{"database":"biostudies-arrayexpress","file_versions":[],"scores":null,"additional":{"submitter":["Nadezhda Azbukina"],"organism":["Homo sapiens"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15660"],"description":["To pattern novel posterior brain regions, we used a multiplexed morphogen screen with a single-cell sequencing readout to test 48 concentrations and combinations of 10 morphogens involved in brain patterning. This screen revealed the emergence of regions harboring under-represented cell types, such as medulla glycinergic neurons and cerebellum glutamatergic cells, that expanded mesencephalon and rhombencephalon models."],"repository":["biostudies-arrayexpress"],"sample_protocol":["Library Construction - Then collected samples were processed for highly multiplexed single-nucleus RNA sequencing (snRNA-seq) using a split-pool combinatorial barcoding kit (ParseBiosciences, WT Mega kit v2, dual-index version RX200).","Sample Collection - After 5 weeks (36 days) in culture 1-4 organoids per condition were dissociated individually using CyBio FeliX liquid handler robot with thermoshaker. For dissociation we used a papain-based dissociation kit as for the time course. Each organoid was dissociated using 820uL of enzyme mix 1 and 12uL of enzyme mix 2. Then each individual single cell suspension has been followed by nuclei isolation, as described for the time course (lysis time 2.5 minutes). Nuclei fixation and permeabilization procedures were performed according to the manufacturer specification (ParseBiosciences, nuclei fixation kit v2.1.2, WN400).","Sample Treatment - The majority of morphogens were provided between day 8 and 14 (Supplementary Table 5), always using base medium as indicated in the original protocol. Exception being SHH, which was provided from day 1 to day 7, and CHIR, from day 5 to day 14, as in the original protocol; insulin, being provided from day 1 to 14 as in the published cerebellum protocol18; FGF-19 - from day 15 onwards, as it has been known to promote neural differentiation. We have used the following morphogens: SHH (R&D Systems, 1845-SH-025/CF), CHIR-99021 (Tocirs, 4423), insulin (Sigma, I9278), R-Spondin-3 (Peprotech, 120-44), R-Spondin-2 (Peprotech, 120-43), retinoic acid (Sigma-Aldrich, R2625), FGF-8 (STEMCELL Technologies, 78204), FGF-19 (Peprotech, 100-32), FGF-17 (Peprotech, 100-27), FGF-2 (Mitenyli Biotech, 130093840), BMP4 (Mitenyli Biotech, 130-111-167).","Nucleic Acid Extraction - Then collected samples were processed for highly multiplexed single-nucleus RNA sequencing (snRNA-seq) using a split-pool combinatorial barcoding kit (ParseBiosciences, WT Mega kit v2, dual-index version RX200).","Sequencing - Libraries were sequenced with Novaseq S1 flowcell, paired end, cycles: 91/8/8/86.","Growth Protocol - Brain organoids were generated from the WTC cell line. For the morphogen screen experiment protocol 113 was used as a baseline and also serves as a control. Organoids were plated in ultra low-attachment plates (Corning, CLS7007), 5 organoids per condition. On day 15 they were transferred in 6-well plates, placing all 5 organoids per condition in one well and kept on shaker, as in the original protocol."],"figure_sub":["Organization","MINSEQE Score","Assays and Data","Processed Data","MAGE-TAB Files"],"data_protocol":["Data Transformation - We used Parse Biosciences Software (v1.0.4) to demultiplex barcodes, map to hg38 human transcriptome and generate count matrix, which was further processed using scanpy python package (v1.10.3)71. Cells were filtered out on the basis of unique molecular identifier (UMI) counts (>1000, <30000) and the fraction of mitochondrial genes (<10). Then 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. Then highly variable genes were estimated and total UMI counts as well as fraction of mitochondrial genes were regressed out. After that PCA was performed, followed by neighbours estimation and leiden clustering. An additional quality-control step included glycolysis signature calculation. We have followed the approach, described previously, by selecting genes that belong to GO terms 'canonical_glycolysis' and using tl.score_genes() function to estimate glycolysis score. The same procedure was done for ‘aerobic electron transport chain’. Then, for each cell we calculated differences between glycolysis score and aerobic score and filtered out leiden clusters, which had median differences more than 0.05. After that, highly variable genes were calculated again, followed by scaling and PCA, as described above. Subsequently, we have calculated new clusters using leiden algorithm and UMAP embeddings to get 2D representation of data."],"omics_type":["Metabolomics","Unknown","Transcriptomics","Genomics","Proteomics"],"instrument_platform":["Illumina NovaSeq 6000"],"pubmed_abstract":["Patterning 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."],"study_type":["RNA-seq of coding RNA from single cells"],"species":["Homo sapiens"],"pubmed_title":["Multi-omic human neural organoid cell atlas of the posterior brain"],"pubmed_authors":["Hsiu-Chuan Lin","Zhisong He","Nadezhda 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 Treutlein","Nadezhda Azbukina"],"additional_accession":[]},"is_claimable":false,"name":"scRNA-seq of posterior brain organoid morphogen screen","description":"To pattern novel posterior brain regions, we used a multiplexed morphogen screen with a single-cell sequencing readout to test 48 concentrations and combinations of 10 morphogens involved in brain patterning. This screen revealed the emergence of regions harboring under-represented cell types, such as medulla glycinergic neurons and cerebellum glutamatergic cells, that expanded mesencephalon and rhombencephalon models.","dates":{"release":"2026-03-16T00:00:00Z","modification":"2026-03-16T12:27:46.985Z","creation":"2025-10-03T17:26:02.868Z"},"accession":"E-MTAB-15660","cross_references":{"ENA":["ERP181015"],"Biostudies":["E-MTAB-15659"],"EFO":["EFO_0002944","EFO_0004170","EFO_0003789","EFO_0005684","EFO_0005518","EFO_0003816","EFO_0004184","EFO_0003969"],"doi":["10.1101/2025.03.20.644368"]}}