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11,105 results for “single cells”
Code and data for 'Bacillus subtilis histidine kinase KinC activates biofilm formation by controlling heterogeneity of single-cell responses'
<p>Code and data used in the paper 'Bacillus subtilis histidine kinase KinC activates biofilm formation by controlling heterogeneity of single-cell responses' https://doi.org/10.1128/mBio.01694-21</p>
Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data"
<p>Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data".</p> <p>Additional mitochondrial gene sequences for Danio rerio, Homo sapiens, and Mus musculus.</p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data : </strong></p> <p>1. Table1_full_version.docx : Marker genes for cell clusters of global Seurat analysis<br> 2. Table2_full_version.docx : List of the Immgen samples used to generate the reference compendium for CMAP signature generation<br> 3. Table5_full_version.docx : Top 20 marker genes for cell clusters of the Seurat analysis on selected cDC1s</p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. Immgen_cell_types.cls : Microarray Phase 1 expression</p> <p>2. Immgen_norm_exp_data.gct : Microarray Phase 1 class</p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
Single Cell Phenotypic Profiling to Identify a Set of Immune Cell Protein Biomarkers for Relapsed and Refractory Diffuse Large B Cell Lymphoma: A Single-Center Study
<p>Diffuse large B-cell lymphoma (DLBCL) is the most common invasive type of non-Hodgkin lymphoma. Cell-of-origin (COO) classification is related to patients’ prognoses. Primary drug resistance in treatment for DLBCL has been observed. The specific serum biomarkers in these patients who suffer from relapsed and refractory (R/R)-DLBCL remains unclear. In the current study, using single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF), we determined and verified immune cell biomarkers at the mRNA and protein levels in single-cell resolution from 18 diagnostic peripheral blood mononuclear cell (PBMC) specimens collected from patients with R/R DLBCL. As controls, five PBMC specimens from healthy volunteers were obtained. We identified a panel of 35 surface marker genes for the features of R/R DLBCL unique cell cluster by scRNA-seq of eight R/R DLBCL patient samples and validated its efficiency in an external cohort consisting of 10 R/R DLBCL patients by CyTOF. The cell clustering and dimension reduction were compared among R/R DLBCL samples in CyTOF Space with COO as well as the C-MYC expression designation. Immune cells from each patient occupied unique regions in the 32-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Significant heterogeneity observed in subgroups was mainly attributed to individual differences among samples and not to expression differences in a single, homogeneous immune cell subpopulation. The marker panel showed reliability in labeling R/R DLBCL without any influence from COO stratification and C-MYC expression designation. Furthermore, we compared all the markers between R/R DLBCL and normal samples. A total of 12 biomarkers were significantly overexpressed in R/R DLBCL relative to the normal samples. Therefore, we further optimized the diagnostic biomarker panel of R/R DLBCL comprising CD82, CD55, CD36, CD63, CD59, IKZF1, CD69, CD163, CD14, CD226, CD84, and CD31. In summary, we developed a novel set of biomarkers for the diagnoses of patients with R/R DLBCL. Detections procedures at single-cell resolution provide precise biomarkers which may substantially overcome intertumoral and intratumoral heterogeneity among primary samples. The findings confirmed that each case was unique and may comprise multiple, genetically distinct subclones.</p> <p>Here we uploaded the dataset of CyTOF for external validation. For more detailed information, please contact Dr zheng (zenki_zheng@163.com)</p> <p> </p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Single-Cell Transcriptomic Atlas of Human Cardiac Arteries
<p>This dataset contains the processed single-cell RNA sequencing data and code in article "Single-Cell Transcriptomic Atlas of Different Human Cardiac Arteries Identifies Cell Types Associated With Vascular Physiology". Please refer to the article for the detailed sample information, sequencing and data processing methods.</p> <p>cardiac_arteries_processed_data.zip: Processed single-cell RNA sequencing data from Cellranger output.</p> <p>merged_all_samples: The output of Cellranger aggr, merging processed data of all samples.</p> <p>Abbreviations in samples: AO, aorta; CA, coronary artery; PA, pulmonary artery.</p> <p>notebook.zip: Jupyter notebooks containing code for data analysis.</p>
Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors - Scripts & Source data
<p>Source data and scripts to reproduce the figures that are part of the publication "Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors".</p> <p>Journal of Cell Science (2022) 135, jcs259685, DOI: 10.1242/jcs.259685</p> <p> </p> <p>An earlier version of this work is published as a preprint: "Heterogeneity and dynamics of ERK and Akt activation by G protein-coupled receptors depend on the activated heterotrimeric G proteins", DOI: <a href="https://doi.org/10.1101/2021.07.27.453948">10.1101/2021.07.27.453948</a></p>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Object for single-cell mouse HSC development data
<p>This file contains SingleCellExperiment object generated from mouse HSC development data, including raw and log-normalized counts, gene regulatory network inferred by PUIC, transcription factor activity matrix estimated by metaRegulon.</p>
Dataset for the paper "High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells", DOI:10.1038/s41929-022-00772-9
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Authors: Asad Mehmood, Mengjun Gong, Frédéric Jaouen, Aaron Roy, Andrea Zitolo, Anastassiya Khan, Moulay-Tahar Sougrati, Mathias Primbs, Alex Martinez Bonastre, Dash Fongalland, Goran Drazic, Peter Strasser, Anthony Kucernak</p> <p>Title: High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells</p> <p>Journal: Nature Materials</p> <p>DOI: 10.1038/s41929-022-00772-9</p> <p>Please cite the above reference if you wish to use this data </p> <p> </p> <p>DOI of data: 10.5281/zenodo.6411262</p>
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
<p class="MsoNormal">Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31<sup><span>-</span></sup>CD45<sup><span>-</span></sup>Ter119<sup><span>-</span></sup>CD24<sup><span>-</span></sup> NPCs from Tg-SwDI, WT, and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.</p>
Single-cell datasets for distribution-based sketching
<p>Contains preprocessed single-cell data for sketching single-cell samples. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy.</p> <ul> <li>The HIV Vaccine Trials Network (HVTN) Flow Cytometry dataset (hvtn_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZZZV (<a href="http://flowrepository.org/id/FR-FCM-ZZZV">http://flowrepository.org/id/FR-FCM-ZZZV</a>).</li> <li>The preeclampsia CyTOF dataset (preeclampsia_preprocessed.h5ad) was originally downloaded from the Flow Repository under Repository ID FR-FCM-ZYRQ (<a href="http://flowrepository.org/id/FR-FCM-ZYRQ">http://flowrepository.org/id/FR-FCM-ZYRQ</a>).</li> <li>The NK-Cell CyTOF dataset (nk_cell_preprocessed.h5ad) from Ref. (<a href="https://www.nature.com/articles/ncomms14825">https://www.nature.com/articles/ncomms14825</a>) was originally downloaded from (<a href="https://github.com/eiriniar/CellCnn">https://github.com/eiriniar/CellCnn</a>).</li> <li>The multiple sclerosis (MS) single-cell RNA sequencing dataset of peripheral blood samples (ms_preprocessed.h5ad) from Ref. (<a href="https://www.nature.com/articles/s41467-019-14118-w">https://www.nature.com/articles/s41467-019-14118-w</a>) was originally accessed from the Gene Expression Omnibus using the accession code GSE138266 (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266</a>).</li> </ul>
Other supporting data for our manuscript "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"
<p>Other supplementary data for our paper "Mapping the Single Cell Transcriptomic Response of Murine Diabetic Kidney Disease to Therapies"</p>
Supplementary data for 'scROSHI - robust supervised hierarchical identification of single cells'
<p>This record contains some of the supplementary files for the manuscript "scROSHI - robust supervised hierarchical identification of single cells" by Prummer et al.</p> <p>The file "scROSHI_Fig03_counts.zip" contains SingleCellExperiment R objects as RDS files, "sce_A.RDS", "sce_B.RDS", "sce_C.RDS", corresponding to the three samples shown in Figure 3. The 'assay' slot of the SingleCellExperiment objects contains the gene x cell raw count matrix, the 'colData' slot contains the description for each cell: barcode, celltype_major, celltype_final, cnv_status.</p> <p>Please see https://github.com/ETH-NEXUS/scROSHI for code generating the cell type labels.</p>
Single-cell phenotypic characteristics of tolerance under recurring antibiotic exposure in Escherichia coli
<p>Non-heritable drug resistance, such as tolerance and persistence towards antibiotics, is little characterized compared to genetic resistance. Tolerance and persistence allow cells to survive application of antibiotics that are bactericidal to non-tolerant cells. Non-heritable drug resistance challenges antibiotic treatment, particularly of recurrent infections, and have implications towards heritable resistance evolution. Tolerant cells have commonly been characterized as growth arrested cells prior and during antibiotic application that quickly resume growth post-application. Here, we explore characteristic of tolerant and susceptible <em>E. coli</em> single bacteria cells to different levels of recurrent antibiotic exposure and quantify their occurrence. In using a high throughput single-cell microfluidic device, we find that tolerant cells reduce their growth rate by about 50%, but contrary to previous findings do not go into growth arrest or near growth arrest. The growth reduction is induced by antibiotic exposure and not caused by a stochastic switch or predetermined state as previously described. Cells exhibiting constant intermediate growth survived best under antibiotic exposure and selection did not primarily act on fast growing cells, as expected for a β-lactam antibiotic. Control experiments on population cultures confirmed and challenged scaling of single cell findings to population level processes. Our findings suggest a prevalent type of tolerance that differs from previously described tolerance and persister characteristics. Our described characteristics and its high frequency of occurrence supports acclaims of an underappreciated role of tolerant cells towards resistance evolution.</p>
Single-cell datasets for temporal gene expression integration
<p>Contains loom files and preprocessed adata objects to compare methods for temporal gene expression integration. Loom files can be accessed using the 'read' function in Scvelo. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy.</p> <p>The raw single-cell RNA sequencing datasets can be found under the following accession codes.</p> <ul> <li> <p>Mouse embryonic cell cycle dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/nbt.3102">10.1038/nbt.3102</a>) was originally downloaded from ArrayExpress with the accession code E-MTAB-2805</p> </li> <li> <p>Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/10.1182/blood-2016-05-716480</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE81682</p> </li> <li> <p>NKT cell differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/ni.3437">10.1038/ni.3437</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE74596.</p> </li> <li> <p>Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org</a>/<a href="https://doi.org/10.1038/nature19348">10.1038/nature19348</a>) was originally downloaded from the Gene Expression Omnibus with the accession codes GSE70236, GSE70240, GSE70244</p> </li> <li> <p>LPS stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1016/j.cels.2017.03.010">10.1016/j.cels.2017.03.010</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE94383.</p> </li> <li> <p>INF-gamma stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41587-020-00803-5">10.1038/s41587-020-00803-5</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE161465.</p> </li> <li> <p>AML chemotherapy dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41591-018-0233-1">10.1038/s41591-018-0233-1</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE116481.</p> </li> <li> <p>AML diagnosis/relapse dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41375-021-01338-7">10.1038/s41375-021-01338-7</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE126068.</p> </li> <li> <p>MS case control PBMC and CSF datasets from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41467-019-14118-w">10.1038/s41467-019-14118-w</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE138266.</p> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.