Category Archives: Induced Pluripotent Stem Cells


The Enormous Potential of Induced Pluripotent Stem Cells (iPSCs) in Biomedical Research and Health Care – Medriva

In the realm of biomedical research and health care, one of the most promising advancements in recent years involves induced pluripotent stem cells (iPSCs). These cells, which can be reprogrammed to behave like embryonic stem cells, have vast potential for understanding and treating a broad range of diseases, including diabetes, cancer, and neurological disorders. Theyre also being used to develop new drugs and could pave the way for personalized medicine.

iPSCs are adult cells that have been genetically reprogrammed to an embryonic stem cell-like state. This means they can potentially transform into any cell type in the body, making them a valuable resource for regenerative medicine and disease modeling. For example, they can be used to create patient-specific cell lines, which can then be used to study the mechanisms of disease at a cellular level, or to test potential treatments.

One of the significant advantages of iPSCs is their use in studying genetic diseases. By creating iPSCs from the cells of patients with specific genetic conditions, researchers can observe how these diseases develop and progress at a cellular level. This can provide invaluable insights into the underlying mechanisms of these conditions and could lead to the development of new, more effective treatments.

Moreover, iPSCs are playing a crucial role in drug discovery. They offer a more accurate and efficient way to test potential new drugs. Traditionally, new drugs are tested in animal models before being trialed in humans. But iPSCs provide a way to test these drugs on human cells, potentially speeding up the process and reducing reliance on animal testing.

Beyond disease study and drug development, iPSCs hold immense promise in the realm of regenerative medicine. They offer the potential to grow patient-specific tissues and organs for transplantation. This could revolutionize treatment for a variety of conditions, including heart disease, diabetes, and neurological disorders.

Furthermore, iPSCs have the potential to usher in a new era of personalized medicine. By creating patient-specific cell lines, treatments can be tailored to the individual, increasing their effectiveness and reducing the risk of adverse effects.

Despite their enormous potential, the use of iPSCs is not without challenges and ethical considerations. Issues such as the risk of tumorigenesis, the efficiency of reprogramming, and the possibility of immune rejection must be addressed. Moreover, the ethical implications surrounding the use of human cells in research and clinical applications must also be carefully considered.

Nonetheless, as our understanding and techniques improve, iPSCs are set to play an increasingly significant role in biomedical research and health care. With their potential to revolutionize disease study, drug development, regenerative medicine, and personalized healthcare, they represent one of the most exciting areas of modern medicine.

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The Enormous Potential of Induced Pluripotent Stem Cells (iPSCs) in Biomedical Research and Health Care - Medriva

Induced Pluripotent Stem Cells Global Market Report 2023-2028 – Key Market Drivers Include Use of iPSCs in … – PR Newswire

DUBLIN, Jan. 10, 2024 /PRNewswire/ --The"Induced Pluripotent Stem Cells: Global Markets 2023-2028" report has been added toResearchAndMarkets.com's offering.

This study focuses on the market side of iPSCs rather than the technical side. Different market segments for this emerging market are covered. For instance, product function-based market segments include molecular and cellular engineering, cellular reprogramming, cell culture, cell differentiation, and cell analysis. Application-based market segments include drug development and toxicity testing, academic research, and regenerative medicine. iPSC-derived cell type-based market segments include hepatocytes, neurons, cardiomyocytes, endothelial cells, and other cell types.

Other cell types are comprised of astrocytes, fibroblasts, and hematopoietic and progenitor cells, among other substances. Geographical-based market segments include the U.S., Asia-Pacific, Europe, and the Rest of the World. The research and market trends are also analyzed by studying the funding, patent publications, and research publications in the field.

This report focuses on the market size and segmentation of iPSC products, including iPSC research and clinical products. The market for iPSC-related contract services is also discussed. iPSC research products are defined as all research tools, including iPSCs and various differentiated cells derived from iPSCs, various related assays and kits, culture media and medium components (e.g., serum, growth factors, inhibitors), antibodies, enzymes, and products that can be applied for the specific purpose of executing iPSC research. For this report, iPSC products do not cover stem cell research and clinical products that are broadly applicable to any stem cell type.

This report discusses key manufacturers, technologies, and factors influencing market demand, including the driving forces and limiting factors of the iPSC market's growth. Based on these facts and analysis, the market trends and sales for research and clinical applications are forecast through 2028.

One particular focus on the application of iPSCs was given to drug discovery and development, which includes pharmaco-toxicity screening, lead generation, target identification, and other preclinical studies; body-on-a-chip; and 3D disease modeling. Suppliers and manufacturers of iPSC-related products are discussed and analyzed based on their market shares, product types, and geography. An in-depth patent analysis and research funding analysis are also included to assess the overall direction of the iPSC market.

Detailed technologies such as those for generating iPSCs, differentiating iPSCs and controlling the differentiation, and large-scale manufacturing of iPSCs and their derivative cells under Good Manufacturing Practice (GMP) compliance or xeno-free conditions are excluded from the study. They are beyond the scope of this report.

The induced pluripotent stem cell market has been analyzed for four main geographic regions: The U.S., Europe, Asia-Pacific, and the Rest of the World (RoW). The report will provide details with respect to induced pluripotent stem cells.

The Report Includes

Companies Profiled

Key Topics Covered:

Chapter 1 Introduction

Chapter 2 Summary and Highlights

Chapter 3 Market Overview

Chapter 4 Market Dynamics

Chapter 5 Induced Pluripotent Stem Cell Applications

Chapter 6 Induced Pluripotent Stem Cell Market Segmentation and Forecast

Chapter 7 Induced Pluripotent Stem Cells Research Application Market

Chapter 8 Induced Pluripotent Stem Cell Contract Service Market

Chapter 9 Clinical Application Market Trend Analysis

Chapter 10 Competitive Landscape

For more information about this report visit https://www.researchandmarkets.com/r/i3vsaw

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Induced Pluripotent Stem Cells Global Market Report 2023-2028 - Key Market Drivers Include Use of iPSCs in ... - PR Newswire

ROR2 expression predicts human induced pluripotent stem cell differentiation into neural stem/progenitor cells and … – Nature.com

Cell culture

Commercially available hiPSC lines were used in this study (Supplementary Table 1). HiPSC lines were obtained from RIKEN Cell Bank (201B7, 253G1, 409B2, HiPS-RIKEN-1A, HiPS-RIKEN-2A, and HiPS-RIKEN-12A), American Type Culture Collection (ATCC-DYR0110 hiPSC and ATCC-HYR01103 hiPSC), JCRB Cell Bank (Tic), and System Biosciences (human mc-iPS). HiPSCs were screened for mycoplasma contamination and hiPSCs used in this study were mycoplasma-free. Undifferentiated hiPSCs were maintained on an iMatrix-511 (Nippi) in StemFit AK02 medium (Ajinomoto). All cells were cultured at 37C in a humidified atmosphere containing 5% CO2 and 95% air.

Differentiation of hiPSCs into NS/PCs was induced, as previously reported, with a few modifications. For adhesive differentiation, hiPSCs were detached through incubation with StemPro Accutase (Thermo Fisher Scientific) containing 10M Y-27632 for 10min and seeded onto 24-well cell culture plates (BD Biosciences) coated with iMatrix at a density of 25,000 cells/cm2 for 23days before NS/PC induction. Confluent hiPSCs were treated with 10M of the ALK inhibitor SB431542 (Stemgent) and 500ng/mL of Noggin (R&D systems) in DMEM/F12 medium containing 20% KSR. The medium was replaced on days 1 and 2. On day 6 of differentiation, SB431542 was withdrawn, and increasing amounts of N2 media (25%, 50%, and 75%) were added to the knockout serum replacement medium every 2days while maintaining 500ng/mL of Noggin. For suspension differentiation, hiPSCs were treated with 10M Y-27632 for 1h at 37C and dissociated with StemPro Accutase (Thermo Fisher Scientific) containing 10M Y-27632 for 10min to generate single-cell suspensions and suspended in B27N2-based medium [DMEM/F12 with 15mM HEPES, 5% B27, and 5% N2 supplements (Life Technologies), 10M SB431542, 2M Dorsomorphin (Fujifilm), and 10ng/mL bFGF (R&D systems)]. The completely dissociated cells were seeded into ultralow attachment 96-well plates (PrimeSurface 96-well, Sumitomo Bakelite) at 9,000 cells/well, centrifuged at 700g for 3min (quick aggregation). The medium was changed daily for up to 10days; for the first 3days, 10M of Y-27632 was added. Total RNA was obtained from 40 wells of neuro spheres per sample. For microarray analysis, hiPSCs were differentiated into NS/PCs using a STEMdiff SMADi Neural Induction Kit (Stem Cell Technologies) according to the manufacturers instructions. Briefly, hiPSCs were maintained on an iMatrix-coated plate in StemFitAK02 media (Ajinomoto) before NS/PC induction. Cells were harvested using Accutase (Thermo Fisher Scientific); 2106 cells were transferred to a Matrigel-coated 6-well plate in STEMdiff Neural Induction Medium+SMADi (Stem Cell Technologies) supplemented with 10M Y-27632. The medium was replenished daily with warmed (37C) STEMdiff Neural Induction Medium+SMADi until the culture was terminated. Cells were passaged every 7days, and RNA was extracted from cells harvested at passages (days 7, 14, and 21).

Total RNA was isolated from hiPSCs or differentiated cells using the RNeasy Mini Kit (Qiagen) and treated with DNase I according to the manufacturers instructions. qRT-PCR was performed using a QuantiTect Probe One-Step RT-PCR Kit (Qiagen) on a STEPONEPLUS Real-Time PCR System (Applied Biosystems). The expression levels of target genes were normalized to those of the GAPDH transcript or 18S rRNA, which were quantified using TaqMan human Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) control reagents (Applied Biosystems) or eukaryotic 18S rRNA endogenous controls (Applied Biosystems), respectively. The probes and primers were obtained from Sigma-Aldrich. The used primer and probe sequences are listed in Supplementary Table 2. PCA was performed using SYSTAT 13 software (Systat Software Inc.) after data standardization (z-scoring) for each NS/PC marker gene.

To identify microarray probe sets related to the differentiation of hiPSCs into NS/PC, correlations between the intensity value rank of the filtered probe sets and the PC1 rank in the 10 hiPSC lines were determined by calculating Spearmans rank correlation coefficients (rs), as described in a previous study26. Probe sets exhibiting statistically significant correlations (P<0.01) were selected. When n=10 data points, the observed value of rs should exceed 0.794 (positively correlated) or less than 0.794 (negatively correlated) to be considered statistically significant (P<0.01).

ROR2 KD cells were generated by infecting R-2A cells with MISSION Lentiviral Transduction Particle expressing ROR2-targeted shRNAs (#1: TRCN0000199888, #2: TRCN0000001492) or MISSIONpLKO.1-puro Control Non-Mammalian shRNA Control Transduction Articles (Sigma, SHC002V), according to the manufacturers instructions. Media containing viruses were collected 48h after transfection, and the cells were transduced with the viruses using 8g/mL polybrene (Sigma-Aldrich) for 24h. The cells were selected using 2g/mL puromycin (Gibco) for 48h.

The cell lysates were used for western blotting analysis. Proteins were separated using sodium dodecyl sulfatepolyacrylamide gel electrophoresis, transferred to PVDF membranes (Bio-Rad), and blocked for 60min in Blocking One (Nacalai tesque). Primary antibody dilutions were prepared in Can Get Signal immunoreaction enhancer solution (TOYOBO) as follows: anti-ROR2 antibody (AF2064; R&D Systems) 1:1000, anti--actin antibody (A5441; Sigma-Aldrich) 1:2000. Membranes were incubated with HRP-conjugated anti-mouse IgG (Invitrogen) or anti-goat IgG (Invitrogen). Proteins were visualized using ECL Prime Western Blotting Detection Reagent (GE Healthcare) and the ChemiDoc Touch Imaging System (Bio-Rad).

HiPSC-derived NS/PC or forebrain neuron was fixed in 4% paraformaldehyde in PBS (Nacalai) for 20min at 25C. After washing with PBS, the cells were permeabilized with 0.2% Triton-X100 (Merk) in PBS for 15min and blocked with Blocking One (Nacalai) for 30min. The samples were incubated for 1h with primary antibodies (anti-PAX6 antibody [PRB-278P-100, BioLegend], anti-MAP2 antibody [MAB8304, R&D systems], and anti-GAD1 antibody [AF2086, BioLegend]). Indirect immunostaining was performed with the secondary antibody (anti-rabbit IgG/Alexa Fluor 555 [A27039, Thermo Fisher Scientific], anti-goat IgG/Alexa Fluor 488 [A32814, Thermo Fisher Scientific], and anti-mouse IgG/Alexa Fluor 488 [A28175, Thermo Fisher Scientific]) for 1h and examined under a BZ-X810 fluorescence microscope (Keyence).

ROR2 overexpression cells were generated by infecting 253G1 cells with lentiviral particles expressing ROR2. Briefly, the nucleotide sequence of the human ROR2 open reading frame (NM_004560) was de novo synthesized (Eurofins Genomics) and cloned into the pLVSIN-EF1 puromycin vector (Takara Clontech). Lentivirus packaging and virus infection were performed as described above.

Total RNA was extracted from hiPSC-derived NS/PC cells using an RNeasy Mini Kit (QIAGEN) according to the manufacturers instructions. Total RNA (100ng per sample) was used as the input for the Clariom D Assay (Thermo Fisher Scientific). Target preparation was performed using a Gene Chip WT PLUS Reagent Kit (Thermo Fisher Scientific) according to the manufacturers instructions. Hybridization was performed in a Gene Chip Hybridization Oven 645 for 16h at 45C. Gene chips were scanned using a GeneChip Scanner 3000. Array quality control was performed using Transcriptome Analysis Console software (version 4.0.2.15). The National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) accession number for the microarray data is GSE233228.

Differentiation of hiPSCs into mature nerves was performed according to the manufacturers instructions using the STEMdiff Forebrain Neuron Differentiation Kit (#08600, STEMCELL Technologies) for forebrain-type nerves and the STEMdiff Midbrain Neuron Differentiation Kit (#100-0038, STEMCELL Technologies) for midbrain nerves. Using the STEMdiff SMADi Neural Induction Kit (Stem Cell Technologies) monolayer culture protocol described above, hiPSCs were differentiated into NS/PC, and mature neural differentiation was induced.

For midbrain neuron differentiation, hiPSC-derived NS/PCs (day21, passage 3) were detached using Accutase and seeded into PLO (Sigma)-and laminin (Sigma)-coated 12-well plate at a density of 1.25105 cells/cm2 culture in STEMdiff Neural Induction Medium+SMADi medium for 24h. The complete medium was replaced daily for 6days with STEMdiff Midbrain Neuron Differentiation Medium. The midbrain neural precursors (day 7) were detached using ACCUTASE and seeded into PLO-and Laminin-coated 12-well plate at a density of 5104 cells/cm2 in STEMdiff Midbrain Neuron Maturation medium with a half-medium change every 23days for 14days.

For forebrain-type neuron differentiation, hiPSC-derived NS/PCs (day21, passage 3) were detached using Accutase and then seeded into PLO-and Laminin-coated 12-well plate at a density of 1.25105 cells/cm2 culture in STEMdiff Neural Induction Medium+SMADi medium for 24h. The full medium was replaced daily for 6days with STEMdiff Forebrain Neuron Differentiation medium. The forebrain neural precursors (day7) were detached using Accutase and seeded into PLO- and Laminin-coated 12-well plate at a density of 5104 cells/cm2 in STEMdiff Forebrain Neuron Maturation media with a half-medium change every 23days for 14days.

Statistical analyses were performed using Prism 9 software (version 9.5.1; GraphPad Software Inc.). Data are presented as meanstandard deviation (SD). For comparison between two groups the t-test was applied; in cases where another statistic test was applied, it is mentioned accordingly. Statistical significance was set at P<0.05.

Original post:
ROR2 expression predicts human induced pluripotent stem cell differentiation into neural stem/progenitor cells and ... - Nature.com

RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations … – Nature.com

Cell culture

Human iPSC line, OILG-3, was obtained from the Wellcome Sanger Institute and cultured in StemFlex medium (Thermo Fisher) on Vitronectin (Thermo Fisher)-coated culture dishes. Cells were detached using TrypLE (Thermo Fisher) and re-seeded at 4104 cells per well into 6-well plates for routine maintenance. For the first 24h after passaging, cells were treated with 10M Y-27632 (Wako). SpCas9-expressing OILG cells were generated as previously described36.

Selected gRNAs (Supplementary Table1) were cloned into pKLV2-U6gRNA5(BbsI)-PGKpuroBFP-W. Lentivirus was produced individually by transfecting 293FT cells together with lentiviral packaging plasmids, psPAX2 and pMD2.G using LipofectamineLTX37. The resulting viral supernatants were then pooled in an equal volume ratio. OILG-Cas9 (1.56105) cells were transduced with the pooled lentivirus at 89% transduction efficiency and maintained until harvesting without passaging. On days 2, 3, 4, and 5 after transduction, 8104 BFP+ cells were collected using an MA900 cell sorter (Sony), then resuspended at 1106 cells/mL in 0.05% BSA in PBS. These cells were then subjected to 5 scRNA-seq library preparation using a Chromium Next GEM Single Cell 5 Library & Gel Bead Kit following the manufacturers protocol with minor modifications to simultaneously capture guide RNA molecules. Briefly, a spike-in oligo (5-AAGCAGTGGTATCAACGCAGAGTACCAAGTTGATAACGGACTAGCC-3) was added to the reverse transcription reaction. The small DNA fraction isolated after cDNA clean-up was then used to generate a gRNA sequencing library with the primers listed in Supplementary Table2. PCR was performed using 2KAPA Hi-Fi Master Mix with the following program: 95C for 3 min, 12 cycles of 98C for 15 sec and 65C for 10 sec, followed by 72C for 1 min. The resulting gene expression libraries and gRNA libraries were pooled at a molecular ratio of 7:1 and sequenced using NovaSeq with 26 cycles for read 1, 91 cycles for read 2, and 8 cycles for the sample index.

A digital expression matrix with gRNA assignment was obtained using the CRISPR Guide Capture Analysis pipeline of Cell Ranger 5.0.0 (10x Genomics). The generated expression matrix was processed using Seurat (version 4.0.3)38. Single cells were filtered to leave cells with>200 and<10000 expressed genes and<20% reads from mitochondrial genes. The expressions were normalized using the sctransform method of Seurat. Only cells bearing a single gRNA were used for downstream analysis.

We investigated GRNs whose nodes were TFs only. Below, we adopt a 1-origin indexing system for all vectors and matrices. Consider a model that represents the propagation of the KO effect from the KO gene g on the GRN. Let G denote the number of genes included in the GRN. The G-dimensional gene expression vector ({{{{{{{{bf{E}}}}}}}}}_{g,{K}^{{prime} }}^{{prime} }) of a cell including the up to ({K}^{{prime} })-th order regulatory effect from the KO gene g is modeled as follows:

$$begin{array}{r}{{{{{{{{bf{E}}}}}}}}}_{g,{K}^{{prime} }}^{{prime} }=mathop{sum }limits_{{k}^{{prime} }=1}^{{K}^{{prime} }}{left({{{{{{{{bf{M}}}}}}}}}_{g}odot {{{{{{{bf{A}}}}}}}}right)}^{{k}^{{prime} }}{{{{{{{{bf{X}}}}}}}}}_{g}+{{{{{{{{bf{b}}}}}}}}}_{{K}^{{prime} }},end{array}$$

(3)

where Xg is a G-dimensional vector of which gth component is the expression change of gene g due to its KO, and the other components are zero. When the cell is the wild type, i.e. no gene is knocked out (g=0), X0 is a zero vector. ({{{{{{{{bf{b}}}}}}}}}_{{K}^{{prime} }}) is the G-dimensional expression vector corresponding to the wild type. A is a GG matrix and Ai,j(ij) represents the strength of regulation from gene j to i; that is, the change in gene i expression due to a unit amount change in gene j expression. Ai,j(i=j) represents effects such as degradation and self-regulation (Supplementary Note1).denotes an element-wise product. Eq. (3) is an extension of Eq. (1) with a mask matrix Mg representing that the KO gene g is no longer regulated by other genes:

$${{{{{{{{{{bf{M}}}}}}}}}_{g}}}_{i,j}=left{begin{array}{ll}0quad &(i=g)\ 1quad &(i,ne ,g).end{array}right.$$

(4)

Thus, (mathop{sum }nolimits_{{k}^{{prime} } = 1}^{{K}^{{prime} }}{({{{{{{{{bf{M}}}}}}}}}_{g}odot {{{{{{{bf{A}}}}}}}})}^{{k}^{{prime} }}{{{{{{{{bf{X}}}}}}}}}_{g}) represents the expression change from the wild type due to gene KO.

From the scCRISPR analysis, we obtained the G-dimensional gene expression vector Ec,t in cell c sampled at time t and G-dimensional vector Xc,t representing the decrease in expression of the KO gene in the cell (t=1,,T,c=1,,Ct). Here, T is the number of time points, and Ct is the number of cells sampled at time t. Note that here, in contrast to Eq. (2) in the Results section, the subscript of E have been changed from g,t to c,t. The KO gene in cell c sampled at time t is identified by the presence of gRNA and denoted by gc,t. The calculation of Xc,t from gc,t will be explained in a later section.

Suppose we have the gene expression data ({{{{{{{{bf{E}}}}}}}}}_{g,{K}^{{prime} }}^{{prime} }; ({K}^{{prime} }=1,cdots ,,max_{K}^{{prime} })), in which the effects of different maximum orders of ({K}^{{prime} }) regulation appear, we can infer the GRN A by fitting Eq. (3) to the data. However, it is impossible to synchronize the sampling time t of the cells and the time at which the effects appear for up to the ({K}^{{prime} })-th order of regulation from the KO gene. Hence, the maximum order of regulation from the KO gene in the cells at sampling time t is unknown. Thus, RENGE estimates the value from the data. By introducing a term w(t,k,gc,t) representing the strength of the effect of the k-th order of regulation at time t when the gene gc,t is knocked out, we can express Eq. (3) as follows:

$${{{{{{{{bf{E}}}}}}}}}_{c,t}=mathop{sum }limits_{k=1}^{K}w(t,k,{g}_{c,t}){({{{{{{{{bf{M}}}}}}}}}_{c,t}odot {{{{{{{bf{A}}}}}}}})}^{k}{{{{{{{{bf{X}}}}}}}}}_{c,t}+{{{{{{{{bf{b}}}}}}}}}_{t}$$

(5)

$$w(t,k,{g}_{c,t})=frac{1}{1+{exp }^{-({alpha }_{{g}_{c,t}}+beta t-gamma k)}},$$

(6)

where w(t,k,gc,t) is assumed to be monotonically increasing with respect to t and monotonically decreasing with respect to k, thus, as time progresses, the effects of higher-order regulation become more apparent. ({alpha }_{{g}_{c,t}},,beta ,,gamma) are the parameters to be estimated, and 0,0. The parameter ({alpha }_{{g}_{c,t}}) represents the time required for the effect of the KO of gene gc,t to appear and is assumed to differ with each KO gene. is related to a rate constant at which the regulation step progresses with respect to time t, and is a parameter representing the degree of decrease in the effect of higher-order regulation. Mc,t is obtained by replacing the subscripts of the mask matrix in Eq. (4) with the relation g=gc,t. The parameters to estimate are ({{{{{{{bf{A}}}}}}}},,{{{{{{{{bf{b}}}}}}}}}_{t}; (t=1,cdots ,,T),,{alpha }_{{g}_{c,t}} ({g}_{c,t}=1,cdots ,,{G}_{ko}),,beta ,,gamma), where Gko is the number of KO genes.

The parameters are estimated by minimizing the following objective function:

$$L = mathop{sum}limits_{t=1}^T mathop{sum}limits_{c=1}^{C_t} left| {{{{{mathbf{m}}}}}}_{c,t} odot left[{{{{{mathbf{E}}}}}}_{c,t}{-}left{mathop{sum}limits_{k=1}^K w(t,k,,g_{c,t}) ({{{{{mathbf{M}}}}}}_{c,t} odot {{{{{mathbf{A}}}}}} )^k {{{{{mathbf{X}}}}}}_{c,t} + {{{{{mathbf{b}}}}}}_t right}right]right|_{2}^{2} \ + lambda_1 mathop{sum}limits_{i,j=1}^G left|left{{{{{{mathbf{A}}}}}}right}_{i, j}right| + lambda_2 mathop{sum}limits_{k=1}^K mathop{sum}limits_{i, j=1}^G left{{{{{{mathbf{A}}}}}}^kright}_{i,j}^2,$$

(7)

where {A}i,j denotes the i,j element of the matrix A,denotes the element-wise product, and mc,t is the mask vector for cell c at time t:

$${{{{{{{{{{bf{m}}}}}}}}}_{c,t}}}_{i}=left{begin{array}{ll}0quad &(i={g}_{c,t})\ 1quad &(i,ne ,{g}_{c,t})end{array}right..$$

(8)

The first term in Eq. (7) is the squared error between the predictions of the model and the data. mc,t is used to ignore the squared error of KO gene gc,t expression in cell c at time t because mRNA of KO gene gc,t may still be expressed even when the functional protein is lost when using the CRISPR system. The last two terms in Eq. (7) are the L1 and L2 regularization terms of the parameter A, respectively. To suppress the magnitude of each element of not only A but also Ak(k2), an L2 regularization term was added for Ak(k=1,K). Note that the L1 regularization term was only added for A and not for Ak(k2) because A represents a GRN and thus is expected to be sparse, but Ak(k2) is not necessarily sparse. The objective function is minimized using the L-BFGS-B method implemented in scipy.minimize. K,1,2 are hyperparameters that are set to values that minimize cross-validation loss using Bayesian optimization with Optuna39.

One of the RENGE inputs, Xc,t, is a G-dimensional vector representing the decrease in expression of the target gene due to its KO in cell c at time t. Here, we assumed that when the target gene is entirely knocked out, the gene expression is decreased to zero. That is, the decrease in expression equals the average expression in control cells. However, in scCRISPR analysis, the target gene is not necessarily knocked out even in cells where the corresponding gRNA is detected. It is therefore necessary to distinguish between cells in which the transcriptome is affected by the KO and cells in which the KO fails and thus the transcriptome is not affected. RENGE uses the concept of perturbation probability, defined as the probability that gRNA detected in a cell has an effect on the transcriptome. RENGE calculates the perturbation probability pc(c=1,,C) for each cell c in the same way as MIMOSCA13, where C is the total number of cells.

Xc,t is defined as the decreased expression of the KO gene gc,t multiplied by pc:

$${{{{{{{{bf{X}}}}}}}}}_{c,t,i}=left{begin{array}{ll}-{p}_{c}cdot frac{1}{{C}_{t}^{ctrl}}mathop{sum }limits_{j = 1}^{{C}_{t}^{ctrl}}{{{{{{{{bf{E}}}}}}}}}_{j,t,i}^{ctrl}quad &(i={g}_{c,t})\ 0quad &(i,ne ,{g}_{c,t}),end{array}right.$$

(9)

where ({C}_{t}^{ctrl}) is the number of control cells at time t and ({{{{{{{{bf{E}}}}}}}}}_{j,t,i}^{ctrl}) is the expression of gene i in control cell j at time t.

RENGE calculates the p-value for each element of the matrix A, which indicates the strength of regulation, using the bootstrap method as follows. Let the data set be denoted by ({{{{{{{bf{D}}}}}}}}=mathop{bigcup }nolimits_{t = 1}^{4}({{{{{{{{bf{X}}}}}}}}}_{t},{{{{{{{{bf{E}}}}}}}}}_{t})). The bootstrap data set D1,,DN is created by sampling cells with replacement, keeping the number of cells for each KO gene at each time point (N=30 by default). For each Dl(l=1,,N), apply RENGE and estimate Al. Given Al(l=1,,N), calculate the sample variance Var({A}i,j)(i,j=1,,G) of {A}i,j. Assuming the null distribution of {A}i,j is ({{{mathcal{N}}}}(0, Var({{{{{{{{{bf{A}}}}}}}}}}_{i,j}))), RENGE calculates the p-value pi,j of {A}i,j as follows:

$${p}_{i,j}=left{begin{array}{ll}2left(1-{Phi }^{-1}right.({{{{{{{{{bf{A}}}}}}}}}}_{i,j}/Var({{{{{{{{{bf{A}}}}}}}}}}_{i,j}))quad &({{{{{{{{{bf{A}}}}}}}}}}_{i,j},ge, 0)\ 2left({Phi }^{-1}right.({{{{{{{{{bf{A}}}}}}}}}}_{i,j}/Var({{{{{{{{{bf{A}}}}}}}}}}_{i,j}))quad &({{{{{{{{{bf{A}}}}}}}}}}_{i,j}, < ,0),end{array}right.$$

(10)

where is the cumulative distribution function of the standard normal distribution. The q-value is then calculated using the Benjamini-Hochberg procedure to control for multiple hypothesis testing. Since RENGE cannot infer self-regulation, all downstream analyses, including method comparison and network analysis, were performed by excluding self-regulation.

The following existing methods were compared with RENGE: GENIE39, dynGENIE340, BINGO32, MIMOSCA13, and scMAGeCK16. GENIE3 predicts the expression of a gene from that of other genes using a tree-based ensemble. The importance of one gene for the prediction of another indicates the strength of the interaction between the genes. Although it exhibited superior performance in the benchmark of GRN inference from scRNA-seq data11, GENIE3 cannot handle information on KO genes or time series data. In this study, one cell was treated as one sample, and time information was ignored. In each cell, the expression of the target KO gene was set to 0 regardless of its measured mRNA expression.

dynGENIE3 is a modified version of GENIE3 that is appropriate for time-series data; however, it cannot handle KO gene information. In this study, at each time point, the expression of each cell for each KO gene was averaged to produce a time series data set of (number of KO genes +1). In each time-series data set, the expression of the KO gene was set to 0.

BINGO is a method used to infer GRNs from time-series expression data by modeling gene expression dynamics with stochastic differential equations involving nonlinear gene-gene interactions. It can also handle KO information. BINGO takes two types of input data, time-series expression data (as data.ts) and KO gene data (as data.ko). The time-series data was constructed in the same way as for dynGENIE3, and KO gene data was constructed based on gRNA assignment.

MIMOSCA was developed for scCRISPR-screening data, and performs a linear regression of expression data using the gRNA detected in each cell and other information as covariates. This method can handle the index of the time point from which each cell is derived as a covariate, but not the time-series information. In this study, we used MIMOSCA by setting gRNA and the index of timepoint as covariates.

scMAGeCK includes scMAGeCK-LR and scMAGeCK-RRA, both GRN inference methods for the scCRISPR-screening data. scMAGeCK-LR performs linear regression similar to MIMOSCA. scMAGeCK-RRA uses Robust Rank Aggregation (RRA) to detect genes with expression changes in each KO. However, it cannot handle time information, so we applied scMAGeCK by ignoring the time information of each cell.

Recently, SCEPTRE41 and Normalisr42 were shown to improve the inference of associations between perturbations and gene expression in scCRISPR analysis. However, since these methods were developed for the high multiplicity-of-infection (MOI) scCRISPR analysis data, they were not examined in this study, which used low MOI data.

To benchmark the methods, simulated data were generated using dyngen, a GRN-based simulator of scRNA-seq data. A total of 750 GRNs, consisting of 100 genes, were generated by setting num_tfs=100. In detail, 250 GRNs were generated for each of the three backbones (linear, converging, and bifurcating conversing) defined in dyngen. We used the backbones with only one steady state because they are cases similar to the real data of hiPS cells we obtained in this study.

The ground-truth GRNs were used for the simulation by dyngen. Initially, the simulation was run without KO for simtime_from_backbone(backbone) time to obtain a steady state for each backbone. Subsequently, a gene was knocked out, and the simulation was run for 100 steps from the steady state. After the KO, a total of 100 cells were sampled at four time points in regular intervals. The parameter values used in dyngen are presented in Supplementary Table6.

We ran the simulation knocking out each of the 100 genes in each GRN and obtained expression data of 100 genes sampled from 100 cells under 100 KOs. Note that here we performed a single-gene KO multiple times. For each GRN, the expression data subset was constructed by extracting the cells corresponding to the KO genes included in the randomly selected set M of genes. For each backbone, the 250 GRNs were divided into 5 sets, each of which included 50 GRNs. GRNs in each set have a different size M(M=20,40,60,80,100). The ratio of KO genes for each data set is (frac{| M| }{100}). We found that in some GRNs of bifurcating converging backbone, single-gene KO does not cause substantial expression variation, possibly due to the GRN structure (Supplementary Fig.3). The amount of expression variation caused by single-gene KO (MIMOSCA score) was calculated using the GGko matrix calculated by MIMOSCA as follows:

$${{{{{rm{MIMOSCA}}}}}}_{{{{{rm{score}}}}}},=frac{{sum }_{i,j}| {{{{{{{{{boldsymbol{beta }}}}}}}}}}_{i,j}| }{{G}_{ko}}.$$

(11)

Since RENGE assumes that single-gene KO causes a substantial amount of expression variation, we excluded GRNs with MIMOSCA_score<2. Consequently, we used 248 GRNs for linear backbones, 233 GRNs for converging backbones, and 133 GRNs for bifurcating converging backbones, resulting in a total of 614 GRNs. To normalize the count data generated by dyngen and stabilize variance, we applied sctransform of Seurat38. The resulting data were used to infer GRNs by each method. The results for all the 750 GRNs are shown in Supplementary Fig.2.

To evaluate the agreement between the inferred GRN and the ground-truth GRN, we first calculated the agreement of the presence and absence of regulation using the AUPRC ratio, while ignoring the sign of the regulation. AUPRC is a common metric that measures the agreement between the inferred and ground-truth GRNs. The AUPRC ratio is the AUPRC divided by that of a random predictor, and it was averaged for all GRNs and M KO gene sets for each KO gene ratio. The AUPRC ratio for each of the positive and negative regulations was then calculated as follows: for positive regulations the confidence level of regulation was set to 0 if it was negative, and only positive regulations were considered; negative regulation was similarly calculated.

We selected the genes to be included in the GRN of hiPSCs as follows. Let d2 be the coefficient matrix obtained by applying MIMOSCA to the day 2 cell population. ({{{{{{{{{{boldsymbol{beta }}}}}}}}}_{d2}}}_{i,j}) represents the expression variation of gene i when gene j is knocked out. The expression variation score vi of gene i was defined as ({v}_{i}={sum }_{j}| {{{{{{{{{{boldsymbol{beta }}}}}}}}}_{d2}}}_{i,j}|), and the top 80 non-KO genes with large vi were selected. A total of 103 genes with 80 non-KO genes and 23 KO genes constituted the node set for the focal system in this study.

The ChIP-Atlas, a database for ChIP-seq data, was used to validate the GRN inferred from the hiPSC data. ChIP-seq data for 19 genes from human pluripotent stem cells was obtained. We used cell types included in the cell-type class Pluripotent stem cell defined in the ChIP-Atlas that did not contain derived in the cell type name. Note that the data labeled as ChIP-seq data for RUNX1T1 in ChIP-Atlas was excluded because it was actually ChIP-seq data for RUNX1-ETO. The 19 genes with ChIP-seq data consisted of 9 KO genes and 10 non-KO genes (Supplementary Table3). The confidence level for the binding of a TF to DNA is expressed as (-10 times {log }_{10},({{mbox{MACS2}}}; q{{mbox{-value}}})). If the confidence level of the binding of gene j to gene i in the region of TSS10kb was higher than the predetermined ChIP threshold, we assumed that regulation occurred from gene j to gene i. This means that the ground-truth network depends on the ChIP threshold; the higher the ChIP threshold, the more reliable the regulations in the ground-truth network. We calculated the AUPRC ratio for the ground-truth GRNs of various confidence levels changing the ChIP threshold from 0 to the maximum confidence value in the data.

The rank correlation coefficient between the confidence level of each regulation was calculated using each method and the confidence level of the ChIP-seq data ((-10 times {log }_{10},({{mbox{MACS2}}}; q{{mbox{-value}}}))). For RENGE, MIMOSCA, and scMAGeCK, we used (-{log }_{10}(q,{{mbox{-value}}},)) as the confidence level, and for GENIE3, dynGENIE3, and BINGO, we used the output value of each tool itself (confidence values or weights).

We examined the details of the inferred regulations for each method by comparing it with the ground-truth network with the ChIP threshold=300. There were 237 regulations, the same number that was observed in the ground-truth network, that were extracted for the GRNs inferred by each method, in order of confidence score of the regulation. These regulations were classified as follows. Suppose the regulation from gene j to gene i was inferred. If the length k of the shortest path from gene j to gene i in the ground-truth network was 1, it was classified as direct; while if k>1, it was classified as indirect. If there was no path from gene j to gene i, it was classified as no path.

Having inferred the GRN of 103 genes by RENGE, we focused on regulation with FDR<0.01 and calculated the out-degree for each gene which is shown in Fig.5b. Using this GRN, we validated our hypothesis that gene pairs with a similar set of target genes are likely to form a proteincomplex. Using the regulatory coefficient matrix A estimated by RENGE, the regulatory correlation coefficients were calculated for all gene pairs in the network as follows:

$$R={co{r}_{sp}({{{{{{{{bf{A}}}}}}}}}_{:,i},{{{{{{{{bf{A}}}}}}}}}_{:,j})| 1le i,,jle G},$$

(12)

where A:,i denotes the i-th column of A and corsp(x,y) denotes the Spearmansrank correlation coefficient between x and y. If corsp(A:,i,A:,j) is close to 1, gene i and gene j regulate the same genes in the same direction, and if close to -1, they regulate the same genes in the opposite direction.

We compared the regulatory correlation with the protein complex data from the three databases. First, curated complexes were obtained from the CORUM3.0 database. We used all complexes in which at least 66% of their component genes were included in the 103 genes in the GRN15. When a gene pair was included in the same complex, the gene pair was assigned to be in the CORUM complex. Second, protein-protein interaction scores were obtained from the v11.5 of STRING (9606.protein.physical.links.v11.5.txt.gz). The protein-protein interaction scores for gene i and gene j are denoted as PPIi,j. Among the gene pairs in R, those with PPIi,j=0 were assigned STRING score low, and those with the top 10% of PPIi,j among gene pairs with PPIi,j>0 were assigned STRING score high. Third, colocalization scores for the DNA binding of TFs were obtained from the ChIP-Atlas, using data for the cell type class of pluripotent stem cells.

Let ({D}_{S}=mathop{bigcup }nolimits_{t = 1}^{4}({{{{{{{{bf{X}}}}}}}}}_{S,t},{{{{{{{{bf{E}}}}}}}}}_{S,t})) be a data set containing control cells and cells in which genes in the gene set S are knocked out, and O={1,,23} be the indices of the genes knocked out in the hiPSC data. We trained the RENGE model using the dataset DO{j} excluding cells in which the gene j(j=1,,23) was knocked out. The trained RENGE model was then used to predict the expression changes of the other genes when gene j was knocked out. We calculated the Pearson correlation coefficient between the predicted and measured expression changes for the gene j KO using D{j}.

All the underlying statistical details were provided earlier in the Methods section.

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

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The first multi-chamber heart organoids developed – Drug Target Review

The first multi-chamber cardioids derived from hiPSCs have enabled scientists to investigate heart development and defects.

Researchers, led by Dr Sasha Mendjan at the Institute of Molecular Biotechnology (IMBA) of the Austrian Academy of Sciences, have developed the first multi-chamber heart organoids that reflect the organs intricate structures. This promises advanced screening platforms for understanding heart development, drug development and toxicology studies.

The leading cause of death worldwide is cardiovascular disease, yet there are limited therapies for it. Similarly, one in 50 babies born suffer from a congenital heart defect but scientists have little understanding of why these occur. However, the team at IMBA have produced a new physiological organoid model that comprises the major regions of the human heart, enabling scientists to study cardiac disease and development.

In 2021, the Mendjan lab developed the first chamber-like heart organoid formed from human induced pluripotent stem cells (hiPSCs). hiPSCs have many benefits, such as overcoming the ethical and immune-compatibility issues faced due to the use of human embryonic stem cells (hESCs). hiPSCs can be derived from patient-specific somatic cells (eg, skin fibroblasts and hematopoietic cells) and be directly reprogrammed by defined factors to induce pluripotency. These hiPSCs displayed similarities in morphology, proliferation, feeder dependence, surface markers, gene expression, promoter activities, in vitro differentiation potential, and teratoma formation characteristics to hESCs.1

These heart organoids, named cardioids, were self-organising and mimicked the development of the hearts left ventricular chamber in the very early days of embryogenesis. Dr Mendjan said: These cardioids were a proof-of-principle and an important step forwardWhile most adult diseases affect the left ventricle, which pumps oxygenated blood through the body, congenital defects affect mostly other heart regions essential to establish and maintain circulation.

For the new study, the IMBA scientists furthered this work and derived organoid model of each developing heart structure individually. Dr Mendjan explained: Then we asked: If we let all these organoids co-develop together, do we get a heart model that co-ordinately beats like the early human heart?

The researchers grew the left and right ventricular and the atrial organoids together. Dr Mendjan remarked: Indeed, an electrical signal spread from the atrium to the left and then the right ventricular chambers just like in early foetal heart development in animalsWe now observed this fundamental process in a human heart model for the first time, with all its chambers.

We now observed this fundamental process in a human heart model for the first time, with all its chambers.

This model allowed the team to investigate how regional gene expression differences led to specific chamber contraction patterns and the intricate communication between them.

Also, insight was gained into early heart development, especially how the human heart starts beating, which was previously unknown. One of the studys first authors Alison Deyett, a PhD student in the Mendjan group detailed: At first, the left ventricular chamber leads the budding right ventricular and atrium chambers at its rhythm. Then, as the atrium develops two days later the ventricles follow the atrial lead. This mirrors what is seen in animals before the final leaders, the pacemakers, control the heart rhythm.

Multi-chamber cardioids also allowed the scientists to study chamber-specific defects. The team established a screening platform for defects for a proof-of-principle experiment, in which they investigated how teratogens and mutations affect hundreds of heart organoids simultaneously.

Thalidomide, a well-known teratogen in humans, as well as retinoid derivatives, that are used in treatments against leukaemia, psoriasis, and acne, are known to cause severe heart defects in the foetus. Both teratogens induced similar, serious compartment-specific defects in the heart organoids. Similarly, mutations in three cardiac transcription factor genes resulted in chamber-specific defects observed in human development. Dr Mendjan summarised: Our tests show that multi-chamber cardioids recapitulate embryonic heart development and can uncover disruptive effects on the whole heart with high specificity. We do this using a holistic approach, looking at multiple readouts simultaneously.

Someday, multi-chamber heart organoids could be used for toxicology studies and to develop novel drugs with heart chamber-specific effects. Drug-induced cardiotoxicity is the leading cause of drug attrition during the development process,2 so these organoids are promising for the future.

Dr Mendjan said: For example, atrial arrhythmias are widespread, but we currently dont have good drugs to treat it. One reason is that no models existed comprising all regions of the developing heart working in a coordinated manner so far.

Developing heart organoids from patient-derived stem cells may provide insight into developmental defects and its potential treatment and prevention, which the Mendjan lab hope to understand further.

This study was published in Cell.

1 Ho Beatrice Xuan, Pek Nicole Min Qian, Soh Boon-Seng. Disease Modeling Using 3D Organoids Derived from Human Induced Pluripotent Stem Cells. International Journal of Molecular Sciences (IJMS) [Internet]. 2018 March 21 [2023 December 7];19(4)936. Available from: https://doi.org/10.3390/ijms19040936

2 Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, et al. Lessons Learned from the Fate of AstraZenecas Drug Pipeline: a Five-Dimensional Framework. Nature Review Drug Discovery. 2014 May 16 [2023 December 7];13(6)419-431. Available from: https://www.nature.com/articles/nrd4309

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The first multi-chamber heart organoids developed - Drug Target Review

Efficient protocol for the differentiation of kidney podocytes from … – Nature.com

Human iPSC culture

All the experiments involving hiPSCs were approved by the ethics committee of Kansai Medical University (Approval Number: 2020197). We obtained the written informed consent of the donors from whom hiPSCs were derived. The study was performed according to the principles of the Declaration of Helsinki, as revised in 2013, and relevant institutional guidelines. Human iPSCs (585A1, 253G1, and HiPS-RIKEN-2F) were maintained with feeder-free cells using NutriStem hPSC XF (05-100-1A, Sartorius AG, Goettingen, Germany) on plates coated with iMatrix-511 silk (892021, Matrixome, Osaka, Japan) at 37C in a 5% CO2 incubator. Single cells were prepared from hiPSC colonies (7090% confluent) using Accutase (AT104, Innovative cell technologies, CA, USA) for subsequent passage and the induction of podocyte differentiation.

We generated podocytes from hiPSCs by modifying a previously reported differentiation protocol16 (Fig.1A). Human iPSCs were seeded at 3000 cells/well in 96 well low-cell-binding V-bottom plates, which were cultured in 200L NutriStem medium containing 10M Y27632 (FCS-10-2301-25, Focus biomolecules, PA, USA) at 37C for 24h. The medium was changed to DMEM Hams/F12 medium (048-29775, Fujifilm, Osaka, Japan) containing 2% B27 supplement (17504044, Thermo Fisher Scientific, MA, USA), 1ng/mL human activin A (338-AC, R&D Systems, MN, USA), and 20ng/mL fibroblast growth factor 2 (FGF2, 064-04541, Fujifilm). After 24h, cell aggregates were cultured for 6days in a medium (DMEM Hams/F12 medium) containing 2% B27 supplement and 10M CHIR99021 (10-1279, Focus biomolecules) that was changed every 2days. Subsequently, the medium was changed to one containing 10ng/mL human activin A, 3ng/mL human bone morphogenetic protein 4 (BMP4, PROTP12644, R&D System), 3M CHIR99021, and 100nM retinoic acid (RA, 302-79-4, Fujifilm). After a further 72h, this medium was switched to one containing 1M CHIR99021 and 10ng/mL FGF9 (273-F9, R&D Systems) without medium change to induce the differentiation of NPCs.

Differentiation of hiPSCs into podocyte. (A) Timeline and factors involved in the differentiation of hiPSCs into podocytes. (B) mRNA expression of podocyte-associated genes (NEPHRIN, PODOCIN, and SYNAPTOPODIN) during the 24days of culture. Results are shown as the meanSD of 6 samples. Statistical analysis was performed using one-way ANOVA with Bonferronis test. **p<0.01, ***p<0.001. (C) Immunostaining for markers of podocytes (NEPHRIN and PODOCIN) and F-Actin in differentiated cells, with nuclei stained with Hoechst. (D) mRNA expression of podocyte-associated genes (NEPHRIN, PODOCIN, and SYNAPTOPODIN) in hiPSCs, NPCs and differentiated podocytes. Results are shown as the meanSD of 6 samples. Statistical analysis was performed using one-way ANOVA with Bonferronis test. *p<0.05 (E) Protein expression of nephrin and podocin in hiPSCs, NPCs and differentiated podocytes, assessed using western blotting analysis. (F) Protein expression of undifferentiation stem cell marker (OCT-3/4) and nephron progenitor cell marker (SIX2) in hiPSCs, NPCs and differentiated podocytes, assessed using western blotting analysis. (G) Protein expression of nephron progenitor cell marker (SIX2) assessed using western blot analysis. Results are shown as the meanSD of 3 samples. Statistical significance was assessed using Students t-test. *p<0.05.

To generate podocytes, the medium was switched to one containing 3M CHIR99021, and after 24h, to one containing 2M IWR-1 (1127442-82-3, Fujifilm), 5M SB431542 (13031, Cayman Chemical, MI, USA), and 10M RA. After a further 24h, the differentiated cells were cultured for 11days in fresh medium containing 2M IWR-1 and 5M SB431542, which was replaced every 3days. Cell sorting was not performed at all steps.

To construct the monolayer cell culture, the cell aggregates were transferred to a 50-mL centrifuge tube, washed with PBS, then dissociated using Accutase. The cells (2,000 cells/cm2) were then seeded onto iMatrix-511 silk-coated dishes and cultured in DMEM Hams/F12 medium supplemented with 10M Y27632 and 2% B27 supplement. Cells were collected 24h after the treatment with DMEM Hams/F12 medium supplemented with Y27632 and B27 supplement.

To evaluate the involvement of the mTOR pathway in podocyte differentiation, rapamycin (R0161, LKT Laboratories, MN, USA) was administered at various times during the differentiation process and evaluated by mRNA expression using RT-PCR. In addition, S6 downstream of mTOR was inhibited using LY2584702 to further assess its involvement in the mTOR pathway.

RNA was extracted from the cells using ISOGEN II reagent (311-07361, Nippon gene, Tokyo, Japan), then a ReverTra Ace qPCR RT Master Mix (FSQ-201, Toyobo, Osaka, Japan) was used for reverse transcription. Real-time PCR was performed to quantify target mRNA expression using a Rotor-Gene Q (Qiagen) and Thunderbird SYBR qPCR Mix (QPS-201, Toyobo). The specific PCR primers used are listed (Table 1).

Cell lysates were collected using 4Bolt LDS Sample Buffer (B0007, Thermo Fisher Scientific), then electrophoresed on a 10% SDS polyacrylamide gel and blotted onto PVDF membranes. The membranes were incubated with anti-NEPHRIN (29070, Immuno-Biological Laboratories, Gunma, Japan), anti-PODOCIN (MBS9608910, Thermo Fisher Scientific), anti-Phospho-Akt (9271, Cell Signaling Technology, MA, USA), anti-Akt (9272, Cell Signaling Technology), anti-Phospho-mTOR (2971, Cell Signaling Technology), anti-mTOR (2972, Cell Signaling Technology), anti-Phospho-p70 S6 Kinase (9205, Cell Signaling Technology), anti-p70 S6 Kinase (2708, Cell Signaling Technology), anti-Phospho-S6 Ribosomal Protein (2211, Cell Signaling Technology), S6 Ribosomal Protein (2217, Cell Signaling Technology), anti-SIX2 (80170, Cell Signaling Technology), anti-OCT3/4 (611202, BD Biosciences, NJ, USA), and anti- actin (MAB8929, R&D Systems) primary antibodies, then further probed with anti-mouse IgG horseradish peroxidase-linked (A90-131P, Bethyl Laboratories, TX, US) secondary antibody. Specific protein bands were visualized using Pierce Western Blotting Substrate (NCI3106, Thermo Fisher Scientific).

Cultured cells were harvested after detachment using Accutase, then incubated for 30min at 4C with FITC-conjugated anti-PODOCIN antibody diluted 1:20. The cells were then centrifuged, the supernatants removed, and 500-L aliquots of PBS containing 2% StemSure Serum Replacement (191-18375, Fujifilm) added. Data were acquired using a BD FACS Canto II flow cytometer system (BD Biosciences).

Cells were fixed using 4% paraformaldehyde, and blocked with Blocking One (03953-95, Nacalai Tesque, Kyoto, Japan) for 60min at room temperature. Incubations were then performed at 4C overnight using primary anti-NEPHRIN, anti-PODOCIN antibody, and F-Actin (bs-1571R, Bioss Inc., MA, USA) antibody. Then, Alexa Fluor 488-tagged secondary antibody (ab150107, Abcam, Cambridge, UK) was applied for 30min at room temperature, and nuclei and F-actin were stained using 10g/mL Hoechst 33342 (346-07951, DOJINDO Laboratories, Kumamoto, Japan) and Phalloidin-iFluor 647 Conjugate (23127, AAT Bioquest, CA, USA), respectively. The stained cells were evaluated using fluorescence microscopy (BZ-X810, Keyence, Osaka, Japan).

Podocytes differentiated from hiPSCs were seeded at 2000 cells/cm2 onto Transwell inserts in six-well culture plates, pore size 0.4m (3450, Corning, AZ, USA) coated with iMatrix-511 silk. After 24h, DMEM Hams/F12 medium containing 2% B27 supplement, potassium chloride (5mM), urea (25mg/L), and human serum albumin (3g/dL) were added to the lower chambers, whereas the cells were incubated in a medium lacking the latter three substances in the upper chambers. After 24h, the media were collected from both of the chambers. The potassium concentration was measured using reagent for potassium measurement and electrode (EA09, A&T Corporation, Kanagawa, Japan). The urea nitrogen and albumin were measured using CicaLiquid-N UN reagent (77697, Kanto Chemical, Tokyo, Japan) and reagent of modified BCP method for albumin (30155001, Sekisui Medical, Tokyo, Japan), respectively, by an autoanalyzer (JCA-BM8020, JEOL Ltd., Tokyo, Japan).

Data are expressed as meanstandard deviation (SD). All experiments resulted by repeating the experiment three independent times. For the results shown in Figs.1B, 2A, and 3B, statistical analysis was performed using one-way ANOVA, followed by Bonferronis test; and Students t-tests were performed to compare the mean values of two groups for the data shown in Figs.2C and 5B. A p-value of<0.05 was considered to indicate statistical significance.

Effects of an mTOR inhibitor on podocyte differentiation. (A) Evaluation of the timing of rapamycin administration for protocol improvement: (a)13days treatment, (b)11days treatment and (c)7days treatment. (B) mRNA expression of podocyte-associated genes (NEPHRIN, PODOCIN, WT1, and MAFB) in cells treated with 100nM rapamycin at different times (a, b, c). Results are presented as meanSD of 6 samples. Statistical analysis was performed using one-way ANOVA with Bonferronis test. *p<0.05, **p<0.01. (C) mRNA expression of podocyte-associated genes (NEPHRIN, PODOCIN, SYNAPTOPODIN, WT1, and MAFB) in cells treated with various concentrations of rapamycin. Results are shown as the meanSD of 6 samples. Statistical analysis was performed using one-way ANOVA with Bonferronis test. *p<0.05, **p<0.01, ***p<0.001. (D) Protein expression of nephrin and podocin in differentiated podocytes, assessed using western blotting analysis. (E) Protein expression of nephrin and podocin assessed using western blot analysis. Results are shown as the meanSD of 3 samples. Statistical significance was assessed using Students t-test. *p<0.05. (F) Histograms for podocin-positive cells, quantified using FACS: (a) undifferentiated hiPSCs and (b) podocytes differentiated from hiPSCs.

Importance of the mTOR pathway for podocyte differentiation. (A) Protein expression of mTOR, p-mTOR, p70 S6K, p-p70 S6K, S6, p-S6, AKT, and p-AKT, assessed using western blotting analysis. (B) mRNA expression of podocyte-associated genes (NEPHRIN, PODOCIN, SYNAPTOPODIN, WT1, and MAFB) following the addition of the S6 inhibitor LY2584702. Results are shown as the meanSD of 6 samples. Statistical analysis was performed using one-way ANOVA with Bonferronis test. ***p<0.001.

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A better way to study Parkinson’s disease in the lab could lead to … – EurekAlert

image:

Lalitha Madhavan, MD, PhD, and her research team used induced pluripotent stem cell technology to reprogram adult skin cells into brain cells to study Parkinsons disease.

Credit: University of Arizona Health Sciences

A recent study published in Progress in Neurobiology and led by researchers at the University of Arizona College of Medicine Tucson has developed an improved method to study Parkinsons disease in the lab. Along the way, researchers also uncovered clues that may help scientists figure out how to detect Parkinsons earlier and point the way toward better treatments.

Around a million Americans are living with Parkinsons disease, a neurological disorder that causes difficulty in movement, balance and cognition. Symptoms worsen until tasks like walking, talking and swallowing present enormous challenges. While there is no cure, there are treatments that control symptoms but their effectiveness wanes over time and they are associated with unwanted side effects.

Its a slow-developing disorder. We only diagnose the disease at a late stage, when 60-70% of dopamine neurons are dysfunctional or have died off, said Lalitha Madhavan, MD, PhD, associate professor of neurology at the College of Medicine Tucson, part of UArizona Health Sciences. We have treatments, but at that point youre trying to throw a small glass of water on a raging fire. Being able to diagnose the condition at the earliest stages would be a big step.

Madhavans team used cells from Parkinsons patients to create a human-derived laboratory model to study the disease. Using induced pluripotent stem cell technology a powerful technique that transforms adult cells into embryo-like cells that can then mature into any cell type the lab reprogrammed adult skin cells called fibroblasts into brain cells.

Using the reprogrammed neurons, Madhavan Lab researchers discovered several changes in the cells from Parkinsons subjects that differentiated them from cells of healthy individuals. Madhavan hopes this finding can form the basis for better cell-culture systems for studying Parkinsons disease in the lab, potentially leading to improved diagnostics and treatments.

The experiments also showed that skin cells may act as a window into the brain. Skin cells dont cause neurological symptoms, but some of the same changes that damage brain cells might also affect skin cells, producing similar molecular signatures.

We wanted to make neurons from skin biopsies using this fantastic technology; however, we noted along the way that the fibroblasts themselves seemed to have signatures that differentiated individuals with Parkinsons. We started to dig deeper into that, Madhavan said. Its exciting that weve shown that connection, and that it tells us skin cells could perhaps be used to diagnose the disease early.

The team hopes that, in the future, doctors will be able to catch Parkinsons disease earlier by examining skin cells for signs that the disease is brewing.

This could be a system in which we could very carefully diagnose people at early stages, Madhavan said, adding that her team received a patent on a method for examining skin cells for molecular signs that correlate to Parkinsons disease.

They are now investigating how skin cells change over time to learn more about how the disease progresses and how to identify it early. Tech Launch Arizona, the University of Arizonas technology commercialization office, is helping protect the innovation and developing strategies to take it from the laboratory to the marketplace where it can impact the lives patients and their doctors.

Madhavan says that if we could catch Parkinsons disease earlier, doctors could prescribe currently available treatments that can slow disease progression. Simultaneously, scientists could work to develop next-generation Parkinsons drugs that target the disease in its early stages.

Because a patients skin cells are easy to access especially compared to brain cells Madhavan also hopes the system could be used for a precision-medicine approach, matching patients with optimized treatments based on a skin biopsy and lab test showing which drug might work best based on their unique genetic profile.

Weve been putting Parkinsons into one big bucket when actually different people express it differently, she said. This system would allow us to carefully classify Parkinsons and assess treatments more effectively based on such a classification.

The lead authors on the study were Mandi Corenblum, MS, senior research specialist, and Aiden McRobbie-Johnson, physiological sciences graduate student. Co-authors include Kelsey Bernard and Timothy Maley, graduate students in neuroscience and physiological sciences; Emma Carruth, undergraduate student in physiology; Moulun Luo, PhD, associate research professor of medicine; Lawrence Mandarino, PhD, professor of medicine; Maria Sans-Fuentes, PhD, BIO5 Institute statistician; Dean Billheimer, PhD, professor in the UArizona Mel and Enid Zuckerman College of Public Health and director of statistical consulting at the BIO5 Institute; and Erika Eggers, PhD, professor of physiology and member of the BIO5 Institute.

The study was supported mainly by a Michael J Fox Foundation grant (MJFF 18366) and in part by grants from the National Eye Institute, a division of the National Institutes of Health, under award nos. R01EY026027 and NSF1552184.

Progress in Neurobiology

Randomized controlled/clinical trial

Cells

Parallel neurodegenerative phenotypes in sporadic Parkinsons disease fibroblasts and midbrain dopamine neurons

22-Oct-2023

Declaration of Competing Interest None.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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A better way to study Parkinson's disease in the lab could lead to ... - EurekAlert

Lab-grown ‘small blood vessels’ point to potential treatment for major … – EurekAlert

image:

Disease mural cells stained for calponin (mural cells marker, green), collagen IV (magenta) and DAPI (nuclei, blue)

Credit: Alessandra Granata/University of Cambridge

Cambridge scientists have grown small blood vessel-like models in the lab and used them to show how damage to the scaffolding that supports these vessels can cause them to leak, leading to conditions such as vascular dementia and stroke.

The study, published today in Stem Cell Reports, also identifies a drug target to plug these leaks and prevent so-called small vessel disease in the brain.

Cerebral small vessel disease (SVD) is a leading cause of age-related cognitive decline and contributes to almost half (45%) of dementia cases worldwide. It is also responsible for one in five (20%) ischemic strokes, the most common type of stroke, where a blood clot prevents the flow of blood and oxygen to the brain.

The majority of cases of SVD are associated with conditions such as hypertension and type 2 diabetes, and tend to affect people in their middle age. However, there are some rare, inherited forms of the disease that can strike people at a younger age, often in their mid-thirties. Both the inherited and spontaneous forms of the disease share similar characteristics.

Scientists at the Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, used cells taken from skin biopsies of patients with one of these rare forms of SVD, which is caused by a mutation in a gene called COL4.

By reprogramming the skin cells, they were able to create induced pluripotent stem cells cells that have the capacity to develop into almost any type of cell within the body. The team then used these stem cells to generate cells of the brain blood vessels and create a model of the disease that mimics the defects seen in patients brain vessels.

Dr Alessandra Granata from the Department of Clinical Neurosciences at Cambridge, who led the study, said: Despite the number of people affected worldwide by small vessel disease, we have little in the way of treatments because we dont fully understand what damages the blood vessels and causes the disease. Most of what we know about the underlying causes tends to come from animal studies, but they are limited in what they can tell us.

Thats why we turned to stem cells to generate cells of the brain blood vessels and create a disease model in a dish that mimics what we see in patients.

Our blood vessels are built around a type of scaffolding known as an extracellular matrix, a net-like structure that lines and supports the small blood vessels in the brain. The COL4 gene is important for the health of this matrix.

In their disease model, the team found that the extracellular matrix is disrupted, particularly at its so-called tight junctions, which zip cells together. This leads to the small blood vessels becoming leaky a key characteristic seen in SVD, where blood leaks out of the vessels and into the brain.

The researchers identified a class of molecules called metalloproteinases (MMPs) that play a key role in this damage. Ordinarily, MMPs are important for maintaining the extracellular matrix, but if too many of them are produced, they can damage the structure similar to how in The Sorcerers Apprentice, a single broom can help mop the floor, but too many wreak havoc.

When the team treated the blood vessels with drugs that inhibit MMPs an antibiotic and anti-cancer drug they found that these reversed the damage and stopped the leakage.

Dr Granata added: These particular drugs come with potentially significant side effects so wouldnt in themselves be viable to treat small vessel disease. But they show that in theory, targeting MMPs could stop the disease. Our model could be scaled up relatively easily to test the viability of future potential drugs.

The study was funded by the Stroke Association, British Heart Foundation and Alzheimers Society, with support from the NIHR Cambridge Biomedical Research Centre and the European Unions Horizon 2020 Programme.

Reference Al-Thani, M, Goodwin-Trotman, M. A novel human 1 iPSC model of COL4A1/A2 small vessel disease unveils a key pathogenic role of matrix metalloproteinases. Stem Cell Reports; 16 Nov 2023; DOI: https://doi.org/10.1016/j.stemcr.2023.10.014

Stem Cell Reports

Experimental study

Cells

A novel human 1 iPSC model of COL4A1/A2 small vessel disease unveils a key pathogenic role of matrix metalloproteinases

16-Nov-2023

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Animal procedures

Animal experimentation at the IRB Barcelona was performed according to protocols approved by the Science Park of Barcelona (PCB) Ethics Committee for Research and Animal Welfare. Mice were housed in a specific pathogen-free facility on a 12-hour lightdark cycle at an ambient temperature of 2024C and humidity of 3070%. Adult mice were fed ad libitum with SAFE R40 pellet diet (https://safe-lab.com/safe_en/) containing 0.02mg per kg body weight vitamin B12. In general, mice of 816 weeks of age of both sexes were treated with 1mg ml1 doxycycline hyclate BioChemica (PanReac, A2951) in the drinking water (supplemented with 7.5% sucrose) for 7d. Antibiotic treatment was conducted using a broad-spectrum cocktail (1mg l1 each of ampicillin (BioChemica, A0839), neomycin sulfate and metronidazole (Sigma, M1547); 0.5mg l1 vancomycin (Cayman Chemical, CAY-15327) all dissolved in water supplemented with 7.5% sucrose) for 3 weeks before doxycycline initiation and was maintained during doxycycline treatment. Vitamin B12 (Sigma, V2876) supplementation was provided at 1.25mg l1 and folate supplementation was provided as folic acid (Sigma, F7876) at 40mg l1 in the drinking water, both for 7d concomitant with doxycycline treatment. For the B12 bolus experiment, mice were administered 5g vitamin B12 (Sigma, V2876) dissolved in water by oral gavage on day 6 after the start of doxycycline treatment, and blood samples were taken by submandibular collection just before and 24h after the bolus. OSKM transgenic mice are the i4F-B strain (derived on a C57/BL6J background and bred in house) described in ref. 3 and are available upon request. WT mice were i4F-B WT littermate controls where specified, or WT C57/BL6J (Charles River France).

Mice were treated with 2.5% (wt/vol) DSS, colitis grade (36,00050,000; MP Biomedicals, MFCD00081551) in drinking water for 5 consecutive days. On day 5, the DSS was removed and drinking water was supplemented with doxycycline hyclate BioChemica (1mg ml1; PanReac, A2951; with 7.5% sucrose) for 48h, after which regular water was returned. Mice in the B12 experimental group also received supplementation of vitamin B12 (1.25mg l1; Sigma, V2876) from the point of DSS removal (that is, day 5) until experimental endpoint. The MAT2Ai group received FIDAS-5 (MedChemExpres, HY-136144) and were dosed with 20mg per kg body weight per day dissolved in PEG400 by oral gavage as previously described79.

On day 9 (relative to the start of DSS administration), food was withdrawn from mice for 4h, after which mice were gavaged with FITCdextran (MW 4,000; Sigma-Aldrich, FD4) at a dose of 44mg per 100g of body weight dissolved in PBS. Food restriction was maintained for 3 additional hours, at which point blood was sampled by submandibular vein bleeding. Whole blood was diluted at a ratio of 1:4 in PBS, and 100l of blood/PBS mixture from each mouse was loaded into a 96-well plate. Fluorescence intensity was measured on a BioTek Synergy H1 Microplate Reader (excitation 490nm; emission 520nm).

Fresh stool samples were collected directly from mice and snap frozen. gDNA was isolated using a QIAamp DNA Stool Mini Kit (QIAGEN, 51504) according to the manufacturers protocols.

Libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina (E7370L) according to the manufacturers protocol. Briefly, 50ng of DNA was fragmented to approximately 400bp and subjected to end repair plus A-tailing, ligation of NEB adaptor and Uracil excision by USER enzyme. Then, adaptor-ligated DNA was amplified for eight cycles by PCR using indexed primers. All purification steps were performed using AMPure XP Beads (A63881). Final libraries were analysed using an Agilent DNA 1000 chip to estimate the quantity and check size distribution, and were then quantified by qPCR using the KAPA Library Quantification Kit (KK4835, KapaBiosystems) before amplification with Illuminas cBot. Libraries were sequenced (2125bp) on Illuminas HiSeq 2500.

Reads were aligned to the mm10 genome using STAR 2.7.0a with default parameters80. DNA contaminated reads were filtered out from the analysis. The first and final ten bases of the non-contaminated reads were trimmed using DADA2 1.10.1 (ref. 81). Taxonomic assignments were carried out through Kaiju 1.7.0 (ref. 82) using the microbial subset of the NCBI BLAST non-redundant protein database (nr). Resulting sequencing counts were aggregated at genus level. Reads that could not be assigned to any specific genus were classified to the nearest known taxonomic rank (marked by the term _un). The gut microbial compositional plot displays the relative abundances (percentage) at genus level. Only the 17 most abundant taxa are shown, while the rest were moved to the others category. For all genera, the treatment effect (finish versus start) was compared between OSKM and control (WT) mice. This was accounted in a model with an interaction term (drug:treatment) using DESeq2 with default options83. The paired nature of the experimental design was taken into account in the model as an adjusting factor.

Decontamination from host and trimming was done following the same routines as for the taxonomic analysis. Cleaned sequences for all samples were assembled into contigs using megahit 1.2.4 (ref. 84), and prodigal 2.6.3 (ref. 85) was then used to predict the open reading frames inside the obtained contigs. Protein mapping and KEGG and COG annotations were obtained using the EggNOG mapper 2.0.0 (ref. 86). The abundance of the annotated genes was finally measured by counting aligned reads to them via Bowtie2, version 2.2.2, under default parameters87. Resulting counts data were aggregated at protein level. The treatment effect (finish versus start) was compared between OSKM and control (WT) mice. This was accounted in a model with an interaction term (drug:treatment) using DESeq2 with default options83. The paired nature of the experimental design was considered in the model as an adjusting factor. The top 500 protein hits from the fitted model (nondirectional set) as well as the top 200 positive hits and the top 200 negative hits (directional sets), in all cases ordered by statistical significance, were used to explore enrichment of functional annotations. In this regard, GO terms for bacteria and archaea were considered using the AmiGO 2 GO annotations database88, removing from the analysis gene sets with few genes (less than 8) and too many genes (more than 499). Statistically enriched GO terms were identified using the standard hypergeometric test. Significance was defined by the adjusted P value using the Benjamini and Hochberg multiple-testing correction. To take into consideration the compositional nature of the data, all DESeq2-based results were complemented with graphical representations of abundance log-ratio (between finish and start matched samples) rankings. This provides a scale invariant way (with regard to the total microbial load) to present the data89.

Blood was collected via submandibular vein bleed (D0, D2, D4) or intracardiac puncture following deep carbon dioxide anaesthetisation (D7) at approximately 12:0014:00h (46h into the light cycle) of each day. Whole blood was spun down for 10min at 3,381g at 4C and supernatant (serum) was separated and stored at 80C.

Acetonitrile (Sigma-Aldrich), isopropanol (Sigma-Aldrich), methanol (Sigma-Aldrich), chloroform (Sigma-Aldrich), acetic acid (Sigma-Aldrich), formic acid (Sigma-Aldrich), methoxyamine hydrochloride (Sigma-Aldrich), MSTFA (N-methyl-N-(trimethylsilyl) trifluoroacetamide; Sigma-Aldrich), pyridine (Sigma-Aldrich), 3-nitrophenylhydrazine (Sigma-Aldrich), N-(3-dimethylaminopropyl)-N-ethylcarbodiimide hydrochloride (EDC; Sigma-Aldrich) and sulfosalicylic acid (Sigma-Aldrich) as previously described90.

A volume of 25l of serum were mixed with 250l a cold solvent mixture with ISTD (methanol/water/chloroform, 9:1:1, 20C), into 1.5ml microtube, vortexed and centrifugated (10min at 15,000g, 4C). The upper phase of supernatant was split into three parts: 50l was used for gas chromatography coupled to mass spectrometry (GCMS) experiments in the injection vial, 30l was used for the short-chain fatty acid ultra-high performance liquid chromatography (UHPLC)MS method, and 50l was used for other UHPLCMS experiments.

The GCMS/MS method was performed on a 7890B gas chromatography system (Agilent Technologies) coupled to a triple-quadrupole 7000C (Agilent Technologies) equipped with a high-sensitivity electronic impact source (EI) operating in positive mode.

Targeted analysis was performed on an RRLC 1260 system (Agilent Technologies) coupled to a triple-quadrupole 6410 (Agilent Technologies) equipped with an electrospray source operating in positive mode. Gas temperature was set to 325C with a gas flow of 12l min1. Capillary voltage was set to 4.5kV.

Targeted analysis was performed on an RRLC 1260 system (Agilent Technologies) coupled to a triple-quadrupole 6410 (Agilent Technologies) equipped with an electrospray source operating in positive mode. The gas temperature was set to 350C with a gas flow of 12l min1. The capillary voltage was set to 3.5kV.

Targeted analysis was performed on an RRLC 1260 system (Agilent Technologies) coupled to a 6500+QTRAP (Sciex) equipped with an electrospray ion source.

The profiling experiment was performed with a Dionex Ultimate 3000 UHPLC system (Thermo Scientific) coupled to a Q-Exactive (Thermo Scientific) equipped with an electrospray source operating in both positive and negative mode and full scan mode from 100 to 1,200m/z. The Q-Exactive parameters were: sheath gas flow rate, 55 arbitrary units (a.u.); auxiliary gas flow rate, 15 a.u.; spray voltage, 3.3kV; capillary temperature, 300C; S-Lens RF level, 55V. The mass spectrometer was calibrated with sodium acetate solution dedicated to low mass calibration.

The peak areas (corrected to quality control) corresponding to each annotated metabolite identified in the serum of reprogrammable mice (n=6 per group) at day 5 and day 7 after doxycycline treatment were converted to log2 values. Data were represented as log2 fold change (log2 FC) values to each mouse at day 0 (before doxycycline administration). Metabolic pathway impact was calculated by Global ANOVA pathway enrichment and Out-degree Centrality Topology analysis through the MetaboAnalyst 4.0 software91, using KEGG library (2019) as a reference. The colour gradient from white to red indicates the P value, where red is most significant. Bubble size indicates the relative contribution of the detected metabolites in their respective KEGG pathway. Pathway impact scores the centrality of the detected metabolites in the pathway.

A total of 30l of mouse plasma was acidified with 3l solution of 15% phosphoric acid (vol/vol). Afterwards, 42l of methyl tert-butyl ether was added and vigorously mixed using a vortex. After 20min of reequilibration, samples were centrifuged for 10min at 21,130g at 4C. Next, 90l of acetonitrile were added to 10l of the aqueous phase to facilitate protein precipitation. After another cycle of centrifugation, the supernatant was transferred into a vial before LCMS analysis.

The extracts were analysed by a UHPLC system coupled to a 6490 triple-quadrupole mass spectrometer (QqQ, Agilent Technologies) with electrospray ion source (LCESIQqQ) working in positive mode. The injection volume was 3l. An ACQUITY UPLC BEH HILIC column (1.7m, 2.1150mm, Waters) and a gradient mobile phase consisting of water with 50mM ammonium acetate (phase A) and acetonitrile (phase B) were used for chromatographic separation. The gradient was as follows: isocratic for 2min at 98% B, from 2 to 9min decreased to 50% B, for 30s raised to 98%, and finally column equilibrated at 98% B until 13min. The flow rate was 0.4ml min1. The mass spectrometer parameters were as follows: drying and sheath gas temperatures, 270C and 400C, respectively; source and sheath gas flow rates, 15 and 11l min1, respectively; nebulizer flow, 35psi; capillary voltage, 3,000V; nozzle voltage, 1,000V; and iFunnel HRF and LRF, 130 and 100V, respectively. The QqQ worked in MRM mode using defined transitions. The transitions for doxycycline and the collision energy (CE(V)) were 445428(17), 44598(60).

In total, 25l of serum was mixed with 25l of TCEP and 70l of 1% formic acid in methanol. Samples were vortexed and left at 20C for 1h, centrifuged for 10min at 21,130g and 4C and transferred to glass vials for their analysis by LCMS.

LCMS was performed with a Thermo Scientific Vanquish Horizon UHPLC system interfaced with a Thermo Scientific Orbitrap ID-X Tribrid Mass Spectrometer.

Metabolites were separated by HILIC chromatography with an InfinityLab Poroshell 120 HILIC-Z 2.7m, 2.1mm100mm column (Agilent Technologies). The mobile phase A was 50mM ammonium acetate in water, and mobile phase B was acetonitrile. Separation was conducted under the following gradient: 02min, isocratic 90% B; 26min raised to 50% B; 67min, isocratic 50% B; 77.2min, increased to 90% B; 7.210.5min, reequilibration column 90% B. The flow rate was 0.4ml min1. The injection volume was 5l.

Samples were analysed in positive mode in targeted SIM mode and the following setting: isolation window (m/z), 4; spray voltage, 3,500V; sheath gas, 50 a.u.; auxiliary gas, 10 a.u.; ion transfer tube temperature, 300C; vaporizer temperature, 300C; Orbitrap resolution, 120,000; RF lens, 60%; AGC target, 2e5; maximum injection time, 200ms.

SAM (m/z 399.145) was monitored from 57min; Met (m/z 150.0583) from 3.25.2min; SAH (m/z 385.1289) from 46min; Hcy (m/z 136.0428) from 3.45.5min, as previously optimized using pure standards.

Approximately, 20mg of dry and pulverized stool samples were mixed with with 75l of TCEP and 210l of 1% formic acid in methanol. Samples were vortexed and subjected to three freezethaw cycles using liquid nitrogen. Subsequently, samples were left in ice for 1h, centrifuged for 10min at 21,130g and 4C and transferred to glass vials for their analysis by LCMS.

LCMS was performed with a Thermo Scientific Vanquish Horizon UHPLC system interfaced with a Thermo Scientific Orbitrap ID-X Tribrid Mass Spectrometer.

Metabolites were separated by HILIC chromatography with an InfinityLab Poroshell 120 HILIC-Z 2.7m, 2.1mm100mm column (Agilent Technologies). The mobile phase A was 50mM ammonium acetate in water, and mobile phase B was acetonitrile. Separation was conducted under the following gradient: 02min, isocratic 90% B; 26min raised to 50% B; 67min, isocratic 50% B; 77.2min, increased to 90% B; 7.210.5min, reequilibration column 90% B. The flow was 0.4ml min1. The injection volume was 5l.

Samples were analysed in positive mode in targeted SIM mode and the following setting: isolation window (m/z), 4; spray voltage, 3,500V; sheath gas, 50 a.u.; auxiliary gas, 10 a.u.; ion transfer tube temperature, 300C; vaporizer temperature, 300C; Orbitrap resolution, 120,000; RF lens, 60%; AGC target, 2e5; maximum injection time, 200ms. Cyanocobalamin was monitored from (m/z 1355.5747 and m/z 678.291) from 55.5min, as previously optimized using a pure standard.

Mouse serum was diluted at a 1:20 ratio in PBS and holotranscobalamin (holoTC) was measured using an ADVIA Centuar Immunoassay System (SIEMENS) with ADVIA Centuar Vitamin B12 Test Packs (07847260) according to the manufacturers instructions.

Cell pellets were mixed with 50l of TCEP and 140l of 1% formic acid in methanol (containing 150g l1 of Tryptophan-d5 as internal standard). Samples were vortexed and subjected to three freezethaw cycles using liquid nitrogen. Subsequently, samples were left at 20C for 1h, centrifuged for 10min at 21,130g and 4C and transferred to glass vials for their analysis by LCMS/MS.

Samples were analysed with an UHPLC 1290 Infinity II Series coupled to a QqQ/MS 6490 Series from Agilent Technologies (Agilent Technologies). The source parameters applied operating in positive electrospray ionization (ESI) were gas temperature: 270C; gas flow: 15l min1; nebulizer: 35psi; sheath gas heater, 400 a.u.; sheath gas flow, 11 a.u.; capillary, 3,000V; nozzle voltage: 1,000V.

The chromatographic separation was performed with an InfinityLab Poroshell 120 HILIC-Z 2.7m, 2.1mm100mm column (Agilent Technologies), starting with 90% B for 2min, 50% B from minute 2 to 6, and 90% B from minute 7 to 7.2. Mobile phase A was 50mM ammonium acetate in water, and mobile phase B was acetonitrile. The column temperature was set at 25C and the injection volume was 2l.

MRM transitions for SAM (RT: 6.1min) were 399298 (4V), 399250 (12V), 39997 (32V) and 399136 (24V) for M+0, and 400299 (4V), 400251 (12V), 40097 (32V), 400137 (24V), 400250 (12V) and 400136 (24V) for M+1.

Samples were fixed overnight at 4C with neutral buffered formalin (HT501128-4L, Sigma-Aldrich). Paraffin-embedded tissue sections (23m in thickness) were air-dried and further dried at 60C overnight for immunohistochemical staining.

Sections were stained with haematoxylin and eosin (H&E) for histological evaluation by a board-certified pathologist who was blinded to the experimental groups. Additionally, periodic acidSchiff staining (AR16592-2, Artisan, Dako, Agilent) was used to visualize mucus-producing cells on 34-m sections of colon that were counterstained with haematoxylin.

In the reprogramming model, the findings were evaluated by focusing mainly on the appearance of hyperplastic and dysplastic changes of the epithelial cells of the digestive mucosa and pancreatic acini. Inflammation and loss of the intestinal goblet cells were also reported. To document the severity and extension, a semi-quantitative grading system was used based on previously used histological criteria:

Gastric and colon mucosa inflammatory cell infiltrate and multifocal areas of crypt (large intestine) or glandular (stomach) epithelial cell dysplasia were scored from 0 to 5, where 0 indicates absence of lesion and 5 indicates very intense lesions.

Intestinal crypt hyperplasia: 1, slight; 2, twofold to threefold increase of the crypt length; 3, >threefold increase of the crypt length.

Goblet cell loss of the mucosa of the large intestine: 1, <10% loss; 2, 1050% loss; 3, >50% loss.

Histological total score was presented as a sum of all parameters scored for a given tissue.

In the colitis model, the following parameters were semi-quantitatively evaluated as previously described92 as follows:

Inflammation of the colon mucosa: 0, none; 1, slight, 2, moderate; 3, severe.

Depth of the injury: 0, none; 1, mucosa; 2, mucosa and submucosa; 3, transmural.

Crypt damage: 0, none; 1, basal and 1/3 damaged; 2, basal and 2/3 damaged; 3, only the surface epithelium intact; 4, entire crypt and epithelium lost.

Tissue involvement: 0, none; 1, 025%; 2, 2650%; 3, 5175%; 4, 76100%.

The score of each parameter was multiplied by the factor of tissue involvement and summed to obtain the total histological score.

Immunohistochemistry was performed using a Ventana discovery XT for NANOG and Sca1/Ly6A/E, the Leica BOND RX Research Advanced Staining System for H3K36me3, keratin 14 and vitamin B12, and manually for Ki67. Antigen retrieval for NANOG was performed with Cell Conditioning 1 buffer (950-124, Roche) and for Sca1/Ly6A/E with Protease 1 (5266688001, Roche) for 8min followed with the OmniMap anti-Rat HRP (760-4457, Roche) or OmniMap anti-Rb HRP (760-4311, Roche). Blocking was done with casein (760-219, Roche). Antigenantibody complexes were revealed with ChromoMap DAB Kit (760-159, Roche). For H3K36me3 and keratin 14, antigen retrieval was performed with BOND Epitope Retrieval 1 (AR9961, Leica) and for vit B12 with BOND Epitope Retrieval Solution 2 (Leica Biosystems, AR9640) for 20min, whereas for Ki67, sections were dewaxed as part of the antigen retrieval process using the low pH EnVision FLEX Target Retrieval Solutions (Dako) for 20min at 97C using a PT Link (Dako-Agilent). Blocking was performed with Peroxidase-Blocking Solution at room temperature (RT; S2023, Dako-Agilent) and 5% goat normal serum (16210064, Life technology) mixed with 2.5% BSA diluted in wash buffer for 10 and 60min at RT. Vitamin B12 also was blocked with Vector M.O.M. Blocking Reagent (MK-2213, Vector) following the manufacturers procedures for 60min. Primary antibodies were incubated for 30, 60 or 120min. The secondary antibody used was the BrightVision poly HRP-Anti-Rabbit IgG, incubated for 45min (DPVR-110HRP, ImmunoLogic) or the polyclonal goat Anti-Mouse at a dilution of 1:100 for 30min (Dako-Agilent, P0447). Antigenantibody complexes were revealed with 3-3-diaminobenzidine (K346811, Agilent or RE7230-CE, Leica). Sections were counterstained with haematoxylin (CS700, Dako-Agilent or RE7107-CE, Leica) and mounted with Mounting Medium, Toluene-Free (CS705, Dako-Agilent) using a Dako CoverStainer. Specificity of staining was confirmed by staining with a rat IgG (6-001-F, R&D Systems, Bio-Techne), a Rabbit IgG (ab27478, Abcam) or a mouse IgG1, kappa (Abcam, ab18443) isotype controls. See Supplementary Table 5 for primary antibody details.

Ready-to-use reagents from RNAscope 2.5 LS Reagent Kit-RED (322150, RNAScope, ACD Bio-Techne) were loaded onto the Leica Biosystems BOND RX Research Advanced Staining System according to the user manual (322100-USM). FFPE tissue sections were baked and deparaffinized on the instrument, followed by epitope retrieval (using Leica Epitope Retrieval Buffer 2 at 95C for 15min) and protease treatment (15min at 40C). Probe hybridization, signal amplification, colorimetric detection and counterstaining were subsequently performed following the manufacturers recommendations.

Hybridization was performed with the RNAscope LS 2.5 Probe - Mm-Lgr5 - Mus musculus leucine rich repeat containing G-protein-coupled receptor 5 (312178, RNAScope, ACD Bio-Techne). Control probe used was the RNAscope 2.5 LS Probe - Mm-UBC - Mus musculus ubiquitin C (Ubc), as a housekeeping gene (310778, RNAScope - ACD Bio-Techne). The bacterial probe RNAscope 2.5 LS Negative Control Probe_dapB was used as a negative control (312038, RNAScope - ACD Bio-Techne).

Brightfield images were acquired with a NanoZoomer-2.0 HT C9600 digital scanner (Hamamatsu) equipped with a 20 objective. All images were visualized with a gamma correction set at 1.8 in the image control panel of the NDP.view 2 U12388-01 software (Hamamatsu, Photonics).

Brightfield images of immunohistochemistry were quantified using QuPath software93 with standard detection methods. Where the percentage of tissue staining is calculated, pixels were classified as positive and negative using the Thresholder function. Where the percentage of cells is quantified, the Positive Cell Detection function was used.

MEFs were cultured in standard DMEM medium with 10% FBS (Gibco, LifeTechnologies, 10270106) with antibiotics (100U ml1 penicillinstreptomycin; Life Technologies, 11528876). Reprogramming of the doxycycline-inducible 4-Factor (i4F) MEFs with inducible expression of the four Yamanaka factors Oct4, Sox2, Klf4 and cMyc (OSKM) was performed as previously described3. Briefly, i4F MEFs were seeded at a density of 3105 cells per well in six-well tissue culture plates coated with gelatin and treated with doxycycline (PanReac, A2951) 1mg ml1 continuously to induce expression of the OSKM transcription factors in the presence of complete KSR media (15% (vol/vol) Knockout Serum Replacement (KSR, Invitrogen, 10828028) in DMEM with GlutaMax (Life Technologies, 31966047) basal media, with 1,000U ml1 LIF (Merck, 31966047), non-essential amino acids (Life Technologies, 11140035) and 100M beta-mercaptoethanol (Life Technologies, 31350010) plus antibiotics (penicillinstreptomycin, Gibco, 11528876)), which was replaced every 4872h. After 10d, iPS cell colonies were scored by alkaline phosphatase staining according to the manufacturers protocol (AP blue membrane substrate detection kit, Sigma, AB0300). Vitamin B12 (Sigma, V2876; 2M final), MAT2Ai PF-9366 (MedChemExpress, HY-107778; 2M final), SAM (S-(5-adenosyl)-l-methionine iodide, Merck, A4377; 100M final) and NSC636819 (Sigma-Aldrich, 5.31996; 10M final) were added continuously to the culture media and replaced every 4872h.

Reprogramming of WT MEFs was performed as previously described94. Briefly, HEK-293T (American Type Culture Collection, ATCC-CRL-3216) cells were cultured in DMEM supplemented with 10% FBS and antibiotics (penicillinstreptomycin, Gibco, 11528876). Around 5106 cells per 100-mm-diameter dish were transfected with the ecotropic packaging plasmid pCL-Eco (4g) together with one of the following retroviral constructs (4g): pMXs-Klf4, pMXs-Sox2, pMXs-Oct4 or pMXs-cMyc (obtained from Addgene) using Fugene-6 transfection reagent (Roche) according to the manufacturers protocol. The following day, media were changed and recipient WT MEFs to be reprogrammed were seeded (1.5105 cells per well of a six-well plate). Retroviral supernatants (10ml per plate/factor) were collected serially during the subsequent 48h, at 12-h intervals, each time adding fresh media to the 293T cells cells (10ml). After each collection, supernatant was filtered through a 0.45-m filter, and each well of MEFs received 0.5ml of each of the corresponding retroviral supernatants (amounting to 2ml total). Vitamin B12 supplementation (Sigma, V2876; 2M final concertation) began on the same day as viral transduction. This procedure was repeated every 12h for 2d (a total of four additions). After infection was completed, media were replaced by complete KSR media (see above). Cell pellets were harvested on day 5 (relative to the first infection) and histone extracts were processed for immunoblot as described below. On day 14 (relative to the first infection), iPS cell colonies were scored by alkaline phosphatase staining according to the manufacturers protocol (AP blue membrane substrate detection kit; Sigma, AB0300).

Doxycycline-inducible i4F MEFs were cultured as described in Cell culture above, with 1mg ml1 doxycycline, with without continuous vitamin B12 supplementation. At 72h after the addition of doxycycline, cells were transferred to complete KSR media containing a final concentration of 0.5mM l-Serine-13C3 (Sigma-Aldrich, 604887). This is the same concentration of unlabelled l-serine normally found in the complete KSR media, and was generated by ordering custom, serine-free DMEM (Life Technologies, ME22803L1) and custom, serine-free non-essential amino acid mixture (Life Technologies, ME22804L1). Six hours after the addition of labelled media, a subset of wells was harvested by scraping in PBS and centrifugation (300g for 5min); supernatant was removed and pellets were snap frozen. At 72h after the addition of the labelled media (that is, 6days into reprogramming), cells still in culture were transferred back to unlabelled complete KSR media, which was changed every 4872h. iPS cell colonies were analysed by alkaline phosphatase staining according to the manufacturers protocol (AP blue membrane substrate detection kit; Sigma, AB0300) on day 10. Doxycycline and vitamin B12 supplementation were continuous throughout the entire reprogramming protocol, and replenished with every media change (that is, every 4872h).

i4F MEFs were cultured in the presence doxycycline 2M of vitamin B12 over 3 or 10days (culture conditions as described above) and histone extracts were prepared using EpiQuik Total Histone Extraction Kit (EpiGentek, OP-0006-100) according to the manufacturers instructions. Around 200ng of total histone extract was used per well in the EpiQuik Histone H3 Modification Multiplex Assay Kit (Colorimetric; EpiGentek, P-3100) according to the manufacturers instructions.

Histone extracts were prepared using an EpiQuik Total Histone Extraction Kit (EpiGentek, OP-0006-100) according to the manufacturers instructions and quantified using DC Protein Assay Kit (Bio-Rad, 5000111). Whole-cell extracts were prepared in RIPA buffer (10mM Tris-HCl, pH 8.0; 1mM EDTA; 0.5mM EGTA; 1% Triton X-100; 0.1% sodium deoxycholate; 0.1% SDS; 140mM NaCl). A total of 10g of lysate was loaded per lane and hybridized using antibodies against H3K36me3, MS, vinculin, total histone H3 and LI-COR fluorescent secondary reagents (IRDye 800 CW anti-mouse, 926-32210; IRDye 680 CW anti-mouse, 926-68070; IRDye 800 CW anti-rabbit, 926-32211; IRDye 680 CW anti-mouse, 926-68071) all at a dilution of 1:10,000 according to manufacturers instructions. Immunoblots were visualized on an Odyssey FC Imaging System (LI-COR Biosciences). See Supplementary Table 5 for primary antibody details.

GSEAPreranked was used to perform a GSEA of annotations from MsigDB M13537, with standard GSEA and leading edge analysis settings. We used the RNA-seq gene list ranked by log2 fold change, selecting gene set as the permutation method with 1,000 permutations for KolmogorovSmirnoff correction for multiple testing95.

Genes belonging to the leading edge of the GSEA using the Met derivation signature (MsigDB, M13537) in the pancreas of reprogramming mice were selected. These genes were then compared to genes belonging to the leading edge of the same gene signature from i4F MEFs treated with doxycycline in vitro for 72h, as compared to OSKM MEFs treated with vitamin B12 (that is, genes in MsigDB M13537 whose upregulation was relieved by B12 supplementation in vitro). We selected 11 of these genes for which we had qPCR primers available.

Total RNA was extracted from MEFs with TRIzol (Invitrogen) according to the manufacturers instructions. Up to 5g of total RNA was reverse transcribed into cDNA using the iScript Advanced cDNA Synthesis Kit (Bio-Rad, 172-5038; pancreas) or iScript cDNA Synthesis Kit (Bio-Rad, 1708890; all other organs) for RTqPCR. Real-time qPCR was performed using GoTaq qPCR Master Mix (Promega, A6002) in a QuantStudio 6 Flex thermocycler (Applied Biosystem) or 7900HT Fast Real-Time PCR System (Thermo Fisher). See Supplementary Table 6 for primer sequences.

i4F MEFs were cultured in the presence or absence of doxycycline 2M of vitamin B12 (Merck, V2876) over 3days in six-well plates (culture conditions as described above). Cells were fixed with 1% (vol/vol) PFA (Fisher Scientific, 50980487) for 2min and then quenched with 750mM Tris (PanReac AppliChem, A2264) for 5min. Cells were washed twice with PBS, scraped, and spun down at 1,200g for 5min. Pellets were lysed with 100l (per well) lysis buffer (50mM HEPES-KOH pH 7.5, 140mM HCl, 1mM EDTA pH 8, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, protease inhibitor cocktail; Sigma, 4693159001) on ice for 10min, then sonicated using a Diagenode BioRuptor Pico (Diagenode, B01060010) for ten cycles (30s on, 30s off) at 4C. Lysates were clarified for 10min at 8,000g, 1% input samples were reserved, and supernatant was used for immunoprecipitation with Diagenode Protein A-coated Magnetic beads ChIPseq grade (Diagenode, C03010020-660) and H3K3me3 monoclonal antibody (Cell Signaling Technologies, 4909) with 0.1% BSA (Sigma, 10735094001). The following day, cells were washed once with each buffer: low salt (0.1% SDS, 1% Triton X-100, 2mM EDTA, 20mM Tris-HCl pH 8.0, 150mM NaCl), high salt (0.1% SDS, 1% Triton X-100, 2mM EDTA, 20mM Tris-HCl pH 8.0, 5,000mM NaCl), LiCl (0.25M LiCl, 1% NP-40, 1% sodium deoxycholate, 1mM EDTA, 10mM Tris-HCl pH 8.0) and eluted in 1% SDS, 100mM NaHCO3 buffer. Cross-links were reversed with RNase A (Thermo Fisher, EN0531), proteinase K (Merck, 3115879001) and sodium chloride (Sigma, 71376), and chromatin fragments were purified using QIAquick PCR purification kit (Qiagen, 28104).

i4F MEFs were cultured in the presence or absence of doxycycline and the indicated compounds over 3days in six-well plates (culture conditions as described above). After 72h, RNA was extracted using an RNeasy Kit (Qiagen, QIA74106) according to the manufacturers instructions.

The concentration of the DNA samples (inputs and immunoprecipitations) was quantified with a Qubit dsDNA HS kit, and fragment size distribution was assessed with the Bioanalyzer 2100 DNA HS assay (Agilent). Libraries for ChIPseq were prepared at the IRB Barcelona Functional Genomics Core Facility. Briefly, single-indexed DNA libraries were generated from 0.51.5ng of DNA samples using the NEBNext Ultra II DNA Library Prep kit for Illumina (New England Biolabs). Eleven cycles of PCR amplification were applied to all libraries.

The final libraries were quantified using the Qubit dsDNA HS assay (Invitrogen) and quality controlled with the Bioanalyzer 2100 DNA HS assay (Agilent). An equimolar pool was prepared with the 24 libraries and sequenced on a NextSeq 550 (Illumina). 78.9Gb of SE75 reads were produced from two high-output runs. A minimum of 23.97 million reads were obtained for all samples.

The concentration of total RNA extractions was quantified with the Nanodrop One (Thermo Fisher), and RNA integrity was assessed with the Bioanalyzer 2100 RNA Nano assay (Agilent). Libraries for RNA-seq were prepared at the IRB Barcelona Functional Genomics Core Facility. Briefly, mRNA was isolated from 1.5g of total RNA using the kit NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). The isolated mRNA was used to generate dual-indexed cDNA libraries using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (New England Biolabs). Ten cycles of PCR amplification were applied to all libraries.

The final libraries were quantified using the Qubit dsDNA HS assay (Invitrogen) and quality controlled with the Bioanalyzer 2100 DNA HS assay (Agilent). An equimolar pool was prepared with the 12 libraries and submitted for sequencing at the Centre Nacional dAnlisi Genmica (CRG-CNAG). A final quality control by qPCR was performed by the sequencing provider before paired-end 50-nucleotide sequencing on a NovaSeq 6000 S2 (Illumina). Around 77.7Gb of PE50 reads were produced from three NovaSeq 6000 flow cells. A minimum of 55.7 million reads were obtained for all samples (Extended Data Fig. 7).

Total RNA extractions were quantified with a Nanodrop One (Thermo Fisher), and RNA integrity was assessed with the Bioanalyzer 2100 RNA Nano assay (Agilent). Libraries for RNA-seq were prepared at the IRB Barcelona Functional Genomics Core Facility. Briefly, mRNA was isolated from 1.2g of total RNA and used to generate dual-indexed cDNA libraries with the Illumina Stranded mRNA ligation kit (Illumina) and UD Indexes Set A (Illumina). Ten cycles of PCR amplification were applied to all libraries.

Sequencing-ready libraries were quantified using the Qubit dsDNA HS assay (Invitrogen) and quality controlled with the Tapestation HS D5000 assay (Agilent). An equimolar pool was prepared with the 15 libraries for SE75 sequencing on a NextSeq 550 (Illumina). Sequencing output was above 539 million 75-nucleotide single-end reads and a minimum of 28 million reads was obtained for all samples (Extended Data Fig. 7).

All analyses were performed in the R programming language (version 4.0.5)96 unless otherwise stated. Stranded paired-end reads were aligned to the Mus musculus reference genome version mm10 using STAR80 with default parameters. STAR indexes were built using the ENSEMBL annotation version GRC138.97. SAM files were converted to BAM and sorted using sambamba (version 0.6.7)97. Gene counts were obtained with the featureCounts function from the Rsubread package98 with the gtf file corresponding to ENSEMBL version GRC138.97 and parameters set to: isPairedEnd=TRUE and strandSpecific=2. Technical replicates were collapsed by adding the corresponding columns in the count matrix.

We obtained a reprogramming gene signature from published data48 and selected genes with false discovery rate (FDR) lower than 0.05 and fold change between MEF and d3-EFF larger than 2. The reprogramming score was defined as the average of all genes in the signature after scaling the rlog transformed matrix.

Exon counts were generated using the featureCounts function with parameters: isPairedEnd=TRUE, strandSpecific=2, GTF.featureType=exon, GTF.attrType=transcript_id, GTF.attrType.extra=gene_id, allowMultiOverlap=TRUE and useMetaFeatures=FALSE and the same GTF as for gene counts. Technical replicates were collapsed by adding the corresponding counts. For each gene, the longest annotated transcript was selected. Genes with less than four exons of RPKMs lower than exp(2) were discarded from the analysis. Intermediate exons were defined as those from the fourth to the penultimate. A total of 9,365 genes were used to compute the ratio between the intermediate and first exons. Fold changes between untreated and B12-treated samples were computed as the ratio between the exon ratios.

Genes were separated by their expression after transcript length and library size normalization (RPKM). For each sample, we computed the median ratios for genes in each decile.

Data were accessed from GSE131032. Reads were processed and ratios computed as previously described. log2 ratios for all transcripts were summarized through the median by sample. Comparisons between days were performed fitting a linear model to the medians using cage as a covariable. The function glht from the multcomp R package was used to find coefficients and P values.

To select genes most affected by the B12 treatment after reprogramming, we compared ratios between the doxy and MEF conditions and between the doxy and doxy+B12 conditions. Genes that increased the ratios in the first comparison (upper 25th percentile) and decreased the ratio in the second comparison (bottom 25%) were selected for functional enrichment analysis. A hypergeometric test was performed to find significant overlap between the defined gene set and the Biological Processes GO collection99.

Reads were aligned to the mm10 reference genome with bowtie100 version 0.12.9 with parameters --n 2 and --m 1 to keep reads with multiple alignments in one position. SAM files were converted to BAM and sorted using sambamba version 0.6.7.

For each sample, aligned reads were imported into R using the function scanBam from the Rsamtools package101. Whole-genome coverage was computed using the coverage function from the IRanges package102 and binned into 50-bp windows. Gene annotations were imported from Ensembl version GRCm38. The average coverage over gene bodies was computed using the normalizeToMatrix function from the EnrichedHeatmap package103 with parameters extend=1,000, mean_mode=w0 and w=50. Genes were filtered to coincide with those used in the exon ratio calculation from the RNA-seq data. Rows in the heat map were split by the average RNA-seq RPKM values in all samples.

BAM files were transformed to TDF files using the count function from IGVtools (version 2.12.2)104 with parameters --z 7, --w 25 and --e 250. Visualization of TDF files was generated using IGV (version 2.9.4)105.

Data were accessed from GSE109142. Reads were processed and ratios computed as previously described except using the ENSEMBL GRCm38.101 human gene annotation and the hg38 genome assembly version. The log2 ratios for all transcripts were summarized through the median by sample. Comparison between diagnosis status was performed fitting a linear model to the medians with sex and the expression quantiles as covariables. The model was fitted using the lm R function and coefficients and P values with the coeff function.

Unless otherwise specified, data are presented as the means.d. Statistical analysis was performed by Students t-test or one-way analysis of variance (ANOVA) as indicated, using GraphPad Prism v9.0.0, and specific statistical tests as indicated for each experiment for bioinformatic analyses. P values of less than 0.05 were considered as statistically significant. No statistical methods were used to predetermine sample size in the mouse studies, but our sample sizes are similar to those reported in previous publications3,9,16,17,19. Animals and data points were not excluded from analysis with the exception of the MEFs that failed to reprogram in the ChIP experiment, which is clearly detailed in the text. Mice were allocated at random to treatment groups, with attempts to balance initial body weight and sex as possible. The investigators were blinded during histological assessment of the mice; other data collection and analysis was not performed blind to the conditions of the experiments. Data distribution was assumed to be normal, but this was not formally tested. Figures were prepared using Illustrator CC 2019 (Adobe).

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

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Using patients’ own cells, researchers examine connection between … – ND Newswire

Although considered a rare disorder, fragile X syndrome is the most common genetic cause of intellectual disability in the world. Fragile X patients can have a range of mild to severe intellectual disability with the potential for other conditions such as autism, delayed motor development, hyperactivity, behavioral problems and seizures.

Although its well-known that fragile X is caused by the FMR1 gene, its less understood how the disorder physically affects brain development and function.

Christopher Patzke, the John M. and Mary Jo Boler Assistant Professor of Biological Sciences at the University of Notre Dame, is collaborating with fragile X patients and families to study the disorder.

My lab is hoping to find an explanation of the disease symptoms in humans, looking at the disorder at the cellular and molecular level, Patzke said.

By partnering with fragile X expert Dr. Elizabeth M. Berry-Kravis, professor of pediatrics at Rush University and a 1979 graduate of Notre Dame, the Patzke Lab has been able to collect patient tissue samples to create induced pluripotent stem cells. Because these stem cells mimic embryonic stem cells, the lab can then transform those cells into virtually any human cell the researchers want to study.

For this research, Patzke and his team are transforming pluripotent stem cells into brain cells that mimic neurons of someone with fragile X syndrome, creating a human model to study the genetic mutations effect on the brain.

Most of the genes associated with intellectual disability encode for proteins that do something with synapses, Patzke said. So making a cell culture of these fragile X neurons allows us, in a way, to zoom in to single cells and synapses, or the connections between neurons, and learn how these neurons communicate with one another.

The researchers then compare a patients cell culture sample to a corrected-cell culture sample, made via gene editing, to analyze the differences between how the synapses function with and without the FMR1 gene mutation.

Although research into fragile X syndrome is not uncommon, many researchers use animal models to study the FMR1 gene. While some of the research has led to clinical trials, those results have yet to translate into effective benefits for humans. By using tissue from fragile X patients, the goal is to overcome this gap in discovery.

In addition to fragile X syndrome, the Patzke Lab is also studying other disorders that cause intellectual disability including Down syndrome and Kabuki syndrome, another rare disorder.

Patzke is affiliated with Notre Dames Boler-Parseghian Center for Rare and Neglected Diseases, the first basic science rare disease research center in the nation. Focused on both basic and translational research, the center works with families affected by rare diseases to combine studies of patient data and tissue with fundamental biological research in order to better understand disease, identify molecular targets and develop new diagnostics and treatments.

Contact: Brandi Wampler, associate director of media relations, 574-631-2632, brandiwampler@nd.edu

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Using patients' own cells, researchers examine connection between ... - ND Newswire