCA1 patients and three control patients without neurological disorders (lung cancer, leukemia, and cholangiocarcinoma) were used. The details of the SCA1 patients (51-year-old female, 54-year-old female, and 50-year-old male) were described previously90,91,92. Their CAG repeat expansion in the Atxn1 gene was confirmed by PCR and their numbers of CAG repeats were around 50, although the exact numbers were not determined by fragment analysis or Sanger sequencing. Human plasma samples were acquired from SCA1 patients with a PCR-based genetic diagnosis or control patients without neurological disorders. Essential information about the SCA1 patients and controls is shown in Fig.8D. Other clinical information is not linked with samples according to ethics regulations. All ethical regulations to human research participants were followed.

In total, 100 L of human plasma samples that had been diluted 2-fold with saline (OTSUKA normal saline 20mL, Otsuka Pharmaceutical Factory, Tokushima, Japan) was added to a 96-well plate precoated with an anti-ISG15 antibody (#CY-8085, CircuLex Human ISG15 ELISA Kit, MBL, Tokyo, Japan) and incubated for 16h at 4C. Plates were washed and subsequently incubated with a peroxidase-conjugated anti-ISG15 antibody (Atlas Antibodies, HPA004627-100UL, Bromma, Sweden) for 2h at room temperature. For detection, Substrate Reagent (#CY-8085, CircuLex Human ISG15 ELISA Kit, MBL, Tokyo, Japan) was added to each well. The reaction was terminated with Stop Solution (#CY-8085, CircuLex Human ISG15 ELISA Kit, MBL, Tokyo, Japan), and absorbance at 450nm was measured on a microplate reader (SPARK 10M, TECAN, Grodig, Austria). A standard curve was generated using 0, 1.5, 3, 6, and 12ng/mL Recombinant Human ISG15 (UL-601-500, R&D Systems, Minneapolis, MN, USA) diluted with Sample Diluent (326078738, HMGB1 ELISA Kit Exp, Shino-test, Tokyo, Japan).

We analyzed three iPSC lines derived from two SCA1 patients for RNA-seq-based gene expression analysis. For meta-analysis using SCA1 model mice, we collected RNA-seq data from 3 to 17 mice in each time point from NCBI SRA database. Statistical analyses for biological experiments were performed using Graphpad Prism 8. Biological data following a normal distribution are presented as the meanSEM, with Tukeys HSD test or Dunnetts test for multiple group comparisons or with Welchs t-test for two group comparisons. The distribution of observed data was depicted with box plots, with the data also plotted as dots. Box plots show the medians, quartiles, and whiskers, which represent data outside the 25th75th percentile range. To obtain each data, we performed biologically independent experiments. The number of samples was indicated in each figure and figure legends.

This study was performed in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the Japanese Government and National Institutes of Health. All experiments were approved by the Committees on Gene Recombination Experiments, Human Ethics, and Animal Experiments of the Tokyo Medical and Dental University (G2018-082C3, 2014-5-3, and A2021-211A). Human samples including post-mortem brains were provided with informed consent and their use was approved by the Committees on Human Ethics (O2020-002-03).

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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Dynamic molecular network analysis of iPSC-Purkinje cells differentiation delineates roles of ISG15 in SCA1 at the ... - Nature.com

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Genetic algorithm

We used the GA to optimize the conductance of each current so that the AP model reproduced the experimental values in previous studies24,29. We executed the GA optimization using a program implemented in C# with reference to the method of Bot et al.55, and its type was a real-coded GA. We evaluated the degree of adaptation of each model in the population using the score calculated using Eq.(1). We calculated the AP for each model 60s after the initial state. We performed numerical integration to compute APs using the forward Euler method with a time step of 0.01ms. The initial values of ion concentration inside and outside the cell, and temperature were equivalent to the conditions of the experiments. We fixed the intracellular potassium and sarcoplasmic reticulum calcium concentrations to accelerate convergence. We estimated the cell volume from the cell surface area data24,56. We used the same value as that in the Paci model for the ratio of the sarcoplasmic reticulum volume to the cytoplasmic volume23. We used a model population with random values assigned to each conductance as the starting generation. The upper and lower scaling limits were 0.010.0 for GNa, GCaL, and Gf in the ventricular-type model and 0.05.0 for all others. We describe the details of the GA optimization of AP models in the Supplementary Methods.

To estimate magnetic signals from iPS-CMs, we simulated the 2D electrical activity of the cell population. We set the intracellular ion concentrations using adult mouse cardiac AP model values57. We determined the extracellular ion concentrations from the composition of the culture medium and set the temperature to room temperature (RT: 24C). The temperature coefficients (Q10) used in the AP models referred to values from the published literature58. We computed the solution to the partial differential Eq.(2) using the CrankNicolson method, with the spatial step set to x=y=60m and the time step set to t=0.01ms. We determined the averaged cellular resistivity to reproduce the conduction velocity measured in neonatal rat cardiac cell sheets40 (Supplementary Methods and Supplementary Fig. S7). The list of parameters used in the simulation is summarized in Supplementary Table S3. We calculated the magnetic field using BiotSavart's law from the currents flowing in the cells at each time point. We estimated the observed waveforms using integration in the area of each pickup coil. We assumed that the cancellation component of the magnetic field caused by the extracellular return current was negligibly small because the volume of the medium was sufficiently large relative to the spreading of cultured cells. We also checked how much the observed waveforms were affected when the cell position was shifted from directly under the sensor. As a result, we confirmed that a displacement of2mm in the x-axis or y-axis directions had almost no effect on the measurements (Supplementary Fig. S8).

The procedure for dataset preparation is as follows: A peak region (250ms) was cut from the magnetic signals estimated using simulation and subjected to random stretching and scaling. Non-peak regions between peak regions were linearly interpolated to make data of 120s each. For the background noise, four data of the x component of the magnetic field with no current applied (for artificial signal experiments) or eight data of the y component with no cell sample placed (for cell experiments) were used in equal proportions within each dataset. Random time shifts were performed in the superposition of these magnetic signals and noise data. Even when the cycle length and amplitude were fixed, this shift brought diversity to the dataset. Finally, datasets (n=160 for artificial signal experiments or 640 for cell experiments) were generated, including three data types in a 1:1:2 ratio: positive peak direction, negative peak direction, and background noise only. Representative waveforms are shown in Supplementary Fig. S9.

The window used in the spectral calculation with the FSST33 was the Kaiser window, with a size of 512 points. The sidelobe attenuation was 13.6dB. The real and imaginary parts of the spectral were input as separate features. The input values were pre-standardized by subtracting the mean and dividing by the standard deviation. Training was iterated for up to 60 epochs (one epoch means one round of data). The network was validated using the validation data for each epoch. If the validation loss exceeded the previous minimum value more than ten times, it was decided that there was no further improvement and training was stopped. The initial learning rate was set to 0.001 and the learning rate was dropped by a factor of 0.1 every 20 epochs. The training data were divided into segments of 10s lengths and the mini-batch size (a subset of the training data used in one step to evaluate the gradient of the loss function and update the weights) was set to 16.

We calculated the AUROC36 to evaluate network classification performance. The AUROC is the area under the curve plotted with the false positive rate on the horizontal axis and the true positive rate on the vertical axis. The AUROC is 1.0 when separation performance is best and 0.5 when classification is performed randomly. In this study, we defined each data point as positive if it was peak (P) or negative if it was non-peak (N).

From the output label data, we plotted a histogram of the lengths of segments labeled as class P (Fig.4f). Using this histogram as a reference, we estimated the appropriate distribution of peak region lengths, set a lower limit, and identified segments longer than this threshold as peak regions (Fig.1c). We used the average count number for the analysis of samples measured multiple times. We obtained the average waveform by superimposing magnetic signals of 175ms before and after the center position of each peak region and averaging their amplitudes. Then, we repeated the adaptive correlation filter59 ten times to correct for positional fluctuations.

A vector-type SQUID magnetometer12,15 was applied to measure magnetic fields. The vector-type SQUID magnetometer had an axial-type first-order gradiometric pickup coil with a diameter of 15.5mm and two planar-type first-order gradiometric squared pickup coils of 915.5 mm2 and 1115.5 mm2. The baseline length of each gradiometric pickup coil was 50mm. The three gradiometric pickup coils were oriented perpendicular to each other and assembled on a cylindrical bobbin. Three Ketchen-type low-temperature SQUIDs were individually coupled to each pickup coil and simultaneously detected the three independent components of the magnetic field: Bx, By, and Bz. The SQUID readouts were connected to double-integrator type flux-locked loop (FLL) circuits for output linearization and dynamic range improvement. The total noise level, including environmental noise, was 1020 fT/Hz at 10Hz. The SQUID magnetometer was installed in a glass-fiber reinforced plastic (GFRP) cryostat with an MSB. The MSB comprised two 1mm thick mu-metal layers with double front doors. The shielding factor of the MSB was more than 40dB at 10Hz. The GFRP cryostat consisted of a cylindrical main body that stored 6-L liquid helium and a narrow GFRP tube that dropped from the bottom of the main body. The main body was installed in the ceiling of the MSB and only the GFRP tube penetrated the MSB through a hole in its top. The SQUID magnetometer was installed at the bottom end of the GFRP tube and placed at the center of the MSB.

The cell sample was placed on a height-adjustable stage made of non-magnetic materials and adjusted to 3mm from the bottom edge of the pickup coil. Measurements were taken at room temperature, and the FLL readout signals were digitally recorded at the sampling rate of 1kHz with HPF at 3Hz, LPF at 100Hz, and notch filters at 60Hz.

We kept the resistance fixed and varied the output voltage of the function generator to adjust the current that generated magnetic signals. With no filtering, we increased the voltage until the peak of the magnetic signal could be identified by visual inspection and recorded the peak amplitude at that point. Based on that value, we adjusted the voltage to generate the desired magnetic signals. We enhanced the artificial signal (2.74) so that the signal-to-noise ratio was equivalent to that in the cell sample experiment. To confirm the validity of this procedure, we compared the amplitude spectrum densities of the background noise between the artificial signal experiment and the cell sample experiment (Supplementary Fig. S4). Although differences in amplitude existed, the spectral distribution had the same trend within the range of 3.540Hz used to train the LSTM networks.

We implemented the scaled template technique following previous research22. We slid the template (the event waveform of interest to detect) along the time series data and scaled it to fit the data at each position. Then, we divided the template scaling factor by the standard error of the time series data, which was the detection criterion, and we considered the event waveform of interest to be detected when this criterion exceeded a threshold value. The template was a peak waveform of 250ms in length cut from the magnetic signal estimated using numerical simulation, which we also used as the training data in deep learning. To compare the two methods without bias, we set the threshold so that the number of detected peaks from background noise was equal to that of deep learning.

The mouse iPS cell line iPS-MEF-Ng-20D-17 (Expressing GFP by Nanog promoter)44, established by the Center for iPS Cell Research and Application, Kyoto University, was provided by the RIKEN BRC through the National BioResource Project of the Ministry of Education, Culture, Sports, Science, and Technology, Japan. For the culture method, we referred to previous studies44,60,61. To maintain the undifferentiated state of iPS cells, MEFs (EmbryoMax Primary Mouse Embryonic Fibroblasts, PMEF-NL, Neo Resistant, Strain FVB; purchased from Sigma-Aldrich, St Louis, MO, USA), in which cell proliferation was arrested by mitomycin C (Nacalai Tesque, Kyoto, Japan) treatment, were cocultured as feeder cells. The maintenance medium was composed of Dulbecco's modified Eagle's medium (Sigma-Aldrich) with 15% fetal bovine serum (Equitech-Bio Inc., Kerrville, TX, USA), 50 U/ml penicillin, 50g/ml streptomycin (Sigma-Aldrich), 2mM L-glutamine (Sigma-Aldrich), nonessential amino acids (100) (Sigma-Aldrich), 0.1mM 2-mercaptoethanol (FUJIFILM Wako Chemicals, Osaka, Japan), and 0.1% human leukemia inhibitory factor (FUJIFILM Wako Chemicals). The medium was refreshed daily and iPS cells were passaged every two days. Colonies were detached with 0.25% trypsin/1mM EDTA (FUJIFILM Wako Chemicals), dispersed in cell suspension, counted, and 1.0106 cells were seeded into MEFs on 60mm plates.

Based on previous studies37,62, cardiomyocyte differentiation was induced by forming EB. The differentiation medium was Iscove's modified Dulbecco's medium (Sigma-Aldrich) containing 20% fetal bovine serum, 50 U/ml penicillin, 50g/ml streptomycin, 2mM L-glutamine, nonessential amino acids (100), and 0.1mM 2-mercaptoethanol. Mouse iPS cells were suspended at 1.5104 cells/ml in the differentiation medium and seeded 0.2ml into each well of a 96-well U-shaped-bottom microplate (Nunclon Sphera; Thermo Fisher Scientific, Waltham, MA, USA). The plates had a cell-nonadherent surface treatment, which allowed uniform and stable EBs to form. For further differentiation, the culture was switched from floating to adherent on day 5. Plastic dishes of 100mm diameter and MEA (Alpha MED Scientific, Osaka, Japan) were used for magnetic measurement, and glass bottom dishes (AGC Techno Glass, Shizuoka, Japan) were used for fluorescence microscopy. These dishes were coated with 0.1 w/v% gelatin solution (FUJIFILM Wako Chemicals) and one EB was transplanted at the center of each dish. Beating areas began to appear on day 7. Magnetic measurement was performed during days 1921 when the area of differentiated cells was extensive and synchronized beating was observed. Fluorescence microscopy was also performed at this time. To ensure that one peak corresponded to the electrical activity of the entire cell population, samples with a single beating area larger than 3mm square were selected for measurement. To bring the cells closer to the sensor, the cylinder of the MEA was excised to a height of 1mm. For comparison with iPS-CMs, MEFs were also cultured in cloning rings with an inner diameter of 5mm. In the experiment to detect the drug's chronotropic effects from magnetic signals, the medium was replaced with a medium supplemented with isoproterenol at a final concentration of 10M, and magnetic signals were measured from iPS-CMs after incubation for 30min.

Cardiomyocytes were immunostained on day 19 of differentiation, and the expression of cardiomyocyte marker cardiac troponin T and connexin 43 that forms gap junctions was confirmed. iPS-CMs were fixed in 4% paraformaldehyde for 20min at 4C, followed by blocking with 5% goat serum (Nichirei, Tokyo, Japan) and 0.1% Triton-X diluted in Dulbecco's phosphate buffered saline (DPBS) for 20min at RT. The cells were washed three times for 5min with DPBS and incubated with a primary antibody diluted in DPBS containing 1% goat serum for 1h at RT and then overnight at 4C. The primary antibodies were rabbit polyclonal IgG anti cardiac troponin T antibody (1.4g/mL; Proteintech, Rosemont, IL, USA) and rabbit polyclonal IgG anti connexin 43 antibody (10g/mL; Thermo Fisher Scientific). The cells were washed three times for 5min with DPBS with shaking and further incubated with the secondary antibody Alexa fluor 546 goat anti rabbit IgG (Invitrogen, Carlsbad, CA, USA; 1:1000 dilution in DPBS/0.05% Triton X-100) for 30min at RT. The cells incubated with connexin 43 antibody were also treated with Alexa fluor 488 Phalloidin (Thermo Fisher Scientific; 1:50 dilution in DPBS/0.05% Triton X-100) and stained for actin filaments. The cells were washed three times with tris buffered saline for 5min and once with DPBS for 5min, and immersed in 4',6-diamidino-2-phenylindole (DAPI)-added anti-fading agent (Nacalai Tesque). Observation and imaging were performed with an IX71 fluorescence microscope (Olympus, Tokyo, Japan).

We measured FPs using MEA38. We selected the electrode near the center of the beating area and recorded the potential difference between it and a reference electrode not in contact with the cells. To avoid noise when measuring simultaneously with magnetic signals, we output the electrical signals from the probe externally through an IC clip and did not use the attached connector. When measuring FP only, we used it. We performed the measurement at room temperature and recorded data at the sampling rate of 1kHz with HPF at 0.16Hz, LPF at 160Hz, and notch filters at 60Hz.

Deep learning network training and data classification were performed in MATLAB (Mathworks Inc., Natick, MA, USA). The GA for parameter optimization of the AP model and the numerical simulation of the electrical activity of cardiomyocytes were performed using our programs implemented in C#.

Data are presented as meanstandard error of the mean (SEM). Comparisons between two groups were analyzed using the unpaired t-test unless otherwise indicated. For comparisons of three or more groups, when equal variances could be assumed, one-way ANOVA was used, followed by Tukey's test as a post hoc test. When equal variances could not be accepted, the BrownForsythe correction was performed, followed by the GamesHowell test as a post hoc test. Differences between data were considered statistically significant at p<0.05.

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Orthogonal analysis of mitochondrial function in Parkinson’s disease patients | Cell Death & Disease – Nature.com

Human subjects

PD patients were recruited from the outpatient clinic for Movement Disorders of the Department of Neurology of the Leiden University Medical Center (Leiden, the Netherlands) and nearby university and regional hospitals. All participants fulfilled the U.K. Parkinsons Disease Society Brain Bank criteria for idiopathic PD. The study was approved by the medical ethics committee of the Leiden University Medical Center (P12.194/NV/ib), and written informed consent was obtained from all PD patients.

Fibroblasts were isolated at Leiden University Medical Center from skin biopsies derived from the ventral side of the upper leg and cultured under highly standardized conditions as previously described in [14]. Peripheral whole blood was collected from PD patients at Leiden University Medical Center and PBMCs were isolated at the Department of Molecular Genetics at the Erasmus Medical Center in Rotterdam. Control iPSC were obtained from the Eramsus MC iPS Core facility.

Peripheral whole blood from 24 age-matched healthy controls (age >55 years) was obtained from Sanquin Rotterdam (NVT0585.00 Mantel, NVT0585.01 Annex).

Bioinformatic analysis was performed using the Parkinsons Progression Markers Initiative (PPMI) database.

To generate erythroblasts, PD patients peripheral blood mononuclear cells (PBMC) were extracted from 10ml of freshly extracted blood with the use of Lympholyte-H (Cedarline) and Leucosep polypropylene tubes (227290, Greiner) according to manufacturers indications. Briefly, blood was diluted in PBS at a 1:2 ratio and loaded on a 15mL Lympholyte Leucosep tube. Blood was centrifuged at 800g for 25min with no brakes at 4C. Upon removal of the plasma, the PBMC enriched cell fraction was collected, washed several times with sterile PBS and upon PBMCs were cultured in StemSpan SFEM medium (Stemcell Technologies) containing 2mM Ultraglutamine (Lonza), 1% Nonessential aminoacids (NEAA), 1% penicillin/streptomycin, 50ng/ml Stem Cell Factor, 2U/ml Erythropoietin, 1uM Dexamethasone (Sigma), 10ng/ml Interleukin-3 (R&D Systems), 10ng/ml Interleukin-6 (R&D Systems), 40ng/ml IGF-1 (R&D Systems) and 50ug/ml Ascorbic Acid (Merck) for 69 days refreshing half of the medium every other day starting from day 2. Erythroblasts were isolated when reaching 6070% of the total cell population by gradient centrifugation at 1000g for 20minutes at room temperature over Percoll (GE Healthcare). Isolated erythroblasts were frozen in FBS containing 10% DMSO at 80C. Metabolic analysis was performed within 2 days after thawing.

PD patients fibroblasts used in this study were prepared and isolated at Leiden University Medical Center from skin biopsies derived from the ventral side of the upper leg and cultured under highly standardized conditions as previously described in [14]. The study was approved by the medical ethics committee of the Leiden University Medical Center, and written informed consent was obtained from all PD patients.

Fibroblasts were reprogrammed to pluripotent stem cells using the CytoTune-iPS 2.0 Sendai Reprogramming Kit (A16517, Thermo Fisher) according to the manufacturers protocol.

Human iPSC lines were generated as previously described [18]. Briefly, to generate embryoid bodies with neuroepithelial outgrowths (EBs), iPSC colonies were dissociated with 2mg/mL collagenase IV and transferred to non-adherent plates in hESC medium.

(Dulbeccos modified Eagles medium (DMEM)/F12 (Thermo Fisher Scientific), 20% knockout serum (Thermo Fisher Scientific), 1% minimum essential medium/non-essential amino acid (NEAA, Sigma-Aldrich, St Louis, MO, USA), 7nlml1 -mercaptoethanol (Sigma-Aldrich), 1% L-glutamine (Thermo Fisher Scientific) and 1% penicillin/streptomycin (P/S, Thermo Fisher Scientific) supplemented with 10M SB-431542 (Ascent Scientific), 1M dorsomorphin (Tocris), 3M CHIR 99021 (Axon Medchem) and 0.5M Purmorphamine (Alexis) on a shaker in an incubator at 37C/5% CO2. On the second day, medium was replaced with N2B27 medium [DMEM-F12/neurobasal 50:50 (Thermo Fisher Scientific), 1% P/S, 1:100 B27 supplement lacking vitamin A (Thermo Fisher Scientific) and 1:200 N2 supplement (Thermo Fisher Scientific)] containing 10M SB-431542, 1M dorsomorphin, 3M CHIR99021 and 0,5M Purmorphamine. On day 4, N2B27 medium was replaced and supplemented with 3M CHIR99021, 0.5M Purmorphamine, and 150M Ascorbic Acid (Sigma).

On day 6, EBs were slightly triturated and plated on Matrigel-coated (Matrigel - 354277, Corning) plates at a density 1015 EB per well containing smNPC expansion medium (N2B27 medium containing 3M CHIR 99021, 200M Ascorbic Acid, 0.5M Purmorphamine) and expanded for 5 passages before final differentiation. The medium was refreshed every other day.

smNPC were dissociated with Accutase at RT, diluted, and seeded on Poly-D-lysine Matrigel-coated cover glasses in a 12-well plate at the concentration of 5104 cells per well in the Patterning medium [N2B27 medium containing 1ng/mL GDNF (Peprotech), 2ng/ml BDNF (Pepotech), 200M Ascorbic Acid and 0.5M Smoothened Agonist (SAG Pepotech)]. The medium was refreshed every 2 days.

At day 8, the medium wash switched to the Maturation medium containing N2B27 medium, 2ng/ml GDNF, 2ng/ml BDNF, 1ng/mL TGF-b3 (Peprotech), 200M ascorbic Acid and 5ng/ml of ActivinA for the first feeding and 2ng/ml ActivinA for the following feedings. Medium change occurred every third day.

Erythroblasts and iPSCs were washed and resuspended in FC buffer (HBSS w/o calcium and magnesium + 0.5% BSA) and incubated with PE Mouse Anti-Human CD44 antibody (1:25, BD, 550989), FITC Mouse Anti-Human CD71 antibody (1:50, BD, 555536), or 7-AAD (Thermo Fisher, A1310) for 30min at 4C. Mitochondria were stained with Mitotracker Green FM (100nm, Cell Signaling, 9074) and active mitochondria with TMRM (100nm, Thermo Fisher, T668) for 30min at 37C. Cells were detected by flow cytometry using a LSRFortessa Cell Analyzer (BD, USA). Flowjo software (BD, USA) was used for data analysis.

Oxygen consumption rates (OCR) and extracellular acidification rate (ECAR) were measured using a XF-24 Extracellular Flux Analyzer (Agilent Technologies), as previously described [14]. Erythroblasts were seeded at a density of 2105 cells/well on Cell-Tak (Corning, 354240) coated Seahorse plates in unbuffered XF DMEM medium (Agilent Technologies) supplemented with 1mM sodium pyruvate, 2mM glutamine and 10mM glucose or galactose. Immediately after seeding, cells were centrifuged at 200g for 1minute to attach evenly to the bottom of the well and the plate was equilibrated for 30minutes at 37C in the absence of CO2. iPSCs derived from fibroblasts of PD patients and healthy controls were seeded at a density of 8103 cells/well on Seahorse plates and differentiated to dopaminergic neurons over a period of 3 weeks according to the described methodology. On an experimental day, the medium was changed to an unbuffered XF DMEM medium supplemented with 1mM sodium pyruvate, 2mM glutamine and 10mM glucose or galactose. Cells were incubated for 1h at 37C in the absence of CO2, before the Seahorse assay. For each assay, medium and reagent acidity were adjusted to pH 7.4 on the day of the assay, according to the manufacturers procedure. Optimal cell densities were determined experimentally to ensure a proportional response to FCCP (oxidative phosphorylation uncoupler).

After 3 measurements to detect the oxygen consumption ratio baseline, cells were then challenged with sequential injections of mitochondrial toxins: 1M oligomycin (Adenosine triphosphate ATP - synthase inhibitor), 1M FCCP, and 1M antimycin (complex III inhibitor). A minimum number of 5 replicates were performed for each cell line; data represent the mean of the different replicates. Basal respiration (measured as the average OCR rates at the baseline), maximal mitochondrial respiration (maximal respiration), reserve capacity (difference between maximal respiration and basal respiration), and respiration dedicated to ATP production (difference between basal respiration and oligomycin-dependent respiration) were used to investigate mitochondrial bioenergetics. Basal glycolysis, measured as extracellular acidification rate (ECAR) maximal glycolysis and reserve glycolytic capacity (difference between maximal glycolysis and basal glycolysis) were taken into account to investigate glycolytic properties.

Reprogrammed iPSCs cells cultured in an 8-chamber slide were fixed with 4% PFA for 15min at room temperature. After incubation in ice-cold methanol for 10min cells were permeabilized in 0.1% Triton in PBS for 10min and blocked using 1% BSA in PBS/0.05% Tween-20 for 30min. Next, cells were incubated with primary antibodies diluted in blocking buffer overnight at 4C - Mouse Anti-Human TRA1-81 (1:75, Abcam, AB16289#20), Rabbit Anti-Human OCT4 (1:250, Abcam, AB19857#8), Goat Anti-Human NCAM (1:100, R&D, AF2408), Goat-Anti Human SOX17 (1:100, R&D, AF1924) or Mouse Anti-Human Beta-Tubulin (1:1000, Merck, T8660) primary Chicken Anti-Human MAP2 (1:2000, Abcam, AB5392), Mouse Anti-Human TH (1:200, Millipore, MAB318). After washing with PBS cells were incubated with respective secondary Goat Anti-Mouse Alexa Fluor 546 (1:500, Invitrogen, A21045), Goat Anti-Rabbit Alexa Fluor 488 (1:500, Invitrogen, A11008#8a), Donkey Anti-Goat Alexa Fluor 488 (1:500, Invitrogen, A11055) or Goat Anti-Mouse Dylight 594 (1:500, Jackson, 115-515-166#7) antibodies diluted in blocking buffer for 1h at room temperature. Nuclei were stained with Hoechst 33342 (1:1000 in PBS, Thermo Fisher) for 10min. Cells were next washed with PBS, mounted with ProLong Diamond Antifade Mountant (P36965, Thermo Fisher), and imaged using a Leica Stellaris5 confocal microscope.

Blood transcriptome data from the Parkinson Progressive Markers Initiative (PPMI) cohort (PPMI website: https://ida.loni.usc.edu/pages/access/geneticData.jsp#441) were obtained. The libraries were prepared using the NEB/Kapa (NEBKAP) based library prep, with second-strand synthesis. RNA sequencing was performed at Hudson Alphas Genomic Services Lab on an Illumina NovaSeq6000, generating 100 million 125bp paired reads per sample. The Salmon files were imported into R using Tximport. To identify differentially expressed genes between PD groups and controls, the DESeq2 package was used. Normalized counts were subjected to Rlog transformation to improve distances/clustering for the principal component analysis (PCA). The cohort of subjects was divided into subgroups based on the delta-UPDRS-III (MDS-Unified Parkinsons Disease Rating Scale, UPDRS-III at last visit - UPDRS-III at first visit) of PD subjects: those with a delta-UPDRS-III less than 0 (defined as mild) and those with a delta-UPDRS-III greater than 0 (defined as severe), as well as controls (CTRL). A threshold of significance at FDR<0.05 was applied.

Gene Set Enrichment Analysis (GSEA) was conducted on an unfiltered, ranked list of genes. The analysis involved various terms from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome Pathway Databases, Hallmark Gene Set Collection, and WikiPathways (GSEA website: http://www.gsea-msigdb.org/gsea/msigdb/collections.jsp). Pathway information was obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) available at the Molecular Signatures Database (http://www.broadinstitute.org/gsea/msigdb/index.jsp) or from the Hallmark Gene Set Collection (http://www.gsea-msigdb.org/gsea/msigdb/collections.jsp). Gene set enrichment with FDR<0.1 was considered significant. Genes in each PD group were ranked based on the level of differential expression using a signal-to-noise metric and a weighted enrichment statistic.

Transcriptomic analysis was performed using R Studio version 4.2.3. The experiments were conducted with a minimum of three independent biological replicates. GraphPad Prism version 9 (GraphPad Software, La Jolla California USA) was used for all statistical analyses and graphical representations. P values were denoted as *P<0.05, **P<0.01, ***P<0.001, and were considered significant. In the absence of indications, comparisons should be considered non-significant. Comparisons between two groups were analyzed using unpaired two-tailed Students t-tests, and comparisons between more than two groups were analyzed using either one-way ANOVA followed by Dunnetts (comparison of PD means vs. healthy subjects) or Tukeys (comparison of all the means) posthoc test for multiple comparisons.

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Orthogonal analysis of mitochondrial function in Parkinson's disease patients | Cell Death & Disease - Nature.com

Targeting Age-Related Macular Degeneration With Stem Cell Therapy – BioProcess Online

Age-related macular degeneration (AMD), a common eye disorder leading to permanent loss of central vision, is the leading cause of blindness in developed countries. Current treatments for AMD are often ineffective, particularly for dry AMD, the most common form of the disorder. Cell therapy technologies offer potential new treatments for AMD, however, the efficacy of such treatments depends on the ability to deliver therapeutic cells to the target region and to produce enough RPE cells. On the back of the success of Induced pluripotent stem cell (iPSC) technology, a novel stem-cell therapy based upon iPSC-derived RPE cell transplantation was conceptualized as a promising new treatment for AMD and other retinal diseases. Bio-Techne aims to offer a range of animal-free cell culture products and GMP proteins for clinical manufacturing, which may boost the success of cell therapies. iPSC-based cell therapy is seen as a promising solution for AMD patients unresponsive to traditional treatments.

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Targeting Age-Related Macular Degeneration With Stem Cell Therapy - BioProcess Online

PLEKHM2 deficiency induces impaired mitochondrial clearance and elevated ROS levels in human iPSC-derived … – Nature.com

Generation of homozygous PLEKHM2-KO hiPSCs and differentiating into cardiomyocytes

The generation of homozygous PLEKHM2-KO hiPSCs was carried out using the CRISPR-Cas9 system, and the guide RNA (gRNA) was designed and synthesized to target exon 2 of PLEKHM2 (Fig. 1A). Subsequent screening confirmed the successful knockout of PLEKHM2 gene, which revealed a one-nucleotide deletion in one allele and a one-nucleotide insertion in another allele (Fig. 1B and Supplementary Fig. 1B). The cell line under investigation was found to express the human pluripotency markers SSEA4 and OCT4 (Fig. 1C) and tested negative for mycoplasma contamination (Supplementary Fig. 1C). Western blot (WB) analyses revealed the absence of PLEKHM2 protein expression in PLEKHM2-KO hiPSC-CMs at day 20 (Fig. 1D). Wild-type (WT) and PLEKHM2-KO hiPSCs were differentiated into cardiomyocytes using the small molecule-based method (Fig. 1E). The efficacy of hiPSC-CMs differentiation was evaluated using flow cytometry, which indicated that PLEKHM2-KO and WT hiPSC-CMs exhibited similar proportions of cardiac Troponin T (cTnT)-positive cells (around 93%) at day 20 post differentiation (Fig. 1F, G). These results indicate that the PLEKHM2-KO hiPSC-CMs were successfully constructed.

A The PLEKHM2 gene structure and the location of the guide RNA (gRNA) used for epigenome editing with CRISPR/Cas9. B Sequencing analysis confirmed a homozygous PLEKHM2-KO hiPSC line with a 1-nucleotide deletion in one allele and a 1-nucleotide insertion in the other allele. C Pluripotent stem cell markers SSEA4 and OCT4 were detected by immunofluorescence staining in PLEKHM2-KO colonies. Scale bar, 20 m. D Western blot analysis of PLEKHM2 in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at day 20. E Protocol of small molecule-based methods to induce cardiac differentiation. F, G Flow cytometry analysis for cTnT from representative WT and PLEKHM2-KO differentiation at day 20. The results are presented as meansSD of 3 independent experiments. N.S. not significant.

We next investigate the dynamic changes in myocardial contractility and calcium transients of PLEKHM2-KO hiPSC-CMs. The HCell series single myocardial cell function detection system was utilized to measure myocardial contractility [10] (Supplementary Fig. 2A), and the green fluorescent calcium-modulated protein 6 fast type (GCaMP6f) calcium imaging system was employed to track myocardial calcium transients [11] (Supplementary Fig. 2B, C).

At the early stage of myocardial differentiation, specifically on 20 day, no significant alterations in myocardial contractility were observed between the WT hiPSC-CMs and the PLEKHM2-KO hiPSC-CMs. But at day 30, PLEKHM2-KO hiPSC-CMs exhibited a minor reduction in systolic displacement, as well as systolic and diastolic velocities compared to WT hiPSC-CMs, but no change in contractile force. And at day 40, the systolic displacement, contractile force, as well as systolic and diastolic velocities were significantly reduced in PLEKHM2-KO hiPSC-CMs compared to the WT hiPSC-CMs (Fig. 2AE, and Supplementary Fig. 2D, E), showing that the PLEKHM2-KO hiPSC-CMs developed systolic dysfunction phenotype. Calcium transient is a principal mechanism responsible for myocardial contraction, wherein the magnitude of contraction force is contingent upon variations in calcium ion concentration within the cell [12]. Hence, we next evaluated the alterations in calcium transients in the myocardium. In accordance with the trend observed in myocardial contractility, no significant variation was detected in calcium transient of PLEKHM2-KO hiPSC-CMs during the early phase post myocardial differentiation. However, a decline in calcium transient amplitude was observed at day 30, alongside a decrease in upstroke and recovery velocity of calcium transients in PLEKHM2-KO hiPSC-CMs compared to WT hiPSC-CMs, and further exacerbated by 40 day (Fig. 2FJ, and Supplementary Fig. 2F). Interestingly, we also found that compared to WT hiPSC-CMs, the baseline values of calcium transients in PLEKHM2-KO hiPSC-CMs showed a significantly increased at day 40, indicating abnormal calcium handling in PLEKHM2-KO hiPSC-CMs (Supplementary Fig. 2G). These results suggest that abnormal calcium handling is a potential cause of the impaired myocardial contractility in PLEKHM2-deficient cardiomyopathy.

A Representative line scan images of myocardial contractility in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at days 20, 30, and 40. BE Quantification of displacement, force, contraction and relaxation velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs (n=12 cells per group). F Representative line scan images of calcium transients in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at days 20, 30, and 40. GJ Quantification of amplitude, diastolic Ca2+ concentration, upstroke and recovery velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs (n=12 cells per group). K Quantitative PCR analysis of heart failure and calcium handling -related genes in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at days 40. Data are shown as meanSD of 3 independent experiments. L Representative immunofluorescence staining and transmission electron microscope (TEM) of sarcomeric. M Quantification of complete organization, intermediate disorganization, and complete organization in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at days 40 based to immunofluorescence staining (more than 120 cells per group). Scale bar, 10 m. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S. not significant.

Subsequently, we evaluated the expression of key genes involved in heart failure and calcium handling. We found a significant increase in the expression of both NPPB and the MYH7/MYH6 ratio in PLEKHM2-KO hiPSC-CMs compared to WT hiPSC-CMs, whereas ryanodine receptor 2 (RYR2) expression significantly decreased at day 40 (Fig. 2K). Moreover, we observed significantly disordered sarcomeres in PLEKHM2-KO hiPSC-CMs at day 40 (Fig. 2L, M). Moreover, transmission electron microscopy (TEM) showed significant abnormalities in the myofilaments of PLEKHM2-KO hiPSC CMs, including disordered myofilament arrangement and blurred Z-disc morphology (Fig. 2L). Overall, these findings corroborate the strong link between PLEKHM2 deficiency and DCM, which manifests as reduced contractility and impaired calcium handling, along with sarcomeric disorganization and dysregulated expression of heart failure markers.

To assess for potentially pathogenic effects of PLEKHM2-deficient cardiomyopathy, we performed quantitative transcriptome profiling by RNA-seq (Supplementary Fig. 3AC). We identified 8725 differentially expressed genes in PLEKHM2-KO hiPSC-CMs versus WT hiPSC-CMs at day 40, including 4426 upregulated and 4299 downregulated genes (Fig. 3A). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that these dysregulated genes were enriched in pathways mainly involved in regulating autophagy, lysosome, cardiomyopathy, apoptosis and metabolism (Fig. 3B). Of particular a significant finding was that the dysregulated genes were enriched in autophagy with the highest enrichment score in PLEKHM2-KO hiPSC-CMs compared to WT hiPSC-CMs (Fig. 3B). The molecular-level analysis of Gene Ontology (GO) enrichment demonstrated a marked dysregulation in gene expression related to mitochondria, apoptosis, and autophagy in PLEKHM2-KO hiPSC-CMs (Fig. 3C, D). Notably, mitochondria-related pathways show the most significant differences between PLEKHM2-KO and WT hiPSC-CMs (Fig. 3C). Overall, these results indicate that PLEKHM2 deficiency leads to widespread dysregulation of signaling pathways in cardiomyocytes. Subsequently, we conducted quantitative PCR to validate the expression of genes associated with substantial dysregulation of mitochondria, apoptosis, and autophagy in RNA seq. Our results indicate a significant downregulation of BNIP3, DNM1L, OPA1, and MFN1 in addition to an upregulation of TSPO expression in PLEKHM2-KO hiPSC-CMs compared to WT hiPSC-CMs at day 40 (Fig. 3E). It is widely acknowledged that BNIP3 and TSPO participate in various physiological processes such as mitophagy, apoptosis, oxidative stress, and the oxidative respiratory chain [13, 14]. While DNM1L, OPA1, and MFN1 play crucial roles in maintaining and regulating mitochondrial morphology and stability [15, 16]. The observed dysregulation in these genes highlight a disruption of mitochondrial homeostasis in PLEKHM2-deficient cardiomyopathy.

A Volcano plot shows 8725 genes with altered expression in PLEKHM2-KO hiPSC-CMs compared with WT. Blue and red dots indicate genes with increased and decreased expression, respectively, based on a P value<0.05 and a log2 fold change >1 (n=3 for each group). B Enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) databases revealed that pathways related to lysosomal function, autophagy, cardiomyopathy, apoptosis and metabolism were disrupted in PLEKHM2-KO hiPSC-CMs. ARVC, arrhythmogenic right ventricular cardiomyopathy. C Gene Ontology (GO) enrichment analysis showed significant changes in gene expression associated with mitochondrial function, apoptosis and autophagy in PLEKHM2-KO hiPSC-CMs. The color scale indicates the P values of the top 15 altered pathways in GO molecular function and the bubble size reflects the number of genes involved in each pathway. D Heatmap of differentially expressed genes involved in mitochondrial function, apoptosis and autophagy. E Quantitative PCR confirmed the altered expression of a representative subset of genes identified by RNA sequencing in PLEKHM2-KO hiPSC-CMs. Data are shown as meanSD of three independent experiments. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S., not significant.

RNA-seq analysis revealed notable anomalies in autophagy and mitochondrial-related pathways, as indicated by significant findings in the KEGG and GO enrichment analysis. Following this discovery, we proceeded to investigate alterations in mitochondrion and autophagic processes. To further investigate the impacts of PLEKHM2 deficiency on the mitochondrion, we next assessed mitochondrial morphology and content using Mitotracker at day 40. Compared to the typical linear arrangement of mitochondria along sarcomeres in WT hiPSC-CMs, the mitochondria within PLEKHM2-KO hiPSC-CMs display distinctive fragmented and punctate patterns, along with irregular distribution throughout the cytoplasm (Fig. 4A, and Supplementary Fig. 4A, B). TEM revealed matrix swelling, empty spaces, and loose, disordered, and wider cristae in PLEKHM2-KO hiPSC-CMs (Supplementary Fig. 4A, C). This suggest that the PLEKHM2 deficiency significantly affects the localization and tissue structure of mitochondria in cardiomyocytes. Mitochondrial morphology disruption usually trigger mitophagy, which targets damaged or dysfunctional mitochondria for degradation and clearance from the cell, and the number of mitochondria within the cell usually decrease due to their removal [17]. However, further analysis using flow cytometry revealed an increasing mitochondrial content within the PLEKHM2-KO hiPSC-CMs, compared to the WT hiPSC-CMs (Fig. 4B, C). These results suggested that mitochondrial morphological abnormalities and impaired mitochondrial clearance occur in PLEKHM2-KO hiPSC-CMs.

A Mitotracker staining revealed that PLEKHM2-KO altered the mitochondrial structure from the filamentous form aligned with the sarcomere in WT hiPSC-CMs to a punctate and fragmented morphology at day 40. Scale bar, 10 m. B, C Quantification of Mitotracker green intensity obtained by flow cytometry demonstrates a significantly increased fluorescence intensity in PLEKHM2-KO hiPSC-CMs at day 40 as compared with WT hiPSC-CMs (n=4). DF Autophagic flux was assessed in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs using mRFP-EGFP-LC3 adenovirus and subjected them to starvation medium for 0, 1, 2, and 4h at day 40. Representative images and quantification of GFP+, RFP+, and GFP, RFP+ puncta are shown in (D)(F). 12 cells per cell line per condition were analyzed. Scale bars, 10 m. G, H Representative western blot and quantification of P62 expression in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at day 40 (n=3). CQ: chloroquine. Data are shown as meanSD. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S. not significant.

The damaged mitochondria undergo degradation and digestion with the participation of lysosomes [4], while impaired autophagy or lysosomal acidification disorders usually impair mitophagy, resulting in delayed clearance of damaged mitochondria [18]. Consequently, we proceeded to investigate alterations in lysosomal localization and autophagy in the PLEKHM2-KO hiPSC-CMs. In this study, we utilized LAMP1 as a marker to identify lysosomal localization. Our results indicate that lysosomes in PLEKHM2-KO hiPSC-CMs exhibit significant clustering around the nucleus, whereas the lysosomes in the WT hiPSC-CMs demonstrate scattered distribution throughout the cytoplasm (Supplementary Fig. 4C, D), which is consistent with previous study [2]. To investigate the effects of PLEKHM2 deficiency on autophagy, we next monitored alterations in autophagic flux at 0, 1, 2, and 4hours post-starvation. Day 3 after Ad-mRFP-EGFP-LC3 infection, we observed a significant accumulation of autophagosomes (GFP+/RFP+ puncta) with increasing starvation duration in both WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs. Notably, at the 2-hour after starvation, the number and proportion of autophagosome-lysosome fusion (GFP-/RFP+ puncta) in PLEKHM2-KO hiPSC-CMs was significantly lower than that of the WT hiPSC-CMs, indicating that the autophagic degradation of PLEKHM2-KO hiPSC-CMs was impaired (Fig. 4DF). In this study, we observed that in the late phase of autophagy (4hour after staving), most autophagosomes in the WT group accumulated around the nucleus and fused with lysosomes to form autolysosomes [19]. However, in the PLEKHM2-KO hiPSC-CMs, a substantial number of autophagosomes remained scattered within the cytoplasm and were not yet concentrated around the nucleus (Supplementary Fig. 4E), suggesting that PLEKHM2 deficiency affected the aggregation of autophagsomes to the perinuclear and fusion with lysosomes. In the subsequent WB results, we also observed that PLEKHM2 deficiency led to accumulation of p62 (Fig. 4G, H, and Supplementary Fig. 4F, G). In summary, these findings suggested that PLEKHM2 deficiency lead to abnormal lysosomal localization and blocking of autophagic flux, resulting in impaired autophagy and damaged mitochondrial accumulation.

Mitophagy is a fundamental cellular self-cleaning mechanism that plays a critical role in maintaining mitochondrialfunction and preventing the accumulation of reactive oxygen species (ROS) by selectively removing damaged mitochondria [20, 21]. m is a crucial indicator of mitochondrial health and function. To investigate the impact of PLEKHM2 deficiency on mitochondrial function, the carbocyanine compound JC-1, a fluorescent voltage-sensitive dye that possesses membrane-permeant fluorescent lipophilic cationic properties, was utilized to assess m and mitochondrial health. Our results revealed that JC-1 in PLEKHM2-KO hiPSC-CMs, exhibited a robust red fluorescence and weak green fluorescence similar to the WT hiPSC-CMs at day 20. However, over time, the red fluorescence of JC-1 in the PLEKHM2-KO hiPSC-CMs decreased gradually, while the green fluorescence increased (Fig. 5A). Notably, at day 30 and 40, the ratio of aggregate to monomeric JC-1 fluorescence in the PLEKHM2-KO hiPSC-CMs significantly reduced compared to that of the WT hiPSC-CMs (Fig. 5B). Futhrtmore, the destabilization in m lead to the release of cytC from mitochondria, which activated the caspase-3 in PLEKHM2-KO hiPSC-CMs at 40 day (Supplementary Fig. 5AC). These results suggest that PLEKHM2 deficiency leads to extensive depolarization of m and impaired mitochondrial function.

A, B Representative immunofluorescence staining and quantification of JC-1 revealed that mitochondrial monomers (green fluorescence) increased and the mitochondrial aggregates (red fluorescence) decreased gradually in PLEKHM2-KO hiPSC-CMs compared to WT at day 20, 30, and 40 (more than 120 cells per group). Scale bar, 10 m. C Heatmap of differentially expressed genes involved in oxidative stress in PLEKHM2-KO hiPSC-CMs compared to WT hiPSC-CMs. D GSEA analysis revealed dysregulation of the respose to oxidative stress signaling pathway in PLEKHM2-KO hiPSC-CMs. E, F Representative flow cytometry analysis and quantification of cell reactive oxygen species (ROS) intensity demonstrated a continuous increased fluorescence intensity in PLEKHM2-KO hiPSC-CMs at days 20, 30, and 40 as compared with WT hiPSC-CMs. G, H Oxygen consumption rate (OCR) of WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs at 40 day was measured using a seahorse analyzer. Data are shown as meanSD. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S., not significant.

Numerous studies have shown that ROS plays a crucial role in inducing widespread m depolarization by directly triggering mPTP opening. And mPTP opening can further enhance ROS production by impairing m, ultimately triggering a vicious cycle of m depolarization and ROS production [22,23,24]. RNA-seq suggested significant changes in the expression gene profile associated with oxidative stress in PLEKHM2-KO hiPSC-CMs compared to WT hiPSC-CMs (Fig. 5C, D). Thus, to investigate whether the PLEKHM2 deficiency leads to an increase in ROS levels, we assessed the dynamic changes in ROS levels in PLEKHM2-KO hiPSC-CMs. We found a continuous increase in ROS levels in PLEKHM2-KO hiPSC-CMs than WT hiPSC-CMs (Fig. 5E, F), which indicated that PLEKHM2 deficiency causes progressive oxidative stress in hiPSC-CMs. To further investigate the effect of PLEKHM2 deficiency on mitochondrial OXPHOS activity, the oxygen consumption rates (OCR) of WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs was analyzed at day 40 (Fig. 5G). These kinetic results revealed that PLEKHM2 deficiency significantly impaired ATP production, basal respiration and spare capacity (Fig. 5H). These results suggest that PLEKHM2 deficiency causes extensive mitochondrial dysfunction.

Previous studies have shown a strong link between oxidative stress and cardiomyopathy. To investigate whether ROS plays an important role in the pathogenesis of PLEKHM2-deficient cardiomyopathy, we administered oxidative stress activator, lipopolysaccharides (LPS) to WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs, and observed the effects on myocardial mitochondrial function, calcium handling, and contractility at day 40. After LPS administration, both WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs exhibited significantly higher levels of ROS than untreated CMs (Fig. 6A, B). Next, the JC-1 was utilized to assess the effect of LPS treatment on mitochondrial function. Our results showed that LPS treatment induced the same mitochondrial dysfunction phenotype in WT hiPSC-CMs as in PLEKHM2-KO hiPSC-CMs. Moreover, LPS treatment exacerbated the m destabilization in PLEKHM2-KO hiPSC-CMs compared to untreated hiPSC-CMs (Fig. 6C, D). To investigate whether oxidative stress accelerates the progression of PLEKHM2-deficient cardiomyopathy, we evaluated the effects of LPS administration on the calcium transient and myocardial contractility of WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs. We found that LPS treatment decreased calcium transients (Fig. 6EI, and Supplementary Fig. 6) in both WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs compared to untreated hiPSC-CMs. And LPS treatment significantly decreased myocardial contractility (Fig. 6JN) in WT hiPSC-CMs compared to untreated hiPSC-CMs. These results suggested that oxidative stress may play a significant role in mitochondrial dysfunction, abnormal calcium handling and impaired myocardial contractility in the development of PLEKHM2-deficient cardiomyopathy.

A, B Representative flow cytometry analysis and quantification of cellular ROS levels showed that both WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs had significantly increased ROS production compared to untreated controls after LPS exposure (n=4 independent experiments). C, D Representative immunofluorescence staining and quantification of JC-1 revealed that LPS treatment impaired mitochondrial membrane potential of WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs, as indicated by increased green fluorescence (monomeric form) and decreased red fluorescence (aggregated form) of JC-1 (more than 120 cells per group). Scale bar, 10 m. E Representative line scan images of calcium transients in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without LPS treatment at day 40. FI Quantification of amplitude, diastolic Ca2+ concentration, upstroke and recovery velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs (n=12 cells per group) with or without LPS treatment at day 40. J Representative line scan images of myocardial contractility in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without LPS treatment at day 40. KN Quantification of displacement, force, contraction and relaxation velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without LPS treatment at day 40 (n=12 cells per group). Data are shown as meanSD. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S., not significant.

Reduced glutathione (GSH) is an important antioxidant helps to prevent and reduce oxidative stress by neutralizing free radicals, widely used in the treatment of various types of oxidative stress-related diseases, including neurodegenerative diseases, cardiovascular diseases, and diabetes [25]. Hence, we treated PLEKHM2-KO hiPSC-CMs with GSH at day 30 to observe whether it could rescue the disease phenotype caused by PLEKHM2 deficiency. We found that PLEKHM2-KO hiPSC-CMs treated with GSH exhibited a considerable reduction in ROS levels (Fig. 7A, B) and significantly elevation of m level compared to untreated PLEKHM2-KO hiPSC-CMs (Fig. 7C, and Supplementary Fig. 7A). This indicates that inhibiting ROS helps improve mitochondrial function by preventing oxidative stress-induced damage to nearby mitochondria. Next, we observed the effects of GSH on the calcium transient and myocardial contractility of PLEKHM2-KO hiPSC-CMs. After GSH treatment, the diastolic calcium concentration and recovery velocity of PLEKHM2-KO hiPSC-CMs were comparable to that of WT hiPSC-CMs (Fig. 7D-H, and Supplementary Fig. 7B). Similarly, after GSH treatment, the PLEKHM2-KO hiPSC-CMs also showed significant improvements in contractile force (Fig. 7IM). These results further suggested the critical role of oxidative stress in mediating the disease phenotype of PLEKHM2-deficient cardiomyopathy.

A, B Representative flow cytometry analysis and quantification of cellular ROS levels showed that underwent GSH treatment PLEKHM2-KO hiPSC-CMs exhibited a considerable reduction in ROS levels comparable to WT hiPSC-CMs (n=4 independent experiments). C Quantification of JC-1 revealed that GSH treatment increased significantly the m level in PLEKHM2-KO hiPSC-CMs (more than 120 cells per group). D Representative line scan images of calcium transients of WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without GSH treatment at day 40. EH Quantification of amplitude, diastolic Ca2+ concentration, upstroke and recovery velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without GSH treatment at day 40 (n=12 cells per group). I Representative line scan images of myocardial contractility in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without GSH treatment at day 40. JM Quantification of displacement, force, contraction and relaxation velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without GSH treatment at day 40 (n=12 cells per group). Data are shown as meanSD. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S., not significant.

Previous studies have shown that PLEKHM2 deficiency lead to abnormal lysosomal localization and impaired autophagic flux [2], causing damaged mitochondrial accumulation and ROS production. mTORC1 signaling pathway is the main negative regulator of autophagy, inhibiting autophagy by phosphorylating and inactivating key autophagy proteins such as ULK1 and ATG13 [26]. Hence, we investigated whether inhibition of mTORC1 by RAPA could boost autophagy and rescue the mitochondrial dysfunction and ROS generation in PLEKHM2-KO hiPSC-CMs. We found that p-mTOR levels were significantly higher in PLEKHM2-KO hiPSC-CMs than WT hiPSC-CMs (Supplementary Fig. 8A, B). Administration of RAPA significantly reduced the p-mTOR levels in PLEKHM2-KO hiPSC-CMs (Supplementary Fig. 8A, B). We further observed the effect of RAPA on autophagic flux in PLEKHM2-KO hiPSC-CMs. RAPA increased the number of autophagosomes (GFP+/RFP+ puncta) and autophagolysosome (GFP-/RFP+ puncta) in PLEKHM2-KO hiPSC-CMs, indicating that RAPA induced autophagy (Supplementary Fig. 8C). However, the number and proportion of GFP-/RFP+ puncta in PLEKHM2-KO hiPSC-CMs was still significantly lower than that WT hiPSC-CMs (Supplementary Fig. 8C), indicating that rapamycin cannot completely improve obstruction of autophagic flux caused by PLEKHM2 deficiency. We then evaluated the effects of RAPA treatment on mitochondrial function and myocardial contractility in PLEKHM2-KO hiPSC-CMs at day 40. We found that RAPA treatment partially improved m level and reduced ROS generation in PLEKHM2-KO hiPSC-CMs (Supplementary Fig. 8EG). Next, we observed the effects of RAPA treatment on myocardial contraction and calcium transient in PLEKHM2-KO hiPSC-CMs. We found that RAPA treatment enhanced the calcium transient amplitude (Supplementary Fig. 8HL) of PLEKHM2-KO hiPSC-CMs, but the myocardial contractility (Supplementary Fig. 8MR) was still significantly lower than those in WT hiPSC-CMs. These results indicated that administering RAPA cannot completely correct impaired autophagy caused by PLEKHM2 deficiency, but partially improves the disease phenotype of PLEKHM2-deficient cardiomyopathy.

We next investigated whether PLEKHM2-WT overexpression could restore autophagic flux in PLEKHM2-KO hiPSC-CMs and rescued the disease phenotype of PLEKHM2-deficient cardiomyopathy. We found that PLEKHM2-WT overexpression corrected the abnormal lysosomal localization (Supplementary Fig. 9) and increased the number and proportion of GFP-/RFP+ puncta in PLEKHM2-KO hiPSC-CMs, compared to untreated PLEKHM2-KO hiPSC-CMs (Fig. 8A, B). This indicates that PLEKHM2-WT overexpression improve the autophagic degradation in PLEKHM2-KO hiPSC-CMs. We further observed the effects of PLEKHM2-WT overexpression on the mitochondrial function of PLEKHM2-KO hiPSC-CMs. PLEKHM2-KO hiPSC-CMs treated with PLEKHM2-WT overexpression exhibited a significant increase in the m level and decrease in ROS levels compared to untreated PLEKHM2-KO hiPSC-CMs (Fig. 8CF). Subsequently, we evaluated the effects of PLEKHM2-WT overexpression on the calcium transient and myocardial contractility of PLEKHM2-KO hiPSC-CMs. PLEKHM2-WT overexpression significantly enhanced calcium transient amplitude (Fig. 8GK) and myocardial contractility (Fig. 8LP) compared to untreated PLEKHM2-KO hiPSC-CMs. This further demonstrates that PLEKHM2 plays a crucial role in regulating autophagy and clearing damaged mitochondria.

A, B Autophagic flux was assessed in hiPSC-CMs using mRFP-EGFP-LC3 adenovirus. Representative images and quantification of GFP and RFP+ puncta are shown in (A) and (B). C, D Representative immunofluorescence staining and and quantitative analysis of JC-1 revealed that PLEKHM2-WT overexpression ameliorated m of PLEKHM2-KO hiPSCs-CMs (more than 70 cells per group). Scale bars, 10 m. E, F Representative flow cytometry analysis and quantification of cell reactive oxygen species (ROS) intensity demonstrated PLEKHM2-WT overexpression reduced ROS levels of PLEKHM2-KO hiPSCs-CMs. G Representative line scan images of calcium transients of WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without PLEKHM2-WT overexpression at day 40. HK Quantification of amplitude, diastolic Ca2+ concentration, upstroke and recovery velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without PLEKHM2-WT overexpression at day 40 (n=12 cells per group). L Representative line scan images of myocardial contractility in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without PLEKHM2-WT overexpression at day 40. MP Quantification of displacement, force, contraction and relaxation velocity in WT hiPSC-CMs and PLEKHM2-KO hiPSC-CMs with or without PLEKHM2-WT overexpression at day 40 (n=12 cells per group). Data are shown as meanSD. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001; N.S. not significant.

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PLEKHM2 deficiency induces impaired mitochondrial clearance and elevated ROS levels in human iPSC-derived ... - Nature.com

Synthetic Circuits Reveal the Key to Rewinding the Cellular Clock – The Scientist

Most people wonder how their lives would change if they could turn back time and remake past decisions. While a seemingly impossible feat, stem cell biologists Kazutoshi Takahashi and Shinya Yamanaka at Kyoto University first turned back the cellular clock in 2006.1 By overexpressing four transcription factors in fully differentiated fibroblasts, Takahashi and Yamanaka reprogrammed the cells to a pluripotent state and called them induced pluripotent stem cells (iPSC).

Although researchers employ iPSC in the laboratory and the clinic, scientists struggle to efficiently produce large quantities of iPSC.2 Reprogramming is still very inefficient, said Thorsten Schlaeger, a stem cell biologist at the Boston Childrens Hospital. It is still not fully understood why 98 or 99.9 percent of the cells do not end up reprogramming into iPSC. In a recently published Science Advances paper, Schlaeger and his team developed a system for tracking the fate of cells with different transcription factor expression dynamics during reprogramming.3 These findings will enable researchers in the field to improve iPSC yield.

Scientists suspected that the heterogeneity in reprogramming outcomes results from variations in the levels and durations of transcription factors expression. Consequently, several research groups have attempted to correlate the success of reprogramming to the levels of the octamer-binding transcription factor 4 (OCT4), which is one of Takahashi and Yamanakas transcription factors that is essential for the reprogramming process. However, these studies used population-based measurements and failed to consider the contribution of endogenous OCT4 to reprogramming.

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Compelled to overcome these limitations, Schlaegers coauthor Domitilla Del Vecchio, a discipline-straddling mechanical engineer at the Massachusetts Institute of Technology, developed an innovative OCT4 expression system. The idea really was to try to use a more sophisticated way of overexpressing transcription factors to reprogram stem cells, Del Vecchio recalled.

Del Vecchios team designed a synthetic gene circuit to ectopically overexpress a fluorescently-tagged version of the OCT4 transcription factor, while simultaneously blocking the expression of the endogenous OCT4 through microRNA. This allowed the researchers to control the total OCT4 protein levels within the cell and quantify them by measuring the fluorescence. Additionally, the ectopic OCT4 gene was controlled by an inducible and noisy promoter, which meant that the system generated variability in the expression of the OCT4 conjugate and a broad range of trajectories to assess, such as cells that maintained high OCT4 expression throughout reprogramming or those whose expression decreased over time.

Thorsten Schlaeger and Domitilla Del Vecchio developed a synthetic gene circuit-based system that allowed them to control and monitor the total OCT4 protein levels within fibroblasts during reprogramming.

Thorsten Schlaeger and Domitilla Del Vecchio

To determine which OCT4 trajectories successfully reprogrammed human dermal fibroblasts into iPSC, Del Vecchio, Schlaeger, and their team transduced the cells with lentiviral vectors encoding their OCT4 trajectory generator and followed the levels of fluorescently-tagged OCT4 proteins within the cells over time through imaging. The researchers then fixed the resulting cell colonies and immunostained them for two pluripotent stem cell surface markers.

They observed that the colonies fell into three categories: type I colonies were positive for only one of the surface markers; type II colonies showed the exact opposite staining pattern from type I; and type III colonies were positive for both markers. They considered cells within type III colonies as iPSC and categorized the cells within type I and II colonies as incompletely reprogrammed. Despite these differences, cells in all three colony types stably expressed supraphysiological levels of OCT4 during reprogramming, indicating that successfully reprogramming human dermal fibroblasts into iPSC requires consistently high levels of the OCT4 transcription factor. But this parameter alone is not sufficient to promote iPSC generation.

The paper is innovative in a technical sense. It is consistent with work that has been done showing that elevated levels of OCT4 are important for the reprogramming process, said Dean Tantin, a geneticist at the University of Utah, who was not involved in the study.

Although Tantin thought Del Vecchios system presented a clever way to directly examine total OCT4 protein levels within live cells, he suggested that examining protein levels alone may not convey the whole story. The level of a protein based on a fluorescent marker is not the same as the activity of a proteinits ability to bind DNA [or] its ability to regulate transcription once it binds," he noted. "So, I think where the field needs to go now is [to find out] how OCT4 activity is really dynamically regulated during [reprogramming]. he noted. Building on this idea, Tantin and his team recently determined that OCT4 activity during reprogramming and differentiation is redox-regulated, and he suspects that regulation of other reprogramming components will be of interest in the years to come.4

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Del Vecchio hopes that her work will inspire other researchers to think beyond the standard methods they employ to dissect molecular pathways. This study is showing how more sophisticated genetic engineering tools can be used in the context of a highly complex biological process and help you get information that will be difficult to get otherwise, Del Vecchio said.

Schlaeger wants to leverage the knowledge gained in this study to develop off-the-shelf iPSC-based therapies, such as CAR T cells, and he believes that precision engineering will be the key to safely bringing these products to the clinic. With the cells, we want to get to this precise control and that can only be done with complex genetic switches and circuits, Schlaeger said.

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Synthetic Circuits Reveal the Key to Rewinding the Cellular Clock - The Scientist

Characterization of human iPSC-derived sensory neurons and their functional assessment using multi electrode array … – Nature.com

Characterization of the expression of hiPSC-derived sensory neurons

HiPSC-derived sensory neurons (cat #RCDN004N, Reprocell.Inc) were used in this study. To identify characteristics of human iPSC-derived sensory neurons, we confirmed sensory neuron-related genes and proteins expression by real-time PCR and immunocytochemistry (ICC). Real-time PCR shows an increase in Peripherin, Brn3a, TRPV1, TRPM8, Nav1.7, Nav1.8, Piezo2, TRKA, TRKC, TRKB, P2X3, H1R, MrgprX1, CGRP and TAC1 compared with hiPSC cultured 14days in vitro (DIV) (Fig.1). The nociceptor phenotype consists of A-fibers, and C-fibers. C-fibers respond to both peptidergic and non-peptidergic neurotransmitters. HiPSC-derived sensory neurons expressed TRKA (nociceptor marker), IB4 (A-fibers marker), CGRP and TAC1 (peptidergic neurotransmitters) and P2X3 (ATP (non-peptidergic neurotransmitter) receptor) (Figs. 1i,l,o,p and 2k). Furthermore, TRPV1, TRPM8, Nav1.7 and Nav1.8 which are nociceptors receptors were also expressed (Fig.1c,eg). TRPV1 is known to be activated by capsaicin and noxious heat (43C)14. TRPM8 is known to be activated by menthol, noxious (<15C) and non-noxious (2815C) heat15,16. Nav1.7 and Nav1.8 are known to be subtype of voltage-gated sodium channels which is preferentially expressed in nociceptors17,18,19. The mechanoreceptor phenotype consists of relatively large diameter cells that are A-fibers. HiPSC-derived sensory neurons expressed TRKB (mechanoreceptor marker), NF200 (A-fibers marker), TRPM8 and Piezo2 (mechanoreceptor receptors) (Figs. 1e,h,j and 2j)4. TRKC, a proprioceptor marker was expressed in hiPSC-derived sensory neurons (Fig.1k). Thus, these data suggests that the hiPSC-derived sensory neurons generated constitute a heterogeneous population of sensory neuronal subclasses. The expression of TRPA1 in hiPSC-derived sensory neurons was lower than the one in hiPSC (Fig.1d). The expression levels of Brn3a, TRPM8, TRKB and MrgprX1 in hiPSC-derived sensory neurons were comparable to those in human DRG, whereas the others were lower than in hDRG. The reason some genes of hiPSC-derived sensory neurons showed lower expression than hDRG might be due to the immature nature of the hiPSC-derived sensory neurons20. Although we cultured them for a long time, the expression levels of Peripherin, TRPV1, TRPA1, Nav1.7, Nav1.8, H1R, and CGRP were not comparable to the ones in hDRG (Supplementary Fig. S1). Therefore, we confirmed proteins expression by ICC.

Expression of sensory neuron related genes in hiPSC, hiPSC-derived sensory neurons and human DRG. Real-time PCR showed expression of (a) Peripherin, (b) Brn3a, (c) TRPV1, (d) TRPA1, (e) TRPM8, (f) Nav1.7, (g) Nav1.8, (h) Piezo2, (i) TRKA, (j) TRKB, (k) TRKC, (l) P2X3, (m) H1R, (n) MrgprX1, (o) CGRP, (p)TAC1. The square marker, the circle marker and triangle marker indicate expression of genes in hiPSC, hiPSC-derived sensory neurons and human DRG respectively. Three different lot of hiPSC-derived sensory neurons were examined. The line marker represents the mean expression of genes in hiPSC-derived sensory neurons.

Expression of sensory neuron related proteins in hiPSC-derived sensory neurons and their morphology. The cells are stained for (a) TUBB3, (b) Peripherin, (c) Brn3a, (d) TRPV1, (e) TRPM8, (f) Nav1.7, (g) TRKA, (h) TRKB, (i) TRKC, (j) NF200, (k) IB4. DAPI stain of nuclei is shown in blue. (l) Image of iPSC-derived sensory neurons which exhibit a bipolar (red arrowhead), pseudounipolar (yellow arrowhead), or multipolar morphology (green arrowhead). Scale bar represents 50m.

ICC showed expression of TUBB3 (mature neuron marker), Peripherin (peripheral neuron marker) and Brn3a (sensory neuron marker) at 14 DIV (Fig.2ac). TRPV1, TRPM8, Nav1.7, TRKA, TRKB and TRKC were expressed at the membrane (Fig.2di). Since TRPV1, and TRPM8 are receptors of noxious and non-noxious stimulation and are expressed at the membrane, we expected them to be available for characterizing their function using MEA (Fig.2d,e). NF200, a A-fibers marker, was expressed at a higher-intensity in relatively large diameter cells than small diameter cells (Fig.2j). Although adult human DRG do not bind IB4 which is a non-peptidergic C-fibers marker, it was expressed in hiPSC-derived sensory neurons (Fig.2k)21. The research showed expression of IB4 in prenatal human DRG at 8-month of gestation22. This data suggests that hiPSC-derived sensory neurons might be immature.

It is known that when observing rat DRG cells in the early stages of development, their morphology changes from bipolar cells to pseudounipolar cells23. Our hiPSC-derived sensory neurons exhibit a bipolar, pseudounipolar and multipolar morphology (Fig.2l). A majority of the hiPSC-derived sensory neurons were bipolar neurons. This image suggests that our hiPSC-derived sensory neurons contained neurons with different degrees of maturity.

Taken together, hiPSC-derived sensory neurons express sensory neuron-related genes and proteins. They constitute a heterogeneous population of nociceptors, mechanoreceptors, and proprioceptors, and they differ in maturity. Thus, we proceeded to characterize their function next.

We confirmed whether hiPSC-derived sensory neurons responded to capsaicin, menthol, noxious heat (4346C), which are noxious stimuli, and bradykinin, and non-noxious heat (3742C), which is a non-noxious stimulus14,15,16,24. Sensory neurons of DRG are used as in vitro model of nociceptive response. DRG responded to 10nM, 100nM, 1M capsaicin25,26. We measured and compared data before and after drug treatment (Fig.3a). We measured the response to treatment with 100nM capsaicin which resulted in increase in Mean Firing Rate (MFR) and Number of Bursts (NOB), whereas vehicle treatment had no effect on them (Fig.3d,e). Capsaicin-evoked activity is known to be rapid in DRG25. This result showed that neural activity was evoked within 10s after treatment with capsaicin in hiPSC-derived sensory neurons as well (Fig.3b). To determine whether capsaicin-evoked neuronal activity is characteristic of hiPSC-derived sensory neuron, we treated with 100nM capsaicin in hiPSC-derived cortical neurons. As a result, hiPSC-derived cortical neurons did not respond to capsaicin (Fig.3ce). Moreover, we added 100nM AMG9810 which is a TRPV1 antagonist for 60min before treating with capsaicin. A response to capsaicin was not observed in the presence of AMG9810 (Fig. S2a,b). These data suggest that capsaicin-evoked activity occurred via TRPV1 in iPSC-derived sensory neurons. Thus, we can conclude that hiPSC-derived sensory neurons specifically respond to noxious stimulus and could be used in functional assays using MEA.

Capsaicin and menthol responsiveness using MEA. (a) Timeline of drug treatment. Baseline and dose response were recorded for 60s when treating with capsaicin or menthol. Capsaicin experiment raster plots of (b) hiPSC-derived sensory neurons and (c) hiPSC-derived cortical neurons. The triangle marker indicates the time of capsaicin addition. (d, h) Mean Firing Rate normalized to the control. Control firing rate is calculated as firing rate before adding vehicle or drug. (e, i) Number of Bursts normalized to the control. Menthol experiment raster plot of (f) hiPSC-derived sensory neurons and (g) hiPSC-derived cortical neurons. n=3 wells.

Menthol activates TRPM8 which is a nociceptive receptor. Since mouse and rat DRG respond to 10M and 100M menthol, we decided to treat with the same concentrations26,27. The high concentration of menthol resulted in suppressing spontaneous neural activity (Supplementary Fig. S3). This may be due in part to the higher expression of TRPM8 in hiPSC-derived sensory neurons than in human DRG (Fig.1e, Supplementary Fig. S1e). Treatment with 100nM menthol resulted in an increase in MFR and NOB in hiPSC-derived sensory neurons whereas hiPSC-derived cortical neurons did not respond to menthol (Fig.3fi). The data show that menthol got a response from nociceptive-like and non-nociceptive-like DRG neurons28. Since our hiPSC-derived sensory neurons responded to menthol, they may also include functionally non-nociceptive like neurons.

Bradykinin activates nociceptors and causes pain,24. Treatment with 100nM bradykinin resulted in significant increase in MFR and NOB compared to vehicle treatment for 60s (n=3, p<0.05) (Fig.4ac). In contrast to capsaicin and menthol, the onset of bradykinin-evoked neural activity was relatively long (Figs. 3b,f and 4a). Bradykinin-evoked activity increased gradually and reached its mean peak at 60s in DRG25. However, hiPSC-derived sensory neurons were able to respond faster than DRG, because they also responded to an additional stimulation which immediately activated them when bradykinin and vehicle were added against the well of the MEA plate. There is expression of TRKB and Piezo2 relevant to touch sensation in hiPSC-derived sensory neurons, explaining why they may have responded to an additional stimulation.

Bradykinin responsiveness. (a) Bradykinin experiment raster plot of hiPSC-derived sensory neurons. The triangle marker indicates the time of bradykinin addition. (b) Mean Firing Rate after addition of Bradykinin normalized to firing rate before addition of Bradykinin. (c) Number of Bursts after addition of Bradykinin normalized to number of bursts before addition of Bradykinin. n=3 wells, *p<0.05.

MFR were observed to increase gradually in DRG, when temperature increases from 37 to 42C via the stage plate heater, part of the recording system25. We increased the temperature from 37 to 46C via MAESTROs system to confirm responsiveness to noxious heat and non-noxious heat. We observed that MFR and NOB increased gradually and reached their mean peak at 45C and 46C respectively, in hiPSC-derived sensory neurons (Fig.5). In the presence of TRPV1 antagonist, AMG9810, MFR were lower than that of vehicle at 4346C (Fig. S2c,d). Because TRPV1 is known to be activated by noxious heat (43C), these results suggest that TRPV1 may contribute to the response to 4346C in iPSC-derived sensory neurons. The relative levels of MFR (1.540.046) and NOB (1.620.01) at 41C, which is non-noxious heat, were significantly higher than those at 37C. However, MFR decreased gradually in hiPSC-derived cortical neurons with an increase in temperature (Fig.5b,c).

Temperature responsiveness. Raster plots of (a) hiPSC-derived sensory neurons and (b) hiPSC-derived cortical neurons when the temperature is gradually increased from 37 to 46C. (c) Mean Firing Rate normalized to the firing rate at 37C. (d) Number of Bursts normalized to the number of bursts at 37C. The data for the number of bursts in hiPSC-derived cortical neurons isnt shown because one of the three wells didnt produce any burst. n=3 wells, *p<0.05, **p<0.01 compared with the corresponding value at 37C. Functional assessment of hiPSC-derived sensory neurons against itching stimuli

These data suggest that the observed and recorded response is specific to sensory neurons and the hiPSC-derived sensory neuron populations generated in this study are likely to include nociceptors that respond to noxious stimuli like capsaicin, menthol, bradykinin, and noxious heat (43C) and to include mechanoreceptors that respond to non-noxious stimuli (41C).

Atopic dermatitis (AD) is the most common chronic skin disease which causes a global disease burden29. AD causes itch (pruritus) and poor non-health-based quality-of-life. It is known that itch occurs via C-fiber in nociceptors30. Recently, investigating itch has been established by using human sensory neurons from stem and other progenitor cells as in vitro model31. Although substances causing itch treat to their cells, their inhibitors effect arent confirmed using hiPSC-derived sensory neurons. Because we demonstrated that our hiPSC-derived sensory neurons expressed nociceptor genes and proteins, and responded to noxious stimuli, we expected that they also responded to an itch stimulus and its inhibitor.

Histamine is one of the substances that cause itching via C-fiber. The histamine receptor is a four G protein-coupled receptor. Histamine H1 receptor (H1R) is involved in the induction of histamine-induced pruritus32. Since we confirmed that the H1R gene is expressed, we examined whether hiPSC-derived sensory neurons respond to histamine, using MEA. Mouse DRG responded to 100M Histamine, as described in the literature33. HiPSC-derived sensory neurons didnt respond to 100M Histamine but responded to 1mM Histamine (Supplementary Fig. S3df and Fig.6a,b). The MFR gradually increased and reached its mean peak at 25min. Pyrilamine is a histamine H1 receptor inverse agonist. We treated the sensory neuron population with 10M Pyrilamine for 60min before adding Histamine. As a result, Pyrilamine inhibited Histamine-evoked activity (Fig.6a,b). These results suggest that Histamine-evoked activity occurred via H1R in iPSC-derived sensory neurons.

Histamine, H1R inhibitor, pyrilamine and chloroquine responsiveness. (a) The left raster plots have been recorded before histamine addition. The right raster plots have been recorded 25min after histamine addition. Upper raster plots are recorded in pyrilamine absence. Lower raster plots are recorded with presence of pyrilamine. (b, e) Mean Firing Rate normalized to firing rate before drug addition. Raster plots of (c) before chloroquine addition and (d) 5min after chloroquine addition. The experiment with histamine and pyrilamine was performed with n=2 wells each. Experiment with chloroquine was performed with n=3 wells. *p<0.05, **p<0.01 compared with the value recorded before addition or with vehicle.

Chloroquine is a drug that has been used in the treatment to prevent malaria. Histamine-independent pruritus is known to be one of the side effects of chloroquine34. Mrgprs are receptors of chloroquine and are activated by it35. Since we confirmed expression of human MrgprX1 by real-time PCR, we investigated the potential response of hiPSC-derived sensory neurons by chloroquine. DRG are reported to respond to 1mM chloroquine, however the MFR gradually decreased at the same concentration in hiPSC-derived sensory neurons (Supplementary Fig. S3g,h)36. 1M chloroquine increased the MFR and reached the mean peak after 5min of incubation (Fig.6ce). The mean peak after stimulation with chloroquine was reached faster than after stimulation with histamine.

These data showed an example of the effect of an itch inhibitor and different responses between itch inducing drugs. HiPSC-derived sensory neurons may be available for drug discovery against AD.

Nav1.7 subtype of voltage-gated sodium channels is expressed in DRG. Mutations in the gene encoding Nav1.7 are associated with either absence of pain or with exacerbation of pain. Recently, Nav1.7 has been an attractive target to pursue treating pain. ProTx-II is a tarantula venom peptide that preferentially inhibits Nav1.7 over other Nav subtypes37. It suppressed spontaneous neural activity in a time dependent manner in hiPSC-derived sensory neurons (Fig.7ae). The MFR and NOB are significantly diminished after 35min of incubation with 1M ProTx-II (Fig.7d,e). After washing out ProTx-II, the suppressed neural activity gradually recovered. Although, responses was completely blocked by 300nM ProTx-II in rodent DRG, the responses in hiPSC-derived sensory neurons were not blocked at the same concentration38. This may be due in part to the lower expression of Nav1.7 in hiPSC-derived sensory neurons than in human DRG.

Nav1.7 channels inhibitor, ProTx-II and Nav1.7 inhibitor responsiveness. Raster plots of (a) before ProTx-II addition (baseline), (b) 35min after adding ProTx-II and (c) 150min after washing ProTx-II, respectively. (d, i) Mean Firing Rate and (e, j) Number of Bursts normalized to mean firing rate and number of bursts before drug addition. Raster plots of (f) before Nav1.7 inhibitor addition (baseline), (b) 50min after adding Nav1.7 inhibitor and (c) 30min after washing Nav1.7 inhibitor, respectively. n=3 wells, *p<0.05, **p<0.01, ***p<0.001 compared to the value recorded before drug addition.

ProTx-II is known to act not only as a Nav1.7 inhibitor but also to act on Nav1.5 channels and on some T-Type Ca2+ channels39,40. Thus we administered a small molecule inhibitor that is more selective for Nav1.741. Although 300nM of a more selective Nav1.7 inhibitor suppressed MFR and NOB in a time dependent manner in hiPSC-derived sensory neurons, the degree of decrease was lower than that of ProTx-II (Fig.7fj). After washing it out, the suppression of the neural activity was lifted.

These results suggested that hiPSC-derived sensory neurons may serve as a drug screening tool for pain.

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Characterization of human iPSC-derived sensory neurons and their functional assessment using multi electrode array ... - Nature.com

Imaging cAMP nanodomains in human iPSC-derived cardiomyocytes – Nature.com

Cardiac activity is regulated by the -adrenergic pathway. The activation of this pathway triggers a cellular signalling cascade that increases the production of cAMP, a cyclic nucleotide that activates the enzyme protein kinase A (PKA). PKA phosphorylates key proteins involved in cellular contraction, but can also phosphorylate a multitude of other proteins with different functions. To achieve specific effects, cAMP is confined in nanoscale subcellular domains (nanodomains) close to PKA and its targets. The maintenance and regulation of these nanodomains are central to functional signal transduction, and their dysregulation can result in diseases such as heart failure.

I use this technique in human induced pluripotent stem cell (iPSC)-derived cardiomyocytes to study how the maturation of these cells is affected by a newly identified cAMP nanodomain found at gap junctions, which regulate the communication between adjacent cardiomyocytes. To understand the role of the gap junction-associated cAMP nanodomain in human iPSC-derived cardiomyocytes, endogenous levels of protein expression must be maintained to avoid interference with their maturation process. This technique can more broadly be used to study cAMP nanodomains in which overexpression of the target protein might impair cell physiology. This tool will provide unique insights into the processes involved in human iPSC-derived cardiomyocyte maturation and can also be used to identify new targets in the -adrenergic pathway that might be relevant for the treatment of diseases, such as heart failure.

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Imaging cAMP nanodomains in human iPSC-derived cardiomyocytes - Nature.com

Colossal Creates Elephant Stem Cells for the First Time in Quest to Revive the Woolly Mammoth – Singularity Hub

The last woolly mammoth roamed the vast arctic tundra 4,000 years ago. Their genes still live on in a majestic animal todaythe Asian elephant.

With 99.6 percent similarity in their genetic makeup, Asian elephants are the perfect starting point for a bold plan to bring the mammothor something close to itback from extinction. The project, launched by biotechnology company Colossal in 2021, raised eyebrows for its moonshot goal.

The overall playbook sounds straightforward.

The first step is to sequence and compare the genomes of mammoth and elephant. Next, scientists will identify the genes behind the physical traitslong hair, fatty depositsthat allowed mammoths to thrive in freezing temperatures and then insert them into elephant cells using gene editing. Finally, the team will transfer the nucleuswhich houses DNAfrom the edited cells into an elephant egg and implant the embryo into a surrogate.

The problem? Asian elephants are endangered, and their cellsespecially eggsare hard to come by.

Last week, the company reported a major workaround. For the first time, they transformed elephant skin cells into stem cells, each with the potential to become any cell or tissue in the body.

The advance makes it easier to validate gene editing results in the lab before committing to a potential pregnancywhich lasts up to 22 months for elephants. Scientists could, for example, coax the engineered elephant stem cells to become hair cells and test for gene edits that give the mammoth its iconic thick, warm coat.

These induced pluripotent stem cells, or iPSCs, have been especially hard to make from elephant cells. The animals are a very special species and we have only just begun to scratch the surface of their fundamental biology, said Dr. Eriona Hysolli, who heads up biosciences at Colossal, in a press release.

Because the approach only needs a skin sample from an Asian elephant, it goes a long way to protecting the endangered species. The technology could also support conservation for living elephants by providing breeding programs with artificial eggs made from skin cells.

Elephants might get the hardest to reprogram prize, said Dr. George Church, a Harvard geneticist and Colossal cofounder, but learning how to do it anyway will help many other studies, especially on endangered species.

Nearly two decades ago, Japanese biologist Dr. Shinya Yamanaka revolutionized biology by restoring mature cells to a stem cell-like state.

First demonstrated in mice, the Nobel Prize-winning technique requires only four proteins, together called the Yamanaka factors. The reprogrammed cells, often derived from skin cells, can develop into a range of tissues with further chemical guidance.

Induced pluripotent stem cells (iPSCs), as theyre called, have transformed biology. Theyre critical to the process of building brain organoidsminiature balls of neurons that spark with activityand can be coaxed into egg cells or models of early human embryos.

The technology is well-established for mice and humans. Not so for elephants. In the past, a multitude of attempts to generate elephant iPSCs have not been fruitful, said Hysolli.

Most elephant cells died when treated with the standard recipe. Others turned into zombie senescent cellsliving but unable to perform their usual biological functionsor had little change from their original identity.

Further sleuthing found the culprit: A protein called TP53. Known for its ability to fight off cancer, the protein is often dubbed the genetic gatekeeper. When the gene for TP53 is turned on, the protein urges pre-cancerous cells to self-destruct without harming their neighbors.

Unfortunately, TP53 also hinders iPSC reprogramming. Some of the Yamanaka factors mimic the first stages of cancer growth which could cause edited cells to self-destruct. Elephants have a hefty 29 copies of the protector gene. Together, they could easily squash cells with mutated DNA, including those that have had their genes edited.

We knew p53 was going to be a big deal, Church told the New York Times.

To get around the gatekeeper, the team devised a chemical cocktail to inhibit TP53 production. With a subsequent dose of the reprogramming factors, they were able to make the first elephant iPSCs out of skin cells.

A series of tests showed the transformed cells looked and behaved as expected. They had genes and protein markers often seen in stem cells. When allowed to further develop into a cluster of cells, they formed a three-layered structure critical for early embryo development.

Weve been really waiting for these things desperately, Church told Nature. The team published their results, which have not yet been peer-reviewed, on the preprint server bioRxiv.

The companys current playbook for bringing back the mammoth relies on cloning technologies, not iPSCs.

But the cells are valuable as proxies for elephant egg cells or even embryos, allowing the scientists to continue their work without harming endangered animals.

They may, for example, transform the new stem cells into egg or sperm cellsa feat so far only achieved in micefor further genetic editing. Another idea is to directly transform them into embryo-like structures equipped with mammoth genes.

The company is also looking into developing artificial wombs to help nurture any edited embryos and potentially bring them to term. In 2017, an artificial womb gave birth to a healthy lamb, and artificial wombs are now moving towards human trials. These systems would lessen the need for elephant surrogates and avoid putting their natural reproductive cycles at risk.

As the study is a preprint, its results havent yet been vetted by other experts in the field. Many questions remain. For example, do the reprogrammed cells maintain their stem cell status? Can they be transformed into multiple tissue types on demand?

Reviving the mammoth is Colossals ultimate goal. But Dr. Vincent Lynch at the University of Buffalo, who has long tried to make iPSCs from elephants, thinks the results could have a broader reach.

Elephants are remarkably resistant to cancer. No one knows why. Because the studys iPSCs are stripped of TP53, a cancer-protective gene, they could help scientists identify the genetic code that allows elephants to fight tumors and potentially inspire new treatments for us as well.

Next, the team hopes to recreate mammoth traitssuch as long hair and fatty depositsin cell and animal models made from gene-edited elephant cells. If all goes well, theyll employ a technique like the one used to clone Dolly the sheep to birth the first calves.

Whether these animals can be called mammoths is still up for debate. Their genome wont exactly match the extinct species. Further, animal biology and behavior strongly depend on interactions with the environment. Our climate has changed dramatically since mammoths went extinct 4,000 years ago. The Arctic tundratheir old homeis rapidly melting. Can the resurrected animals adjust to an environment they werent adapted to roam?

Animals also learn from each other. Without a living mammoth to show a calf how to be a mammoth in its natural habitat, it may adopt a completely different set of behaviors.

Colossal has a general plan to tackle these difficult questions. In the meantime, the work will help the project make headway without putting elephants at risk, according to Church.

This is a momentous step, said Ben Lamm, cofounder and CEO of Colossal. Each step brings us closer to our long-term goals of bringing back this iconic species.

Image Credit: Colossal Biosciences

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Colossal Creates Elephant Stem Cells for the First Time in Quest to Revive the Woolly Mammoth - Singularity Hub

How stem cells might be used in planned de-extinction of woolly mammoth – Cosmos

Sometimes it takes the smallest thing to undertake a mammoth task.

Thats what researchers behind the attempts to de-extinct the woolly mammoth are hoping as they announced what they believe to be a step forward in their efforts.

One Texas-based company has made de-extinction its business. It is looking at not only de-extinction of the woolly mammoth, but also dodos and Australias thylacine both hunted to extinction within the last 500 years.

The key is an announcement this week from researchers with Colossal Biosciences who say theyve derived induced pluripotent stem cells (iPSCs) from Asian elephants (Elephas maximus).

These iPSCs are reprogrammed to be able to give rise to any cell type in the body.

It means the researchers might be able to investigate the genetic differences between the woolly mammoth (Mammathus primigenius) and their closest living relatives the Asian elephant. They can also test gene edits without needing tissue from living animals.

Woolly mammoths roamed Earth for nearly 800,000 years.

They diversified from the steppe mammoth (Mammuthus trogontherii) at the beginning of the Middle Pleistocene (770,000126,000 years ago). They were closely related to the North American Columbian mammoth (Mammathus columbi) and DNA studies show they occasionally interbred.

Woolly mammoths were up to 3.5 metres tall at the shoulder and could weigh as much as 8 metric tons. (By contrast the Asian elephant is 2-3m and weights up to 5t.)

Mammoths are synonymous with the last Ice Age which ended about 12,000 years ago. Its believed that a combination of a warming climate and human hunting saw woolly mammoth numbers decline.

They died out so recently that some mammoth bodies have been recovered extremely well preserved in ice and snow.

The last stronghold of the woolly mammoth was the Siberian island of Wrangel where they lived until as recently as 4,000 years ago.

When these last mammoths died, the Great Pyramids of Giza were already 600 years old. Stonehenge had been around for 1,000 years and Sumerian poets had begun compiling the works that would over the next 800 years be brought together into the Epic of Gilgamesh.

In the great scheme of geological time, we are tantalisingly close to these remarkable creatures.

The successful formation of Asian elephant iPSCs in the lab is critical to understanding how the woolly mammoths genetic code sets it apart from its modern counterparts.

Which bits of DNA come together to produce features like their shaggy hair, curved tusks, fat deposits and dome skulls? These are the kinds of questions scientists at Colossal now feel they can answer.

It is also possible that the iPSCs can lead to producing elephant sperm and egg cells in the lab. Anyone whos had the birds and the bees chat doesnt need to be told why thats important in de-extinction.

Being able to create these cells in a lab is particularly important given the precariousness of Asian elephant populations.

Fewer than 50,000 Asian elephants remain in the wild according to the World Wildlife Fund. They are listed as endangered on the IUCNs Red List. Attempts to retrieve egg and sperm cells from Asian elephants would be difficult and potentially adverse.

Making elephant iPSCs has been so difficult because of complex gene pathways unique to these animals. Colossals genetic engineers overcame this by suppressing core genes called TP53 which regulate cell growth and halt the duplicating process.

But the work doesnt stop.

Theyre still looking at alternative methods to create iPSCs and maturing the ones theyve already made.

Theres also a lot still to learn about the complex 22-month gestation period of elephants if a healthy woolly mammoth calf is to be produced through in vitro fertilisation of a modern elephant.

Colossals plan is to have a living, breathing woolly mammoth by 2028.

For that to happen, the company is also looking into restoring suitable tundra steppe habitats in Canada and the US where the reborn mammoth population can settle.

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How stem cells might be used in planned de-extinction of woolly mammoth - Cosmos