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.

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. Resources in this dataset:Resource Title: .h5Seurat object - epithelial cells. File Name: EpithelialCells.tarResource Description: Epithelial cells used for data analysis, available in .h5Seurat file format. Untar file before use.

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.

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 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.

The Community package (https://github.com/SoloveyMaria/community) is an R package designed for the analysis of single-cell RNA sequencing data, specifically for inferring interactions between different cell types. The dataset provided here is compatible with the Community tool, allowing for direct utilization.

The dataset associated with this research has undergone peer review and has been published in the journal Cell. The publication can be accessed via the following link: https://doi.org/10.1016/j.cell.2019.06.029. For access to the raw data, please visit: https://singlecell.broadinstitute.org/single_cell/study/SCP259/intra-and-inter-cellular-rewiring-of-the-human-colon-during-ulcerative-colitis?#study-visualize. It's important to note that the data in this repository has undergone batch correction and normalization, and the corresponding metadata has been appropriately adjusted (for detailed insights into the preprocessing steps, you can review the information provided at our paper repository: preprocessing). This processed data serves as the input for the Community tool.

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

This is a spatial transcriptomics dataset generated from human tonsil by Slide-Tags.• Dataset provided by: Broad Institute• Original dataset link: Single-cell portal

The Human Cell Atlas initiative is an ambitious global research effort which aims to describe every cell in the human body across all developmental stages to generate a reference map to accelerate progress in science and medicine particularly relating to human development and disease. The initiative is led by investigators based at the Wellcome Sanger Institute and the Broad Institute of MIT and Harvard and funding support from the Wellcome and NIH. This specific project will assess human development. We aim to interrogate the cellular composition and molecular regulators underpinning development and maturation through single cell RNA-sequencing/spatial transcriptomics and computational algorithms to predict cellular developmental trajectories and cell-cell interactions. We will subsequent validate these findings in situ using allied imaging technologies e.g. immunohistochemistry and RNAscope and in vitro culture of human intestinal organoids (HIOs). This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

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. NGS RNA sequencing has emerged as one of the most effective tools in the molecular biological study of the gene expression and regulation using the complete sequencing of all the RNA species present within one cell or tissue. This would give a snapshot comprehensive representation of the active genes and their expressions. It holds especially valuable contribution in complex biological processes, especially in the development and progression of diseases and cellular reactions to stimuli. The RNA sequencing in NGS uses an approach that has various advantages over the old methods of gene expression analytical methods. It could identify known and unknown transcripts-for example, non-coding RNAs that are increasingly being considered to hold very prominent positions in regulating genes. More importantly, it makes it possible for NGS to be sensitive to measuring the expression of genes that may likely have enabled slight differences in the activation of the genes to be determined. This NGS RNA sequencing is of many applications in several fields of study. For instance, in the medical world, it identifies what type of mutations cause certain diseases; or perhaps what molecular mechanisms may be involved in drug resistance development, thereby helping one determine what might be the best regimen for treatment. NGS promises to unlock pathways for the understanding and illumination of regulatory networks of gene expression in complex eukaryotic organisms, which will provide explanatory means of gene-function evolution and determination of the genetic basis of complex traits. Applications in agriculture: increased crop yield, development of disease-resistant varieties against specific plant diseases, and improvement of nutritional content. 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.

10X single-cell RNA-seq data profiling spontaneously differentiated cells from induced neural progenitors produced by overexpressing transcription factors Induced neural progenitors were differentiated from hESCs by overexpressing transcription factors and spontaneously differentiating before single-cell RNA-seq

Malaria parasites go through major transitions during their complex life cycle, yet the underlying differentiation pathways remain obscure. Here we apply single cell transcriptomics to unravel events that initiate sexual development in preparation for transmission of the parasite from human to mosquito. This proof-of-concept study provides a template to capture transcriptional diversity in heterogeneous parasite populations, with major implications for our understanding of parasite biology and the ongoing malaria elimination campaign. Plasmodium-infected human erythrocytes were single-cell sorted into 384-well plates at three separate time points after LysoPC depletion gametocyte induction (4, 8, 12 hours post induction). Two rows (total of 48 wells) of each 384-well plate were reserved for control samples (no induction). A total of 1152 wells were sequenced, with 144 control wells, and 1008 induced wells.

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. Methods 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 using Seurat 3.0 in R version 3.6.3. An individual Seurat object was generated for each sample, and filtered and clustered individually. Cells with < 300 genes detected were filtered out, as were cells with > 10% mitochondrial gene content. Counts data were log-normalized using the default NormalizeData function and the default scale of 1e4. Then, the top 2000 variable genes were identified using the Seurat FindVariableFeatures function (selection.method = “vst”, nfeatures = 2000), followed by scaling and centering using the default ScaleData function. Principal Components Analysis was carried out on the scaled expression values of the 2000 top variable genes, and the cells were clustered using the first 50 principal components (PCs) as input in the FindNeighbors function, and a resolution of 0.4 in the FindClusters function. Non-linear dimensionality reduction was performed by running UMAP on the first 50 PCs. Following clustering and dimensionality reduction, putative cell doublets were identified using DoubletFinder (McGinnis et al. 2019; https://doi.org/10.1016/j.cels.2019.03.003), assuming a doublet formation rate of 5%. For each sample, the optimal pK value was identified based on the results of paramSweep_vs, summarizeSweep and find.pK functions of the DoubletFinder package. Instead of using the default paramSweep_vs function, we extended the upper range of computed pK values to 1.2. We visually verified cells identified as doublets had high nFeatures (number of genes expressed) by plotting the pANN metric against nFeatures. For samples not showing this correlation, we adjusted the pK value to the next highest peak in the pK/BCmetric plot. Finally, the individual Seurat objects were merged.

The Next-Generation Sequencing (NGS) RNA Sequencing market is experiencing robust growth, driven by advancements in sequencing technologies and their expanding applications across diverse sectors. The market, valued at $3.71 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 20.06% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the increasing prevalence of chronic diseases like cancer and infectious diseases necessitates advanced diagnostic tools, making NGS RNA sequencing a crucial technology for personalized medicine. Secondly, the decreasing cost of sequencing and improved throughput are making the technology more accessible to researchers and clinicians. The development of more efficient and accurate sequencing platforms, such as nanopore sequencing and single-molecule real-time sequencing, contributes significantly to market growth. Furthermore, the rising demand for targeted therapies and the growing adoption of precision medicine strategies are driving increased investment in RNA sequencing research and development. The market segmentation reveals a diverse landscape. Sequencing platforms and consumables comprise a significant segment, alongside sequencing services, indicating strong demand for both technology and expertise. Technological advancements, particularly in nanopore and single-molecule real-time sequencing, are pushing market boundaries. Applications are widespread, including drug discovery (powering personalized drug development), diagnostics (offering early disease detection), and precision medicine (enabling tailored treatments). The end-user segment is dominated by biotechnology and pharmaceutical companies and hospitals and clinics, reflecting the broad applicability of NGS RNA sequencing across the healthcare and research ecosystems. Geographic analysis shows that North America and Europe currently hold substantial market shares, while the Asia-Pacific region is poised for significant growth due to increasing healthcare spending and technological advancements. The presence of major players like Illumina, Thermo Fisher Scientific, and Oxford Nanopore Technologies further validates the market's potential and the ongoing investments in innovation within this dynamic sector. 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.

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

This collection of data accompanies the study: Yeh, Aguirre, Laveroni et al. Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer. (2024). Nature Immunology. Files are provided in the form of RObjects (extension .rds) or tabulated data (extension .csv) to reproduce the results and figures provided in the paper via the the R programming environment using Code provided here.

Data collected and processed and published as a part of this study of tubo-ovarian high grade serous carcinoma (HGSC) includes:

• ~2.5 million single cell spatial transcriptomics profiles from 130 HGSC tumors of 94 patients
• Matching de-identified clinical annotations and clinical outcomes.
• Matching targeted genomic data from the bulk tumor tissues.
• Perturb-seq CRISPR knockout data in ovarian cancer cells in monoculture and co-culture with Natural Killer (NK) cells.

The spatial transcriptomics, Perturb-Seq, and matched H&E (Hematoxylin & Eosin, where available) are also provided via the Single Cell Portal with an interactive interface (SCP2640, SCP2641, SCP2650, SCP2644, SCP2646, SCP2707).

The collection also includes previously published data that was analyzed and used in this study to examine the generalizability of the findings, evaluate immunotherapy predictors, and for data-driven experimental design.

The SeuratObj.zip contains six SeuratObjects matching the five spatial transcriptomcs datasets (Discovery, Validation 1, Validation 2, Test 1 and Test 2) and Perturb-Seq data.

The Yeh2024.zip file includes the study's data and additional datasets/results to reproduce the study's figures. A detailed description of the files included in the repository is provided in `README.txt` and `README.xlsx`

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells. Methods ChIP-seq was performed as previously described (Galli, G.G., et al., Mol Cell, 2015. 60(2): p. 328-37). Briefly, cells were cross-linked in 1% formaldehyde for 10 minutes at room temperature after which the reaction was stopped by addition of 0.125M glycine. Cells were lysed and harvested in ChIP buffer (100 mM Tris at pH=8.6, 0.3% SDS, 1.7% Triton X-100, and 5 mM EDTA) and the chromatin disrupted by sonication using a EpiShear sonicator (Active Motif) to obtain fragments of average 200-500 bp in size. Chromatin extracts were incubated for 16 hours with the following antibodies: ER (Cell Signaling, 13258), FOXA1 (Cell Signaling, 58613), GATA3 (Cell Signaling, 5852), CTCF (Cell Signaling, 2899), H3K27ac (Cell Signaling, 8173) and H3K4me1 (Cell Signaling, 5326). Immunoprecipitated complexes were recovered using Protein G Dynabeads (Invitrogen) and DNA was recovered by reverse-crosslinking and purified using SPRI Select beads (Beckman Coulter). Libraries for ChIP-sequencing were generated using Ovation® Ultralow Library System V2 (NuGEN) and barcodes were added using NEBNext Multiplex Oligos for Illumina (NEB, Index Primers Set 1) according to the manufacturer’s recommendation. All next-generation sequencing experiments were run on a HiSeq2500 (Illumina). Fastq files were aligned to a human reference genome (hg38) using bowtie2 v2.3.4.1 and sorted using samtools v1.8. Duplicates were marked and removed using Picard MarkDuplicates v2.18.7 (http://broadinstitute.github.io/picard), and low quality mapped reads (below 20) were removed using samtools. Samtools view was used to retain reads mapping to human chromosomes and to discard reads mapping to chrM for ATAC-seq samples. Deeptools was used to generate RPKM-normalized bigwig files.

Summary

This metadata record provides details of the data supporting the claims of the related manuscript: “Treatment Scheduling Effects on Evolution of Drug Resistance in Heterogeneous Cancer Cell Populations”.

The related study evaluated crizotinib (ALK/MET inhibitor) and navitoclax (ABT-263; BCL2/BCL-XL inhibitor) combinations in a large design consisting of 696 two-cycle sequential and concomitant treatment regimens with varying treatment dose, duration, and drug holiday length over a 26-day period in MDA-MB-231 TNBC cells.

Type of data: DNA barcoding, single-cell RNA sequencing

Subject of data: Eukaryotic cell lines

Data access

All DNA barcoding data and single-cell sequencing data generated in this study have been deposited in the Sequence Read Archive under project accession number https://identifiers.org/ncbi/insdc.sra:SRP259903 (BioProject accession: PRJNA630413).

The data underlying Supplementary Figure 3 Panel A & B are openly available from the Cancer Cell Line Encyclopedia (CCLE) at https://portals.broadinstitute.org/ccle.

Other datafiles underlying the related manuscript are shared openly as part of this data record. A detailed list of which of these files underlies which manuscript element (figures, supplementary figures, etc) is included in the file ‘Patwardhan_et_al_2021_underlying_data_list.xlsx’.

Corresponding author(s) for this study

Christos Hatzis, Ph.D., Department of Internal Medicine, Breast Medical Oncology, Yale Cancer Center, Yale University School of Medicine, 300 George Street, New Haven, CT 06511, christos.hatzis@yale.edu

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.

We transfected two HD iPSC lines carrying adult onset CAG repeats (42 and 46; both carrying hap.01 and hap.08 diplotype) for mutant-specific NMD-CRISPR/Cas (PX551 vector for spCas9 and PX552 for our test gRNA; experimental group) or empty vector (PX551 vector spCas9 and empty PX552 vector without gRNA; control group) and subsequently developed single cell clones by limited dilution. 12 clonal lines were developed for each group and further validated by Sanger sequencing and MiSeq analysis of genomic DNA. Then, genome-wide RNAseq analysis was performed by the Broad Institute. Sequence data were processed by STAR aligner as part of the Broad Institute's standard RNAseq analysis pipeline. Expression levels of genes were based on transcripts per million (TPM) data computed by the TPMCalculator (https://github.com/ncbi/TPMCalculator). Expression levels in 20,260 protein-coding genes based on Ensembl (ftp://ftp.ensembl.org/pub/release-75/gtf/homo_sapiens/) were normalized, and sub...