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Growth protocol H1 hESCs were differentiated using the protocol published by Nair et al. (Nair et al. 2019). For planar culture (Protocol 1), hESCs were plated onto Geltrex-coated 12-well plates at a density of 5x10^5 in StemFlex medium with 1 mM Y-27632. Differentiations began 24h post-seeding. Cells were treated with RPMI (Gibco) containing 0.2% FBS, 1:5,000 ITS (Gibco), 100 ng/ml Activin A and 3 µM CHIR99021 on day 1. On day 2-3, cells were treated with RPMI containing 0.2% FBS, 1:2,000 ITS and 100 ng/ml Activin A. Morphogens were replaced for specific experiments as described in the manuscript.
Extracted molecule total RNA Extraction protocol For scRNA-seq, the 10x Genomics ChromiumTM controller and Single Cell 3’ Reagent Kits v2 (Pleasanton, CA, USA) were used to generate single cell libraries. Briefly, cells were counted following FACS and cell suspensions were loaded for a targeted cell recovery of 1000-5000 cells per channel.The microfluidics platform was used to barcode single cells using Gel Bead-In-Emulsions (GEMs). For bulk RNA-seq, RNA was extracted using Trizol (Thermo).
For scRNA-seq, RT is performed within GEMs, resulting in barcoded cDNA from single cells. The full length, barcoded cDNA is PCR amplified followed by enzymatic fragmentation and SPRI double sided size selection for optimal cDNA size. End repair, A-tailing, Adaptor Ligation, and PCR are performed to generate the final libraries that have P5 and P7 primers compatible with Illumina sequencing. The libraries were pooled and sequenced using an Illumina NextSeq500 platform with a 150 cycle High Output v2 kit in paired-end format with 26 bp Read 1, 8 bp I5 Index, and 85 bp Read 2. For bulk RNA-seq, SMARTer Stranded Total RNA Sample Prep Kit - HI Mammalian (Takara) was used to generate cDNA libraries for sequencing. The libraries were pooled and sequenced using an Illumina NextSeq500 platform with a 150 cycle High Output v2 kit in single-end format with Read 1: 85 cycle, Read 2: 0 cycle, and single index: 8 cycle. Following sequencing, FASTQ files were generated from the raw sequencing data using the BaseSpace Onsite (Illumina).
Data processing <scRNA-seq> Following sequencing, FASTQ files were generated from the raw sequencing data using cellranger mkfastq (10x Genomics), after which they were mapped to the build 38 of the Genome Reference Consortium human genome (GRCh38) to generate single-cell gene counts using cellranger count (10x Genomics). Low quality score sequences were filtered out. Cellranger aggr (10x Genomics) was used to combine data from multiple samples and ensure that all libraries had the same sequencing depth.
The Seurat R package (4.0.2 version), executed with default parameters unless otherwise specified, was used for quality control, filtering, data preprocessing, scaling and normalization of scRNA-seq raw data. High quality cells with mitochondrial gene content lower than 5%, feature total number higher than 200 and lower than 4000 and RNA count number higher than 2500 were extracted. The top 5000 most variable genes were selected based on their average expression and dispersion, and used to reduce the dimensionality of the data through principal component (PC) analysis. The top 50 PCs were selected for clustering. tSNE (REF) were applied to the selected PCs for visualization of the data. Cluster-specific markers were identified using the Seurat’s FindAllMarkers function. Differentially expressed genes between two clusters were identified using the Seurat’s FindMarkers function with both the parameters min.pct and logfc.threshold set to 0.2.
pySCENIC (0.11.2 version), executed with default parameters (SCENICprotocol: A Scalable SCENIC Workflow for Single-Cell Gene Regulatory Network Analysis n.d.), was applied on the pre-processed scRNA-seq data to identify potential important TFs and their targets. First, potential modules of TFs and targets were identified based on their co-expression using GRNBoost2 (Moerman et al. 2019). Then, each co-expression module underwent cis-regulatory motif analysis using RcisTarget to filter out indirect targets without motif support and retain modules with significant motif enrichment. These RcisTarget-processed modules are referred to as “regulons”. Next, the activity of each regulon in each cell was computed using AUCell (Aibar et al. 2017). The resulting AUC scores were further processed using SCopeLoomR (0.11.0 version) (SCopeLoomR: R Package (compatible with SCope) to Create Generic .loom Files and Extend Them with Other Data E.g.: SCENIC Regulons, Seurat Clusters and Markers, n.d.) and scFunctions (0.0.0.9 version) (Wuennemann n.d.) for downstream analysis and visualization.
<Bulk RNAseq> Transcripts were quantified using salmon (1.4.0 version) (Patro et al. 2017). and annotated using GENCODE (version 36) (protein-coding transcripts only). Differential expression analysis was performed using the limma (3.48.1 version) (Ritchie et al. 2015) and edgeR (3.34.0 version) (Robinson, McCarthy, and Smyth 2010) R packages. A gene was considered as differentially expressed if the absolute change in log2-fold expression was >2, and significant (the FDR was controlled at 5% using the BH method).
Genome_build: GRCh38
Series (1)
GSE185628 Bulk and single-cell transcriptome profiling of hESC-derived definitive endoderm cells induced by small molecules
Relations
BioSample SAMN22211749 SRX12567808
Supplementary data files not provided
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Raw data are available in SRA
Processed data are available on Series record