Remark: for cell cycle analysis - see paper https://arxiv.org/abs/2208.05229 "Computational challenges of cell cycle analysis using single cell transcriptomics" Alexander Chervov, Andrei Zinovyev On the basis of that data we demonstrate the connection between tp53-pathway break and presence of the "fast" cell cycle pattern.

Data and Context

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes (csv file is vice versa). value of the matrix shows how strong is "expression" of the corresponding gene in the corresponding cell. https://en.wikipedia.org/wiki/Single-cell_transcriptomics

Particular data: GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157220 BROAD: https://singlecell.broadinstitute.org/single_cell/study/SCP542/pan-cancer-cell-line-heterogeneity Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135089/ Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Gabriela S. Kinker,*,1,4 Alissa C. Greenwald,*,1 Rotem Tal,1 Zhanna Orlova,1 Michael S. Cuoco,2 James M. McFarland,3 Allison Warren,3 Christopher Rodman,2 Jennifer A. Roth,3 Samantha A. Bender,3 Bhavna Kumar,5 James W. Rocco,5 Pedro ACM Fernandes,4 Christopher C. Mader,3 Hadas Keren-Shaul,6,7 Alexander Plotnikov,6 Haim Barr,6 Aviad Tsherniak,3 Orit Rozenblatt-Rosen,2 Valery Krizhanovsky,1 Sidharth V. Puram,8 Aviv Regev,2 and Itay Tirosh1,#

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. Early review: Cole Trapnell 2015 Defining cell types and states with single-cell genomics https://genome.cshlp.org/content/25/10/1491.short

Recent: "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

Nature. P. Kharchenko: "The triumphs and limitations of computational methods for scRNA-seq" https://www.nature.com/articles/s41592-021-01171-x

Files pertaining to data analyses performed and presented in the preprint, 'Porcine intestinal innate lymphoid cells and lymphocyte spatial context revealed through single-cell RNA sequencing' by Wiarda et al. 2022 are provided in this dataset. Single cell suspensions enriched for lymphocytes were obtained from ileum of two seven-week-old pigs and subjected to single-cell RNA sequencing (scRNA-seq). Peripheral blood mononuclear cells (PBMCs) were collected and processed for scRNA-seq in parallel. scRNA-seq was performed to provide transcriptomic profiles of lymphocytes in porcine ileum, with 31,983 cells annotated into 26 cell types. Deeper interrogation of data revealed previously undescribed cells in porcine intestine, including SELLhi γδ T cells, group 1 and group 3 innate lymphoid cells (ILCs), and four subsets of B cells. Single-cell transcriptomes in ileum were compared to those in porcine blood, and subsets of activated lymphocytes were detected in ileum but not periphery. Comparison to scRNA-seq human and murine ileum data revealed a general consensus of ileal lymphocytes across species. Lymphocyte spatial context in porcine ileum was conferred through differential tissue dissection prior to scRNA-seq. Antibody-secreting cells, B cells, follicular CD4 αβ T cells, and cycling T/ILCs were enriched in ileum with Peyer’s patches, while non-cycling γδ T, CD8 αβ T, and group 1 ILCs were enriched in ileum without Peyer’s patches. Data files included herein are .h5seurat files of the various cell subsets included in analyses of the manuscript. Files may be used to reconstruct different analyses and perform further data query. Scripts for original data analyses are found at https://github.com/USDA-FSEPRU/scRNAseq_Porcine_Ileum_PBMC. Raw data are available at GEO accession GSE196388. Data are available for online query at https://singlecell.broadinstitute.org/single_cell/study/SCP1921/intestinal-single-cell-atlas-reveals-novel-lymphocytes-in-pigs-with-similarities-to-human-cells. Resources in this dataset:Resource Title: Ileum_AllCells. File Name: Ileum_AllCells.tarResource Description: .h5seurat object of all the cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: GutBlood_IntegratedILCs. File Name: GutBlood_IntegratedILCs.tarResource Description: .h5seurat object of ILCs derived from both ileum and PBMC samples. Untar into .h5seurat file before use.Resource Title: Ileum_Bonly. File Name: Ileum_Bonly.tarResource Description: .h5seurat object of B cells and antibody-secreting cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_CD4Tonly. File Name: Ileum_CD4Tonly.tarResource Description: .h5seurat object of non-naive CD4 ab T cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_gdCD8Tonly. File Name: Ileum_gdCD8Tonly.tarResource Description: .h5seurat object of gd and CD8 ab T cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_ILConly. File Name: Ileum_ILConly.tarResource Description: .h5seurat object of innate lymphoid cells (ILCs) derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_MyeloidOnly. File Name: Ileum_MyeloidOnly.tarResource Description: .h5seurat object of myeloid lineage leukocytes derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_NonImmuneOnly. File Name: Ileum_NonImmuneOnly.tarResource Description: .h5seurat object of non-immune cells derived from ileum samples. Untar into .h5seurat file before use.Resource Title: Ileum_TILConly. File Name: Ileum_TILConly.tarResource Description: .h5seurat object of all T cells and innate lymphoid cells (ILCs) derived from ileum samples. Untar into .h5seurat file before use.Resource Title: PBMC_AllCells. File Name: PBMC_AllCells.tarResource Description: .h5seurat object of all cells derived from PBMC samples. Untar into .h5seurat file before use.

The gene expression matrix and metadata were downloaded the Single Cell Portal of the Broad Institute (https://singlecell.broadinstitute.org/single_cell/study/SCP259/intra-and-inter-cellular-rewiring-of-the-human-colon-during-ulcerative-colitis), originally published by Smillie CS, Biton M, Ordovas-Montanes J, et al. Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis. Cell. 2019;178(3):714-730.e22. doi:10.1016/j.cell.2019.06.029. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).

Single cell suspensions enriched for epithelial cells were obtained from duodenum, jejunum, and ileum of a 7.5-week-old pig and subjected to single-cell RNA sequencing (scRNA-seq). scRNA-seq was performed to provide transcriptomic profiles of epithelial cells, with 695 cells annotated into 6 cell types. Deeper interrogation of data revealed previously undescribed cells in porcine intestine, and region-specific gene expression profiles within specific cell subsets. Data herein includes a .h5seurat files of the epithelial cell subsets analyzed. Files may be used to reconstruct different analyses and perform further data query. Scripts for original data analyses are found at https://github.com/USDA-FSEPRU/scRNAseqEpSI_Pilot. Raw data are available at GEO accession GSE208613. Data are available for online query at https://singlecell.broadinstitute.org/single_cell/study/SCP1936/regional-epithelial-cell-diversity-in-the-small-intestine-of-pigs.

• Resource Title: .h5Seurat object - epithelial cells.

  File Name: EpithelialCells.tar

  Resource Description: Epithelial cells used for data analysis, available in .h5Seurat file format. Untar file before use.

Single Cell Databases of Cell Painting Profiles for the Cell Health Project. These data are used to aggregate profiles in a CRISPR knockout experiment. The data are used to predict cell health assays.DataWe collected Cell Painting measurements on a CRISPR experiment. The experiment targeted 59 genes, which included 119 unique guides (~2 per gene), across 3 cell lines. The cell lines included A549, ES2, and HCC44.About 40% of all CRISPR guides were reproducible. This is ok since we are not actually interested in the CRISPR treatment specifically, but instead, just its corresponding readout in each cell health assay.ApproachWe performed the following approach:Split data into 85% training and 15% test sets.Normalized data by plate (z-score).Selected optimal hyperparamters using 5-fold cross-validationTrained elastic net regression models to predict each of the 70 cell health assay readouts, independently.Trained using shuffled data as well.Report performance on training and test sets.We also trained logistic regression classifiers using the same approach aboveSee https://github.com/broadinstitute/cell-health for more details.

Amyloid-β plaques and neurofibrillary tau tangles are the neuropathologic hallmarks of Alzheimer’s disease (AD), but the spatiotemporal cellular responses and molecular mechanisms underlying AD pathophysiology remain poorly understood. Here we introduce STARmap PLUS to simultaneously map single-cell transcriptional states and disease marker proteins in brain tissues of AD mouse models at a voxel size of 95  95  350 nm. This high-resolution spatial transcriptomics map revealed a core-shell structure where disease-associated microglia (DAM) closely contact amyloid-β plaques, whereas disease-associated astrocyte-like cells (DAA-like) and oligodendrocyte precursor cells (OPC) are enriched in the outer shells surrounding the plaque-DAM complex. Hyperphosphorylated tau emerged mainly in excitatory neurons in the CA1 region accompanied by infiltration of oligodendrocyte subtypes into the axon bundles of hippocampal alveus. The integrative STARmap PLUS method bridges single-cell gene expression profiles with tissue histopathology at subcellular resolution, providing an unprecedented roadmap to pinpoint the molecular and cellular mechanisms of AD pathology and neurodegeneration.

This dataset is used by the research Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease, it contains the human digital gene expression matrix and the macaque slide seqv2 dataset publish by the authors. - The data for Cross Species analysis are not included.

You can check the result produced by research:

1. Single Cell Analysis
2. Slide Seq

Context

https://en.wikipedia.org/wiki/Squamous_cell_carcinoma Squamous cell carcinomas (SCCs), also known as epidermoid carcinomas, comprise a number of different types of cancer that result from squamous cells.[1] These cells form on the surface of the skin, on the lining of hollow organs in the body, and on the lining of the respiratory and digestive tracts.

Data - results of single cell RNA sequencing, i.e. rows - correspond to cells, columns to genes. value of the matrix strong is "expression" of the corresponding gene in the corresponding cell.

This particular dataset is from the Broad Institute collection of scRNAseq datasets of cancer lines. See https://www.kaggle.com/datasets/alexandervc/scrnaseq-collection-of-cancer-cell-lines for many other datasets from the same collection.

Inspiration

Single cell RNA sequencing is important technology in modern biology, see e.g. "Eleven grand challenges in single-cell data science" https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6

This dataset hosts files needed to reproduce the Human Retina Cell Atlas (HRCA) reference model using scArches. The HRCA data can be accessed through several interactive browsers, including HCA Data Portal, CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal. Please use these browsers for atlas exploration and visualization. For more information on HRCA, please refer to the HRCA paper (Li et al., bioRxiv 2023) and the Github repository at https://github.com/RCHENLAB/HRCA_reproducibility. This dataset has been used in the tutorial for the HRCA reference model at https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches.

Data description:

1. HRCA_snRNA_allcells_rawcounts.h5ad

This file contains the cell-by-gene count matrix for over 3.1 million single nuclei and more than 36,000 gene features of the HRCA. Gene features are represented by gene symbols. Please refer to the interactive browsers for atlas exploration, where gene features are mapped to Ensembl IDs. In the cell metadata, "sampleid" indicates sample batches of cells, and "celltype" specifies 123 retina cell types.

2. model.pt

This file is the trained reference model using scArches, incorporating 10,000 highly variable features from the full count matrix. It can be directly used for cell type annotation of new retina samples.

3. HRCA_snRNA_allcells_rawcounts_latent.h5ad

This file contains the embeddings of all 3.1 million reference single nuclei generated by the trained reference model using scArches. These embeddings can be used to compare with the embeddings of query data for exploration.

4. HRCA_reference_model_gene_id_and_symbol.csv

This file contains the mapping of Ensembl IDs to gene symbols for the 10,000 features used in the reference model. This mapping can be used to convert the gene features in a query .h5ad file from gene IDs to gene symbols, allowing cell type labels to be predicted using the trained reference model, which uses gene symbols as gene features.

5. query.h5ad

This file contains a cell-by-gene count matrix for a query dataset, designed to support reproducibility in the HRCA reference model tutorial. The "majorclass" column includes pre-annotated major cell classes. Additional details on the tutorial are available at https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches.

6. query_latent.h5ad

This file contains the embeddings of the query data against the trained reference model. These embeddings can be compared with the reference data embeddings for exploration and visualization.

Processed Datasets

  Kuppe et al. (2022): Spatial and single-cell infarction data, obtained via link (Reference: https://www.nature.com/articles/s41586-022-05060-x)
  Reichart et al. (2022): Cardiomyopathies, obtained via link (Reference: https://www.science.org/doi/10.1126/science.abo1984)
  Carraro et al. (2021): Cystic fibrosis data, obtained via link (Reference: https://www.nature.com/articles/s41591-021-01332-7)
  Habermann et al. (2020): Pulmonary Fibrosis data, obtained via link (Reference: https://pubmed.ncbi.nlm.nih.gov/32832598/)
  Velmeshev et al. (2019): Single-cell Autism Spectrum Disorder data, obtained via link and link (Reference: https://www.science.org/doi/full/10.1126/science.aav8130)
  Wu et al. (2021): Processed breast cancer 10× Visium slides, available at link (Reference: https://www.nature.com/articles/s41588-021-00911-1)
  Vicari et al. (2023): Parkinson's disease data, obtained via link (Reference: https://www.nature.com/articles/s41587-023-01937-y)
  Russell et al. (2023): Slide-Seq datasets obtained via the Broad Institute Single Cell Portal:

      Mouse embryonic brain: SCP2170
      Mouse brain: SCP2162
      Human brain: SCP2167
      Human tonsil: SCP2169, SCP2171
      Human melanoma multiome: SCP2176
      Also available under GEO GSE244355

    (Reference: https://www.nature.com/articles/s41586-023-06837-4)

  Kuppe et al. (2022): Acute cardiac infarction, 23 samples, obtained via link (Reference: https://www.nature.com/articles/s41586-022-05060-x)
  Reichart et al. (2022): Cardiomyopathies, 126 samples, obtained via link (Reference: https://www.science.org/doi/10.1126/science.abo1984)
  Simonson et al. (2023): Ischemic cardiomyopathy, 15 samples, obtained via link (Reference: https://www.sciencedirect.com/science/article/pii/S2211124723000979?via=ihub)
  Koenig et al. (2022): Dilated (nonischemic) cardiomyopathy, 38 samples, obtained via link (Reference: https://www.nature.com/articles/s44161-022-00028-6)
  Chaffin et al. (2022): Dilated and hypertrophic cardiomyopathy, 42 samples, obtained via link (Reference: https://www.nature.com/articles/s41586-022-04817-8)
  Armute et al. (2023): Cardiomyopathies, 40 samples, obtained via link (Reference: https://www.nature.com/articles/s44161-023-00260-8)



Dataset Information


classification.tar.xz contains datasets #1-5 used in the classification task for MOFA+/Tensor-cell2cell (Ext Data Figure 5), while single-cell data from Kuppe et al. (#1), was also used for Figure 5.
wu_et_al.tar (#6) contains the 10X visium slides used for the malignancy calling task (Ext Data Figure 2A).
sma.tar (#7) contains the spatial metabolome-transcriptome data used for Figure 3 as well as Ext Data Figs 3&4.
heart_visium.tar contains the 10x visium slides from Kuppe et al. (#1), used in Figure 5 and Ext Data Figure 2B.
slidetags.tar contains the Slide-tags data (#8) used for the co-localisation evaluations shown in Figure 4.
reliability.tar contains pseudobulks of heart atlases (#9-14) and shown in Extended Data Figure 7.

The hypothalamus controls essential social behaviors and homeostatic functions. However, the cellular architecture of hypothalamic nuclei, including the molecular identity, spatial organization, and function of distinct cell types, is poorly understood. Here, we developed an imaging-based cell type identification and mapping method and combined it with single-cell RNA-sequencing to create a molecularly annotated and spatially resolved cell atlas of the mouse hypothalamic preoptic region. We profiled ~1 million cells, identified ~70 neuronal populations characterized by distinct neuromodulatory signatures and spatial organizations, and defined specific neuronal populations activated during key social behaviors in male and female mice, providing a high-resolution framework for mechanistic investigation of behavior circuits. The approach described here opens a new avenue for the construction of cell atlases in diverse tissues and organisms.

Secondary upload of datasets on scPerturb.org

.zip files for ATAC-seq datasets

Adult mouse visual cortex (RPKM values for 24,057 genes and 1,679 cells) with cluster information taken from https://singlecell.broadinstitute.org/single_cell/study/SCP6/a-transcriptomic-taxonomy-of-adult-mouse-visual-cortex-visp#study-download

The size of the NGS RNA Sequencing Market was valued at USD 3.71 Million in 2023 and is projected to reach USD 13.34 Million by 2032, with an expected CAGR of 20.06% during the forecast period. Recent developments include: October 2022: PacBio launched a multiplexed array sequencing (MAS-Seq) kit in partnership with the Broad Institute of MIT and Harvard and 10x Genomics. The kit enables long-read single-cell RNA sequencing to further detect and characterize novel isoforms, novel driver mutations, and cancer fusion genes., March 2022: Quantbio launched the sparQ RNA-Seq HMR kit, an ultra-fast RNA next-generation sequencing (NGS) library preparation tool with integrated ribosomal RNA (rRNA) and globin mRNA depletion. The new kit enables the researchers to quickly and easily generate high-quality stranded transcriptome libraries from difficult FFPE or low-input human, mouse, and rat (HMR) samples in five hours.. Key drivers for this market are: Surge in Products Approvals for RNA-sequencing Platforms and Consumables, Advancements in Precision Medicine; Advantages of NGS-based RNA-sequencing. Potential restraints include: Lack of Standardization, Interpretation Of Complex Data And Lack Of Skilled Professionals. Notable trends are: Sequencing Platform and Consumables Segment is Expected to Hold the Significant Market Share in the NGS-Based RNA-Sequencing Market Over the Forecast Period.

The NGS RNA Sequencing market is booming, projected to reach $16.11 billion by 2033, driven by personalized medicine, decreasing sequencing costs, and technological advancements. Explore market trends, key players (Illumina, Thermo Fisher), and regional insights in this comprehensive analysis. Recent developments include: October 2022: PacBio launched a multiplexed array sequencing (MAS-Seq) kit in partnership with the Broad Institute of MIT and Harvard and 10x Genomics. The kit enables long-read single-cell RNA sequencing to further detect and characterize novel isoforms, novel driver mutations, and cancer fusion genes., March 2022: Quantbio launched the sparQ RNA-Seq HMR kit, an ultra-fast RNA next-generation sequencing (NGS) library preparation tool with integrated ribosomal RNA (rRNA) and globin mRNA depletion. The new kit enables the researchers to quickly and easily generate high-quality stranded transcriptome libraries from difficult FFPE or low-input human, mouse, and rat (HMR) samples in five hours.. Key drivers for this market are: Surge in Products Approvals for RNA-sequencing Platforms and Consumables, Advancements in Precision Medicine; Advantages of NGS-based RNA-sequencing. Potential restraints include: Surge in Products Approvals for RNA-sequencing Platforms and Consumables, Advancements in Precision Medicine; Advantages of NGS-based RNA-sequencing. Notable trends are: Sequencing Platform and Consumables Segment is Expected to Hold the Significant Market Share in the NGS-Based RNA-Sequencing Market Over the Forecast Period.

This is the dataset supporting the EPI-Clone manuscript: scRNA-seq profiling of hematopoietic stem and progenitor cells (HSPCs) was performed with the 3' 10x Genomics profiling. Three experiments are included: Two where HSCs were clonally labeled with the LARRY system, transplanted to recipient mouse and profiled 4-5 months later (post-transplant hematopoiesis), and one where HSPCs were profiled straight from an unperturbed mouse.Dataset is a seurat (v4) object with the following assays, reductions and metadata:ASSAYS:AB: Antibody expression dataRNA: RNA expression profilesintegrated: Integration of DNA methylation data performed across experimental batches with two batch correction methods: CCA (https://satijalab.org/seurat/reference/runcca) and harmony (https://portals.broadinstitute.org/harmony/articles/quickstart.html).DIMENSIONALITY REDUCTIONpca_cca: PCA performed on the integrated data (CCA integration)umap_cca: UMAP computed on the integrated data (CCA integration)umap_harmony: UMAP computed on the integrated data (Harmony integration)METADATAExperiment: The experiment that the cell is from, values are "LARRY main experiment", "LARRY replicate" and "Native hematopoiesis"ProcessingBatch: Experiments were processed in several batches.CellType: Cell type annotationLARRY: Error corrected LARRY barcodepercent.mt: percentage of mitochondrial DNAnCount_RNA: Read count for the RNA modalitynFeature_RNA: Number of RNAs with at least one readnCount_AB: Read count for the surface protein modalitynFeature_AB: Number of ABs with at least one read

Single cell RNA sequencing (drop-seq) data of forebrain organoids carrying pathogenic MAPT R406W and V337M mutations. Organoids were generated from 5 heterozygous donor lines (two R406W lines and three V337M lines) and respective CRISPR-corrected isogenic controls. Organoids were also generated from one homozygous R406W donor line. Single-cell sequencing was performed at 1, 2, 3, 4, 6 and 8 months of organoid maturation., Single-cell transcriptomes were obtained using drop-seq (Macosko et al., 2015, https://doi.org/10.1016/j.cell.2015.05.002). Counts matrices were generated using the Drop-seq tools package (Macosko et al. 2015), with full details available online (https://github.com/broadinstitute/Drop-seq/files/2425535/Drop-seqAlignmentCookbookv1.2Jan2016.pdf). Briefly, raw reads were converted to BAM files, cell barcodes and UMIs were extracted, and low-quality reads were removed. Adapter sequences and polyA tails were trimmed, and reads were converted to Fastq for STAR alignment (STAR version 2.6). Mapping to human genome (hg19 build) was performed with default settings. Reads mapped to exons were kept and tagged with gene names, beads synthesis errors were corrected, and a digital gene expression matrix was extracted from the aligned library. We extracted data from twice as many cell barcodes as the number of cells targeted (NUM_CORE_BARCODES = 2x # targeted cells). Downstream analysis was performed ..., The package Seurat (https://satijalab.org/seurat) in R is required to read the FB1.seurat file

Secondary upload of datasets on scPerturb.org

h5ad files for RNA and protein datasets, created using scanpy 1.9.1

We present a cellular microscopic image dataset for investigating channel-adaptive models. We collected and pre-processed images from three publicly available sources: 1) the WTC-11 hiPSC dataset from the Allen Institute (Viana et al., 2023), 2) the Human Protein Atlas dataset (Thul et al., 2017), and 3) a combined Cell Painting dataset from the Broad Institute (Gustafsdottir et al., 2013; Bray et al., 2017; Way et al., 2021). These images contain 3, 4, or 5 channels with different cellular structures highlighted in each channel. The goal of this dataset is to facilitate the creation and evaluation of novel computer vision models that are invariant to channel numbers.

The data necessary to reproduce the Cell Painting results in the paper Unbiased single-cell morphology with self-supervised vision transformers.