biostudies-arrayexpressNadezhda AzbukinaHomo sapienshttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15826We used a pooled, multiplexed CRISPR/Cas9 gene knock-out (KO) experiment with single-cell transcriptomic readout to perturb and validate selected TF regulomes . We designed gRNAs and generated a pooled lentiviral library targeting 12 TFs with specific midbrain or hindbrain expression or high regulatory centrality. IPSCs carrying a doxycycline-inducible Cas9 cassette in the AAVS1 safe harbor locus were transduced, mosaic organoids were generated, and perturbations were induced at neuroepithelium stage from day 7 to 14. Mosaic organoids were analyzed using scRNA-seq and gRNA amplicon sequencing at day 30 and day 70, recovering 31,857 cells, among which a gRNA was detected in 8711 cells.biostudies-arrayexpressLibrary Construction - Cells positive for fluorescent reporters were selected using FACS and these cells were then loaded onto the 10x Chromium using the Chromium Next GEM Single Cell 5’ v2 Dual Index kit (10x Genomics, PN-1000265) targeting 10,000 cells per reaction with two reactions per time point. Single-cell transcriptome and CRISPR gRNA libraries were prepared following manufacturer protocols.Sample Collection - Five organoids were pooled and dissociated into single cell suspensions at day 30 and day 70. Cells positive for fluorescent reporters were selected using FACS and these cells were then loaded onto the 10x Chromium using the Chromium Next GEM Single Cell 5’ v2 Dual Index kit (10x Genomics, PN-1000265) targeting 10,000 cells per reaction with two reactions per time point.Nucleic Acid Extraction - Cells positive for fluorescent reporters were selected using FACS and these cells were then loaded onto the 10x Chromium using the Chromium Next GEM Single Cell 5’ v2 Dual Index kit (10x Genomics, PN-1000265) targeting 10,000 cells per reaction with two reactions per time point. Single-cell transcriptome and CRISPR gRNA libraries were prepared following manufacturer protocols.Sequencing - The transcriptome libraries and gRNA libraries were pooled and sequenced on the NovaSeq 6000 platform using 26/10/10/90 as cycle parameters.Growth Protocol - The final mixed pool of cells was used to generate Qian organoids following the previously described method (Qian Reference). To induce Cas9 expression and therefore create knockouts in targeted genes, 2 μg/ml doxycycline (Clontech, 631311) was added with each media change starting on day 7 until day 14. Organoids were returned to their normal media on day 15. Fluorescence was monitored throughout culturing to ensure KOs reporters were not silenced throughout differentiation.OrganizationMINSEQE ScoreAssays and DataProcessed DataMAGE-TAB FilesData Transformation - We used Cell Ranger (v.7.0.1) using flag multi to obtain count matrices of transcriptome and gRNAs. We used human transcriptome (hg38, provided by 10x Genomics), and a table of guide sequences for GEX and gRNA reads mapping respectively. To facilitate efficient gRNA detection, the pattern and sequence entries in the feature reference files were adjusted to 12bp instead of 20bp. Then, as with the time course, count matrices were further processed using the Seurat R package. Cells were filtered on the basis of the number of counts (>600,<20,000 for day 30) or detected genes (>500,<7500 for day 70) and the fraction of mitochondrial genes (<0.15 for day 30 and <0.1 for day 70). Transcript counts were normalized to the total number of counts for that cell, multiplied by a scaling factor of 10,000 and subsequently natural-log transformed (NormalizeData()). 10X lanes from the same time points were integrated using CSS method as for the time course data. Datasets were annotated combining label transfer from the time course dataset (using css_project() function of CSS package) and marker gene expression. To assign gRNA labels, the 10x gRNA calling outputs (protospacer_calls_per_cell.csv) were first filtered to retain cells with gRNAs targeting a single gene. For cells with gRNAs targeting multiple genes, additional filtering was applied to identify the best call based on z-scaled UMI > 5 and a total UMI count > 10. Cells lacking gRNAs calls were excluded from further analysis. Cells with effective perturbations were identified using Mixscape68 as implemented in Seurat using cells with all other gRNAs as a control. Regulon activities upon CRISPR-based knockout were quantified using Seurat function AddModuleScore() considering the positive and negative regulons of TF of interest as inferred in Pando. To visualize the detected gRNAs and regulon activities on the UMAP embeddings, their densities were estimated using Nebulosa69 (weight=1 for gRNA). Differentially expressed genes (DEGs) were identified for each cluster with FindMarkers() function in the seurat package using wilcoxon test (log2 fold change >0.5, adjusted p<0.05, detection rate>5%, detection rate difference>10%) and analyzed for gene ontology enrichment using clusterProfiler.MetabolomicsUnknownTranscriptomicsGenomicsProteomicsIllumina NovaSeq 6000Patterning of the neural tube establishes midbrain and hindbrain structures that coordinate motor movement, process sensory input, and integrate cognitive functions. Cellular impairment within these structures underlie diverse neurological disorders, and in vitro organoid models promise inroads to understand development, model disease, and assess therapeutics. Here, we use paired single-cell transcriptome and accessible chromatin sequencing to map cell composition and regulatory mechanisms in organoid models of midbrain and hindbrain. We find that existing midbrain organoid protocols generate ventral and dorsal cell types, and cover regions including floor plate, dorsal and ventral midbrain, as well as adjacent hindbrain regions, such as cerebellum. Gene regulatory network (GRN) inference and transcription factor perturbation resolve mechanisms underlying neuronal differentiation. A single-cell multiplexed patterning screen identifies morphogen concentration and combinations that expand existing organoid models, including conditions that generate medulla glycinergic neurons and cerebellum glutamatergic subtypes. Differential abundance of cell states across screen conditions enables differentiation trajectory reconstruction from region-specific progenitors towards diverse neuron types of mid- and hindbrain, which reveals morphogen-regulon regulatory relationships underlying neuronal fate specification. Altogether, we present a single-cell multi-omic atlas and morphogen screen of human neural organoid models of the posterior brain, advancing our understanding of the co-developmental dynamics of regions within the developing human brain.RNA-seq of coding RNA from single cellsHomo sapiensMulti-omic human neural organoid cell atlas of the posterior brainHsiu-Chuan LinZhisong HeNadezhda Azbukina, Zhisong He, Hsiu-Chuan Lin, Malgorzata Santel, Bijan Kashanian , Ashley Maynard, Tivadar Török, Ryoko Okamoto, Marina Nikolova, Sabina Kanton, Valentin Brösamle, Rene Holtackers, J. Gray Camp, Barbara TreutleinBarbara TreutleinNadezhda AzbukinafalsescRNA-seq CRISPR screening of human posterior brain organoidsWe used a pooled, multiplexed CRISPR/Cas9 gene knock-out (KO) experiment with single-cell transcriptomic readout to perturb and validate selected TF regulomes . We designed gRNAs and generated a pooled lentiviral library targeting 12 TFs with specific midbrain or hindbrain expression or high regulatory centrality. IPSCs carrying a doxycycline-inducible Cas9 cassette in the AAVS1 safe harbor locus were transduced, mosaic organoids were generated, and perturbations were induced at neuroepithelium stage from day 7 to 14. Mosaic organoids were analyzed using scRNA-seq and gRNA amplicon sequencing at day 30 and day 70, recovering 31,857 cells, among which a gRNA was detected in 8711 cells.2026-03-17T00:00:00Z2026-03-17T02:03:49.32Z2025-10-23T12:15:55.169ZE-MTAB-15826ERP182815EFO_0002944EFO_0004170EFO_0003789EFO_0005684EFO_0005518EFO_0003816EFO_000418410.1101/2025.03.20.644368