Category Archives: Induced Pluripotent Stem Cells

Scientists develop world’s first 3D-printed brain tissue that functions like human brain – WION

In a path-breaking scientific endeavour, researchers have created the worlds first 3D-printed brain tissue that behaves like a natural brain tissue. This is being considered a major leap towards the development of advanced solutions to neurological and neurodevelopmental disorders.

This will greatly aid research programmes for scientists specially focused on treatments for a broad range of neurological and neurodevelopmental disorders, such as Alzheimers and Parkinsons disease.

This could be a hugely powerful model to help us understand how brain cells and parts of the brain communicate in humans, Su-Chun Zhang, professor of neuroscience and neurology at UWMadisons Waisman Center, was quoted as saying by Neuroscience.

It could change the way we look at stem cell biology, neuroscience, and the pathogenesis of many neurological and psychiatric disorders, he added.

The 3D printer employed by scientists here ditched the traditional approach in favour of stacking layers horizontally. They situated brain cells, neurons grown from induced pluripotent stem cells, in a softer bio-ink gel than previous attempts had employed.

Watch:Are brain implants the future of computing?

The tissue still has enough structure to hold together but it is soft enough to allow the neurons to grow into each other and start talking to each other, Zhang added.

Yuanwei Yan, a scientist in Zhangs lab, said the tissues stayed relatively thin, which allowed the neurons to easily access oxygen and enough nutrients from the growth media.

The neurons communicate with each other, send signals and interact through neurotransmitters, and even form proper networks with support cells that were added to the printed tissue.

We printed the cerebral cortex and the striatum and what we found was quite striking, Zhang said. Even when we printed different cells belonging to different parts of the brain, they were still able to talk to each other in a very special and specific way, he added.

As per experts, the printing technique offers an advanced level of precision not seen in other approaches, including brain organoids, miniature organs used to study brains. The technique offers control over the types as well as arrangements of cells, with proper organisation and control.

This provides scientists with flexibility in their research endeavours, which paves the way for radical advancements in the field.

(With inputs from agencies)

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Scientists develop world's first 3D-printed brain tissue that functions like human brain - WION

Effect of a retinoic acid analogue on BMP-driven pluripotent stem cell chondrogenesis | Scientific Reports – Nature.com

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Effect of a retinoic acid analogue on BMP-driven pluripotent stem cell chondrogenesis | Scientific Reports - Nature.com

An epigenetic barrier sets the timing of human neuronal maturation – Nature.com

PSC lines and cell culture

Experiments with hPSCs and iPSCss was approved in compliance with the Tri-Institutional ESCRO at Memorial Sloan Kettering Cancer Center, Rockefeller University and Weill Cornell Medicine. hPSC lines WA09 (H9; 46XX) and WA01 (H1; 46XY) were from WiCell Stemcell Bank. The GPI::Cas9 line was derived from WA09 hPSCs. MSK-SRF001 iPSCs were from Memorial Sloan Kettering Cancer Center. hPSCs and iPSCs were authenticated by STR. hPSCs and iPSCs were maintained with Essential 8 medium (Life Technologies A1517001) in feeder-free conditions onto vitronectin-coated dishes (VTN-N, Thermo Fisher A14700). hPSCs and iPSCs were passaged as clumps every 45 days with EDTA (0.5M EDTA/PBS) and routinely tested for mycoplasma contamination. The GPI::Cas9 knock-in hPSCline was generated using CRISPRCas9-mediated homologous recombination by transfecting H9 hPSCs with the Cas9-T2A-Puro targeting cassette downstream of the GPI gene (Supplementary Fig. 6b). Selected clones were validated by genomic PCR and Cas9 mRNA and protein expression by RTqPCR and western blot, respectively and screened for Karyotype banding. CHD5-KO and JADE2-KO WA09 hPSC lines were generated by the SKI Stem Cell Research Core at Memorial Sloan Kettering Cancer Center (MSKCC) via CRISPRCas9 using the following gRNA targets: CHD5, CGTGGACTACCTGTTCTCGG; JADE2, CAGTTTGGAGCATCTTGATG. Mouseepiblast stem cells (EpiSCs) B6.129_4 were a gift from the Vierbuchen laboratory at Memorial Sloan Kettering Cancer Center and were maintained on mouse embryonic fibroblasts as previously described64. Rat primary astrocytes were purchased from Lonza (R-CXAS-520) and cultured according to manufacturer instructions.

hPSCs (passage 4050) were differentiated toward cortical excitatory neurons using an optimized protocol based on dual SMAD inhibition and WNT inhibition as follows. hPSCss were dissociated at single cells using Accutase and plated at 300,000 cells per cm2 onto Matrigel-coated wells (354234, Corning) in Essential 8 medium supplemented with 10M Y-27632. On day 02, cells were fed daily by complete medium exchange with Essential 6 medium (E6, A1516401, Thermo Fisher Scientific) in the presence of 100nM LDN193189 (72142, Stem Cell Technologies), 10M SB431542 (1614, Tocris) and 2M XAV939 (3748, Tocris) to induce anterior neuroectodermal patterning. On day 39 cells were fed daily with Essential 6 medium (E6, A1516401, Thermo Fisher Scientific) in the presence of 100nM LDN193189 (72142, Stem Cell Technologies), 10M SB431542. On day 1020 cells were fed daily with N2/B27 medium (1:1 NB:DMEM/F12 basal medium supplemented with 1 N2 and B27 minus vitamin A) to generate a neurogenic population of cortical NPCs. N2 and B27 supplements were from Thermo. At day 20, NPCs were either cryopreserved in STEM-CELLBANKER solution (Amsbio) or induced for synchronized neurogenesis as following: NPCs were dissociated at single cells following 45min incubation with Accutase and seeded at 150,000 cells per cm2 onto poly-l-ornithine and laminin/ fibronectin-coated plates in NB/B27 medium (1 B27 minus vitamin A, 1% l-glutamine and 1% penicillin-streptomycin in Neurobasal medium) in the presence of 10M Notch pathway inhibitor DAPT for 10 days (until day30). For long-term culture, neurons were maintained in NB/B27 supplemented with BDNF (450-10, PreproTech), GDNF (248-BD-025, R&D biosystems), cAMP (D0627, Sigma) and ascorbic acid (4034-100, Sigma). From day 20 onwards, cells were fed every 45 daysby replacing 50%of the mediumvolume. For neurons-astrocytes co-cultures, rat primary astrocytes were plated onto poly-l-ornithine and laminin/fibronectin-coated plates in NB/B27 medium supplemented with BDNF, GDNF, cAMP and ascorbic acid and allowed to adhere for few days. hPSC-derived neurons at day 25 were dissociated using Accutase and seeded on top of rat astrocytes. Neurons-astrocytes co-cultures were maintained on NB/B27 medium supplemented with BDNF, GDNF, cAMP and ascorbic acid.

Mouse epiblast stem cells (mEpiSCs) B6.129_4 were differentiated as following: on day 0, mEpiSC colonies were lifted from feeders using 0.5Ul1 collagenase IV in HBSS++, dissociated to single-cell solution using Accutase, then plated at 220,000 cells per cm2 on Matrigel-coated wells in mN2/B27 media64 supplemented with 10M Y-27632, 100nM LDN193189, 10M SB431542 and 2M XAV939. Cells were fed daily with mN2/B27 supplemented with 2M XAV939 (day 1), 100nM LDN193189 (day 15), 10M SB431542 (day 15). On day 6 NPCs were dissociated to single-cell suspension using Accutase and replated at 200,000 cells per cm2 onto poly-l-ornithine and laminin/fibronectin-coated plates in NB/B27 medium (10% Neurobasal, 90% Neurobasal A, 1 B27 minus vitamin A, 1% Glutamax, 0.5% penicillin-streptomycin, 0.1% BDNF, 0.1% cAMP, 0.1% ascorbicacid, 0.1% GDNF) supplemented with 10M Y-27632 (day 6) and 10M DAPT (day 6 and 8). Cells were fed every other day by replacing 50% of the medium volume.

On day 1, WA09 (H9) hPSCs were dissociated with EDTA for 10min at 37C and allowed to aggregate into spheroids of 10,000 cells each in V-bottom 96 well microplates (S-Bio) in E8 medium with ROCK inhibitor (Y-27632, 10M) and WNT inhibitor (XAV939, 5M, Tocris 3748). The next day (day 0), the medium was changed to E6 supplemented 100nM LDN193189, 10M SB431542 and 5M XAV939. On day 5, medium was switched to E6 supplemented with 100nM LDN193189, 10M SB431542. On day 8, medium was changed to N2/B27-based organoid medium as previously described65. From day 0 to day 14 medium was replaced every other day. On day 14, organoids were transferred to an orbital shaker on 10cm dishes and half of the medium was changed on a MondayWednesdayFriday schedule. Treatment with 4M GSK343 or DMSO was performed transiently from day 1725 or day 1737 depending on the experiment as indicated in the figures.

For birth-dating experiments of WA09 (H9) hPSC-derived cortical neurons, 3M EdU (5-ethynyl-2-deoxyuridine, A10044 Invitrogen) was added to the culture for 48h in the following time windows: d1819, d2021, d2223, d2425, d2627, d2829. After treatment, EdU was washed out and neurons were fixed at day40 of differentiation and processed for immunostaining. Treatment of hPSC-derived cortical NPCs with small molecules inhibitors of chromatin regulator was performed from day12 to day20 of differentiation (Fig. 4b). Small molecules were washed out and withdrawn starting at day 20 before the induction of synchronized neurogenesis and neurons derived from all the treatments were maintained in the same conditions. Small molecules were dissolved in DMSO and added to the N2/B27 medium at 2 or 4 M depending on the experiment. DMSO in control conditions was added at the corresponding dilution factor as for epigenetic inhibitors.

Treatment of mEpiSC-derived NPCs was performed as follows: For Ezh2i experiments, 0.04% DMSO or 4M GSK343 was added to NPC medium on day 4 and 5. For Ezh2i+ experiments this treatment was extended with 0.02% DMSO or 2M GSK343 being added to medium on day 6, 8 and 10. GSK-J4 was used at 1 M and added to the medium on day 4 and 5.

The following small molecules targeting epigenetic factors were used in the study and purchased from MedChemExpress: GSK343 (HY-13500), UNC0638 (HY-15273), EPZ004777 (HY-15227), GSK2879552 (HY-18632), CPI-455 (HY-100421), A-196 (HY-100201), GSK-J4 (HY-15648F). A List of small molecules and relative molecular target is reported in Extended Data Fig. 3b.

For the morphological reconstruction of WA09 (H9) hPSC-derived neurons, NPCs were infected at day20 with low-titre lentiviruses expressing dTomato reporter. Following induction of neurogenesis, the resulting neurons were fixed at day 25, 50, 75 and 100. The dTomato reporter signal was amplified by immunofluorescence staining and individual neurons were imaged at Zeiss AXIO Observer 7 epi-fluorescence microscope at 10 magnification. Neuronal morphology was reconstructed in Imaris v9.9.1 software using the filamentTracer function in autopath mode and using the nucleus (using DAPI channel) as starting point. Traces were eventually manually corrected for accuracy of cell processes detection. Neurite length and Sholl Analysis (every 10 m radius) measurements were performed in the Imaris platform and extracted for quantifications and statistics. For staining with synaptic markers, cells were cultured on -plate 96 Well Black (Ibidi) and stained for SYN1 and PSD95 antibodies to visualize pre and post -synaptic puncta respectively and MAP2 to visualize neuronal dendrites. Confocal images were acquired using a 63 immersion objective at a Leica SP8WLL confocal laser-scanning microscope. Three fields of view for each sample from two independent differentiations (total of 6 fields of viewpercondition) were analysed as following. Single-plane confocal images were open in Fiji v2.9.0 and puncta were detected using the SynQuant plugin (https://github.com/yu-lab-vt/SynQuant). The z-score for particle detection was adjusted for accuracy of puncta detection. The other parameters were set as default value. Dendrite length was extracted from the reference MAP2 channel.

Cultured cells were fixed with 4% PFA in PBS for 20min at RT, washed three times with PBS, permeabilized for 30min in 0.5% Triton X-100 in PBS and then blocked in a solution containing 5% Normal goat serum or Normal donkey serum, 2% BSA and 0.25% Triton X-100 for 1h at room temperature. Primary antibodies were incubated overnight at 4Cin the same blockingsolution. EdU+ cells were detected using the Click-iT EdU Imaging kit (Molecular Probes) with Alexa Fluor 488 according to manufacturers instructions. Secondary antibodies conjugated to either Alexa 488, Alexa 555 or Alexa 647 (Thermo) were incubated for 45min at 1:400 dilutionin blocking solution. Cell nuclei were stained with 5M 4-6-diamidino-2-phenylindole (DAPI) in PBS.

Organoids were fixed in 4% PFA overnight at 4C, washed 3 times with PBS and cryoprotected in 30% sucrose/PBS. Organoid tissue was sectioned at 30m on a cryostat (Leica 3050S), mounted on microscope slides, allowed to dry at room temperature and stored at 80C. On the day of the staining, slides we defrosted for 20min at room temperature. Sections were first permeabilized in 0.5% Triton X-100 in PBS, blocked for 1h in 5% normal goat serum, 1% BSA, 0.25% triton in PBS and incubated in the same solution with primary antibodies overnight. The next day, sections were washed with PBS and incubated in secondary antibodies for 2.5h at room temperature at 1:400 dilution. DAPI 5M stain was used to identify cell nuclei. Images were captured using a Leica SP8WLL confocal laser-scanning microscope.

The following primary antibodies and dilutions were used: rabbit anti-PAX6 1:300 (901301, Biolegend); rabbit anti-FOXG1 1:500 (M227, Clonetech); mouse anti-Nestin 1:400 (M015012, Neuromics); mouse anti-MAP2 1:200 (M1406, Sigma); chicken anti-MAP2 1:2000 (ab5392, Abcam); rabbit anti-class III -tubulin (TUJI) 1:1,000 (MRB-435P, Covance); mouse anti-Ki67 1:200 (M7240, Dako); rabbit anti-Ki67 1:500 (RM-9106, Thermo Scientific); rabbit anti-TBR1 1:300 (ab183032, Abcam); rabbit anti-TBR1 1:500 (ab31940, Abcam); rat anti-CTIP2 1:500 (ab18465, Abcam); mouse anti-SATB2 1:1,000 (ab51502, Abcam); rabbit anti-synapsin I 1:1,000 (S193, Sigma); mouse anti-PSD95 1:500 (MA1-046, Thermo); mouse anti-neurofilament H 1:500 (non-phosphorylated) (SMI32, Enzo Life science); mouse anti c-FOS 1:500 (ab208942, Abcam); mouse anti-HLA Class I ABC 1:150 (ab70328, abcam); goat anti-RFP 1:1,000 (200-101-379, Rockland); rabbit anti-DsRed 1:750 (632496, Clontech); rabbit anti-H3K27me3 1:200 (9733, Cell Signaling Technologies); rabbit anti-GFAP 1:500 (Z033429-2, Dako); chicken anti-GFP 1:1,000 (ab13970, Abcam); rat anti-SOX2 1:200 (14-9811-82, Thermo); rabbit anti-AQP4 1:500 (HPA014784, SIGMA); sheep anti-EOMES 1:200 (AF6166, R&D). The primary antibodies including anti-GFAP antibody were validated for recognition of human antigens to confirm lack of human astrocytes in our synchronized cortical cultures.

smRNA-FISH was performed on WA09 (H9) hPSC-derived and mEpiSC-derived neurons using ViewRNA Cell Plus Assay Kit (Invitrogen) in RNAse-free conditions according to manufacturers instructions to simultaneously detect RNA targets by in situ hybridization and the neuronal marker MAP2 (Alexa Fluor 647) by immunolabelling. Neurons were plated on -plate 24 Well Black (Ibidi) plates, fixed and permeabilized for 15min at room temperature with fixation/permeabilization solution and blocked for 20min followed by incubation with primary and secondary antibody for 1h at room temperature. Target probe hybridization with mouse or human -specific viewRNA Cell Plus probe sets was carried at 40C under gentle agitation for 2h. Type 1 (Alexa Fluor 546) and type 4 (Alexa Fluor 488) probe sets were used to detect EZH2 and TBP RNA respectively, using the same fluorophore scheme for neurons derivedfrom mEpiSCs and hPSCs. Pre amplification, amplification and fluorescence labelling steps were carried at 40C under gentle agitation for 1h each. Washes were performed as indicated in the kits procedure. Samples were incubated with 5M DAPI to visualize cell nuclei and a coverslip was gently placed inside each well using ProLong Glass Antifade Mountant. z-stack images at 0.4 m step and covering the entire cell volume were acquired using a Leica SP8WLL confocal laser-scanning microscope with a 63 immersion objective at 3 digital zoom. z-stacks were loaded and projected in Imaris v9.9.1 software for RNA puncta visualization and quantification within each single MAP2 positive neuron. Eight different fields of view (25 neurons per field) for each condition (mouse versus human) from two independent batches of differentiations (16 fields of view per condition) were obtained for downstream analysis. The nuclear volume for each neuron was reconstructed and calculated using the Surface function in Imaris Software.

For electrophysiological recordings, neurons were plated in 35mm dishes. Whole-cell patch clamp recordings during the maturation time course were performed at day 25, 50, 75 and 100 of differentiation as previously described22. In brief, neurons were visualized using a Zeiss microscope (Axioscope) fitted with 4 objective and 40 water-immersion objectives. Recordings were performed at 2324C and neurons were perfused with freshly prepared artificial cerebral-spinal fluid (aCSF) extracellular solution saturated with 95% O2, 5% CO2 that contained (in mM): 126 NaCl, 26 NaHCO3, 3.6 KCl, 1.2 NaH2PO4, 1.5 MgCl2, 2.5 CaCl2, and 10 glucose. Pipette solution for recordings in current clamp configuration contained (in mM): 136 KCl, 5 NaCl, 5 HEPES, 0.5 EGTA, 3 Mg-ATP, 0.2 Na-GTP, and 10 Na2-phosphocreatine, pH adjusted to 7.3 with KOH, with an osmolarity of ~290mOsm. For mEPSCs, the pipette solution contained (in mM): 140 CsCl, 10 NaCl, 10 HEPES, 0.5 EGTA, 3 Mg-ATP, 0.2 Na-GTP, and 10 Na2-phosphocreatine, pH adjusted to 7.3 with CsOH. 20M ()-bicuculline methochloride (Tocris), 1M strychnine HCl (Sigma), and 0.5M tetrodotoxin (TTX) (Alomone Labs) were added to aCSF for mEPSC recordings to block GABAA receptors, glycine receptors, and voltage-gated Na+ channels, respectively. Input resistance was measured from a voltage response elicited by intracellular injection of a current pulse (100 pA, 200ms). Membrane voltage was low-pass filtered at 5kHz and digitized at 10kHz using a Multiclamp 700B amplifier connected to a DigiData 1322A interface (Axon Instruments) using Clampex 10.2 software (Molecular Devices). Liquid junction potentials were calculated and corrected off-line. Action potentials were generated in current clamp from currents injected in 10 pA intervals from 0 to 250 pA. Recordings were analysed for: resting membrane potential, input resistance, rheobase, threshold, as well as action potential amplitude, overshoot, duration, half-width, rise and decay. Neurons were held at 80mV and continuous recordings of mEPSCs were made using Axoscope software (Molecular Devices). Data processing and analysis were performed using MiniAnalysis (Synaptosoft) version 6 and Clampfit 10.2 (Molecular Devices). Events were detected by setting the threshold value, followed by visual confirmation of mEPSC detection. Whole-cell patch clamp recordings in neurons derived from DMSO and EZH2i conditions (pipettes 36 M) were performed in aCSF containing (in mM): 125 NaCl, 2.5 KCl, 1.2 NaH2PO4, 1 MgSO4, 2 CaCl2, 25 NaHCO3 and 10 d-glucose. pH and osmolarity were adjusted to 7.4 and 300310mOsm, respectively. For firing recordings, pipettes were filled with a solution containing (in mM): 130 potassium gluconate, 4 KCl, 0.3 EGTA, 10 Na2-phosphocreatine, 10 HEPES, 4 Mg2-ATP, 0.3 Na2-GTP and 13 biocytin. pH and osmolarity were adjusted to 7.3 (with KOH) and 285290mOsm respectively. For mEPSCs recordings the ACSF was supplemented with 1M TTX and 100M 4-AP and pipettes were filled with a caesium-based solution that contained (in mM): 120 CsMeSO4, 8 NaCl, 10 HEPES, 0.3 EGTA, 10 TEA-Cl, 2 Mg2-ATP, 0.3 Na2-GTP, 13.4 biocytin and 3 QX-314-Cl. pH: 7.3 (adjusted with CsOH) and 290295mOsm. Recordings were acquired with a computer-controlled Multiclamp 700B amplifier and a Digidata 1550B (Molecular Devices, California) at a sampling rate of 10kHz and low-pass filtered at 1kHz. pClamp 10 software suite (Molecular Devices) was used for data acquisition (Clampex 10.6) and data analysis (Clampfit 10.6). The quantification of the amplitude and inter-event interval of mEPSCs shown in the cumulative probability plots in Fig. 4j was performed taking all the events together. To isolate the NMDA component from mEPSCs recorded at +40mV, we measured current amplitude 20ms after the mEPSC onset, where AMPA receptors are desensitized (depicted by the dotted line in Extended Data Fig. 5f)66,67,68. For the calculation of the NMDA/AMPA ratio, the amplitude of the NMDA component was then divided by the amplitude of the peak of the AMPA currents recorded at 70mV. Statistical analysis and plots were done in Prism 9 (GraphPad, California). Evoked action potential and traces shown in DMSO versus EZH2i groups in Fig. 4g were elicited with 20 pA injected current.

hPSC-derived cortical neurons were infected with lentiviruses encoding GCaMP6m and cultured on -plate 96 Well Black (Ibidi). In rat astrocytes co-culture experiments, hPSC-derived neurons were infected with GCaMP6m lentiviruses four days before dissociation and prior to seeding onto rat primary astrocytes. For each batch of experiments, the infection and measurement of Ca2+ spikes in neurons under control or genetic/pharmacological perturbation has been done in parallel on the same day to account for the variability in the absolute expression of GCaMP6m due to lentiviral delivery. Ca2+ imaging was performed as previously described69. In brief, on the day of the imaging, cells were gently washed twice in modified Tyrode solution (25mM HEPES (Invitrogen), 140mM NaCl, 5mM KCl, 1mM MgCl2, 10mM glucose, 2mM CaCl2, 10M glycine, 0.1% BSA pH 7.4, pre-warmed to 37C) and equilibrated in imaging buffer for 1-2min (25mM HEPES, 140mM NaCl, 8mM KCl, 1mM MgCl2, 10mM glucose, 4mM CaCl2, 10M glycine, 0.1% BSA pH 7.4, pre-warmed to 37C). GCaMP6m fluorescence was recorded on Celldiscover7 (ZEISS) inverted epi-fluorescence microscope with the 488nm filter under environmental control (37C; 95% O2, 5% CO2) using ZEN Blue 3.1 software at the Bio-Imaging Resource Center (BIRC) at Rockefeller University. Neuronal cultures were imaged for ~3min at a frame rate of 46 frames per second (600800 frames per time lapse) using a 10 or 20 objective.

hPSC-derived cortical brain organoids were infected with lentiviruses encoding GCaMP6m at day 45 of differentiation and cultured in BrainPhys Imaging Optimized Medium (Stem Cell Technologies) for a week before the imaging. On the day of the imaging, DMSO control and organoids transiently treated with GSK343 were equilibrated in imaging buffer for 30min and transferred into imaging cuvettes. GCaMP6m fluorescence on intact organoids was recorded by light-sheet microscopy on TruLive3D Imager (Bruker) under environmental control (37C; 95% O2 5% CO2). Multiple fields of view from 34 organoids per condition from 2 independent batches each were imaged for ~24min at a frame rate of 510 frames per second at 31.3 effective magnification.

Analysis was performed as previously described69. In brief, the live-imaging image stack was converted to TIFF format and loaded into optimized scripts in MATLAB (Mathworks) R2020b and R2021b. Region of Interest (ROI) were placed on the neuron somas to calculate the raw GCaMP6m intensity of each neuron over time. The signal intensity of each raw trace was normalized to baseline fluorescence levels (F/F0) for spike detection. Single-neuron amplitude was calculated from the normalized GCaMp6m intensity for all the detected spikes in each trace (mean F/F0 of detected spikes for each neuron). Single-neuron frequency was calculated as the number of detected spikes in each trace per minute of recording. Network activity was assessed by calculating the synchronous firing rate, defined as the number of detected synchronous Ca2+ spikes from all ROI in one FOV per minute of recording. In Figs. 1k and 4k, coloured lines depict the normalized (F/F0) GCaMP6m signal traces of individual neurons in 1 field of view during 1min of imaging; the black line is the averaged normalized GCaMP6m signal among neurons in 1 field of view. Images in Figs. 1j Fig. 4m were displayed as royal lookup table in FIJI. Supplementary Videos16 show 20 frames per second, Supplementary Videos7 and 8 show 100 frames per second.

Microscopy images were visualized with Adobe Photoshop 2022, Fiji 2.9.0 or Imaris software version 9.9.1. Morphological reconstruction of neurons was performed using Imaris software version 9.9.1. Ca2+ imaging analysis was performed using MATLAB software. Quantification of immunofluorescence images was performed in FIJI (ImageJ) version 2.9.0 or using the Operetta high content imaging system coupled with Harmony software version 4.1 (PerkinElmer).

Cells were collected and lysed in RIPA buffer (Sigma) with 1:100 Halt Protease and Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific) and then sonicated for 330sec at 4C. Protein lysates were centrifugated for 15min at more than 15,000rpm at 4C and supernatant was collected and quantified by Precision Red Advanced Protein Assay (Cytoskeleton). 510g of protein were boiled in NuPAGE LDS sample buffer (Invitrogen) at 95C for 5min and separated using NuPAGE 412% Bis-Tris Protein Gel (Invitrogen) in NuPAGE MES SDS Running Buffer (Invitrogen). Proteins were electrophoretically transferred to nitrocellulose membranes (Thermo Fisher Scientific) with NuPAGE Transfer Buffer (Invitrogen). Blots were blocked for 60min at room temperature in TBS-T+5% nonfat milk (Cell Signaling) and incubated overnight in the same solution with the respective primary antibodies at 4C. The following primary antibodies were used: mouse anti-neurofilament H 1:500 (non-phosphorylated) (SMI32; Enzo Life science); mouse anti-syntaxin 1A 1:500 (110 111; SYSY); mouse anti-actin 1:500 (MAB1501; Millipore); mouse anti-Cas9 1:500 (14697; Cell Signaling Technology); rabbit anti-CHD3 1:1,000 (ab109195, Abcam); rabbit anti-KDM5B 1:1,000 (ab181089, abcam). The following secondary antibodies were incubated for 1h at room temperature at 1:1,000 dilution: anti-mouse IgG HRP-linked (7076; Cell Signaling Technology) and anti-rabbit IgG HRP-linked (7074; Cell Signaling Technology). Blots were revealed using SuperSignalTM West Femto Chemiluminescent Substrate (Thermo Fischer Scientific) at ChemiDoc XRS+ system (Bio-Rad). Chemiluminescence was imaged and analysed using Image lab software version 6.1.0 (Bio-Rad). Controls samples were run within each gel and the signal intensity of protein bands of interest was normalized to the intensity of the actin band (loading control) for each sample on the same blot. Uncropped and unprocessed images are shown in Supplementary Figure 1. One sample t-test on Fig. 3d was performed by comparing the mean of logFC for each genetic perturbation with the hypothetical mean logFC = 0 (null hypothesis of no changes). Two-tailed ratio-paired t-test in Fig. 4c was calculated on normalized marker/actin expression in manipulations versus DMSO.

Samples were collected in Trizol. Total RNA from hPSC-derived samples was isolated by chloroform phase separation using Phase Lock Gel-Heavy tubes, precipitated with ethanol, and purified using RNeasy Mini Kit (Qiagen) with on-column DNA digestion step. RNA from mouse cells was isolated using Direct-zol microprep kit (Zymo research, R2060). cDNA was generated using the iScript Reverse Transcription Supermix (Bio-Rad) for RTqPCR and quantitative PCR (qPCR) reactions were performed using SsoFast EvaGreen Supermix (Bio-Rad) according to the manufacturers instructions in 96 or 384-well qPCR plates using CFX96 and CFX384 Real-Time PCR Detection systems (Bio-Rad) using 510ng cDNA / reaction. Primers were from Quantitect Primer assays (QUIAGEN) except for the ones in Supplementary Table 4. Results were normalized to the housekeeping genes GAPDH or TBP.

A Cas9-T2A-PuroR cassette flanked by 5 and 3 homology arms for the GPI locus was generated by NEBuilder HiFi DNA Assembly Cloning Kit of PCR-amplified fragments according to manufacturers instruction. EF1alpha-GCaMP6m lentiviral vector was generated by PCR amplification of GCaMP6m from pGP-CMV-GCaMP6m (Addgene 40754) using with Q5 High Fidelity master mix (NEB) and subcloned into pWPXLd (Addgene 12258) into BamH1 and EcoRI restriction site using standard cloning methods. For the simultaneous expression of gene-specific gRNA under transcriptional control of U6 promoter and dTomato fluorescent reporter driven by EF1alpha promoter, the SGL40.EFs.dTomato vector (Addgene 89398) was modified by inserting a P2A-Basticidin cassette downstream of dTomato sequence to generate the SGL40.EFs.dTomato-Blast backbone. gRNA sequences specific to each gene were designed using SYNTEGO CRISPR design tool (https://www.synthego.com/products/bioinformatics/crispr-design-tool) and validated using CRISPOR tool70 (http://crispor.tefor.net). DNA oligos (IDT) were annealed and subcloned into BsmBI restriction sites of SGL40.EFs.dTomato-Blast lentiviral backbone by standard cloning methods. Lentiviruses were produced by transfection of HEK293T cells (ATCC) using the Xtreme Gene 9 DNA transfection reagent (Sigma) with the respective lentiviral vectors along with the packaging vectors psPAX2 (Addgene, 12260) and pMD2.G (Addgene, 12259). Arrayed CRISPR gRNA lentiviral libraries were produced simultaneously. Viruses were collected 48h post transfection, filtered with 0.22-m filters and stored in aliquots at 80C.The sequence of each gRNA used is reported in Supplementary Table 5.

Total RNA was extracted as described above. Sample for RNA-seq during chronological maturation at hPSC, NPC, d25, d50, d75 and d100 timepoints were submitted for TruSeq stranded ribo-depleted paired-end total RNA-seq at 4050 million reads at the Epigenomic Core at Well Cornell Medical College (WCMC). Samples for RNA-seq studies on neurons upon perturbation with epigenetic inhibitors were submitted for paired-end poly-A enriched RNA-seq at 2030 million reads to the MSKCC Integrated Genomic Core. Quality control of sequenced reads was performed by FastQC. Adapter-trimmed reads were mapped to the hg19 human genome using versions 2.5.0 or 2.7.10b of STAR71. The htseq-count function of the HTSeq Python package version 0.7.172 was used to count uniquely aligned reads at all exons of a gene. For the chronological maturation studies, the count values were transformed to RPKM to make them comparable across replicates. A threshold of 1 RPKM was used to consider a gene to be present in a sample and genes that were present in at least one sample were used for subsequent analyses. Differential gene expression across timepoints or treatments with epigenetic inhibitors was computed using versions 1.16 or 1.22.2 of DESeq2 respectively73. Variance stabilizing transformation of RNA-seq counts was used for the PCA plots and for heat maps of gene expression. For downstream analysis of trends of gene expression, transcripts were first grouped into monotonically upregulated and monotonically downregulated based on the characteristics of their expression from d25 to d100 and further categorized in strict: all the transitions satisfy the statistical significance criteria and relaxed: d25 versus d100 transition satisfy the significance criteria and intermediate transitions may not. For all comparisons a significance threshold of false discovery rate (FDR)5% was used. Monotonically upregulated (strict): (d50 versus d25: FDR5%) and (d100 versus d25: FDR5%) and (d100 versus d50: FDR5%) and (d50 versus d25:logFC > 0) and (d75 versus d50: logFC > 0) and (d100 versus d25 logFC > d50 versus d25 logFC). Monotonically downregulated (strict): (d50 versus d25:FDR5%) and (d100 versus d25: FDR5%) and (d100 versus d50: FDR5%) and (d50 versus d25:logFC <0) and (d75 versus d50: logFC <0) and (d100 versus d25 logFC 0) and ((d100 versus d25:logFC >= d50 versus d25: logFC) OR (d75 versus d50: logFC > 0)). Monotonically downregulated (relaxed): (d100 versus d25: FDR5%) and (d50 versus d25:logFC <0) and ((d100 versus d25:logFC <= d50 versus d25: logFC) OR (d75 versus d50: logFC <0)). GSEA74 was performed on d50 versus d25 and d100 versus d50 pairwise comparisons to test enrichment in KEGG pathways or gene sets from MSigDB using the following parameters: FDR5%, minimum gene-set size=15, maximum gene-set size=500, number of permutations = 1000. GO term analysis was performed using v6.8 DAVID75 (http://david.abcc.ncifcrf.gov/knowledgebase/). Venn diagrams were generated using Biovenn76.

The score for maturation in neurons upon epigenetic inhibition and control conditions (Extended Data Fig. 7b,c). was computed based on the geometric distribution of samples in the three-dimensional coordinate system defined by PCA1, 2 and 3. For each condition (treatment and day of differentiation), the coordinates defining the position of the samples in the 3D PCA space were determined based on the average across replicates. The DMSO d25 coordinates were set as the origin. The vectors defining maturation trajectories for each treatment and timepoint were then measured as the connecting segments between sample coordinates. The vector linking DMSO d25 and DMSO d50 conditions was used to define the chronological maturation trajectory and set as a reference (control vector) to calculate a similarity score for each treatment at any given timepoint. To account for vector magnitude and directionality, the dot product metric treatment vectorcontrol vector was used to calculate the scores. Gene expression correlation heat maps in Extended Data Fig7d were created from either all genes or maturation genes only by computing Pearson correlation and then running agglomerative hierarchical clustering using complete linkage. k-Means clustering in Extended Data Fig7e was performed on z-score converted normalized counts and run using the kmeans function in R with nstart = 25 and k=2:10, stopping when clusters became redundant (k=4).

ATAC-seq libraries were prepared at the Epigenetic Innovation Lab at MSKCC starting from ~50,000 live adherent cells plated on 96-wells. Size-selected libraries were submitted to the MSKCC Genomic core for paired-end sequencing at 4060 million reads. Quality control of sequenced reads was performed by FastQC (version 0.11.3) and adapter filtration was performed by Trimmomatic version 0.36. The filtered reads were aligned to the hg19 reference genome. Macs2 (version 2.1.0)77 was used for removing duplicate reads and calling peaks. Differentially accessible peaks in the atlas were called by DESeq2 version 1.1673. To define dynamic trends of chromatin accessibility during neuronal maturation as shown in Fig. 3g, agglomerative hierarchical clustering using Wards linkage method was done on the union of differentially accessible peaks in pairwise comparisons between d25, d50, d75 and d100 samples. HOMER findMotifsGenome.pl (version 4.6)78 was used to investigate the motif enrichment in pairwise comparisons and unbiasedly clustered groups of peaks. Motif enrichment was also assessed by KolmogorovSmirnov and hypergeometric tests as previously described79. ATAC-seq peaks in the atlas were associated with transcription factor motifs in the updated CIS-BP database80,81 using FIMO82 of MEME suite version 4.1183. Hypergeometric test was used to compare the proportion of peaks containing a transcription factor motif in each group (foreground ratio) with that in the entire atlas (background ratio). Odds ratio represents the normalized enrichment of peaks associated with transcription factor motifs in the group compared to the background (foreground ratio/background ratio). Odds ratio1.2 and transcription factor expression from parallel RNA-seq studies (reaching1 RPKM) in neurons at any timepoint (d25, d50, d75, d100) was used to filter enriched transcription factor motif.

CUT&RUN was performed from 50,000 cells per condition as previously described84 using the following antibodies at 1:100 dilution: rabbit anti-H3K4me3 (aab8580, abcam); rabbit anti-H3K9me3 (ab8898, abcam); rabbit anti-H3K27me3 (9733, Cell Signaling Technologies); rabbit anti-H3K27ac (309034, Active Motif), normal rabbit IgG (2729, Cell Signaling Technologies). In brief, cells were collected and bound to concanavalin A-coated magnetic beads after an 8min incubation at room temperature on a rotator. Cell membranes were permeabilized with digitonin and the different antibodies were incubated overnight at 4C on a rotator. Beads were washed and incubated with pA-MN. Ca2+-induced digestion occurred on ice for 30min and stopped by chelation. DNA was finally isolated using an extraction method with phenol and chloroform as previously described84. Library preparation and sequencing was performed atthe MSKCC Integrated Genomic Core.

Sequencing reads were trimmed and filtered for quality and adapter content using version 0.4.5 of TrimGalore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore) and running version 1.15 of cutadapt and version 0.11.5 of FastQC. Reads were aligned to human assembly hg19 with version 2.3.4.1 of bowtie2 (http://bowtie-bio.sourceforge.net/bowtie2/index.shtml) and MarkDuplicates of Picard Tools version 2.16.0 was used for deduplication. Enriched regions were discovered using MACS2 with a p-value setting of 0.001 and a matched IgG or no antibody as the control. The BEDTools suite version 2.29.2 (http://bedtools.readthedocs.io) was used to create normalized read density profiles. A global peak atlas was created by first removing blacklisted regions (https://www.encodeproject.org/annotations/ENCSR636HFF) then merging all peaks within 500bp and counting reads with version 1.6.1 of featureCounts (http://subread.sourceforge.net). Reads were normalized by sequencing depth (to 10 million mapped fragments) and DESeq2 (v1.22.2) was used to calculate differential enrichment for all pairwise contrasts. Clustering was performed on the superset of differential peaks using k-means clustering by increasing k until redundant clusters arose. Gene annotations were created by assigning all intragenic peaks to that gene, and otherwise using linear genomic distance to transcription start site. The annotations in each cluster were used to intersect with the RNA-seq time series by plotting the average expression z-score of all peak-associated genes which are differentially expressed across any stage. Motif signatures and enriched pathways were obtained using Homer v4.11 (http://homer.ucsd.edu). Tracks of CUT&RUN peaks were visualized in Integrative Genomics Viewer version 2.8.9 (IGV, Broad Institute).

Neuronal cultures at day 27 of differentiation were washed three times in PBS, incubated with Accutase supplemented with Neuron Isolation Enzyme for Pierce (Thermo 88285) solution at 1:50 at 37C for 4560min and gently dissociated to single-cell suspensions via pipetting. After washing in PBS, single-cell suspensions were diluted to 1,000 cells per l in 1 PBS with 0.04% BSA and 0.2Ul1 Ribolock RNAse inhibitor (Thermo EO0382) for sequencing. scRNA-seq was performed at the MSKCC Integrated Genomic Core for a target recovery of 10,000 cells per sample using 10X Genomics Chromium Single Cell 3 Kit, version 3 according to the manufacturers protocol. Libraries were sequenced on an Illumina NovaSeq. The CellRanger pipeline (Version 6.1.2) was used to demultiplex and align reads to the GRCh38 reference genome to generate a cell-by-gene count matrix. Data analysis was performed with R v4.1 using Seurat v4.2.085. Cells expressing between 200 and 5,000 genes and less than 10% counts in mitochondrial genes were kept for analysis. Gene counts were normalized by total counts per cell and ScaleData was used to regress out cell cycle gene expression variance as determined by the CellCycleScoring function. PCA was performed on scaled data for the top 2,000 highly variable genes and a JackStraw significance test and ElbowPlot were used to determine the number of principal components for use in downstream analysis. A uniform manifold approximation and projection (UMAP) on the top 12 principal components was used for dimensional reduction and data visualization. FindNeighbors on the top 12 principal components and FindClusters with a resolution of 0.3 were used to identify clusters. Published scRNA-seq datasets for hPSC cortical differentiation were from Yao et al.86 (PMID: 28094016) and Volpato et al.87 (PMID: 30245212). To compare our dataset to those generated by Yao et al.86 and Volpato et al.87, Seurats anchor-based integration approach85 was used using FindIntegrationAnchors with 5,000 features. Single-cell hierarchical clustering and plotting for Extended Data Fig. 1h was performed with HGC88 using the Louvain algorithm. Single-cell RNA-seq analysis for mouse cortical development in Fig. 3f,g were from the published dataset from Di Bella et al.41 Data were processed using the same pipeline as in the original publication and developmental trajectories were inferred using v1.1.1.URD algorithm89.

Sample sizes were estimated based on previous publications in the field. Investigators were not blinded to experimental conditions. However, for knockout and small molecule treatment studies, samples were de-identified respect to the molecular target. Transcriptional and genomic studies were performed with the same bioinformatic pipeline between conditions.Statistics and data representation were performed in PRISM (GraphPad) version 8,9 or 10, excel and R software version 3.5.2 or 4.1. Statistical tests used for each analysis are indicated in the figures legend. Data are represented as arithmetical means.e.m. unless otherwise indicated.

Independent replication from representative micrographs were as following. Fig. 1b, 6 experiments; Fig. 1j, 3 experiments; Fig. 1n, 2 experiments, Fig. 2d, 2 experiments; Fig. 3c, 2 experiments for each genetic perturbation; Fig. 4m, 4 experiments; Supplementary Fig. 2a, 4 experiments; Supplementary Fig. 2f, 3 experiments; Supplementary Fig. 6e, 1 experiment; Extended Data Figs. 6a, 2 experiments; Extended Data Figs. 6c, 2 experiments; Supplementary Fig. 8e, 2 experiments for d12 and d16.

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

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An epigenetic barrier sets the timing of human neuronal maturation - Nature.com

New Opportunities for Human Stem Cells in Research and Clinical Applications – STAT

About

iPSCs (induced pluripotent stem cells), an inexhaustible source of functional human cells, have enormous potential for biomedical research and health care. They are being used to study genetic disease, new drug development, and treatment of a broad range of disorders diabetes, cancer, neurological illnesses, and more. This forum explores uses of iPSCs for new strategies in preclinical and clinical applications.

Jesse McQuarters, Branded Content Editor at STAT

Ilyas Singe, M.D., Ph.D. Chief Scientific Officer, FUJIFILM Cellular Dynamics, Inc.

Ilyas was the inaugural director of the Stem Cell Translation Laboratory at NCATS/NIH, where he led the development of innovative technologies to help translate iPSC technology into clinical applications and drug discovery.

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New Opportunities for Human Stem Cells in Research and Clinical Applications - STAT

BlueRock takes up option on iPSC cell therapy candidate OpCT-001 – The Pharma Letter

German pharma major Bayers (BAYN: DE) independently operated company BlueRock Therapeutics today revealed it has exercised its option to exclusively license OpCT-001 under a 2021 deal with FUJIFILM Cellular Dynamics and Opsis Therapeutics.

OpCT-001 is an induced pluripotent stem cell (iPSC) derived cell therapy candidate for the treatment of primary photoreceptor diseases and is the lead cell therapy candidate being developed under the strategic

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BlueRock takes up option on iPSC cell therapy candidate OpCT-001 - The Pharma Letter

Engineered cartilage and osteoarthritis – Boston Children’s Answers – Boston Children’s Discoveries

About one in seven adults live with degenerative joint disease, also known as osteoarthritis (OA). In recent years, as anterior cruciate ligament (ACL) injury and other joint injuries have become more common among adolescent athletes, a growing number of 20- and 30-somethings have joined the ranks of aging baby boomers living with chronic OA pain.

Key takeaways

Treatments for degenerative joint disease are limited, largely because the cartilage that protects the joints doesnt regenerate after birth. Without a way to stimulate regrowth of damaged cartilage, most treatments focus on managing symptoms. And with few curative treatment options, OA remains one of the leading causes of pain and disability in the United States.

Boston Childrens researcher April Craft, PhD, and her team want to change that. Their approach: grow cartilage in the lab that could be used to replace damaged articular tissues in patients joints.

The team first set out to understand how cartilage and joint tissues develop naturally and how stem cells differentiate into cartilage cells, or chondrocytes. The next step was to replicate that process in the lab, putting cells through the same stages of development.

In a study published this year in BMJ, members of the Craft Lab described their approach for generating cartilage from induced pluripotent stem cells (iPSC). Derived from patients own cells, iPSCs can give rise to virtually any type of cell in the body, including chondrocytes. The team generated cartilage-like tissues from two patients with progressive pseudorheumatoid arthropathy of childhood (PPAC), a genetic condition that causes severe premature joint degeneration.

We chose to study PPAC because joint degeneration in this condition progresses rapidly toward a state that is indistinguishable from end-stage OA, says Craft. Our iPSC model of PPAC cartilage will help us learn about this devastating disease. Their findings may possibly apply more broadly to OA from acute injuries or chronic overuse, as well as provide the basis for future therapeutics development.

Using cartilage engineered in the Craft Lab, the team has successfully repaired damaged joint tissues in rats and is preparing to test the procedure in large animals.

Because joint-lining cartilage is avascular and the implanted chondrocytes will be encased by the cartilage tissue itself, there is a reduced likelihood of implant rejection. Because of this, Craft believes that someday off-the-shelf cartilage for human patients could be created using one cell line. If so, live cartilage tissues could be produced, stored, and delivered to surgical teams as needed to replace damaged cartilage.

In some ways, the procedure resembles the most advanced cell therapy for cartilage: autologous chondrocyte implantation. In this two-procedure process, chondrocytes are harvested from one area of the body, expanded in number, and then implanted into the damaged area.

Off-the-shelf cartilage implants would allow patients to undergo just one surgical procedure rather than two. Replacing damaged cartilage with a piece of new cartilage that was generated ahead of time would omit the delay in manufacturing associated with autologous cartilage harvesting, reduce the rehabilitation time, and allow patients to return to their normal activities sooner after surgery.

The first humans to receive this novel implant would likely be patients who have pain and joint damage but havent yet progressed to severe degeneration. And eventually, it could be tried in others, such as athletes with joint damage.

This could have a profound impact on people as they age as well as athletes experiencing joint pain, says Craft.

Learn more about the Craft Lab and the Orthopedic Department.

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Engineered cartilage and osteoarthritis - Boston Children's Answers - Boston Children's Discoveries

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Report Includes

Companies Profiled

Key Topics Covered:

Chapter 1 Introduction

Chapter 2 Summary and Highlights

Chapter 3 Market Overview

Chapter 4 Market Dynamics

Chapter 5 Induced Pluripotent Stem Cell Applications

Chapter 6 Induced Pluripotent Stem Cell Market Segmentation and Forecast

Chapter 7 Induced Pluripotent Stem Cells Research Application Market

Chapter 8 Induced Pluripotent Stem Cell Contract Service Market

Chapter 9 Clinical Application Market Trend Analysis

Chapter 10 Competitive Landscape

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

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

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

Cell culture

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cell culture

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

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

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

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

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

(3)

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

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

(4)

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

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

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

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

(5)

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

(6)

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

The parameters are estimated by minimizing the following objective function:

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

(7)

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

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

(8)

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

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

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

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

(9)

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

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

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

(10)

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

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

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

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

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

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

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

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

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

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

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

(11)

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

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

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

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

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

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

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

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

(12)

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

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

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

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

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

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