Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,503
datasets available to search
ShareScore release 0.9.0
Dataset results
1,503 results for “Lymph nodes”
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
Dataset for a machine learning tool to improve lymph node staging with FDG-PET/CT
<p>This upload provides Open Data associated with the publication "A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [<sup>18</sup>F]FDG-PET/CT parameters" by Rogasch JMM <em>et al.</em> (2022).</p> <p>The upload contains the anonymized dataset with 10 features necessary for the final GBM model that was presented in the publication. However, the original full dataset with 40 features was excluded from this Open Data repository because it may not comply with strict rules of data anonymization. The full dataset can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request.</p> <p>Besides the dataset, this upload provides the original python and R scripts that were used as well as their output.</p> <p>A description of all files can be found in "content_description_2022_11_19.txt".</p> <p>A user-friendly web tool that implements the final machine learning model can be found here: <a href="https://baumgagl.github.io/PET_LN_calculator/">PET_LN_calculator</a> </p>
Downsampling of CT-Lymph-Node Dataset for Body Part Regression Tutorial
<p>Down sampling of the<a href="https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes#19726546f04e74ab3631480694fcb72cac2e5477"> CT Lymph Node</a> dataset from the TCIA.<br> The files were down sampled to a pixel spacing of 7 mm/pixel. Through zero padding and cropping, all images are provided in the size of 64px x 64 px. Moreover, the HU values were clipped between -1000 HU and 1500 HU and rescaled to -1 and 1. To avoid aliasing effects, an additional Gaussian smoothing filter was applied before down sampling.</p> <p>This dataset was created for a Body Part Regression tutorial.</p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
Density analysis of lymph node anlagen in whole mount acquired mouse embryos
<p>Raw data and script permitting to analyse the density of cervical, mandibular and axillary lymph node anlagen in whole mount aquired mouse embryos.</p>
Benchmarking eliminative radiomic feature selection for head and neck lymph node classification - Supplemental data
<p>Supplementary files for the publication "Benchmarking eliminative radiomic feature selection for head and neck lymph node classification"</p>
Accompanying dataset for: A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes
<p>This dataset accompanies the manuscript titled “A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes”, A. Radtke et al., bioRxiv, 2022. [<a href="https://doi.org/10.1101/2022.06.03.494716">doi: 10.1101/2022.06.03.494716</a>]</p> <p>The dataset contains the processed scRNA-seq information from human lymph nodes, both normal and from Follicular Lymphoma (FL) patients analyzed in this work as a Seurat object. The scRNA-seq information was saved in the rds format for viewing and analysis using the R programming language (to load it in R: <em>scrna_seq_data <- readRDS("scRNA_seq_data_object.rds")</em>).</p> <p>Additionally, the dataset contains comma-separated-value tables describing human lymph nodes, both normal and from Follicular Lymphoma (FL) patients. The files are formatted using the anatomical structures (AS), cell types (CT), and biomarkers (B), ASCT+B format defined by the Human BioMolecular Atlas Program (HuBMAP) for use with the <a href="http://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter visualization tool</a>. Details on the structure of ASCT+B tables and the Reporter tool can be found in the <a href="https://doi.org/10.5281/zenodo.5944386">standard operating procedure</a> authored by the ASCT+B working group. </p> <p><strong>ASCT+B Table Details</strong></p> <p>In support of a human reference atlas (Regev et al., 2017; Snyder et al., 2019), the Human BioMolecular Atlas Program (HuBMAP) is creating machine readable tables that catalog the anatomical structures (AS), cell types (CT), and biomarkers (B) found in human organs (Börner et al., 2021). ASCT+B tables facilitate data integration across multimodal assays and support comparisons between normal and diseased tissues. In addition, they are readily visualized with the <a href="http://hubmapconsortium.github.io/ccf-asct-reporter/">ASCT+B Reporter</a>, a web based tool.</p> <p><br> For these reasons, we created 10 ASCT+B tables from the datasets included in our study. To construct these tables, we used the <a href="https://doi.org/10.48539/HBM573.SHCQ.259">Lymph Node v1.1 ASCT+B table</a> as a starting point. The presence or absence of anatomical structures was determined by visual inspection of images and quantitative image analysis of cellular communities. Certain anatomical structures were absent from the excisional biopsies of FL patients e.g., capsule, medulla, hilum, etc. In contrast, the lack of primary follicles, mantle zones, polarized germinal centers (GC), and negligible interfollicular cortex and paracortex in FL LNs reflects changes arising from malignancy. Cell types were defined based on gene biomarkers from bulk and single cell RNA sequencing (RNA-seq) and protein biomarkers from the highly multiplexed imaging method, IBEX (Radtke et al., 2022; Radtke et al., 2020). Whenever possible, cell types captured across assays were defined by both gene and protein biomarkers. However, several cell types were only profiled by bulk RNA-seq, scRNA-seq, or IBEX imaging. In these instances, only assay-specific biomarkers are included in the ASCT+B tables. Whenever possible, we used agreed upon ontology terms to define cell types; however, our study identified several unique cell types not included in ontology databases such as DC-SIGN+ follicular dendritic cells (FDCs). Furthermore, the Reporter does not allow visualization of similar cell types (DC-SIGN- FDCs versus DC-SIGN+ FDCs) in the same anatomical structure if a shared Cell Ontology (CL) identifier is used (FDC: CL:0000442). In these instances, we removed the CL term to allow the Reporter to display the various subpopulations discovered in this study. Cell types were placed in their respective anatomical structures using domain knowledge, visual inspection of images, and quantitative image analysis.</p> <p><strong>Reporter Usage Instructions</strong></p> <ul> <li>Visualizing an individual ASCT+B table: <ol> <li>Go to <a href="https://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter</a></li> <li>Launch Playground</li> <li>Click on Upload tab</li> <li>Attach CSV final of ASCT+B table</li> <li>Use the toolbars on the left to adjust display. Typical parameters include: Tree Height (1400),Tree width (1000), Bimodal Distance X (500), and Bimodal Distance Y (50). </li> <li>Toggle between gene and protein biomarkers by clicking drop down menu under Biomarkers tab on left-side of screen.</li> </ol> </li> <li>Comparing non-FL and FL tables to the Lymph Node v1.1 ASCT+B table: <ol> <li>Go to <a href="https://hubmapconsortium.github.io/ccf-asct-reporter/">Reporter</a></li> <li>Select “go to visualization” to compare new tables to a master table for lymph node</li> <li>Click check box next to lymph node and select version of published master table v1.1</li> <li>Click submit</li> <li>Click compare button at top right tool bar</li> <li>Attach CSV file of non-FL and FL ASCT+B tables </li> <li>Pick colors </li> <li>Go to bottom of panel and click add</li> <li>Click compare</li> <li>Adjust settings for tree height, tree width, bimodal distance x, bimodal distance y, ontology ID on or off, biomarker type (gene or protein), etc.</li> </ol> </li> </ul> <p><strong>References</strong></p> <ul> <li>Börner, K., Teichmann, S.A., Quardokus, E.M., Gee, J.C., Browne, K., Osumi-Sutherland, D., Herr, B.W., Bueckle, A., Paul, H., Haniffa, M., et al. (2021). Anatomical structures, cell types and biomarkers of the Human Reference Atlas. Nature Cell Biology 23, 1117-1128.</li> <li>Radtke, A.J., Chu, C.J., Yaniv, Z., Yao, L., Marr, J., Beuschel, R.T., Ichise, H., Gola, A., Kabat, J., Lowekamp, B., et al. (2022). IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues. Nature Protocols.</li> <li>Radtke, A.J., Kandov, E., Lowekamp, B., Speranza, E., Chu, C.J., Gola, A., Thakur, N., Shih, R., Yao, L., Yaniv, Z.R., et al. (2020). IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues. Proc Natl Acad Sci U S A 117, 33455-33465.</li> <li>Regev, A., Teichmann, S.A., Lander, E.S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017). The Human Cell Atlas. Elife 6.</li> <li>Snyder, M.P., Lin, S., Posgai, A., Atkinson, M., Regev, A., Rood, J., Rozenblatt-Rosen, O., Gaffney, L., Hupalowska, A., Satija, R., et al. (2019). The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187-192.</li> </ul> <p> </p>
Immunofluorescence of human metastatic lymph node
<p><strong>Description:</strong></p> <p>Immunofluorescence staining of a consecutive, 10 µm-thick section (IF section #1), consecutive to Open-ST metastatic lymph node section #2</p> <p>Data is provided as a standard, non-compressed pyramidal OME.TIFF file with 3 channels, converted with QuPath.</p> <p><strong>Channels:</strong></p> <p>0 (cyan): DAPI<br>1 (yellow): panCK<br>2 (magenta): VIM</p> <p><strong>Methods:</strong></p> <p>Immunofluorescent (IF) staining was performed on the first cryosection of the metastatic lymph node reserved for validations, as shown in the experimental setup in Figure 2D. This slide was reserved at -80°C for ~15 months, before proceeding to IF staining. Steps were performed at room temperature unless stated otherwise. Upon drying the slide, the OCT was removed in a 10-minute PBS wash. Next, the section was fixed with 4% formaldehyde (Sigma-Aldrich, F8775) for 15 minutes and then washed with DPBS (no calcium, no magnesium, Gibco™, 14190169) three times. Blocking and permeabilization was done by incubating in 0.25% Triton-X (Sigma-Aldrich, T8787) and 5% normal donkey serum (Biozol Diagnostica, SBA-0030-01) in DPBS for 1 hour. </p> <p>The section was incubated overnight at 4°C in the dark, with primary-conjugated antibodies diluted in a DPBS buffer with 0.1% Triton-X and 5% normal donkey serum, as follows: 1:100 for Pan Cytokeratin (mouse mAb, clones AE1and AE3, eFluor™ 570 conjugate, ThermoFisher, Cat# 41-9003-82), 1:50 for Vimentin (mouse mAb, clone V9, Alexa Fluor® 750 conjugate, Bio-Techne, Cat# NBP1-97670AF750). Following three 5-minute DPBS washes, DAPI staining was performed with 1 ug/mL DAPI (Bio-Trend, #40011) in DPBS for 10 min in the dark. After three DPBS rinses, the section was dried and then mounted with 85% glycerol. </p> <p>Images were acquired on the Leica Thunder DMi8 imager using a Leica DFC 9000GT sCMOS fluorescence camera, a 20X objective and the Leica Application Suite (LAS) X software (v.3.9.0.28093). Thunder instant computational clearing was performed on the image.</p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data</strong></p> <ul> <li>custom_201216_m_moFluMemB_processedData.tar.gz : pre-processed data of FB5P-seq protocol (Attaf et al., 2020) on memory B cells sorted from single-cell suspensions of lungs with enzymatic digestion of lung tissue at 37°C, with index sorting information for a panel of antibodies identifying subsets of memory B cells.</li> <li>moFluMemB_DockerImages.tar.gz: Docker images used by the analysis</li> <li>moFluMemB_SingularityImages.tar.gz: Singularity images used by the analysis (conversion of the docker images)<br> </li> </ul> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5565863</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung - 10x_191105_m_moFluMemB
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data:</strong> 10x_191105_m_moFluMemB_processedData.tar.gz : pre-processed data of 10x 5’ scRNA-Seq on memory B cells sorted from single-cell suspensions of spleen, lymph nodes and lungs with mechanical dissociation of lung tissue at 4°C.</p> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5566674</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung - 10x_190712_m_moFluMemB
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data: 1</strong>0x_190712_m_moFluMemB_processedData.tar.gz : pre-processed data of 10x 5’ scRNA-Seq on memory B cells sorted from single-cell suspensions of spleen, lymph nodes and lungs with enzymatic digestion of lung tissue at 37°C.</p> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5566674</strong></p> <p><strong>10.5281/zenodo.5565863</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
H&E colorectal lymph node metastasis nuclei dataset
<p>H&E colorectal lymph node metastasis nuclei dataset (neoplastic/non-neoplastic, 224x224 pixels at 40x magnification)</p> <p>This description will be updated soon.</p>
The TCR assigns naive T cells to a preferred lymph node
<p>Naive T cells recirculate between the spleen and lymph nodes where they mount immune responses when meeting dendritic cells presenting foreign antigen. As this may happen anywhere, naive T cells ought to visit all lymph nodes. Here, deep sequencing almost-complete TCR-repertoires led to a comparison of different lymph nodes within and between individual mice. We find strong evidence for a deterministic CD4/CD8 lineage choice and a consistent spatial structure. Specifically, some T cells show a preference for one or multiple lymph nodes, suggesting that their TCR interacts with locally presented (self-)peptides. These findings are mirrored in TCR-transgenic mice showing localized CD69-expression, retention, and cell division. Thus, naïve T cells intermittently sense antigenically dissimilar niches, which is expected to affect their homeostatic competition.</p>
Attenuation of chronic T cell responses through constitutive COX2-dependent prostanoid synthesis by lymph node fibroblasts
<p>Raw data (in microsoft excel format) which were used to generate the figures in this manuscript. One worksheet per figure.</p>
Prognostic value of a modified pathological staging system for gastric cancer based on the number of retrieved lymph nodes and metastatic lymph node ratio raw data
<p><span>Clinical data from the US Surveillance, Epidemiology, and End Results (SEER) Program from 2010-2015 (https://seer.cancer.gov/) was extracted and analyzed as training set, data from 2016-2017 was adopted as internal validation set. Data from The Cancer Genome Atlas Program (TCGA) (https://portal.gdc.cancer.gov/) and prognosis data from Gastrointestinal surgery Department, Third Affiliated Hospital of Sun Yat-sen University were applied as external validation sets. </span></p> <p><span>Screening criteria for gastric cancer cases were as follow: exclusion of cases with only autopsy or death certificate, cases where initial tumor location was not stomach, patients with stage 0 and stage IV, cases without radical surgery, non-adenocarcinoma cases, death cases within one month after operation, and cases with unknown lymph node information and AJCC TNM stage.</span></p> <p><span>The study analyzed various factors such as age of diagnosis (<50 years, 50-69 years, >69 years), gender, race (white, black, other), AJCC T stage (T1-T4b), AJCC TNM stage (I-III), primary tumor location (stomach body, antrum/pylorus, cardia/fundus, greater gastric recurve, lesser gastric recurve, overlapping area, NOS), Clinical features such as tumor size (≥5cm,<5cm, unknown), tumor grade (I-IV), chemotherapy, radiotherapy, number of lymph nodes retrieved and number of metastases, and lymph node positive rate. The populations of American Indian/Alaskan and Asian/Pacific Islander were classified as "other" due to small sample sizes. Tumor grade was also analyzed, with grades I-IV representing highly differentiated, moderately differentiated, poorly differentiated, and signed-ring cell carcinoma, respectively. Overall survival (OS) is the time from cancer diagnosis to death from any cause, while disease-specific survival (DSS) is the time from cancer diagnosis to death specifically due to the disease.</span></p> <p><strong><span> </span></strong></p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 26 as continuation of dataset <strong>10.5281/zenodo.7494413.</strong> The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of those parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p> <p>Additionally, this repository contains the metadata of the patients analyzed in this study, the panel information and the single-cell data that was extracted from the multiplexed images together with the associated metadata in SingleCellExperiment format for analysis in R.</p> <p>The analysis.zip folder contains files that were written out during the analysis according to the scripts in https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The single-cell and other data outputs from CellProfiler can be found in the cpout.zip file.</p> <p>The IF_whole_sections.zip file contains the IF images of the primary breast cancer sections (czi files) and the extracted single-cell data.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 21 and 25 of the matched primary breast cancer and lymph node metastasis study presented in Fischer and Jackson et al., 2023. The code that was used to process and analyze this data can be found at https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of the respective parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p>
RNAseq transcriptome of draining lymph node (LN) and tumor of MC38 murine tumors treated with cryoablation and chitosan/IL-12
<p class="MsoNormal">Focal ablation technologies are routinely used in the clinical management of inoperable solid tumors but often result in incomplete ablations leading to high recurrence rates. Adjuvant therapies capable of safely eliminating residual tumor cells are therefore of great clinical interest. Interleukin 12 (IL-12) is a potent antitumor cytokine that can be localized intratumorally through coformulation with <span>viscous biopolymers</span> including chitosan (CS) solutions. The objective of this research was to determine if localized immunotherapy with CS/IL-12 could prevent <span>tumor recurrence after cryoablation (CA). Tumor recurrence, overall survival, and protective immunity were assessed. Systemic immunity was evaluated in spontaneously metastatic and bilateral tumor models. Temporal bulk RNA sequencing was performed on tumor and draining lymph node samples.</span> In multiple murine tumor models, the addition of CS/IL-12 to CA reduced recurrence rates by 30–55%. Altogether, this cryo-immunotherapy induced complete durable regression of large tumors in 80–100% of treated animals. <span>Mice</span> treated with CA plus adjuvant CS/IL-12 were partially or completely protected from tumor rechallenge. <span>Systemically,</span> CS/IL-12 prevented lung metastases when delivered as a neoadjuvant to CA. However, CA plus CS/IL-12 had minimal antitumor activity against established, untreated abscopal tumors. Adjuvant anti-PD-1 therapy delayed the growth of abscopal tumors. Transcriptome analyses revealed early immunological changes in <span>the dLN</span>, followed by a significant increase in gene expression associated with immune suppression and regulation. Cryo-immunotherapy with localized CS/IL-12<span> reduces recurrences and</span> enhances the elimination of large primary tumors<span>. This focal combination therapy also induces significant systemic</span> antitumor immunity <span>although further studies are necessary</span>.</p>
Transcriptomics and proteomics reveal distinct biology for lymph node metastases and tumor deposits in colorectal cancer
<p>Spatial transcriptomic data (counts_DSP_afterQC_normalisation.csv) derived using the Nanostring GeoMx digital spatial profiler platform to analyse tumor deposits and lymph node metastases from 10 patients with colorectal cancer. 264 AOIs of cancer transcriptome atlas data. Normalised using Q3 normalisation, for further information on methods see associated publication. </p>
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.