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3,441 results for “Immune cells”
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>
sirselim/immunecell_methylation_paper_data: First release of data for immune cell epigenetics (methylation) manuscript
<p>This is the first release of the data to be made public and accessible with the manuscript.</p>
Data used in analyses of Danaher et al. 2024 "Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells"
<p>CosMx 1000-plex data and R code from childhood-onset lupus nephris samples, generated for the article Danaher et al. 2024 "Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells".</p>
Immune checkpoint molecule TIGIT regulates kidney T cell functions and mediates acute kidney injury
<p>This dataset includes the single cell RNA-seq data associated with the publication listed in the title (abstract below).<br> <br> CellRanger.zip contains the raw transcript counts as produced by CellRanger. There is one folder per sample. The samples are indicated by the folder name (e.g. KO_Ctrl). <br> <br> We have also included .h5ad files that contain of cells that passed quality control, as described in the manuscript.<br> <br> adTIGIT_raw_031422.h5ad contains all passing cells and the raw counts, as well as cell annotations ('celltype')<br> <br> adTIGIT_Tonly_091421.h5ad contains only T cells, .X contains the normalized data and T-cell sub-type ('cluster').<br> <br> Abstract: T cells mediate pathologic and reparative processes during acute kidney injury (AKI) but exact mechanisms regulating kidney T cell functions are unclear. This study identified upregulation of the novel immune checkpoint molecule, TIGIT, on mouse and human kidney T cells following AKI. TIGIT-expressing kidney T cells produced proinflammatory cytokines and had effector and central memory phenotype. Kidney Tregs were predominantly TIGIT+ and reduced after ischemia reperfusion (IR) injury. TIGIT deficient mice had protection from both ischemic and nephrotoxic AKI. Single cell RNA sequencing led to discovery of possible downstream targets of TIGIT. TIGIT mediates AKI pathophysiology, is a promising target for developing AKI therapy, and is being increasingly studied in human cancer therapy trials.</p>
Data: Fate of intravenously administered umbilical cord mesenchymal stromal cells and interactions with the host's immune system
<p>This data set includes all the raw data collected for the following article: "Fate of intravenously administered umbilical cord mesenchymal stromal cells and interactions with the host's immune system".</p>
Processed data and scripts supporting the manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma"
<p>This submission contains the compiled count table, processed R objects and various scripts and output files accompanying our manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma" (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-38886-8)</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>
Data and code for Symbiotic bacteria, immune-like sentinel cells, and the response to pathogens in a social amoeba
<p>This repository contains data and code to do the analyses in the paper "Symbiotic bacteria, immune-like sentinel cells, and the response to pathogens in a social amoeba" published in Royal Society Open Science. The paper investigates how infection by <em>Paraburkholderia</em> symbionts affects host <em>Dictyostelium discoideum</em>'s immune function.</p>
Malaria drives unique regulatory responses across multiple immune cells during human infection
<p>To investigate malaria driven transcriptional changes in specific immune cell subsets, we sorted live PBMCs from 6 malaria infected donors (day 0), and two subsequent time points after drug treatment (day 7 and 28), along with PBMCs from 2 healthy controls. We performed scRNAseq of these cells, and used clustering and sub-clustering to identify specific immune cell subsets. Differential gene analysis between day 0 and day 28 was performed for each cell cluster and sub-cluster. Key transcriptional changes were confirmed at the protein level with additional donor samples.</p>
Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease
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Progesterone signaling in oviductal epithelial cells modulates the immune response to support preimplantation embryonic development
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Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
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Histological analyses data for: Multinucleated giant cells are hallmarks of ovarian aging with unique immune and degradation-associated molecular signatures
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A single-cell atlas of circulating immune cells over the first two months of age in extremely premature infants
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An in vivo microscopy dataset of immune cells for the characterization of apoptotic cell death
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Data from: Single cell RNA-seq analysis reveals that prenatal arsenic exposure results in long-term, adverse effects on immune gene expression in response to Influenza A infection
<p>Arsenic exposure via drinking water is a serious environmental health concern. Epidemiological studies suggest a strong association between prenatal<i> </i>arsenic exposure and subsequent childhood respiratory infections, as well as morbidity from respiratory diseases in adulthood, long after systemic clearance of arsenic.<i> </i>We investigated the impact of exclusive prenatal arsenic exposure on the inflammatory immune response and respiratory health after an adult influenza A (IAV) lung infection. C57BL/6J mice were exposed to 100 ppb sodium arsenite<i> in utero,</i> and subsequently infected with IAV (H1N1) after maturation to adulthood. Assessment of lung tissue and bronchoalveolar lavage fluid (BALF) at various time points post IAV infection reveals greater lung damage and inflammation in arsenic exposed mice versus control mice. Single-cell RNA sequencing analysis of immune cells harvested from IAV infected lungs suggests that the enhanced inflammatory response is mediated by dysregulation of innate immune function of monocyte derived macrophages, neutrophils, NK cells, and alveolar macrophages. Our results suggest that prenatal arsenic exposure results in lasting effects on the adult host innate immune response to IAV infection, long after exposure to arsenic, leading to greater immunopathology. This study provides the first direct evidence that exclusive prenatal exposure to arsenic in drinking water causes predisposition to a hyperinflammatory response to IAV infection in adult mice, which is associated with significant lung damage.</p>
Preexisting memory CD4 T cells in naïve individuals confer robust immunity upon vaccination
<p>Data set accompanying the publication "Preexisting memory CD4 T cells in naïve individuals confer robust immunity upon vaccination".</p> <p>In this study, we utilize high-throughput sequencing to profile the memory CD4 TCRβ repertoire and track vaccine-specific and epitope-specific TCRβ clonotypes following the de novo administration of hepatitis B (HepB) vaccine in healthy HepB-naïve individuals.</p> <p>The data set is comprised out of the following parts:</p> <ul> <li>repTCRb: Folder containing CD4+ TCR repertoire data from volunteers. Some of these samples are also available in ImmuneAccess (https://clients.adaptivebiotech.com/pub/deneuter-2018-cmvserostatus).</li> <li>peptideTCRab: Folder containing peptide-specific or peptide-pool-specific TCR data</li> <li>df_all: Meta and FC data for samples and volunteers</li> <li>freqCD154: CD154-based group definitions</li> <li>groups.csv: Group definitions used for classes</li> </ul>
Data from: The effects of body mass on immune cell concentrations of mammals
Theory predicts that body mass should affect the way organisms evolve and use immune defenses. We investigated the relationship between body mass and blood neutrophil and lymphocyte concentrations among 250+ terrestrial mammalian species. We tested whether existing theories (e.g., Protecton Theory, immune system complexity, and rate of metabolism) accurately predicted the scaling of immune cell concentrations. We also evaluated the predictive power of body mass for these leukocyte concentrations compared to sociality, diet, life history, and phylogenetic relatedness. Phylogeny explained >65% of variation in both lymphocytes and neutrophils, and body mass appeared more informative than other interspecific trait variation. In the best-fit mass-only model, neutrophils scaled hypermetrically (b = 0.11) with body mass whereas lymphocytes scaled isometrically. Extrapolating to total cell numbers, this exponent means that an African elephant circulates 13.3 million times the neutrophils of a house mouse, whereas their masses differ by only 250k-fold. We hypothesize that such high neutrophil numbers might offset the i) higher overall parasite exposure that large animals face and/or ii) the higher relative replication capacities of pathogens to host cells.
Single-cell immune repertoire sequencing of two convalescent COVID-19 patients
<p>Single-cell immune repertoire sequencing of two convalescent COVID-19 patients using 10x genomics 5' immune profiling. Resulting output files are from the count and vdj functions from 10x genomic's cellranger v3.1.0. </p>
Labeling and tracking of immune cells in ex vivo human skin
<p>Example imaging datasets linked to the publication 'Labeling and tracking of immune cells in ex vivo human skin' (doi: 10.1038/s41596-020-00435-8).</p> <p>Additional information filenames:<br> - m = ex vivo murine skin<br> - h = ex vivo human skin<br> - mCD8= anti-murine CD8-AF594 nanobody staining (red)<br> - hCD8= anti-human CD8-AF594 nanobody staining (red)<br> - Hoechst= Hoechst 33342 staining (grey)<br> - CD1a= anti-hCD1a-AF488 staining (green)<br> - CD103= anti-hCD103-AF488 staining (green)<br> - SHG = second harmonic generation (cyan)</p> <p>Imaris x64 v9.2.0.</p>
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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.