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2,955 results for “immune response”
Neutrophil and emergency granulopoietic drivers of sepsis immune suppression and an extreme response to infection
<p>The dysregulated host response to infection leading to organ dysfunction is highly heterogeneous. It is currently poorly delineated by sepsis as a clinical syndromic classification, thus confounding immunotherapy trials. Here we establish the pathophysiology and potential therapeutic targets of a specific extreme response to infection state (sepsis response signature SRS1), characterised by immune compromise and poor outcome. We first derive a whole blood single-cell multi-omic atlas of the sepsis response (2727,993 cells, n=39), finding an increase in IL1R2+ immature neutrophils in SRS1, which we confirmed by CyTOF and RNA-sequencing (n=53). We next uncovered high activity of neutrophil STAT3 gene expression programs in SRS1, which were shared across multiple infectioius disease settings (n=1044) irrespective of the clinical definition of the patient cohorts. We observed elevated plasma G-CSF and IL-6 in SRS1, suggesting heightened emergency granulopoiesis (EG). We therefore characterised patient and healthy control hematopoietic stem cells (HSCs) using single-cell RNA/chromatin accessibility multi-omics (29,366 cells, n=27), identifying SRS1-specific EG transcriptional skewing, together with STAT3 and EG master regulator CEBPB epigenetic signatures. Our findings establish a common cellular axis present across extreme responses to infection, reveal its hematopoietic origin, and nominate G-CSF and IL-6 as potential therapeutic targets for the SRS1 state.</p> <p> </p> <p>The present data deposit includes processed and quality-controlled data tables for:</p> <p>1. Whole blood leukocytes profiled with the BD Rhapsody platform in a cohort of 39 sepsis patients (RNA and protein count matrices, as well as their accompanying metadata table)</p> <p>2. Circulating HSCs in blood profiled with the 10X multiomics platform in a cohort of 27 sepsis patients (RNA and ATAC-seq count matrices, as well as their accompanying metadata tables)</p>
Cellular and Humoral Immune Responses after Immunisation with Low Virulent African Swine Fever Virus in the Large White Inbred Babraham Line and Outbred Domestic Pigs
<p>Raw data for manuscript. Contains temperature, clinical scores, qPCR, blood cell numbers and immune responses over time for two groups of pigs immunised with low virulent African swine fever virus and challenged with highly virulent virus. Data for each panel or figure is displayed on a separate worksheet in the file. The readme worksheet contains a brief description of each figure. The majority of data is displayed in an XY table format, with the number of days post immunisation with low virulent virus indicated.</p>
Data from: Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep
<p>This dataset contains additional files from the manuscript: "Transcriptomic meta-analysis reveals unannotated long non-coding RNAs related to the immune response in sheep".</p> <p>The files included are:</p> <p>- All novel lncRNA transcript annotation GTF file ( lncrnas.gtf )</p> <p>- High-confidence lncRNA gene annotation GTF file ( lncrnas_evidence.gtf )</p> <p>- All novel lncRNA transcript annotation GTF file remapped to the ARS-UI_Ramb_v2.0 genome ( lncrnas_remapped_v2.gtf )</p> <p>- Raw count estimates of the extended annotation ( rawcounts.csv )</p> <p>- TPM values of the extended annotation ( tpmcounts.csv )</p> <p>- Supplementary data to the published article (.xlsx, .pdf)</p> <p> </p>
Peripheral MC1R activation modulates immune responses and confers neuroprotection in a mouse model of Parkinson's disease
<p>Raw data sets for the manuscripts</p> <p>This work was supported by NIH grants R01NS102735 and R01NS110879, the Farmer Family Foundation Initiative for Parkinson’s Disease Research and the MJFF and ASAP [ASAP-000312].</p>
Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies
<p>Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2<strong> </strong>lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.</p>
Fig. 1 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 1. Analysis of the population of the potato ladybird beetle with the species-specific PCR-markers of the gene COI mtDNA. А – species-specific marker for H. vigintioctopunctata, 400 b.p.; Б – species-specific marker for H. vigintioctomaculata, 406 b.p.; М – marker of the lengths of fragments 100 b.p. ladder; 1–3 – Primorsky krai: Chuguevsky district; 4–6 – Amurskaya oblast; 7–17 – Primorsky krai: Timiryazevsky.
Fig. 3 in Protease inhibitors of fodder plants as a factor of immune response influencing the physiological state of the potato ladybird beetle Henosepilachna vigintioctomaculata (Coleoptera: Coccinellidae)
Fig. 3. Sinergetic activity of the protainases of trypsin type (in an insect) and trypsin inhibitors (in a plant) in the course of feeding on different potato varieties.
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>
Evolutionary gain and loss of a pathological immune response to parasitism
<p><span>Parasites impose fitness costs on their hosts. Biologists often assume that natural selection favors infection-resistant hosts. Yet, when the immune response itself is costly, theory suggests selection may instead favor loss of resistance. Intraspecific variation in immune costs are rarely surveyed in a manner that tests evolutionary patterns, and there are few examples of adaptive loss of resistance. Here, we show that when marine threespine stickleback colonized freshwater lakes they gained resistance to the freshwater-associated tapeworm, <em>Schistocephalus solidus</em>. Extensive peritoneal fibrosis and inflammation is a commonly observed phenotype that contributes to suppression of cestode growth and viability, but also impose a substantial cost of reduced fecundity. Combining genetic mapping and population genomics, we find that opposing selection generates immune system differences between tolerant and resistant populations, consistent with divergent optimization.</span></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>
A model of within-host interactions between host resources, macroparasite infection and immune response
<p>This project was designed to mathematically investigate the of different host parasite-mitigation strategies on host condition. The R code herein comprises:</p> <ul> <li>An ODE model of within-host interactions between a macroparasite (e.g. helminth) infection, host resource levels and host immune response, and the consequent effects on host condition. In brief, resources are ingested and utilised by the host, leading to inceased condition. The host is infected by a parasite, which matures and establishes within the host; both age stages cause harm to the host, decreasing host condition. The presence of the parasite stimulates an immune response, which can either target larval or adult parasites (a resistance strategy), or ameliorate the harm they cause (a tolerance strategy). The host can also reduce resource intake in order to also reduce ingestion of parasite infective stages (an avoidance strategy). Resistance responses have an associated immunopathology, in that the immune response also harms the host.</li> <li>Code to plot model trajectories over time.</li> <li>Code to plot multiple trajectories as a heat map, in which the x-axis is time and the y-axis is a parameter representing the host investment in its parasite-mitigation strategy.</li> <li>Code to calculate the optimum host investment for each strategy, over various sets of parameter values, as determined by maximising mean host condition over a given timeframe, and to plot the output.</li> <li>The same are also provided for an ODE model in which the total immune response is allocated between the two resistance responses and tolerance (a combined strategy). The optimisation code optimises both the total investment in immune repsonse, and how much is allocated to the three different individual strategies.</li> </ul> <p>The model and results are described in detail in the associated manuscript. We also provide here the simulated datasets in which the optimum host investments were calculated over a range of different parameter values, as these take several hours to run on a standard desktop computer..</p>
Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy
<p>Data associated with Chen, Nieman, Spurrell et al "Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy" Nature Immunology, 2024. </p> <p>(1) Multiplex RNA scope. Processed cell-level data from 3 datasets, separated into manually selected "Stem Immunity" regions and "Tumor" regions, in separate files. </p> <p>(2) GeoMx. Unprocessed GeoMex data, with standard fields provided by Nanostring software. </p> <p>(3) MERFISH. Transcript-level information, including CellPose and Baysor segmentation. </p> <p>(4) Co-registered multispectral RNA and protein images. Processed cell-level data from 46 patients. Patient-level meta data information is available in supplementary table 1, tab B_Patient_Metadata. Fluorophore channel information is Figure 1a. </p> <p>Paper: <a title="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2" href="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2">https://www.nature.com/articles/s41590-024-01792-2</a></p>
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>
Fig. 6 in Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes afer exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles
Fig. 6. Transcription of the 23S gene of Enterobacteria (428 bp) and ribosomal protein S5 gene (782 bp) from rRNA samples of Trichoplusia ni NL strain larval midguts, afer exposure to 10 g of neem leaves, determined by reverse transcriptase polymerase chain reaction (RT-PCR). PCR product of RNA not subject- ed to RT-PCR was taken as a negative control. Lane 1, DNA ladder 100 bp; lane 2, PCR product of plasmid DNA with the Enterobacteria insert as positive control; lane 3, PCR products of the 23S gene of Enterobacteria and the ribosomal protein S5 gene of T. ni from unexposed larvae; lanes 4, 6, and 8, PCR of control RNA; lane 5, RT-PCR products in gut from VOC-exposed T. ni larva, showing both 23S and ribosomal protein S5 gene amplification (1st replication); lane 7, RT-PCR products in gut from VOC-exposed T. ni larva, showing both 23S and ribosomal protein S5 gene amplification (2nd replication).
Fig. 3 in Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes afer exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles
Fig. 3. Mortality for NL and Gto strains of Trichoplusia ni exposed as neonate larvae for 7 d in sealed containers to VOCs from 1 or 10 g of dried neem stems compared with the unexposed controls. Data represent the mean ± standard deviation of 3 replicate experiments per treatment (90 larvae per replicate were tested).
Fig. 1 in Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes afer exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles
Fig. 1. Setup of the bioassay container for neem VOC exposure of Trichoplusia ni neonates. A) View of tray with 30 cups placed inside the 11 L plastic container with airtight lid for VOC exposure; B) view of tray with 30 cups with artificial diet infested with 3 neonates each and cardboard lid to allow VOC exchange; C) view of 1 L container with artificial diet (bottom) and 1 oz (29.6 mL) cups (top) with 1 g milled dried neem stems or leaves.
Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae in Role of potato immune factors in the trophic responses of Henosepilachna vigintioctomaculata Motschulsky, 1858
Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae
The skin commensal yeast Malassezia triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation
<p>Data accompanying the publication: Sparber et al. The skin commensal yeast <em>Malassezia</em> triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation. 2019. Cell Host & Microbe. https://doi.org/10.1016/j.chom.2019.02.002</p>
Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease
<p><strong>Background </strong></p> <p>Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population. <strong> </strong></p> <p><strong>Methods</strong></p> <p>We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiological information. Differential gene expression was determined by DESeq2.</p> <p><strong>Results</strong></p> <p>No differences in demographics or composition of white blood cell populations between groups were identified at baseline. Out of 213 genes associated with NTM-PD in the non-CF population, only two were significantly different in our cystic fibrosis NTM-PD cohort. Gene set enrichment analysis of the differential expression results showed that CF individuals who developed NTM-PD had higher expression levels of genes involved in the interferon (α and γ), tumor necrosis factor, and IL6-STAT3-JAK pathways. <strong> </strong></p> <p><strong>Conclusion</strong></p> <p>In contrast to the non-cystic fibrosis population, the gene expression signature of patients with cystic fibrosis who develop NTM-PD is characterized by increased innate immune responses.</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>
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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.