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1,395 results for “Tumor immunity”
Dataset of 'HIV infection is associated with compromised tumor microenvironment adaptive immune reactivity in Hodgkin Lymphoma'
<p><span><span>§<span> </span></span></span><strong><span>:</span></strong><span>The data were generated using the i) GeoMx Digital Spatial Profiler (DSP) platform developed by Nanostring Technologies. GeoMx analysis utilizes <em>in situ </em>RNA hybridization with Whole Atlas Transcriptome probe (Nanostring) and ii) HTG platform (Immune Response kit) Our dataset comprises samples from donors categorized as HLposHIVnegEBVneg, HLposHIVposEBVpos, or HLposHIVnegEBVpos (HL: Hodgkin Lymphoma). Regions of interest (ROI) were spatially profiled to capture distinct molecular signatures associated with these donor categories.</span></p>
A Single-Cell Tumor Immune Atlas for Precision Oncology
<p><strong>Publication version of the Single-Cell Tumor Immune Atlas</strong></p> <p>This upload contains:</p> <ul> <li><strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas</li> <li><strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas</li> <li><strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li><strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells</li> </ul> <p>All the files contain the following patient/sample metadata variables:</p> <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> <p>If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file.</p> <p>For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>.</p> <p>h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.</p>
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.
<p>Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type.</p> <p>To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to:</p> <p>- The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt</p> <p>- The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt</p> <p>- The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt</p> <p>- The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak</p> <p>- Bed files listing ATAC-Seq fragments for each sample: *.bed</p> <p>We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip".</p> <p>The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript. </p>
Conventional therapy induces tumor immunoediting and modulates the immune contexture in colorectal cancer
<p>Cancer immunotherapies for patients with colorectal cancer (CRC) continue to lag behind other solid cancer types with the exception of 4% of patients with microsatellite-instable tumors. Thus, there is an urgent need to broaden the clinical benefit of checkpoint blockers to CRC by combining conventional therapies to sensitize tumors to immunotherapy. However, the impact of conventional drugs on immunoediting and hence, imposing positive selection towards less immunogenic variants, and on the tumor immune contexture in CRC remains elusive.</p> <p>In this study, we performed comprehensive multimodal profiling using longitudinal samples from metastatic CRC patients undergoing neoadjuvant therapy with mFOLFOX6 and Bevacizumab. Exome-sequencing, RNA-sequencing and multiplexed immunofluorescence imaging was carried out on tumor samples obtained before and after therapy and the data was analyzed using established methods. The results of the analysis were extrapolated to publicly available datasets (TCGA and CPTAC). In order to identify a surrogate marker, an explainable artificial intelligence method was developed using a transformer-based analytical pipeline for the identification of features in H&E images associated with specific biological processes, followed by manual evaluation of highly informative tiles by a pathologist.</p> <p>We expect that the results of this project will provide a deeper understanding of the tumor-immune interactions and will allow the development of more robust combinatorial therapeutic strategies for MSS CRC.</p>
Lactate Increases Stemness of CD8+ T Cells to Augment Anti-Tumor Immunity
<p>The immunological role of lactate in antitumor immunity is not well understood. In this study, we report lactate treatment significantly augments antitumor efficacy of immune checkpoint blockade or T cell vaccine therapy in multiple tumor models. Single cell transcriptomics and flow cytometry analysis revealed an increased subpopulation of stem-like TCF-1-expressing CD8<sup>+</sup> T cells upon lactate treatment.</p>
Tumor-Immune Microenvironment Revealed by Imaging Mass Cytometry in a Metastatic Sarcomatoid Urothelial Carcinoma with a Prolonged Response to Pembrolizumab - IMC data
<blockquote> <p>Sarcomatoid urothelial carcinoma (SUC) is a rare subtype of urothelial carcinoma (UC), that typically presents at an advanced stage compared to more common variants of UC. Locally advanced and metastatic UC have a poor long-term survival following progression on first-line platinum-based chemotherapy. Antibodies directed against the programmed cell death 1 protein (PD-1) or its ligand (PD-L1) are now approved to be used in these scenarios. The need for reliable biomarkers for treatment stratification is still under research. Here we present a novel case report of the first Image Mass Cytometry (IMC) analysis done in SUC to investigate the immune cell repertoire and PD-L1 expression in a patient who presented with metastatic SUC and experienced a prolonged response to the anti-PD1 immune checkpoint inhibitor pembrolizumab after progression on first line chemotherapy. This case report provides an important platform for translating these findings to a larger cohort of UC and UC variants.</p> </blockquote> <p>We make available TIFF files containing imaging mass cytometry data for 4 regions of interest of a sample of metastatic sarcomatoid urothelial carcinoma. The order of the axis in the image stacks is "CYX". The CSV files indicate the identity of the channels.</p>
Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma
<p><strong>- melanoma_IMC_data.zip</strong></p> <p>The zip file contains the raw IMC images (in the raw_tiff folder) and corresponding single cell masks (in the mask folder) associated with the paper "Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma". The MCD files by CyTOF IMC were exported to a multi-channel TIFF file including 41 channels, and the order of the channel was provided in the <strong>Melanoma_panel.csv</strong>. </p> <p><strong>- Melanoma_code_data.zip</strong></p> <p>The zip file contains the 4 folders described as follows: </p> <ul> <li>Folder ”data“: the processed data for the result shown in paper<br> - Folder "input": <br> - sc_data.csv: the single cell protein expression data;<br> - Folder "abundance": the cell type abundance files;<br> - Folder "clidata": the response and survival data for 4 melanoma datasets used in the paper; <br> - Folder "hc_result": TME archetypes annotation for each sample/ROI from hierarchical clustering;<br> - Folder "ICB": data for ICB analysis (presented in FigS3);<br> - Folder "meta": panel file for clustering;<br> - Folder "RNAseq_data": the RNAseq data for 4 melanoma datasets used in the paper;<br> - Folder "RNAseq_deconv": the result of cell type deconvolution from bulk RNAseq; <br> - Folder "spatial": data for neighbourhood analysis (presented in Fig3, FigS4).<br> - Folder "output": intermediate result for analysis.</li> <li>Folder "Rscript": R scripts for reproducing results in the paper.<br> - generate_Figs.Rmd: ploting figures presented in the paper;<br> - functions.R: functions used for analysis;<br> - Clustering.Rmd: determining cell types based on marker intensities;<br> - Spatial_analysis.Rmd: neighbourhood analysis to get significant interction/avoidance cell relationships.</li> <li>Folder "Figs": figures presented in paper.</li> <li>Folder "HE_figs": the H&E image and the ROIs distribution for each sample.</li> </ul>
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
<p>Manganese porphyrins reportedly exhibit synergic effects when combined with irradiation. However, an in-depth understanding of intratumoral heterogeneity and immune pathways, as affected by Mn porphyrins, remains limited. Here, we explored the mechanisms underlying immunomodulation of a clinical candidate, MnTnBuOE-2-PyP<sup>5+</sup> (BMX-001, MnBuOE), using single-cell analysis in murine carcinoma<em> </em>model. Mice bearing 4T1 tumors were divided into 4 groups: control, MnBuOE, radiotherapy (RT), combined MnBuOE, and radiotherapy (MnBuOE/RT). In epithelial cells, epithelial-mesenchymal transition, TNF-α signaling via NF-кB, angiogenesis, and hypoxia-related genes were significantly downregulated in the MnBuOE/RT compared to the RT. All subtypes of cancer-associated fibroblasts (CAFs) were reduced in MnBuOE and MnBuOE/RT. Inhibitory receptor-ligand interactions, in which epithelial cells and CAFs interacted with CD8+ T cells, were significantly lower in the MnBuOE/RT than in the RT. Trajectory analysis showed that DC maturation-associated markers were increased in MnBuOE/RT. M1 macrophages were significantly increased in the MnBuOE/RT compared to the RT, whereas myeloid-derived suppressor cells were decreased. CellChat analysis showed that the number of cell-cell communications was the lowest in the MnBuOE/RT. Our study is the first to provide evidence for the combined radiotherapy with a novel Mn porphyrin clinical candidate, BMX-001 from the perspective of each cell type within the tumor microenvironment.</p>
Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer
<p>Wallen ZD, Ko H, Nesline MK, Hastings SB, Strickland KC, Previs RA, Zhang S, Pabla S, Conroy J, Jackson JB, Saini KS, Jensen TJ, Eisenberg M, Caveney B, Sathyan P, Severson EA, Ramkissoon SH. <strong>Real-world comprehensive genomic and immune profiling reveals distinct age- and sex-based genomic and immune landscapes in tumors of patients with non-small cell lung cancer.</strong> <em>Front Immunol.</em> 2024 Jun 21;15:1413956. doi: <a href="https://doi.org/10.3389/fimmu.2024.1413956">10.3389/fimmu.2024.1413956</a>. PMID: <a href="https://pubmed.ncbi.nlm.nih.gov/38975340/">38975340</a>; PMCID: <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC11224431/">PMC11224431</a>.</p> <p><strong>ABSTRACT</strong></p> <p>Younger patients with non-small cell lung cancer (NSCLC) (<50 years) represent a significant patient population with distinct clinicopathological features and enriched targetable genomic alterations compared to older patients. However, previous studies of younger NSCLC suffer from inconsistent findings, few studies have incorporated sex into their analyses, and studies targeting age-related differences in the tumor immune microenvironment are lacking. We performed a retrospective analysis of 8,230 patients with NSCLC, comparing genomic alterations and immunogenic markers of younger and older patients while also considering differences between male and female patients. We defined older patients as those ≥65 years and used a 5-year sliding threshold from <45 to <65 years to define various groups of younger patients. Additionally, in an independent cohort of patients with NSCLC, we use our observations to inform testing of the combinatorial effect of age and sex on survival of patients given immunotherapy with or without chemotherapy. We observed distinct genomic and immune microenvironment profiles for tumors of younger patients compared to tumors of older patients. Younger patient tumors were enriched in clinically relevant genomic alterations and had gene expression patterns indicative of reduced immune system activation, which was most evident when analyzing male patients. Further, we found younger male patients treated with immunotherapy alone had significantly worse survival compared to male patients ≥65 years, while the addition of chemotherapy reduced this disparity. Contrarily, we found younger female patients had significantly better survival compared to female patients ≥65 years when treated with immunotherapy plus chemotherapy, while treatment with immunotherapy alone resulted in similar outcomes. These results show the value of comprehensive genomic and immune profiling (CGIP) for informing clinical treatment of younger patients with NSCLC and provides support for broader coverage of CGIP for younger patients with advanced NSCLC.</p> <p><strong>DATA AVAILABILITY: </strong></p> <p>De-identified, individual-level patient data, genomic variants, and individual immune gene expression data used in the manuscript can be found in this repository (https://zenodo.org/record/11396552). An R markdown file with R code used to perform the analyses and generate figures is also provided in the repository along with the data. All versions of software used are provided in the Methods section of the manuscript. Raw sequencing data were derived from routine clinical testing of real-world patients and cannot be shared publicly. Data for immune gene expression signatures are not publicly available due to a non‑provisional patent filing covering the methods used to generate and analyze these data but are available from the corresponding author on reasonable request.</p>
Spatial Tumor-Immune Analysis: Insights from Pathology Slides and Breast Cancer Survival
<p>Cancer is the second leading cause of death in the US. Among the various forms of cancer, breast cancer and lung cancer are particularly significant due to their prevalence and impact. Breast cancer in particular contributing to around 30\% of all new female cases each year, while also having some of the highest mortality rates. Scientists and doctors rely on pathology slides to aid in the discovery of a cure, diagnose patients, and provide treatment. These slides play a crucial role in examining samples and identifying any abnormalities. The primary goal of this project was to analyze pathology slides from 873 cancer patients in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA). We developed STAIN (Spatial Tumor and Immune Analysis for Novel insights) with the hypothesize that quantitative analysis of cell type specific clusters in the spatial context can lead to novel insights on patient survival. First, we identified tumor and immune cells using a HD-Yolo algorithm. Then we identify tumor clusters and immune cell clusters. Next, descriptive statistics such as Jaccard distance, Hausdorff distance, Wasserstein distance, tumor density, and immune cell density were derived and correlated with the patients survival while adjusting for clinical attributes such as patient age and tumor stage using Cox proportional Hazard models. The results discover spatial attributes and known clinical risk features associated with survival. </p>
INSPIRE-seq simultaneously selects nanobodies for immune epitopes in the complex tumor microenvironment
<p>scRNAseq of CD45 magnetic microbeads enriched cells were isolated form Py8119 bearing mice (three mice per pool/group) two hours after injection of either PBS, insertless phage display, CD45, DCs, or CD8 specific VHHs phage display libraries. </p>
A Study of TAK-981 in People With Advanced Solid Tumors or Cancers in the Immune System
ClinicalTrials.gov study NCT03648372. IPD Sharing: YES. Countries: 1. Publications: 1.
Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
Open the record for dataset details and reuse information.
Data from: DGKα/ζ inhibition lowers the TCR affinity threshold and potentiates anti-tumor immunity
<p>Checkpoint blockade immunotherapies expand neoantigen- or virus-specific T cells, and poor responsiveness to immunotherapy is associated with lower mutational burden in tumors of non-viral origin. Although mouse models demonstrate that lower affinity T cells recognizing self-antigens can contribute to tumor control if sufficiently activated, therapeutic options for enhancing T cell priming are limited. Diacylglycerol kinases (DGKs) attenuate DAG signaling by converting DAG to phosphatidic acid, thereby suppressing pathways downstream of TCR signaling. Using a novel dual DGK alpha and zeta inhibitor (DGKi), tumor-specific CD8 T cells with different affinities (TRP1high and TRP1low), and a series of altered peptide ligands, we demonstrate that inhibition of DGKα/ζ can lower the signaling threshold for T cell priming. TRP1high and TRP1low CD8 T cells produced more IL-2, IFNγ, and other effector cytokines in the presence of cognate antigen and DGKi. Effector TRP1high- and TRP1low-mediated cytolysis of tumor cells with low antigen load was MHC-restricted, mediated by IFNγ, and augmented by DGKi. Adoptive T cell transfer into mice bearing pancreatic or melanoma tumors synergized with single-agent DGKi or DGKi and αPD1, with increased expansion of low-affinity T cells and increased cytokine production observed in tumor infiltrates of treated mice. Collectively, our findings highlight DGKα/ζ as therapeutic targets for augmenting tumor-specific CD8 T cell function.</p>
Characterization of the tumor-immune microenvironment in hepatocellular carcinoma by highly multiplexed imaging mass cytometry
<p>Imaging mass cytometry data of 54 HCC patients. </p> <ul> <li>DC_img_normalized: Preprocessed and normalized multistack .tiff images. Each stack represents one channel. Channel annotations are stored in the ICICohort_panel.csv file. ROIs are located in the tumor, interface and adjacent liver as indicated in the file name.</li> <li>DC_cellmasks: Masks identifying individual cells on the images.</li> <li>DC_stromamasks: Masks identifying stromal and parenchymal regions on the image.</li> <li>DCCohort_panel.csv: table containing channel information (metal tag and marker).</li> </ul> <p>Patient metadata may be found as supplementary table 2 of DOI <a href="https://doi.org/10.1136/gutjnl-2024-332837" target="_blank" rel="noopener noreferrer"> 10.1136/gutjnl-2024-332837 </a>.</p>
Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment
<p>Source data for the paper, "Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment."<br><br>This includes nanostring gene expression profiling of primary human tumor material ("fresh" in the data, these samples are taken directly after enzymatic digestion) and the corresponding 3D Patient-Derived Explant Culture (PDEC). <br><br>transfer_257851_files_0a119f4d.zip refers to the mouse spatial transcriptomics data.<br><br>DE_results_Metformin/LPS files refer to differentially expressed genes of human monocyte-derived dendritic cells of 6 individual donors to control untreated dendritic cells after 24hr treatment. <br><br> Also includes the original Seurat.rds file for the scSEQ of primary tumor material (fresh) vs. PDEC<br><br><br><br><br> </p>
Immune repertoires of de-identified TCGA tumor samples assembled by TRUST4
<p>We applied TRUST4 to assemble the immune repertoire data from TCGA tumor samples. Because TCGA has restricted access permission, the sample IDs are de-identified and the sequence is at the amino acid level. The data is used in the study of "Comprehensive characterizations of immune receptor repertoire in tumors and cancer immunotherapy studies". The format is:</p> <p>CancerType_RandomID Chain_Type CDR3_AminoAcid VGene JGene ConstantGene Abundance</p> <p>(Note that the deidentified RandomID is different from the previous version (version 1))</p>
BAF155 Methylation Drives Metastasis By Hijacking Super-enhancers and Subverting Anti-tumor Immunity
<p>Subunits of the chromatin remodeler SWI/SNF are the most frequently disrupted genes in cancer. However, how post-translational modifications (PTM) of SWI/SNF subunits elicit epigenetic dysfunction remains unknown. Arginine-methylation of BAF155 by coactivator-associated arginine methyltransferase 1 (CARM1) promotes triple negative breast cancer (TNBC) metastasis. Herein, we discovered the dual roles of methylated-BAF155 (me-BAF155) in promoting tumor metastasis: activation of super-enhanceraddicted oncogenes by recruiting BRD4, and repression of interferon / pathway genes to suppress host immune response. Pharmacological inhibition of CARM1 and BAF155 methylation not only abrogated the expression of an array of oncogenes, but also boosted host immune responses by enhancing the activity and tumor infiltration of cytotoxic T cells. Moreover, strong me-BAF155 staining was detected in circulating tumor cells from metastatic cancer patients. Despite low cytotoxicity, CARM1 inhibitors strongly inhibited TNBC cell migration in vitro, and growth and metastasis in vivo. These findings illustrate a unique mechanism of arginine methylation of a SWI/SNF subunit that drives epigenetic dysregulation, and establishes me-BAF155 as a therapeutic target to enhance immunotherapy efficacy.</p>
Mutant IDH1 inhibition induces dsDNA sensing to activate tumor immunity
<p>Isocitrate Dehydrogenase 1 (IDH1) is the most commonly mutated metabolic gene across human cancers. Mutant IDH1 (mIDH1) generates the oncometabolite (R)-2-hydroxyglutarate, disrupting enzymes involved in epigenetics and other processes. A hallmark of IDH1-mutant solid tumors is T cell exclusion, whereas mIDH1 inhibition in preclinical models restores anti-tumor immunity. Here, we define a cell-autonomous mechanism of mIDH1-driven immune evasion. IDH1-mutant solid tumors show striking, selective hypermethylation and silencing of the cytoplasmic dsDNA sensor, CGAS, compromising innate immune signaling. mIDH1 inhibition restores DNA demethylation, derepressing CGAS and transposable element (TE) subclasses. dsDNA produced by TE-reverse transcriptase activates cGAS, triggering viral mimicry and stimulating anti-tumor immunity. Thus, we demonstrate that mIDH1 epigenetically suppresses innate immunity and link endogenous reverse transcriptase activity to the mechanism of action of an FDA-approved oncology drug.</p>
TGF-β neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice
<p>Deep TCR sequencing was performed using the TCR Profiling Kit from MiLaboratories (Mouse α/β TCR RNA; Kit MiLaboratories; TMMR-001). Deep TCR sequencing was analyzed using the MiXCR software from MiLaboratories per manufacturer's recommendations. The files correspond to the TCR-beta sequences.<br>The files are named as follows:</p> <p> </p> <table> <tbody> <tr> <td>file_name</td> <td>cell type sequenced</td> <td>tissue of origin</td> <td>treatment</td> <td>mouse_id</td> </tr> <tr> <td>21BA1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>23BA2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>25BA3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>27BA4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>29BA5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>22BA1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>1</td> </tr> <tr> <td>24BA2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>2</td> </tr> <tr> <td>26BA3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>3</td> </tr> <tr> <td>28BA4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>4</td> </tr> <tr> <td>30BA5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>bintrafusp alpha</td> <td>5</td> </tr> <tr> <td>31CON1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>6</td> </tr> <tr> <td>33CON2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>7</td> </tr> <tr> <td>35CON3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>8</td> </tr> <tr> <td>37CON4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>9</td> </tr> <tr> <td>39CON5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>control</td> <td>10</td> </tr> <tr> <td>32CON1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>6</td> </tr> <tr> <td>34CON2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>7</td> </tr> <tr> <td>36CON3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>8</td> </tr> <tr> <td>38CON4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>9</td> </tr> <tr> <td>40CON5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>control</td> <td>10</td> </tr> <tr> <td>1aPDL11dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>3aPDL12dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>5aPDL13dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>7aPDL14dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>9aPDL15dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>2aPDL11SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>11</td> </tr> <tr> <td>4aPDL12SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>12</td> </tr> <tr> <td>6aPDL13SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>13</td> </tr> <tr> <td>8aPDL14SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>14</td> </tr> <tr> <td>10aPDL15SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-PDL1 antibody</td> <td>15</td> </tr> <tr> <td>11aTGFB1dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>13aTGFB2dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>15aTGFB3dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>17aTGFB4dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>19aTGFB5dLN.clones_TRB.tsv</td> <td>T cells</td> <td>tumor-draining lymph node</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> <tr> <td>12aTGFB1SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>16</td> </tr> <tr> <td>14aTGFB2SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>17</td> </tr> <tr> <td>16aTGFB3SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>18</td> </tr> <tr> <td>18aTGFB4SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>19</td> </tr> <tr> <td>20aTGFB5SP.clones_TRB.tsv</td> <td>T cells</td> <td>spleen</td> <td>anti-TGF-beta antibody</td> <td>20</td> </tr> </tbody> </table>
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.