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

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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Lab-grown 'small blood vessels' point to potential treatment for major ... - EurekAlert

Vitamin B12 is a limiting factor for induced cellular plasticity and … – Nature.com

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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Vitamin B12 is a limiting factor for induced cellular plasticity and ... - Nature.com

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

Seven Salk scientists named among best and most highly cited … – Salk Institute

November 15, 2023 November 15, 2023

LA JOLLASalk Professors Joseph Ecker, Ronald Evans, Satchidananda Panda, Rusty Gage, and Kay Tye, as well as Assistant Professor Jesse Dixon, have been named to the Highly Cited Researchers list by Clarivate. The 2023 list includes 6,849 researchers from 67 countries, all of whom demonstrate significant and broad influence reflected in their publication of multiple highly cited papers over the last decade. This is the ninth consecutive year that Ecker and Gage have made the list. Joseph Nery, a research assistant II in the Ecker lab, was also included on the list.

The Highly Cited Researchers list identifies and celebrates exceptional individual researchers at Salk, whose significant and broad influence in their fields translates to impact in their research community and innovations that make the world healthier, more sustainable, and more secure, says David Pendlebury, Head of Research Analysis at the Institute for Scientific Information at Clarivate. Their contributions resonate far beyond their individual achievements, strengthening the foundation of excellence and innovation in research.

Joseph Ecker Ecker is a professor in the Plant Molecular and Cellular Biology Laboratory, the director of the Genomic Analysis Laboratory, the Salk International Council Chair in Genetics, and a Howard Hughes Medical Institute investigator. His current research focuses on genomic and epigenomic regulation in plants and mammals and the application of DNA sequencing technologies for genome-wide analysis of DNA methylation, chromatin conformation, transcription, and gene function in single cells.

Ronald Evans Evans is a professor, the director of the Gene Expression Laboratory, and the March of Dimes Chair in Molecular and Developmental Biology. An expert in the essential roles of hormone receptors in reproduction, growth, and metabolism, Evans has identified novel pathways involved in cancer and metabolic diseases that are targetable by drugs that activate these receptors. More than a dozen approved drugs have been developed with Evans' technology for the treatment of leukemia, prostate cancer, breast cancer, liver disease, diabetes, and hypertension.

Satchidananda Panda Panda is a professor in the Regulatory Biology Laboratory and the director of the Wu Tsai Human Performance Alliance at Salk. He aims to understand how diet, exercise, and sleep affect cells and molecules in our body and to leverage this knowledge to elevate performance and reduce chronic diseases.

Rusty Gage Gage is a professor in the Laboratory of Genetics, the Vi and John Adler Chair for Research on Age-Related Neurodegenerative Disease, and the former president of the Salk Institute. He is a neuroscientist who studies the plasticity, adaptability, and diversity of the brain. By reprogramming human skin cells and other cells from patients with neurologic and psychiatric diseases into induced pluripotent stem cells, induced neurons, and organoids, his work is deciphering the progression and mechanisms that lead to disorders such as Alzheimer's disease, Parkinsons disease, bipolar disease, depression, and autism spectrum disorder.

Kay Tye Tye is a professor in the Systems Neurobiology Laboratory and the Wylie Vale Chair. She seeks to understand the neural-circuit basis of emotion that leads to motivated behaviors such as social interaction, reward-seeking, and avoidance. Her findings may help to inform treatments for a multitude of neuropsychiatric conditions such as anxiety, depression, addiction, and impairments in social behavior.

Jesse Dixon Dixon, a physician-scientist, is an assistant professor in the Gene Expression Laboratory and a member of the Salk Cancer Center faculty. He is a molecular biologist who uses molecular and computational approaches to explore how our genomes are organized in cells and how abnormal genome folding leads to human diseases such as cancer. His team is also developing new methods to study gene organization and gene function in single cells.

Joseph Nery Nery is a research assistant in the Ecker lab. He has been at the Salk Institute since 2006, where he specializes in epigenetics and runs computational analyses for the lab.

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Seven Salk scientists named among best and most highly cited ... - Salk Institute

Perspectives of current understanding and therapeutics of Diamond … – Nature.com

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Perspectives of current understanding and therapeutics of Diamond ... - Nature.com

Reprogramming of human peripheral blood mononuclear cells into … – Nature.com

OCT4 alone was insufficient to reprogram PBMCs into iMSCs directly

Previously, we reported that lentivirally expressed OCT4 could directly reprogram human cord blood CD34+ hematopoietic progenitor cells into iMSCs with very high efficiency11. Therefore, we first tried to convert human PBMCs into iMSCs by overexpressing OCT4 alone using a clinically relevant vector system. Isolated human PBMCs were cultured in a Stemline-based erythroid medium for six days to expand erythroid progenitors. Using the nucleofection method, 2106 expanded PBMCs were transfected with our modified oriP/EBNA1-based episomal vector, which expressed OCT4 under a strong SFFV promoter (Fig.1a), as we previously described11. Cells were then cultured in MSC medium11 supplemented with small molecules that promote reprogramming (3M CHIR99021, 10M forskolin, 10M ALK inhibitor (SB431542), and 5M tranylcypromine hydrochloride)15. However, there was no MSC-like colony formation 2 weeks later, indicating that OCT4 alone was insufficient to convert human PBMCs into iMSCs directly (Fig.1b).

a Schematic diagram of the episomal vector plasmids. SFFV is the spleen focus-forming virus U3 promoter; WPRE, posttranscriptional regulatory element; SV40PolyA, polyadenylation signal from SV40 virus; OriP, EBV (EpsteinBarr virus) origin of replication; EBNA1, EpsteinBarr nuclear antigen 1. b Colony formation at day 14 after nucleofection with 2106 PBMCs and maintenance in MSC culture conditions. c Reprogramming efficiency with different combinations of reprogramming factors. Error bars indicate standard deviation. n=3 biologically independent samples for each group. d Fluorescence-activated cell sorting (FACS) analysis of iMSCs 8 days after reprogramming with different factor combinations. SOX2 induced iPSCs generation (TRA-1-60+ cells). However, SOX9 did not induce detectable TRA-1-60+ cells. e Colony formation at day 14 after nucleofection with 1106 PBMCs (control) or CD34+-depleted PBMCs followed by maintenance in MSC culture conditions.

Our previous studies showed that BCL-XL is a critical reprogramming factor in blood cell reprogramming9,16, which increased the reprogramming efficiency by 10-fold when converting PBMCs into iPSCs using Yamanaka factors16. Here, we observed that transfection of PBMCs with OCT4, BCL-XL, and MYC (OBM) led to the formation of MSC-like colonies 2 weeks later (Fig.1b), albeit at low efficiency. The combination of any two of the OBM factors failed to generate iMSC colonies (Fig.1b). To improve the reprogramming efficiency further, we examined OBM with different combinations of other factors for generating iPSCs, including KLF4 and SOX2. KLF4 moderately improved iMSC generation, whereas SOX2 increased reprogramming efficiency by ~5-fold (Fig.1c and Supplementary Data1). However, the presence of SOX2 in the reprogramming cocktail resulted in ~12% of reprogrammed cells expressing iPSC markers, e.g., TRA-1-60 (Fig.1d) and NANOG (Supplementary Fig.1), even in MSC expansion culture conditions. Since iPSCs may induce teratomas, the SOX2-containing approach is not clinically prudent.

We decided to replace SOX2 with SOX9 because SOX9 plays an important role in skeletal development and chondrogenesis17,18. Surprisingly, SOX9 showed greater potency than SOX2 in iMSC reprogramming (Fig.1c). As expected, SOX9 virtually abolished the generation of TRA-1-60-expressing cells (Fig.1d). To ensure the absence of undetectable levels of iPSCs after reprogramming with SOX9, we cultured iMSCs in iPSC medium for 1 week. Phenotyping analysis of cultured cells showed no expression of iPSC markers. These data suggested that SOX9 restricted cell fate to iMSCs, whereas SOX2 would overshoot the reprogramming of a proportion of PBMCs beyond the stage of iMSCs. Moreover, after reprogramming with SOX9, PBMCs transformed morphologically to spindle-like cells resembling MSCs within 46 days, whereas SOX2-reprogrammed cells did not display spindle-like morphology (Supplementary Fig.2a).

Although PBMCs are composed of many different cell types, based on our previous studies3,16,19, we hypothesized that the CD34+ cell subset in peripheral blood was the most amenable to reprogramming to iMSCs. After six days of culture in hematopoietic stem cell expansion medium, the percentage of CD34+ cells in PBMCs increased from <1% to ~45%. When we depleted CD34+ cells from PBMCs before inducing reprogramming, no MSC-like colonies were observed (Fig.1e). These results suggested that the five reprogramming factors converted the CD34+ hematopoietic stem cells and progenitors but not the matureblood cells into iMSCs.

Having observed that the combination of OCT4, BCL-XL, MYC, KLF4, and SOX9 (named as 5F) induced the highest levels of PBMC conversion without overshooting the iMSC reprogramming process, we used the five factors (5F) for reprogramming in subsequent experiments. In all, 57 days after nucleofection of PBMCs with 5F, dozens of MSC-like colonies were observed. At approximately 2 weeks, reprogrammed cells resembled MSCs with typical spindle-like morphology (Fig.2a). The expression of MSC markers such as CD90 and CD73 increased from ~5% of reprogrammed cells by ~1 week to ~15% and 40% of the cells, respectively, by week 2 and >75% by week 3 (Fig.2b and Supplementary Data2). Four weeks after reprogramming with 5F, almost all cells expressed typical MSC markers: CD29 (99.7%), CD73 (95.3%), CD90 (96%), and CD166 (80%) (Fig.2c, d). The expression of hematopoietic markers such as CD45 and CD34 was negligible (Fig.2e). In addition, OCT4+ cells were not detectable (Supplementary Fig.3). Next, we evaluated the immunomodulatory potential of the iMSCs. We found that our 5F iMSCs were able to significantly suppress T-cell proliferation (CD4+ and CD8+ T-cell subsets) after 3 or 6 days (Fig.2f, Supplementary Fig.4a, and Supplementary Data3) co-culture with PBMCs. To further determine if the reprogramming to iMSCs or their expansion in culture may cause any chromosomal abnormalities, we performed digital karyotyping using SNP arrays. We did not identify any chromosomal abnormalities after either 1 week or 4 weeks of in vitro culture (Supplementary Figs.57). These data demonstrated that human PBMCs can be efficiently reprogrammed into iMSCs using our nonintegrating episomal vector system.

a Representative images of human PBMCs and iMSCs 14 days after reprogramming with five factors (5F). Scale bar represents 100m. b Changes in the percentage of cells expressing the MSC markers CD73 and CD90 as measured by flow cytometry of 5F-transfected PBMCs over time.c,d Flow cytometry plots of typical MSC marker expression (CD29, CD73, CD90, CD166)at 4 weeks after reprogramming. n=3 biologically independent samples for time point. eBlood cell markers (CD45 and CD34) were assessed 4 weeks after transfection of reprogramming factors. f iMSCs significantly inhibited T-cell proliferation after 3 days of co-culture with PBMCs. **P=0.0007. Error bars indicate standard deviation. n=3 biologically independent samples for each group.

To assess the essentiality of the five factors, we performed reprogramming by omitting a single factor in separate experiments. PBMCs from various donors were used. Surprisingly, we found that skipping OCT4, a critical factor for blood cell reprogramming, still allowed the generation of a considerable number of MSC-like colonies (Fig.3a and Supplementary Data4). In addition, PBMCs could be converted to iMSCs without KLF4, although at a ~35% decreased efficiency (Fig.3a). Omitting SOX9 not only significantly reduced the number of colonies formed but the reprogrammed cells were round in shape instead of spindle-like MSCs suggesting that SOX9 played a pivotal role in determining the MSC fate (Supplementary Fig.2b). By comparison, hardly any colonies were formed in the absence of BCL-XL or MYC. Taken together, SOX9, BCL-XL, and MYC were indispensable for reprogramming PBMCs into iMSCs.

a Reprogramming efficiency with the five-factor combination and removing one of the five factors. One-way ANOVA and Dunnetts multiple comparisons test, *P<0.05 vs. 5F group, ***P<0.001 vs. 5F group. ns: not significant. Error bars indicate standard deviation. n=5 for each group from biological independent donors. b Flow cytometry analysis of the MSC marker CD73 4 weeks after transfection with 5F, 4FnoO (no OCT4), and 4FnoK (no KLF4). c Flow cytometry analysis of the MSC markers CD73 and CD90 at 2, 3, and 4 weeks after transfection with 5F, 4FnoO (no OCT4), or 4FnoK (no KLF4). df RTqPCR analysis of osteogenesis-, adipogenesis-, and chondrogenesis-related genes in iMSCs reprogrammed with 5F, 4FnoO, and 4FnoK 2 weeks after multilineage differentiation. Tukeys multiple comparisons test, *P<0.05, 4FnoO vs. 5F and 4FnoK group. #P<0.05, 4FnoO vs. 4FnoK group. n=4 biologically independent samples for each group. Error bars indicate standard deviation (SD). g Multilineage differentiation of iMSCs reprogrammed with 5F, 4FnoO, or 4FnoK. Cells were cultured in osteogenic, adipogenic, or chondrogenic induction medium for 24 weeks and stained with Alizarin Red (osteogenesis), Oil Red O (adipogenesis), or Alcian blue (chondrogenesis), respectively. Scale bars represent 200m.

The iMSCs generated with the three different combinations of reprogramming factors, 5F, 4FnoO (5F minus OCT4), and 4FnoK (5F minus KLF4), were morphologically similar: they were all spindle-shaped, resembling MSCs (Supplementary Fig.2b). We evaluated the proliferation of the iMSCs generated from different conditions and compared it with primary human bone marrow MSCs (BMMSCs) (Supplementary Fig.2c). Primary human BMMSCs showed slowed proliferation after ~1 month in culture. The iMSCs reprogrammed from PBMCs displayed an enhanced in vitro proliferative capacity compared with BMMSCs. While the 5F iMSCs and 4FnoK iMSCs have similar proliferation ability, the 4FnoO iMSCs showed slower proliferation compared with the other two types of iMSCs (5F iMSCs and 4FnoK iMSCs). More than 100-fold more 5F iMSCs were generated than the human primary BMMSCs after ~1 month culture. In addition, >90% of the reprogrammed cells expressed the MSC marker CD73 (Fig.3b) 4 weeks after vector transfection. To monitor the reprogramming process in more detail, we evaluated the expression of the MSC markers CD73 and CD90 at 2-, 3-, and 4-week post-transfection (Fig.3c). We found that more than 60% of cells reprogrammed from either 5F or 4FnoK conditions became CD90+ by week 2, whereas only ~6% of cells from 4FnoO were CD90+, suggesting that OCT4 promoted the formation of CD90+ cells.

A characteristic feature of MSCs is the potential for trilineage differentiation into osteoblasts, adipocytes, and chondrocytes20. To assess the functionality of iMSCs reprogrammed with 5F, 4FnoO, or 4FnoK, we cultured iMSCs in three lineage-specific induction media, followed by RTqPCR analysis on the marker genes of osteogenesis, adipogenesis, and chondrogenesis.

The expression levels of runt-related transcription factor 2 (RUNX2), an early marker of osteogenic commitment, as well as the later osteogenic markers SP7 and alkaline phosphatase (ALP), were significantly decreased in the 4FnoO-reprogrammed iMSCs compared with 5F- or 4FnoK-reprogrammed iMSCs (P=0.01 and 0.03, respectively; Tukeys multiple comparisons test, Fig.3d and Supplementary Data5). To confirm the osteogenic commitment, we assessed calcium deposits by Alizarin Red S staining. Mineralization was observed in iMSCs reprogrammed with either 5F or 4FnoK but not in 4FnoO-reprogrammed iMSCs (Fig.3g).

Regarding chondrogenic differentiation, there was no significant difference in the expression of chondrogenic marker genes such as ACAN among the three groups (Fig.3e and Supplementary Data5). Alcian blue staining, which stains for aggrecans associated with MSC chondrogenic potential, also showed no significant difference among the three groups (Fig.3g). However, SOX9 expression was significantly reduced in 4FnoO iMSCs (4FnoO vs. 4FnoK, P=0.005). These data suggested that omitting OCT4 also impaired the chondrogenic differentiation potential of iMSCs. Taken together, these five factors were necessary for the generation of iMSCs with unbiased differentiation potential. Conversely, reprogramming without OCT4 led to the formation of dysfunctional iMSCs.

After the induction of adipogenic differentiation, lipoprotein lipase (LPL) and fatty acid-binding protein 4 (FADP4) were expressed at substantially lower levels in 4FnoO iMSCs than in either 5F or 4FnoK iMSCs (Fig.3f and Supplementary Data5). We used Oil Red O staining to visualize lipid droplets in functional adipocytes. Consistent with the adipogenic gene expression data, iMSCs reprogrammed without OCT4 failed to differentiate into functional adipocytes (Fig.3g). Of interest, omitting KLF4 led to the expression of higher levels of adipocyte markers and the formation of larger oil droplets, suggesting that KLF4 played a role in restricting adipogenic-biased MSCs.

To evaluate the immunomodulatory potentials of iMSCs reprogrammed with 5F, 4FnoO, or 4FnoK, we compared a list of major immunoregulatory cytokines, chemokines, and soluble factors secreted by MSCs21,22 using the normalized gene counts from the RNA-seq data (Supplementary Fig.4b and Supplementary Data6). We found that compared with 5F iMSCs, in addition to impaired trilineage differentiation potential, the 4FnoO iMSCs showed significantly reduced gene expression on many immunoregulatory cytokines/chemokines, such as IL-10, HGF, VCAM1, CCL2, CXCL14 (Supplementary Fig.4b). Both 5F and 4FnoK iMSCs showed comparable levels of immunoregulatory cytokines/chemokines gene expression compared to the primary human bone marrow-derived MSCs23.

To investigate the mechanisms underlying the distinct features of iMSCs reprogrammed with different factors (i.e., 5F, 4FnoO, and 4FnoK), we conducted transcriptome analysis 4 weeks after reprogramming factor transfection. We chose 4 weeks because >90% of the reprogrammed cells expressed MSC markers at this time point, and the nonintegrating episomal viral vectors were cleared from the reprogrammed cells7. First, we investigated the differentially expressed genes (DEGs) between the 5F, 4FnoO, or 4FnoK iMSCs. DEG analysis identified 827 significantly down- and 538 significantly upregulated genes in 4FnoO iMSCs compared to 5F iMSCs (FDR<0.05 and fold change (FC)>2, Fig.4a and Supplementary Data7). Of note, 5F and 4FnoK iMSCs showed similar transcriptomes with only 24 DEGs, consistent with their seemingly identical differentiation potentials (Supplementary Fig.8). Hierarchical clustering analysis identified a set of genes highly enriched in 5F and 4FnoK iMSCs, some of which were reported as MSC lineage signature genes, such as SRPX, S1PR3, ROBO2, NCAM1, COL5A1, and COL4A1 etc24,25,26 (Fig.4b and Supplementary Data7). Furthermore, the 4FnoO iMSCs displayed a significant decrease in the expression of mesoderm-regulating genes, including SOX4, SALL4, and TWIST1 (Supplementary Data7). We speculated that these downregulated genes might be associated with the impaired functionality of 4FnoO iMSCs. We then performed Gene Ontology (GO) enrichment analyses to explore the pathways associated with genes expressed at low levels in 4FnoO iMSCs. We found that 1365 DEGs were enriched in the biological processes of axonogenesis, extracellular structure organization, ossification, and cartilage development (Fig.4c). The top identified Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were the PI3K-Akt signaling and calcium signaling pathways (Fig.4d). These data helped explain the functional defects in osteogenesis of 4FnoO iMSCs and further understanding of the role of OCT4 in reprogramming PBMCs into iMSCs.

a Volcano plot showing differentially expressed genes identified in 4FnoO iMSCs compared with 5F iMSCs. Each dot represents a gene. The red dots are genes significantly upregulated (right) or downregulated (left) in 4FnoO iMSCs (Cutoff: P<10e6, fold change>2). b Heatmap showing the top 30 differentially expressed genes between 5F iMSCs and 4FnoO iMSCs (ranked by p-value). c, d Dot plots showing the top Gene Ontology (GO) biological process (BP) terms (c) and KEGG pathways (d) enriched from DEGs in 4FnoO iMSCs compared to 5F iMSCs. e PCA of RNA-seq from iMSCs 4 weeks after reprogramming with 5F, 4FnoO, or 4FnoK, primary human bone marrow-derived MSCs (BMMSC) and primary adipose-derived MSCs (AdMSC). For each condition, iMSCs were reprogrammed from PBMCs derived from three biologically independent donors. f Pearson correlation analysis of iMSCs and primary MSCs. g Comparison of twenty-four genes previously determined to be specific to the MSC lineage between primary MSCs and iMSCs.

To compare the iMSCs reprogrammed from PBMCs with primary human MSCs, we downloaded RNA-seq data generated from primary human bone marrow-derived MSCs (BMMSC)23 and primary human adipose-derived MSCs (AdMSC). First, we analyzed the transcriptional similarity of the iMSCs in our study to the primary human MSCs using principal component analysis (PCA) (Fig.4e). The reduction of the multi-dimensional dataset into two principal component (PC) dimensions enables the unbiased comparison and visualization of the transcriptomes between samples. As expected, the results showed that 4FnoO iMSCs were distinct from the other two iMSC groups (Fig.4e), consistent with the impaired differentiation potential of 4FnoO iMSCs when compared with 5F and 4FnoK iMSCs. The transcriptomes of human BMMSC and AdMSC were very similar to each other. Furthermore, the variation captured in PC1 demonstrated closer similarity of 5F and 4FnoK iMSCs with the primary MSCs compared to 4FnoO iMSCs, which tended to cluster further away from BMMSC and AdMSC (Fig.4e). Pearson correlation analysis confirmed that the 4FnoK and 5F iMSCs retained strong transcriptome correlation with the primary MSCs, while the 4FnoO iMSCs had less correlation with the primary MSCs (Fig.4f). A panel of 24 MSC lineage genes25,26 were compared between the primary MSCs and our iMSCs (Fig.4g). The 4FnoO iMSCs showed distinct expression patterns of these MSC signature genes that contrasted strongly with other groups. Noteworthy is that COL4A1, COL5A1, LOX, NNMT, which are known to be upregulated in MSCs versus fibroblasts24,27, were downregulated in 4FnoO iMSCs.

Genome-wide chromatin accessibility can provide mechanistic insights at the molecular level into cell fate decisions, especially during the reprogramming process. Thus, we performed ATAC-seq28 analysis on iMSCs 4 weeks after reprogramming PBMCs with 5F, 4FnoO, or 4FnoK. Open chromatin regions were identified as peaks in the ATAC-seq dataset. Furthermore, after peak calling, the relative genomic distribution of ATAC peaks showed reduced peaks within promoter regions in iMSCs generated without OCT4 (Fig.5a). In contrast, these cells had more open chromatin at intron regions. These results suggested that OCT4 may preferentially bind promoter regions to promote chromatin accessibility during reprogramming.

a Genomic location of ATAC-seq peaks from 5F, 4FnoO, and 4FnoK iMSCs. b PCA using normalized ATAC-seq counts from 5F, 4FnoO, and 4FnoK iMSCs, and two datasets from bone marrow-derived CD34+ cells (SRR2920489 and SRR2920490). For each condition, the chromatin accessibility was profiled from iMSCs that were reprogrammed from two biologically independent donors. c Heatmap showing ATAC-seq signals with the top 200 most different peaks (ranked by padj). Red represents chromatin regions with more mapped reads, suggesting possible chromatin openness. Gray represents chromatin regions with fewer mapped reads, suggesting closed chromatin. d Selected genomic views of the ATAC-seq data using IGV (2.8) for the indicated groups. For each gene, all genome views are on the same vertical scale. e The bar plot showing RNA-seq gene expression values for the respective genes shown above in the genome view. RNA-seq gene expression levels are shown as log2() normalized read counts. n=3 biologically independent samples for each group. *P0.05; error bars indicate standard deviation.

Similar to what was observed in the RNA-seq transcriptomic data, PCA of normalized ATAC-seq read counts showed that chromatin accessibility of three groups of iMSCs (5F, 4FnoO, and 4FnoK) were well-separated from each other, in which the accessible chromatin regions were mainly different in 4FnoO cells (PC1=52% variance, Supplementary Fig.9). However, in contrast to the similar transcriptomes between 5F and 4FnoK iMSCs (Supplementary Fig.8 and Fig.4a), ATAC-seq analysis showed that therewas aclear separation between 5F and 4FnoK iMSCs (PC2=19%, Supplementary Fig.9). These data suggested that both OCT4 and KLF4 facilitate chromatin remodeling during reprogramming. To compare the changes in chromatin accessibility during reprogramming, we downloaded the ATAC-seq data of primary CD34+ cells from bone marrow (SRR2920489, SRR2920490)29, which are similar to our reprogramming-initiating cells in this study. The datasets were processed using the same analysis pipeline. PCA revealed that CD34+ hematopoietic progenitor cells clustered separately from the three groups of reprogrammed iMSCs (Fig.5b), whereas 5F iMSCs and 4FnoK iMSCs were clustered closely with each other.

We also noticed that some chromatin regions remained closed in both CD34+ and 4FnoO iMSCs, whereas the same regions were in an open configuration in the 5F and 4FnoK iMSCs (Fig.5c). These data suggested that OCT4, but not KLF4, played a critical role in opening chromatin during the reprogramming process. More specifically, OCT4 opened the chromatin of the stemness-associated gene SALL4, Wnt signaling-related genes such as SFRP4, microtubule-binding and glutamate receptor binding-related genes JAKMIP2 and SYNDIG1, and MSC lineage signature gene NNMT (Fig.5d). These genes with reduced ATAC-seq peaks in 4FnoO iMSCs also showed significantly reduced mRNA expression, indicating a consistency between transcriptome and chromatin accessibility data (Fig.5e and Supplementary Data8).

DNA methylation is the most common epigenetic modification of the genome to control gene expression in mammalian cells30 and the differentiation or self-renewal of MSCs13. To determine the effects of reprogramming factors on methylation levels and patterns in iMSCs, we assessed genome-wide CpG methylation profiles in 5F, 4FnoO, and 4FnoK iMSCs at week four using RRBS. First, we profiled CpG methylation patterns on five different genomic features (all sites, promoters, exons, introns, and transcription start sites (TSSs) (Fig.6a, b and Supplementary Data9). We found that iMSCs reprogrammed without OCT4 showed a globally hypermethylated CpGs compared to iMSCs reprogrammed with OCT4 (Fig.6a, b). Specifically, when reprogramming in the absence of OCT4, we identified 10,760 differentially methylated cytosines (DMCs) (20%, q=0.1, Supplementary Data10), of which 9004 DMCs were hypermethylated and 1756 DMCs were hypomethylated (4FnoO vs. 5F). Among these sites, 7.7% were within promoter regions, and 7.9% werewithin exon regions (Fig.6c). In contrast, there was no significant difference in CpG methylation within all five genomics features in the iMSCs when reprogrammed in the absence of KLF4 (Fig.6a, b). Of the 3849 CpG sites significantly different (20%, q=0.1) between the 5F and 4FnoK groups, 3698 CpG sites were hypermethylated, and 151 sites were hypomethylated. When measuring the average methylation against the distance to the TSS, there was a global hypermethylation pattern in the iMSCs reprogrammed without OCT4 (Fig.6d, p<0.0001), suggesting that OCT4 was critical for global demethylation during reprogramming of PBMCs to iMSCs.

a The bar graph showing the methylation levels of all sites, promoters, exons, and intron regions from 5F, 4FnoO, and 4FnoK iMSCs. n=2 biologically independent samples for each group. b The methylation levels of the TSS region. n=2 biologically independent samples for each group. c The percentage of differentially methylated CpGs (DMCs) between 5F and 4FnoO iMSCs annotated within the promoter, exon, intron, and intergenic regions shown in the pie chart. d The average methylation levels surrounding the TSSs (5000 to +5000bp) in 5F, 4FnoO, and 4FnoK iMSCs. e Hierarchical clustering and heatmap analysis of 13,974 DMCs. f The bar plot showing the log2() normalized read counts from RNA-seq. n=3 biologically independent samples for each group. *P<0.05; error bars indicate standard deviation.

We performed hierarchical clustering on six RRBS datasets and generated a heatmap using the beta value of all common CpG sites. As expected, two datasets from 4FnoO clustered together, enriched a set of hypermethylated DMCs that were not observed in the 5F and 4FnoK datasets (Fig.6e). Since the cells reprogrammed from 5F and 4FnoK were very similar in their transcriptomes, chromatin openness, and methylation levels, we focused on our comparisons in the iMSCs programmed using 5F vs. 4FnoO. We annotated 10,760 DMCs and identified 665 differentially methylated genes (DMGs) between 5F and 4FnoO iMSCs (Supplementary Data10) which were subject to GO enrichment analysis (Supplementary Fig.10). Similar to the GO enrichment analysis based on RNA-seq data, DMGs were enriched in axonal guidance signaling and mesenchyme development. Of note, POU5F1, SALL4, NCAM1, HDAC4, and MSC lineage signature gene COL5A1 were significantly hypermethylated in iMSCs reprogrammed using 4FnoO compared with the iMSCs programmed using 5F (Supplementary Data10), suggesting that these genes might be associated with the impaired functionality in the 4FnoO iMSCs.

Demethylation may occur passively. DNMT1 is the most abundant DNA methyltransferase in mammalian cells and is considered the key methyltransferase responsible for DNA methylation maintenance, and its inhibition will result in passive demethylation. We found that the expression levels of DNMT1 in iMSCs reprogrammed with or without OCT4 were similar (Fig.6f and Supplementary Data8), suggesting minimal role of DNMT1 in OCT4-mediated demethylation. We then suspected that active DNA demethylation might have contributed to the global hypomethylation. Active DNA demethylation is mainly regulated by ten-eleven translocation (TET) enzymes31. We observed that the expression of TET1, but not TET2, was significantly reduced when reprogramming without OCT4 (Fig.6f), suggesting that TET1 might have contributed to OCT4-induced global demethylation. Meanwhile, the expression level of DNMT3B was significantly increased when reprogramming without KLF4, suggesting a role of KLF4 in regulating DNA methylation homeostasis via de novo DNA methyltransferase DNMT3B (Fig.6f).

To assess the influence of methylation on gene expression, we performed integration analysis of DMGs and DEGs datasets. We found the co-occurrence of 67 genes between 5F and 4FnoO iMSCs (Fig.7a and Supplementary Table1). Hypergeometric test was applied to show that the overlap is significant. Our analysis suggested that the observed difference in functionality between 5F and 4FnoO iMSCs might be a consequence of the difference in the methylation status of these 67 genes. Among these genes, ZFHX4, SLC8A2, NCAM1, TFPI2, and SALL4 were the most differentially expressed (Fig.7b). When PBMCs were reprogrammed without OCT4, not only were these genes significantly hypermethylated on either promoters or exons compared to PBMCs reprogrammed with OCT4 (Supplementary Data10), but some chromatin regions of these genes also remained inaccessible/closed (Fig.7c). Consistent with the hypermethylation of the four genes, their transcription levels were close to zero (Fig.7d and Supplementary Data8).

a Venn diagram illustrating the overlap between the differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between 5F iMSCs and 4FnoO iMSCs. A total of 1365 DEGs and 665 DMGs were identified; 67 of these were both differentially expressed and differentially methylated. b Volcano plot showing 67 overlapping genes between the DEG and DMG. pCutoff=10e6, log2 FC>1). c Selected genomic views of the ATAC-seq data using IGV (2.8) for the indicated groups. For each gene, all genome views are on the same vertical scale. d The bar plot showing the RNA-seq gene expression values for the respective genes, which are shown above in the genome view. RNA-seq gene expression levels are shown as log2() normalized read counts. *, P<0.05; error bars indicate standard deviation. n=3 biologically independent samples for each group. e Heatmap showing the normalized gene read count after log2() transformation from RNA-seq.

ZFHX4, a transcription-related zinc finger protein involved in the mesodermal commitment pathway, is upregulated in both embryonic stem cell-derived and bone marrow (BM)-derived MSCs32,33. These reports, together with our findings, indicate that ZFHX4 may serve as an MSC marker. In addition, neural cell adhesion molecule (NCAM), also called CD56, is expressed on human MSCs and was proposed as a marker for human MSC isolation34,35. Also, CD56+ cells showed increased colony formation ability, suggesting CD56 expression enriches MSCs with self-renewal potency36. On the other hand, BM-MSCs from NCAM-deficient mice exhibited defective migratory ability and significantly impaired adipogenic and osteogenic differentiation potential37.

Many genes have been proposed as MSC surface marker genes, but no consensus has been reached yet. To screen possible trilineage differentiation function associated MSC markers, we compared ten well-established MSC surface markers between primary MSCs and our iMSCs (Fig.7e and Supplementary Fig.11). We found that other than NCAM1, four additional MSC surface markers (CD90, PDGFRB, CD82, and FZD5) were highly expressed in both primary MSCs and 5F/4FnoK iMSCs but downregulated in 4FnoO iMSCs (Fig.7e). Taken together, integrated analysis of multiomics data lead to the identification of putative functional MSC markers, and our dataset enables the mining for additional MSC surface markers that co-associate with functional potential.

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Novel microelectrode array system enables long-term cultivation and analyses of brain organoid – Medical Xpress

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A human brain organoid (colored red) grew on the hammock-like mesh structure of a Mesh-MEA (green) for one year. The scanning electron micrograph shows how the brain organoid has grown around the mesh filaments and microelectrodes. Credit: Max Planck Institute for Molecular Biomedicine

Brain organoids are self-organizing tissue cultures grown from patient cell-derived induced pluripotent stem cells. They form tissue structures that resemble the brain in vivo in many ways. This makes brain organoids interesting for studying both normal brain development and for the development of neurological diseases. However, organoids have been poorly studied in terms of neuronal activity, as measured by electrical signals from the cells.

A team of scientists led by Dr. Thomas Rauen from the Max Planck Institute for Molecular Biomedicine in Mnster, Germany, in collaboration with Dr. Peter Jones' group at the NMI (Natural and Medical Sciences Institute at the University of Tbingen, Germany), has now developed a novel microelectrode array system (Mesh-MEA) that not only provides optimal growth conditions for human brain organoids, but also allows non-invasive electrophysiological measurements throughout the entire growth period. This opens up new perspectives for the study of various brain diseases and the development of new therapeutic approaches.

The study is published in the journal Biosensors and Bioelectronics.

Nerve cells communicate through chemical signals (neurotransmitters), which are converted into electrical signals that pass information from one nerve cell to the next. This is also the way in which the neurons in the brain organoids communicate with each other.

"To find the causes of various brain diseases and new therapeutic approaches, it is not enough to simply look at nerve cells under the microscope. You also need to know how the nerve cells workhow they communicate with each other," says Thomas Rauen.

However, current systems for recording the communication between nerve cells in brain organoids have their limitations. In the relatively large brain organoids, the sensors either do not get close enough to the nerve cells or they destroy parts of the organoid tissue when they penetrate it.

Now, Dr. Thomas Rauen's team, in collaboration with Dr. Peter Jones' team, has developed a novel microelectrode array system (Mesh-MEA) that not only provides optimal growth conditions for human brain organoids, but also enables non-invasive electrophysiological measurements throughout the growth period of the brain organoids.

The scientists designed a kind of hammock for the brain organoids. "The hammock-like mesh structure provides 61 microelectrodes for electrophysiological measurements of neuronal network activity," explains Dr. Peter Jones.

The current study shows that brain organoids can not only be cultured on the newly developed Mesh MEA for up to one year but can also be continuously electrophysiologically analyzed during this period. "This is a great achievement because it allows us to study brain organoids for much longer than before. Normal human brain development takes a very long time, and neurodegenerative diseases also develop slowly," says Rauen.

The key to the current success is that the brain organoids enveloped the filaments and continued to grow on the spider web-like Mesh-MEA scaffold. Dr. Katherina Psathaki from CellNanOs at the University of Osnabrck was able to show this using an electron microscope. She analyzed brain organoids in their Mesh-MEA hammock one year after the start of cultivation.

"The images clearly confirm that the brain organoids develop in the suspended Mesh-MEA net structure. The microelectrodes are located in the center of the brain organoid tissue," adds Thomas Rauen.

The scientists observed spontaneous neuronal activity recorded by the microelectrodes in the brain organoids. "There was continuously recurring, synchronized neuronal activity throughout the recording phase, suggesting the formation of neuronal networks as seen in vivo," says Thomas Rauen.

Although brain organoids cannot represent all the functions of the human brain, Peter Jones and Thomas Rauen are convinced that the electrophysiological analysis of brain organoids using their newly developed Mesh-MEA system will open up the possibility of simulating specific functional aspects of human brain development and its diseases in the laboratory, which has not been possible until now.

More information: Matthew McDonald et al, A mesh microelectrode array for non-invasive electrophysiology within neural organoids, Biosensors and Bioelectronics (2023). DOI: 10.1016/j.bios.2023.115223

Journal information: Biosensors and Bioelectronics

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