<HashMap><database>biostudies-arrayexpress</database><scores/><additional><submitter>Elisa Balmas</submitter><organism>Homo sapiens</organism><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15711</full_dataset_link><description>This dataset comprises droplet-based single-cell RNA-seq (10x Genomics) from first heart field–derived left ventricle (LV) cardioids generated from WTC11 hiPSCs (TTN-mEGFP) carrying tetracycline-inducible shRNAs targeting GATA4 or CTCF, as well as a scrambled (SCR) control. Cardioids were profiled at day 4.5 and day 7.5 to capture early and later stages of LV differentiation under ± tetracycline (isogenic) conditions, with two biological replicates per time point. The dataset enables comparison of cellular composition, cardiomyocyte maturation trajectories (pseudotime), and knockdown-specific shifts in LV development. In brief, GATA4 KD expands immature/mesodermal populations and yields less-mature CM clusters, whereas CTCF KD produces more-mature CM states at day 4.5 and alters CM abundance by day 7.5 (as readouts; see figure panels for details). Each cell is annotated with the following information: line/perturbation (GATA4, CTCF, SCR), treatment (±Tet), time point (d4.5, d7.5), and replicate. Method details for scRNA-seq preparation (pooling 16 cardioids/condition, CMO multiplexing, FACS, library prep, and sequencing) are provided in the paper’s Methods and Supplementary Methods.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Nucleic Acid Extraction - 10X genomics technology with CG000388 Rev B (with cell multiplexing oligos). Cells were loaded on a chip G.</sample_protocol><sample_protocol>Library Construction - the single cell library was created with CG000388 Rev B (with cell multiplexing oligos).</sample_protocol><sample_protocol>Sample Collection - Digestion with versene and trippleE. Then cells were labeled with CMO, and sorted for vital cells and then loaded on the 10X controller. We processed the organoids with trypsin and obtained a single-cell suspension. We then labeled the cells with cell multiplexing oligos (CMO) before loading on a reaction of 3' Next GEM single cellv3.1 kit.  Cells were washed with PBS and 1% BSA (Miltenyi) and then labeled with CMOs following the 10x Genomics protocol (CG000391 RevA). After three washes with PBS and 1% BSA, cells were incubated for 15 minutes at room temperature with Fixable Viability Dye eFluor™ 780 (Invitrogen 1:1000 dilution). Cells were washed again and sorted with a SONY SH800S sorter with a 100 µm chip in FACS tubes, then pooled together in equal quantities, and resuspended at 1,600 cells/µL to be loaded on the 10X chromium with a target of 16,000 cells per reaction.</sample_protocol><sample_protocol>Sequencing - Cell multiplexing barcodes were pulled in molar ratio compared to the gen expression library 1:6 and libraries were loaded on 3 lanes of a Novaseq 6000 with standard 10X 3' sequencing parameters reported in the userguide (R1=28, indexes=10, R2=90).</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>Processed Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><data_protocol>Data Transformation - an RData file is attached with the full processed data (cds_filtered_all.RData and cds_final.RData). Data can be read in R or in Rstudio and then it can be read and used with monocle 3. Additional R files with data annotation (seurat_meta_norm.RData) and counts data (seurat_meta_norm.RData) are  provided to be analyzed with different platforms. Additionally, the output of this pipeline filtered_feature_bc_matrix files (barcodes.tsv.gz and features.tsv.gz) is also provided to allow independent filtering and analysis.</data_protocol><data_protocol>Data Transformation - Data provided are row. Use the atached aggregation.csv and config_multi.csv files to run cell ranger aggr (aggregation.csv) and then cell ranger multi (config_multi.csv)</data_protocol><omics_type>Metabolomics</omics_type><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><instrument_platform>Illumina NovaSeq 6000</instrument_platform><study_type>RNA-seq of coding RNA</study_type><species>Homo sapiens</species><pubmed_authors>Sara Bianchi</pubmed_authors><pubmed_authors>Elisa Balmas</pubmed_authors></additional><is_claimable>false</is_claimable><name>Single-cell RNA-seq of differentiating inducible knockdown (with CTCF and GATA4 shRNA) Left Ventricle cardioids</name><description>This dataset comprises droplet-based single-cell RNA-seq (10x Genomics) from first heart field–derived left ventricle (LV) cardioids generated from WTC11 hiPSCs (TTN-mEGFP) carrying tetracycline-inducible shRNAs targeting GATA4 or CTCF, as well as a scrambled (SCR) control. Cardioids were profiled at day 4.5 and day 7.5 to capture early and later stages of LV differentiation under ± tetracycline (isogenic) conditions, with two biological replicates per time point. The dataset enables comparison of cellular composition, cardiomyocyte maturation trajectories (pseudotime), and knockdown-specific shifts in LV development. In brief, GATA4 KD expands immature/mesodermal populations and yields less-mature CM clusters, whereas CTCF KD produces more-mature CM states at day 4.5 and alters CM abundance by day 7.5 (as readouts; see figure panels for details). Each cell is annotated with the following information: line/perturbation (GATA4, CTCF, SCR), treatment (±Tet), time point (d4.5, d7.5), and replicate. Method details for scRNA-seq preparation (pooling 16 cardioids/condition, CMO multiplexing, FACS, library prep, and sequencing) are provided in the paper’s Methods and Supplementary Methods.</description><dates><release>2025-10-14T00:00:00Z</release><modification>2026-05-27T06:01:40.088Z</modification><creation>2025-10-14T14:24:15.228Z</creation></dates><accession>E-MTAB-15711</accession><cross_references><ENA>ERP182143</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003816</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>