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252 results for “Tumor heterogeneity”
Sparks et al, Heterogeneity in tumor chromatin-doxorubicin binding revealed by in vivo fluorescence lifetime imaging confocal endomicroscopy: In vitro data
<p>Data is divided into three folders:</p> <ul> <li>Sparks_et_al_FIG2_Histone_vs_free_GFP <ul> <li>data for Sparks et al Figure 2</li> <li>main text section: <em>'FRET between chromatin-bound GFP and doxorubicin'</em></li> </ul> </li> <li>Sparks_et_al_FIG3_in_vitro_dose_response <ul> <li>data for Sparks et al Figure 3#</li> <li>main text section:<em> 'FLIM endomicroscope can monitor doxorubicin cellular uptake'</em></li> </ul> </li> <li>Sparks_et_al_SuppFIG2_endoscope_spectral_cross_talk <ul> <li>data for Sparks et al Supplementary Figure 2</li> <li>Supplementary information</li> </ul> </li> </ul> <p><strong>Cell lines</strong></p> <p>IGROV-1 cell lines were cultured in CO<sub>2</sub> dependent media with 10% fetal bovine serum and 1% Pen Strep at 37 ˚C. Before experiments, cells were grown to 80% confluence. For measuring doxorubicin uptake by fluorescence an IGROV-1 cell line stably expressing GFP fused to Histone-1 (H1) was made using the PiggyBac transposon system. As a control to show that effect of doxorubicin on GFP depends on whether it is fused to H1 or not, a stable whole cell expression of GFP by lentiviral transfection and selection by Geneticin was made. For bioluminescence imaging of xenograft tumors, all IGROV-1 cell lines were made to stably express firefly luciferase.</p> <p>To investigate the effect of doxorubicin on other histones, IGROV-1 cells were transiently transfected with a Histone-2B-GFP plasmid (gift from Kurt Anderson) using the Lipofectamine® 2000 reagent.</p> <p>IGROV-1 cells were obtained from Crick institute cell services and confirmed as IGROV-1 by Short Tandem Repeats (STR) profiling and no mycoplasma was detected.</p> <p><strong>In vitro experiments</strong></p> <p>IGROV-1 cells were grown to 80% confluence in 75 ml flasks before being re-plated in 12 or 24 well plates or 35 ml glass bottomed dishes and allowed to attach to the surface for 24 hours before experiments.</p> <p>To study how the fluorescence of GFP labelled H1 labelled IGROV-1 cells changes with doxorubicin treatment, fluorescence intensity and lifetime distributions were measured from cells after 3 hours of incubation with doxorubicin of varying concentrations (0, 0.18, 0.9, 1.8, 9, 18 µM) by serial dilutions of a stock solution with PBS. After 3 of hours, cells were washed in PBS then fixed for 20 minutes in 4% PFA. Cells were then imaged in PBS. Doxorubicin hydrochloride (Sigma-Aldrich, D1515-10 mg) was dissolved in PBS to a concentration of 9 mM and stored at -20˚C.</p>
Sparks et al, Heterogeneity in tumor chromatin-doxorubicin binding revealed by in vivo fluorescence lifetime imaging confocal endomicroscopy: in vivo data
<p>Data is divided into three folders:</p> <ul> <li>Sparks_et_al_FIG_6_IP_intranodule_heterogeneity <ul> <li>data for Sparks et al Figure 6</li> <li>main text section: <em>'FRET between chromatin-bound GFP and doxorubicin'</em></li> </ul> </li> <li>Sparks_et_al_FIG4_5_6_IP_IV_chemo_comparison <ul> <li>data for Sparks et al Figures 4,5 & 6</li> <li>main text section:<em> 'FLIM endomicroscope can monitor doxorubicin cellular uptake'</em></li> </ul> </li> <li>Sparks_et_al_FIG6_IP__internodule_heterogeneity <ul> <li>data for Sparks et al Figure 6</li> <li>main text section: <em>'Intra-tumor heterogeneity'</em></li> </ul> </li> </ul> <p><strong>In vivo experiments</strong></p> <p>Murine xenografts were prepared by intraperitoneal (IP) injection of IGROV-1 cancer cells. IGROV-1 cells were grown to 80% confluence before being trypsinized and re‑suspended in PBS at a concentration of cells per ml. cells were injected into ICRF nude mice. After 14 days post-injection, the presence of intraperitoneal tumors was confirmed by bioluminescence imaging. Briefly, an IVIS bioluminescence imaging system was used to image isoflurane anesthetized mice. 100 µl of D-luciferin (luciferase substrate) at 30mg ml<sup>-1</sup> was injected IP 10 minutes before recording of bioluminescence images. The presence of peritoneal tumors was confirmed if bioluminescence signals from the peritoneum were above background noise 10-30 minutes after D‑luciferin injections. Following confirmation of tumors, in vivo fluorescence imaging experiments were carried out after 21 days. To study differences in drug uptake between intravenous or intraperitoneal delivery, prior to imaging mice were subject to IP or IV doxorubicin-based chemotherapy for 1.5, 3 or 24 hours. Imaging involved terminal procedures, mice were anesthetized then peritoneal tumors were exposed by minor surgery and inspected with the CEM.</p> <p>All animal model procedures were approved by The Francis Crick Institute Biological Ethics Committee and UK Home Office authority provided by Project License 70/8380.</p> <p> </p> <p> </p>
ChromoPhyloGen: characterizing copy number alteration patterns in heterogeneous tumor cell populations at Single-Cell Resolution
<p>The human liver cancer cell line Huh7 was obtained from the American Type Culture Collection (ATCC). Huh7 cells were cultivated in Dulbecco's Modified Eagle Medium (DMEM, Gibco, C11995), supplemented with 1% penicillin/streptomycin (Gibco, 15140122), and 10% fetal bovine serum (FBS, Excell, FSP500). Huh7 cell line was maintained under a 95% O2 and 5% CO2 humidified atmosphere in an incubator at 37˚C. </p> <p>The scDNA-seq library was performed using the Chromium Single cell DNA Library & Gel Bead kit (10x Genomics, PN1000040) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip C and D (10x Genomics, 1000022 and 1000042, respectively) according to the manufacturer's user guide and subsequently run on a thermocycler. The barcoded libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p>The scRNA-seq library was generated using the 10x Genomics Chromium Single Cell 3' & Gel Bead Kit v3 (10x Genomics, PN100075) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip B (10x Genomics,1000154) according to the manufacturer's protocol and subsequently run on a thermocycler. The 3' gene expression libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p> </p>
Heterogeneity of RNA editing in mesothelioma and how RNA editing enzyme ADAR2 affects mesothelioma cell growth, response to chemotherapy and tumor microenvironment
<p>Raw data supporting the manuscript</p>
Proteomic characterization of intra-tumor heterogeneity in human endometrial cancer.
<p>Endometrial cancer (EC) is one of the most frequently diagnosed gynecological cancers worldwide, and its prevalence has increased by more than 50% over the last two decades. Despite the understanding of the major signaling pathways driving the growth and metastasis of EC cells, clinical trials targeting these signaling pathways in human patients have reported poor outcomes. Heterogeneous nature of EC is suspected to be one of the key reasons for the failure of targeted therapies. However, no study so far has explored EC heterogeneity within the same patient at the proteomic level. In this study, we isolated proteins from tumor tissue samples obtained from different sites of EC from individual patients (~2-4 samples/patient). We then performed a SWATH-based comparative proteomic analysis, using liquid chromatography-tandem mass spectrometry (LC-MS/MS), to profile the protein content of different areas within EC tissue. Our results highlighted an average of 1424 unique proteins in 20 patient-derived EC tissues with a confidence corresponding to a false discovery rate below 1%. We have identified protein biomarkers that differentiate between premenopausal vs postmenopausal cancer, macroscopic vs microscopic tumor, more vs less invasive cancer, and DNA mismatch repair defective vs intact tumor. Furthermore, the data revealed a list of unique proteins that, for the same patient, exist only in a single EC location but are not present in other locations within the same tumor. Overall, our proteomic analysis highlighted that tumor tissue samples collected from different sites of EC within the same patient can harbor diverse protein profiles. Importantly, this study sets the foundation for further investigations into the mechanisms of endometrial heterogeneity and some of the proteins identified here may represent potential novel EC drug targets.</p>
Data from: Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer
Open the record for dataset details and reuse information.
Tools for prediction of tumor heterogeneity by a machine learning approach
<p>The package includes R codes and datasets. We applied three classification algorithms: Support Vector Machine, Random Forest, and Naïve Bayes. Datasets include tab-delimited files of mutation and gene expression profile for stomach cancer. </p>
Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability
<p>"Pan-cancer inference of intra-tumor heterogeneity reveals associations with different forms of genomic instability", F. Raynaud, M. Mina, D. Tavernari and G. Ciriello</p> <p>These files are necessary to generate the supplementary table containing:</p> <p>#Sample_name #Cancer_type #Cancer_subtype #Mean_reads_per_mutations #Number_of_mutations #Number_of_altered_segments #Number_of_clones #TreeScore #Number_of_mutations_first_clone #Number_of_mutations_other_clones</p> <p>REQUIREMENTS</p> <p>Phylogenies generated by PhyloWGS (PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors](<a href="http://genomebiology.com/2015/16/1/35">http://genomebiology.com/2015/16/1/35</a>), Deshwar et al.)</p> <p>Output files from PhyloWGS for each sample:</p> <ul> <li> <p>top_k_trees which contains the best phylogenies (50 by default) with the label of each clone, the population frequency of each clone, the number of children of each clone, number of mutations in the clone, the labels of the mutations</p> </li> <li> <p>top_k_trees1, top_k_trees2, ... , top_k_treesN output file for the best k trees (50 by default)</p> </li> </ul> <p>FILES</p> <p>*Molecular data for the tumor types: CESC, UCEC, UVM, THCA, KICH, BRCA, SKCM, ACC, CRC, STAD, BLCA, LUAD, KIRP, PRAD, LIHC; has been collected in July 2015 *Molecular data for the tumor types: SARC, PAAD, MESO, LGG, GBM, DLBC, UCS, THYM, TGCT, PCPG, OV, LUSC, LAML, KIRC, HNSC, ESCA, CHOL; has been collected in 2018</p> <p>from the FireHose (<a href="https://gdac.broadinstitute.org/">https://gdac.broadinstitute.org/</a>) and cBioPortal (Cerami et al., 2012) (<a href="http://www.cbioportal.org/">http://www.cbioportal.org/</a>) data repositories for The Cancer Genome Atlas (TCGA). Only TCGA datasets publicly available at that time were used in our study.</p> <p>-Mutation files (MAF format): combined_2015_2018_MAF.maf.bz2</p> <p>-Copy number segmentation files: combined_seg_2015_2018.seg.bz2</p> <p>-analyze_public.py: Python file to generate the Supplementary Table run: python2.7 analyze_public.py</p> <p>- All input data and results from PhyloWGS: PhyloWGS_input_output.tar.bz2 </p>
Small extrachromosomal circular DNA harboring targeted tumor suppressor gene mutations supports intratumor heterogeneity in mouse liver cancer induced by multiplexed CRISPR/Cas9
<p>These files include raw image data from our study titled "Small extrachromosomal circular DNA containing targeted tumor suppressor mutations supports intratumoral heterogeneity in multiplex CRISPR/Cas9-induced mouse liver cancer".</p>
The Effect of Immunological Heterogeneity of Tumor Microenvironment in the Prognosis of Gastric Cancer
ClinicalTrials.gov study NCT04819958. IPD Sharing: NO. Countries: 1. Publications: 6.
Understanding tumor heterogeneity in melanoma brain metastasis using spatial transcriptomics and multi-regional bulk sequencing
<p>Melanoma brain metastasis (MBM) exhibits extensive inter- and intra-tumor heterogeneity, driven by a complex tumor microenvironment (TME). The aim with this study was to profile the MBMs by using a multi-omics approach, integrating spatial transcriptomics with bulk exome, proteome, and transcriptome profiling. We identified significant patient-specific variations in immune cell infiltration, particularly in B/plasma cells, myeloid cells, and cancer-associated fibroblasts (CAFs). Notably, immunotherapy-treated patients showed enrichedpathways related to EMT, IFN-γ signaling, oxidative phosphorylation, T-cell signaling, inflammation and DNA damage, which aligned with distinct cellular compositions observed in the spatial analysis. We also uncovered considerable intra-tumor heterogeneity, especially at the protein level, revealing differential expression patterns of key tumor and immune-related markers. The correlation between mRNA and protein data highlighted consistent enrichment of critical pathways across multi-omics layers. These findings provide a comprehensive view of MBM's molecular and cellular landscape, emphasizing the importance of addressing tumor heterogeneity in developing effective therapeutic strategies.</p>
Data for paper 'Formation of motile cell clusters in heterogeneous model tumors: The role of cell-cell alignment' (PRE, 2024)
<p>The data provided in this repository is generated for the publication ‘Formation of motile cell clusters in heterogeneous model tumors: The role of cell-cell alignment’ by Quirine J.S. Braat, Cornelis Storm and Liesbeth M.C. Janssen and published in Physical Review E.</p> <p>The data is generated using the Cellular Potts Model in CompuCell3D [1] that can be retrieved from GitHub. The simulations contain a more detailed description of the data, and the data provided here can be reproduced using the appropriate simulation code and parameters. These can be found on GitHub via <a href="https://github.com/QBraat/Cluster-Formation-Alignment">https://github.com/QBraat/Cluster-Formation-Alignment</a>.</p> <p>The data in this repository has been divided into the following sets: </p> <ol> <li><strong>EmptyLayer_Random.zip</strong>. this data set belongs to section III.A and section III.C and contains detailed information about the cells’ positions, orientation as a function of time for the active cells in free space.</li> <li><strong>EmptyLater_Random_single.zip</strong>. this data set belongs to section III.A and contains processed data about the order parameter and cluster size as a function of time for the active cells in free space.</li> <li><strong>ConfluentLayer_Random_single.zip</strong>. this data set belongs to section III.B and contains the processed data about the order parameter and cluster size as a function of time for the active cells in a confluent layer. </li> <li><strong>ConfluentLayer_Random_FiniteSize.zip</strong>. this data set belongs to the data in the supplementary information.</li> <li><strong>ConfluentLayer_Random_InitialBlock.zip</strong>. this data set belongs to the data in the supplementary information.</li> </ol> <p>Other than these sets, there is an additional data set ‘<strong>ConfluentLayer_Random.zip</strong>’, which contains the detailed information about the cells’ positions, orientation etc. as a function of time for the confluent layer simulations in section III.B and section III.C. This data set has not been included as it contains a significantly large amount of data, but can be received upon request from the authors.</p> <h3>Detailed information about the data set</h3> <p>Each data point in the paper is generated by running 200 simulations using the simulation code for a set of parameters. For each combination of parameters (tau, gamma), we either got the full dynamic information (Full) or only the dynamic evolution of the mean cluster size and the steady state values for the mean cluster size and order parameter (Single).</p> <p><strong>Confluent layer (fraction = 0.25</strong><strong>)</strong></p> <table> <tbody> <tr> <td> <p><strong>tau / gamma</strong></p> </td> <td> <p><strong>1.0</strong></p> </td> <td> <p><strong>0.5</strong></p> </td> <td> <p><strong>0.2</strong></p> </td> <td> <p><strong>0.1</strong></p> </td> <td> <p><strong>0.05</strong></p> </td> <td> <p><strong>0.02</strong></p> </td> <td> <p><strong>0.01</strong></p> </td> <td> <p><strong>0.005</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> </tr> <tr> <td> <p>500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>2500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>4000 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Single</p> </td> <td> <p>Full</p> </td> </tr> </tbody> </table> <p><strong> </strong></p> <p><strong>Empty layer (fraction = 0.25</strong><strong>) </strong></p> <table> <tbody> <tr> <td> <p><strong>tau / gamma</strong></p> </td> <td> <p><strong>1.0</strong></p> </td> <td> <p><strong>0.5</strong></p> </td> <td> <p><strong>0.2</strong></p> </td> <td> <p><strong>0.1</strong></p> </td> <td> <p><strong>0.05</strong></p> </td> <td> <p><strong>0.02</strong></p> </td> <td> <p><strong>0.01</strong></p> </td> <td> <p><strong>0.005</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> <td> <p><strong>0.001</strong></p> </td> </tr> <tr> <td> <p>500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>2500 mcs</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>4000 mcs</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> <td> <p>Single</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Other simulations </strong></p> <p>Apart from the main results, we also ran simulations with different parameter settings to get more insights into the dynamic behavior.</p> <table> <tbody> <tr> <td> <p><strong>Simulations</strong></p> </td> <td> <p><strong>Settings</strong></p> </td> <td> <p><strong>Storage type</strong></p> </td> </tr> <tr> <td> <p>Different fraction active cells</p> </td> <td> <p>Confluent layer, fraction = 0.1, other parameters as before</p> </td> <td> <p>Single</p> </td> </tr> <tr> <td> <p>No alignment</p> </td> <td> <p>Confluent Layer + Empty Layer, fraction = 0.25, gamma = 0, tau = 2500 mcs</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>Initially aligned cluster</p> </td> <td> <p>Fully aligned cluster, Confluent Layer + Empty Layer, gamma = 1.0, 0.01, 0.001 and tau = 2500 mcs</p> </td> <td> <p>Full</p> </td> </tr> <tr> <td> <p>Finite-size effects</p> </td> <td> <p>Confluent Layer, number of cells = 100, 400, 9000, 1600, 2500 cells</p> </td> <td> <p>Single</p> </td> </tr> </tbody> </table> <p> </p> <h2>Data format</h2> <p>When the simulations are run with full data export, the following files are generated: </p> <ul> <li>data_cells_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS) </li> <li>cell.id: number of the cell </li> <li>cell.type: type of the Cells (active = 2, passive = 1) </li> <li>xCOM: x-coordinate of the center of mass </li> <li>yCOM: y-coordinate of the center of mass </li> <li>zCOM: z-coordinate of the center of mass (always equal to 0 in 2D) </li> <li>pol_angle: angle with respect to the x-axis of the active force orientation. </li> <li>cluster.id: number of the cluster to which the cells belongs.</li> </ul> </li> <li>data_clusters_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>cluster_id: number of the cluster (corresponding to the number cluster.id in data_cells_<settings>.dat </li> <li>cluster_size: number of cells in the given cluster </li> </ul> </li> <li>metadata_<settings>.dat: <ul> <li>Information about the full set of simulation parameters</li> </ul> </li> </ul> <p>When the simulations are run with single data export, the following files are generated: </p> <ul> <li>single_export_<settings>.dat: <ul> <li>steadyS: steady state value of the mean cluster size (mean calculated after 60000 mcs)</li> <li>steadyS_std: standard deviation of the steady state value of the mean cluster size</li> <li>steadyP: steady state value of the polarity order parameter </li> <li>steadyP_std: standard deviation of the steady state value of the polarity order parameter </li> </ul> </li> <li>St-single-export_<settings>.dat: <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>St: mean cluster size at given time stamp</li> <li>St_std: standard deviation of the mean cluster size at a given time stamp </li> </ul> </li> <li>S-max-single-export_<settings>.dat <ul> <li>mcs: time stamp in Monte Carlo Steps (MCS)</li> <li>Smax: largest cluster detected at a given time stamp</li> </ul> </li> <li>metadata_<settings>.dat: <ul> <li>Information about the full set of simulation parameters</li> </ul> </li> </ul>
Data from: Reporting tumor molecular heterogeneity in histopathological diagnosis
Background: Detection of molecular tumor heterogeneity has become of paramount importance with the advent of targeted therapies. Analysis for detection should be comprehensive, timely and based on routinely available tumor samples. Aim: To evaluate the diagnostic potential of targeted multigene next-generation sequencing (TM-NGS) in characterizing gastrointestinal cancer molecular heterogeneity. Methods: 35 gastrointestinal tract tumors, five of each intestinal type gastric carcinomas, pancreatic ductal adenocarcinomas, pancreatic intraductal papillary mucinous neoplasms, ampulla of Vater carcinomas, hepatocellular carcinomas, cholangiocarcinomas, pancreatic solid pseudopapillary tumors were assessed for mutations in 46 cancer-associated genes, using Ion Torrent semiconductor-based TM-NGS. One ampulla of Vater carcinoma cell line and one hepatic carcinosarcoma served to assess assay sensitivity. TP53, PIK3CA, KRAS, and BRAF mutations were validated by conventional Sanger sequencing. Results: TM-NGS yielded overlapping results on matched fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues, with a mutation detection limit of 1% for fresh-frozen high molecular weight DNA and 2% for FFPE partially degraded DNA. At least one somatic mutation was observed in all tumors tested; multiple alterations were detected in 20/35 (57%) tumors. Seven cancers displayed significant differences in allelic frequencies for distinct mutations, indicating the presence of intratumor molecular heterogeneity; this was confirmed on selected samples by immunohistochemistry of p53 and Smad4, showing concordance with mutational analysis. Conclusions: TM-NGS is able to detect and quantitate multiple gene alterations from limited amounts of DNA, moving one step closer to a next-generation histopathologic diagnosis that integrates morphologic, immunophenotypic, and multigene mutational analysis on routinely processed tissues, essential for personalized cancer therapy.
Data from: Reporting tumor molecular heterogeneity in histopathological diagnosis
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Tractable mouse TNBC models capture the heterogeneous tumor immune microenvironment and adaptation to PD-L1 blockade
GEO Series GSE290815. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
In vivo intratumoral Heterogeneity in a dish: Scalable Forebrain Organoid Models of Embryonal Brain Tumors for High-Throughput Personalized Drug Discovery (human organoids+cell lines subset 1)
GEO Series GSE269254. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Multi-omic profiling unveils molecular landscapes and heterogeneous tumor microenvironment in sinonasal squamous cell carcinoma
GEO Series GSE278149. Homo sapiens. 40 samples. Type: Methylation profiling by genome tiling array; Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
GEO Series GSE81861. Homo sapiens. 1220 samples. Type: Expression profiling by high throughput sequencing.
Single-cell landscapes of primary glioblastomas and matched organoids and cell lines reveal variable retention of inter- and intra-tumor heterogeneity [scWGS]
GEO Series GSE173279. Homo sapiens. 16 samples. Type: Other.
Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastomas [scRNA+ATAC-seq]
GEO Series GSE240984. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
ScienceDex guides
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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.