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1,545 results for “microenvironment”
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>
Datasets: Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment
<p>The following are necessary data files for the manuscript "Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment":</p> <ul> <li>.zip files for TMA_1, TMA_2, TMA_3, and TMA_4 are .mcd files acquired from imaging mass cytometry (IMC) for each slide of the pancreas TMA slide series</li> <li>pancreas_TMA_sample_info.xlxs includes info on all samples of the TMA slide series that were imaged by IMC</li> <li>custom_gates_0.zip includes histoCAT-derived single cell data files from all IMC samples in the pancreas TMA to be used for single cell analyses in R</li> <li>PDAC_IMC.RDS is a Seurat object of the IMC-derived PDAC single cell data to use for single cell and spatial analyses</li> <li>PDAC_sce is a SingleCellExperiment object of IMC-derived PDAC single cell data to use for spatial analyses</li> <li>mat.RDS is a distance matrix of PDAC cell types to use in R to generate network graph (Figure 2)</li> </ul> <p> </p> <p> </p>
Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer
<p>A dataset with spatial immune cell information on a lung cancer cohort from Uppsala University Hospital, Sweden, with anonymized clinical data. For more information please refer to the 'readme' file and the original study (https://doi.org/10.1016/j.ejca.2023.02.012).</p>
Additional data: Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring
<p>This repository provides additional data for the manuscript titled "Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring", currently under revision at Nature Genetics. The primary data cohort has been deposited in the HTAN data portal. This repository includes processed 10x Xenium spatial transcriptomic data for six TH-MYCN mice (three chemotherapy-treated and three treatment-naive) as well as processed scRNA-seq data for CHLA15 and CHLA20 neuroblastoma (NBL) cells. The scRNA-seq data includes mono-cultured, co-cultured cells with THP-1 macrophages, and co-culture cells treated with Afatinib/CRM197. </p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces (Figures)
<p>High resolution figures related to the below manuscript:</p> <p>Atul Deshpande, Melanie Loth, et al., <a href="https://doi.org/10.1101/2022.06.02.490672">Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces</a>. <em>bioRxiv</em> 2022. doi:10.1101/2022.06.02.490672</p>
Single cell RNA-seq Data - Dissecting the functional reprogramming of the microenvironment in bone marrow fibrosis at the single cell level
<p>We provide results regarding the bioinformatic analysis of scRNA-seq from distinct bone marrow fibrosis mouse models and human samples.</p> <p> </p> <p>These include:</p> <p>Robjects&Markdown - R markdown and R objects with QC statistics, UMAP and final scRNA-seq data sets.</p> <p>Markers - Excel tables with cluster specific marker genes.</p> <p>DE Genes - Excel table with DE genes when comparing cells in control vs. disease condition per cluster.</p> <p>GO Analysis - Gene enrichment analysis of either DE genes. These are divided by either UP or down regulated genes.</p>
Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment
<p>Processed single-cell RNA-seq from the study </p> <ul> <li>10X Genomics CellRanger output (barcodes.tsv, genes.tsv, matrix.mtx) for each each sample</li> <li>Metadata</li> <li>Seurat object of the integrated scRNAseq dataset</li> <li>Xenium object of the spatial transcriptomic data</li> </ul> <p>Distinct Stromal Cell Populations Define the B-cell Acute Lymphoblastic Leukemia Microenvironment</p> <p>Mauricio N. Ferrao Blanco<sup>1</sup>, Bexultan Kazybay<sup>1</sup>, Mirjam Belderbos<sup>1</sup>, Olaf Heidenreich<sup>1</sup>, Hermann Josef Vormoor<sup>1,2</sup></p> <p><sup>1 </sup>Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands</p> <p><sup>2 </sup>University Medical Center Utrecht, Utrecht, the Netherlands</p> <p><strong>Abstract</strong></p> <p>The bone marrow microenvironment plays a critical role in B-cell acute lymphoblastic leukemia (B-ALL) progression, yet its cellular heterogeneity remains poorly understood. Using single-cell RNA sequencing on patient-derived of bone marrow aspirates from pediatric B-ALL patients, we identified two distinct mesenchymal stromal cell (MSC) populations: early mesenchymal progenitors and adipogenic progenitors. Spatial transcriptomic analysis further revealed the localization of these cell types and identified a third stromal population, osteogenic-lineage cells, exclusively present in the bone biopsy. Functional <em>ex vivo</em> assays using sorted stromal populations derived from B-ALL patient bone marrow aspirates demonstrated that both early mesenchymal and adipogenic progenitors secrete key niche-supportive factors, including CXCL12 and Osteopontin, and support leukemic cell survival and chemoresistance. Transcriptomic profiling revealed that B-ALL cells interact differently with stromal subtypes. Notably, adipogenic progenitors, but not early mesenchymal progenitors, provide support to leukemic cells through interleukin-7 and VCAM1 signaling. Stromal cells from B-ALL patients exhibited an enhanced adipogenic differentiation capacity compared to healthy controls. Moreover, co-culture experiments showed that B-ALL cells induce adipogenic differentiation in healthy MSCs through a cell contact-dependent mechanism. Adipogenic progenitors were also enriched in relapse samples, implicating them in disease progression. These findings highlight the complexity of the B-ALL microenvironment and identify different specialized stromal niches with which the leukemic cells can engage.</p> <p> </p> <p> </p> <p> </p>
Imaging Mass Cytometry Dataset of exhausted and non-exhausted breast cancer microenvironments
<p>A cohort of human breast tumor samples were annotated as having an "exhausted" or "non-exhausted" immune environment based on CyTOF characterization of T cell phenotypes (see Wagner et al. 2019). 12 samples (6 exhausted, 6 non-exhausted) were then selected for further analysis by Imaging Mass Cytometry (IMC) with the goal to compare the two immune environment types and to comprehensively characterize exhaustion-associated spatial features of the tumor microenvironment. For IMC, two consecutive FFPE sections of each sample were stained with two different antibody panels (Protein Panel and RNAscope Panel), and 4-10 regions of interest (ROIs, 1mm x 1mm) were measured on each section. ROIs on consecutive sections were registered manually to be as spatially close as possible.</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>
Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study
<p><strong>Supplementary Materials: </strong>The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, <strong>Table ST1</strong> – qMIDS<sup>V2 </sup>Gene panel primer sequences; <strong>Figure S1</strong> – qMIDS<sup>V1</sup> vs qMIDS<sup>V2</sup> 384-well assay format and protocols; <strong>Figure S2.</strong> Individual target gene expression pattern in 1761 samples; <strong>Figure S3.</strong> Various statistical methods used for gene selection analysis on 1761 clinical samples; <strong>Figure S4. </strong>Diagnostic performance comparison between qMIDS<sup>V2</sup> vs qMIDS<sup>V2* </sup>(with 4 less effective genes removed from the panel of 14 target genes of qMIDS<sup>V2</sup>); <strong>Figure S5</strong>. Effect of removing individual genes from the 14-target gene panel qMIDS<sup>V2</sup> (qV2) on diagnostic test performance based on the UK patient cohort data.</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>
Conserved angio-immune subtypes of the cancer microenvironment predict response to immune checkpoint blockade therapy
<p>Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment. However, only a fraction of the patients respond to ICB therapy. Accurate prediction of patients to likely respond to ICB would maximize the efficacy of ICB therapy. The tumor microenvironment (TME) dictates tumor progression and therapy outcome. Here, we classify the TME by analyzing the transcriptome from 11,069 cancer patients based on angiogenesis and T-cell activity. We find three distinct angio-immune TME subtypes conserved across 30 non-hematological cancers. There is a clear inverse relationship between angiogenesis and anti-tumor immunity in TME. Remarkably, patients displaying TME with low angiogenesis with strong anti-tumor immunity show the most significant responses to ICB therapy in four cancers. Re-evaluation of the renal cell carcinoma clinical trials provides compelling evidence that the baseline angio-immune state is robustly predictive of ICB responses. This study offers a rationale for incorporating baseline angio-immune scores for future ICB treatment strategies.</p>
Supplementary movies for: "The mammalian membrane microenvironment regulates the sequential attachment of bacteria to host cells"
<p><strong>Supplementary Movie 1: Live visualization of bacterial attachment to host cells. </strong></p> <p>Microscopic visualizations of <em>E. coli</em> VHH adhesion to HeLa GFP. Maximum intensity projection of a 1-hour confocal microscopy time-lapse at 0.1 fps accelerated 100x. Overlay of bacteria (red) location of initial contact (circles) and their corresponding tracks (colored lines). Scale bar: 50 µm.</p> <p> </p> <p><strong>Supplementary Movie 2: High-speed visualization of bacterial attachment to host cells upon contact.</strong></p> <p>Close-up on microscopic visualizations of <em>E. coli</em> VHH <em>E. coli</em> adhesion to HeLa GFP. Maximum intensity projection of 5-minutes confocal microscopy time-lapses at 1 fps accelerated 10x. Overlay of bacteria (red) location of initial contact (circles) and their corresponding tracks (colored lines).</p> <p> </p> <p><strong>Supplementary Movie 3: Attachment of <em>E. coli</em> VHH to GFP-coated coverslips. </strong></p> <p><em>E. coli</em> VHH in flow binding to the edge of a GFP-functionalized coverslip used as a substrate for a microfluidic channel. Maximum intensity projection of 5-minutes confocal microscopy time-lapse at 1 fps accelerated 10x. Scale bar: 50 µm.</p> <p> </p> <p><strong>Supplementary Movie 4: HeLa GFP cells actively pull bacteria towards their cell body. </strong></p> <p>Maximum intensity projection of a confocal time-lapse experiment at 0.1 fps accelerated 100x of <em>E. coli</em> VHH (red), HeLa GFP (CD80) (green) and 3 mm/s flow. Scale bar: 10 µm.</p> <p> </p> <p><strong>Supplementary Movie 5: Bacteria sequester GFP upon attachment. </strong></p> <p>Maximum intensity projection of 20-minutes epifluorescence microscopy time-lapse at 1 frame per minutes accelerated 60x. <em>E. coli</em> VHH were added on HeLa GFP under static conditions at an MOI of 200 for a couple of minutes and washed 3 times before imaging. Scale bar: 10 µm.</p> <p> </p> <p><strong>Supplementary Movie 6: Flagella promote unspecific transient binding. </strong></p> <p>Maximum intensity projection of a confocal time-lapse experiment at 6 frames per minutes accelerated 100x of flagellated <em>E. coli</em> VHH (red), HeLa GFP (CD80) (green) and 3 mm/s flow. Scale bar: 10 µm.</p>
Evaluating the role of the nuclear microenvironment in gene function by population-based modeling
<p>This repository accompanies the manuscript "<strong>Evaluating the role of the nuclear microenvironment in gene function by population-based modeling</strong>", available in <em>Nature Structural & Molecular Biology</em>.</p> <p>It contains the files for the population of 3D structures for GM12878 in 200-kb resolution generated using IGM (https://github.com/alberlab/igm) and the derived structural features. Please see README.txt for more information.</p> <p>For any inquiries please reach out to Dr. Frank Alber (falber@g.ucla.edu).</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>
VISION Invited lecture - Genomic instability, microenvironment and telomere homeostasis in colorectal cancer
<p>Recording and presentation of the invited lecture that took place online on 4 November 2021 - <strong>Pavel Vodička, MD, Ph.D. - Genomic instability, microenvironment and telomere homeostasis in colorectal cancer.</strong></p> <p>Pavel Vodicka<sup>1,2,3</sup>, Sona Vodenkova<sup>1</sup>, Michal Kroupa<sup>1,3</sup>, Alena Opattova<sup>1,2,3</sup>, Kristyna Tomasova<sup>1,3</sup>, Ludmila Vodickova<sup>1,2,3</sup></p> <p><sup>1</sup> Institute of Experimental Medicine, Czech Acad. Sci., Videnska 1083, Prague 4, Czech Rep.</p> <p><sup>2</sup> Inst. Biology and Med. Genet., 1st Faculty of Medicine, Charles University, Albertov 6, Prague 2, Czech Rep.</p> <p><sup>3</sup> Biomedical Center, Faculty of Medicine in Pilsen, Charles University Prague, Pilsen, 30100, Czech Rep.</p> <p>Colorectal cancer (CRC) continues to be one of the leading malignancies and causes of tumour-related deaths worldwide. Both impaired DNA repair mechanisms and disrupted telomere length homeostasis represent potential culprits in CRC onset, its dissemination in the body and prognosis. Above parameters are becoming critical as prognostic markers, since CRC therapy is based on compounds interacting with DNA. DNA repair capacity in CRC patients have recently been studied in order to address prediction of therapy response. Due to the substantial interindividual variations in DNA repair capacities and relative telomere length, these markers may pose a possible contribution in individualized therapeutical regimen of CRC patients. Telomere attrition, responsible for replicative senescence in healthy cells, may become a hallmark of malignant transformation of the cell due to by-passing cell cycle checkpoints. Telomerase – a key enzyme keeping homeostasis of telomere - is almost ubiquitous in advanced solid cancers, including CRC, and its expression is fundamental to cell immortalization.<br> Here we present our data based on the investigation of base excision repair capacities and relative telomere length in tumor tissues and adjacent non-malignant mucosa of sporadic CRC patients. The relative gene expression of telomerases is monitored as well. Particular attention will be dedicated to the CRC phenotypes and clinicopathological characteristics. We also addressed telomere homeostasis in peripheral blood lymphocytes of CRC patients in several consecutive samplings (at diagnosis, immediately after treatment and in additional follow-up intervals), which could provide us the insight into the treatment response. This aspect is of particular relevance, since there is currently a persistent effort to develop therapeutics, which are telomerase-specific and gentle to non-malignant tissue. However, in practice, we are at the dawn of clinical trials. Additionally, emerging crosstalks between DNA repair and telomere length homeostasis may cast some lights on a dynamic of genomic instability, a fundamental hallmark (or cause) of cancer.</p> <p>Acknowledgement: GACR 21-04607X, 19-10543S, AZV NV18/03/00199</p>
Relapsed multiple myeloma demonstrates distinct patterns of immune microenvironment and malignant cell-mediated immunosuppression
<p> </p> <table> <tbody> <tr> <td>rowID</td> <td>filename</td> <td>date</td> <td>batch</td> <td>group</td> <td>ID</td> </tr> <tr> <td>1</td> <td>20200121_bm003696_RMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>RMM</td> <td>bm003696</td> </tr> <tr> <td>2</td> <td>20200121_bm054122_DRMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>DRMM</td> <td>bm054122</td> </tr> <tr> <td>3</td> <td>20200121_bm054496_RMM_tx_02.FCS</td> <td>20200121</td> <td>1</td> <td>RMM</td> <td>bm054496</td> </tr> <tr> <td>4</td> <td>20200121_bm059645_NDMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>NDMM</td> <td>bm059645</td> </tr> <tr> <td>5</td> <td>20200121_bm060791_NDMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>NDMM</td> <td>bm060791</td> </tr> <tr> <td>6</td> <td>20200121_bm064862_DRMM_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>DRMM</td> <td>bm064862</td> </tr> <tr> <td>7</td> <td>20200121_REF2_tx_01.FCS</td> <td>20200121</td> <td>1</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>8</td> <td>20200124_BM038232_RMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>RMM</td> <td>BM038232</td> </tr> <tr> <td>9</td> <td>20200124_BM051757_NDMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>NDMM</td> <td>BM051757</td> </tr> <tr> <td>10</td> <td>20200124_BM052673_DRMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>DRMM</td> <td>BM052673</td> </tr> <tr> <td>11</td> <td>20200124_BM053393_NDMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>NDMM</td> <td>BM053393</td> </tr> <tr> <td>12</td> <td>20200124_BM053570_RMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>RMM</td> <td>BM053570</td> </tr> <tr> <td>13</td> <td>20200124_BM059775_DRMM_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>DRMM</td> <td>BM059775</td> </tr> <tr> <td>14</td> <td>20200124_REF2_TAX_01.FCS</td> <td>20200124</td> <td>2</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>15</td> <td>20200128_3-1-BM060915-DRMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>DRMM</td> <td>BM060915</td> </tr> <tr> <td>16</td> <td>20200128_3-2-BM064896-DRMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>DRMM</td> <td>BM064896</td> </tr> <tr> <td>17</td> <td>20200128_3-3-BM059424-NDMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>NDMM</td> <td>BM059424</td> </tr> <tr> <td>18</td> <td>20200128_3-4-BM059429-NDMM_TX_02.FCS</td> <td>20200128</td> <td>3</td> <td>NDMM</td> <td>BM059429</td> </tr> <tr> <td>19</td> <td>20200128_3-5-BM043778-RMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>RMM</td> <td>BM043778</td> </tr> <tr> <td>20</td> <td>20200128_3-6-BM008007-RMM_TX_01.FCS</td> <td>20200128</td> <td>3</td> <td>RMM</td> <td>BM008007</td> </tr> <tr> <td>21</td> <td>20200129_3-7-ref2_tax_01.FCS</td> <td>20200129</td> <td>3</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>22</td> <td>20200204_5-1_BM054866-DRMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>DRMM</td> <td>BM054866</td> </tr> <tr> <td>23</td> <td>20200204_5-2_BM065069-DRMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>DRMM</td> <td>BM065069</td> </tr> <tr> <td>24</td> <td>20200204_5-3_BM035491-NDMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>NDMM</td> <td>BM035491</td> </tr> <tr> <td>25</td> <td>20200204_5-4_BM0333015-NDMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>NDMM</td> <td>BM0333015</td> </tr> <tr> <td>26</td> <td>20200204_5-5_BM0052990-RMM-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM0052990</td> </tr> <tr> <td>27</td> <td>20200204_5-5_BM0052990-RMM-Tx_02.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM0052990</td> </tr> <tr> <td>28</td> <td>20200204_5-5_BM052990-RMM-Tx_02.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM052990</td> </tr> <tr> <td>29</td> <td>20200204_5-6_BM052692-RMM-Tx_02.FCS</td> <td>20200204</td> <td>5</td> <td>RMM</td> <td>BM052692</td> </tr> <tr> <td>30</td> <td>20200204_5-7 -Ref2-Tx_01.FCS</td> <td>20200204</td> <td>5</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>31</td> <td>20200207_6-1_BM063515_DRMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>DRMM</td> <td>BM063515</td> </tr> <tr> <td>32</td> <td>20200207_6-2_BM054226_DRMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>DRMM</td> <td>BM054226</td> </tr> <tr> <td>33</td> <td>20200207_6-3_BM008346_NDMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>NDMM</td> <td>BM008346</td> </tr> <tr> <td>34</td> <td>20200207_6-4_BM008718_NDMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>NDMM</td> <td>BM008718</td> </tr> <tr> <td>35</td> <td>20200207_6-5_BM052764_RMM_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>RMM</td> <td>BM052764</td> </tr> <tr> <td>36</td> <td>20200207_6-6_Ref2_Tax_01.FCS</td> <td>20200207</td> <td>6</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>37</td> <td>20200211_7-1_BM061912_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM061912</td> </tr> <tr> <td>38</td> <td>20200211_7-2_BM059328_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM059328</td> </tr> <tr> <td>39</td> <td>20200211_7-3_BM065082_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM065082</td> </tr> <tr> <td>40</td> <td>20200211_7-4_BM008353_DRMM_TAX_01.FCS</td> <td>20200211</td> <td>7</td> <td>DRMM</td> <td>BM008353</td> </tr> <tr> <td>41</td> <td>20200211_Ref2_Tax_02.FCS</td> <td>20200211</td> <td>7</td> <td>REF</td> <td>REF2</td> </tr> <tr> <td>42</td> <td>20200214_4-1_BM062618_DRMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>DRMM</td> <td>BM062618</td> </tr> <tr> <td>43</td> <td>20200214_4-2_BM062255_DRMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>DRMM</td> <td>BM062255</td> </tr> <tr> <td>44</td> <td>20200214_4-3_BM032596_NDMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>NDMM</td> <td>BM032596</td> </tr> <tr> <td>45</td> <td>20200214_4-4_BM047845_NDMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>NDMM</td> <td>BM047845</td> </tr> <tr> <td>46</td> <td>20200214_4-5_BM042666_RMM_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>RMM</td> <td>BM042666</td> </tr> <tr> <td>47</td> <td>20200214_4-6_Ref2_Tax_01.FCS</td> <td>20200214</td> <td>4</td> <td>REF</td> <td>REF2</td> </tr> </tbody> </table> <p> </p> <p>*files with row ID #26 and #27 need to be concatenated since they represent the same sample acquired over 2 files</p>
Microscopic images and schematics illustrating processes of microenvironment sensing and cortical actomyosin partitioning in T cells
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Conserved angio-immune subtypes of the cancer microenvironment predict response to immune checkpoint blockade therapy
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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.