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3,441 results for “Immune cells”
Immune single-cell RNA-seq data from PyMT-M tumor and its peripheral blood samples
<p><em>Immune single-cell RNA-seq data collection and preprocessing</em></p> <p>Blood and tumor samples were harvested from PyMT-M tumor-bearing mice. Blood samples (n=3) are collected retro-orbitally using caliper tubes and processed with red blood cell lysis buffer (Tonbo Biosciences) before library preparation. A tumor sample are dissociated with Tumor Dissociation Kit following the manufacturer’s instructions (Miltenyi Biotec). After isolation and filtering through a 70µm filter, CD45+DAPI- cells were sorted using FACSAria cell sorter (BD Biosciences) at the Cytometry and Cell Sorting Core. The single-cell libraries were prepared using Chromium Controller (10X Genomics) at the Single Cell Genomics Core and sequenced using NovaSeq 6000 at the Genomics and RNA Profiling Core of Baylor College of Medicine. The FASTQ files were processed using Cell Ranger pipelines (10X Genomics) to generate feature-barcode matrices.</p> <p><em>Integrating immune single-cell RNA-seq data from the blood and tumor of PyMT-M mouse</em></p> <p>We followed the Seurat tutorial on single-cell RNA integration from <a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>. Specifically, both datasets were library-size normalized and log-scaled. Then, variable genes from both datasets were extracted, and overlapped variable genes were used as anchors to integrate the two datasets to generate a combined immune single-cell RNA-seq dataset.</p> <p> </p> <p><em>Immune cell type annotation using SingleR</em></p> <p>After having the integrated immune single-cell RNA-sequencing data, we performed the standard pipeline for clustering, including scaling the expression data, performing dimension reduction using PCA and UMAP, and finding clusters by a shared nearest neighbor (SNN) modularity optimization (<a href="https://satijalab.org/seurat/articles/integration_introduction.html">https://satijalab.org/seurat/articles/integration_introduction.html</a>). Then, we used the R package <em>SingleR </em>to assign cell-type labels to each identified cluster using the <em>ImmGen</em> reference data from the Immunological Genome Project.</p> <p> </p>
Data from: The CD73 immune checkpoint promotes tumor cell metabolic fitness
<p>CD73 is an ectonucleotidase overexpressed on tumor cells that suppresses anti-tumor immunity. Accordingly, several CD73 inhibitors are currently being evaluated in the clinic, including in large randomized clinical trials. Yet, the tumor cell-intrinsic impact of CD73 remains largely uncharacterized. Using metabolomics, we discovered that CD73 significantly enhances tumor cell mitochondrial respiration and aspartate biosynthesis. Importantly, rescuing aspartate biosynthesis was sufficient to restore proliferation of CD73-deficient tumors in immune-deficient mice. Seahorse analysis of a large panel of mouse and human tumor cells demonstrated that CD73 enhanced oxidative phosphorylation (OXPHOS) and glycolytic reserve. Targeting CD73 decreased tumor cell metabolic fitness, increased genomic instability and suppressed poly ADP ribose polymerase (PARP) activity. Our study thus uncovered an important immune-independent function for CD73 in promoting tumor cell metabolism and provides the rationale for previously unforeseen combination therapies incorporating CD73 inhibition.</p>
A novel immune-related prognostic signature based on Chemoradiotherapy sensitivity predicts long-term survival in patients with esophageal squamous cell carcinoma.
<p>Raw Data for PeerJ</p>
High risk clear cell renal cell carcinoma microenvironments contain protumour immunophenotypes lacking specific immune checkpoints.
<p>Final Seurat file (rds) produced from the immune cell analysis performed in "<strong>High risk clear cell renal cell carcinoma microenvironments contain protumour immunophenotypes lacking specific immune checkpoints</strong>.<em><strong>"</strong></em></p>
MCT1 gene silencing enhances the immune effect of dendritic cells on cervical cancer cells
<p>Statistics for Figure 1-6.</p> <p><strong>Figure S1</strong> Cube maps for the levels of expression for DC markers after sLA exposure and LPS challenge. DCs were first infected with Ad-shCtrl and Ad-shMCT1 adenoviruses for 24 h, then co-cultured with 50 mM sLA or PBS for 48 h, and finally stimulated with 1 ug/mL LPS for 24 h. Flow cytometry was used to detect the expression of CD1a (A), CD80 (B), CD83 (C), CD86 (D) and MHCII (E) in DCs. The column height represents the average value, and the bar of the column is the standard deviation. P value was calculated by Tukey’s honestly significant difference (HSD) test following analysis of variance (ANOVA) testing between multiple groups or by Student's t test between (PBS vs sLA treatment</p> <p>Table S1. Statistics for Figure S1.</p>
MCT1 gene silencing enhances the immune effect of dendritic cells on cervical cancer cells
<p>Figur 1. xlsx, Results of ANOVA as presented in Figure 1.</p> <p>Figur 2. xlsx, Results of ANOVA as presented in Figure 2.</p> <p>Figur 3. xlsx, Results of ANOVA as presented in Figure 3.</p> <p>Figur 4. xlsx, Results of ANOVA as presented in Figure 4.</p> <p>Figur 5. xlsx, Results of ANOVA as presented in Figure 5.</p> <p>Figur 6. xlsx, Results of ANOVA as presented in Figure 6.</p> <p>Figur s1. xlsx, Results of ANOVA as presented in Figure s1.</p> <p>supplementary figure. docx, <strong>Figure S1</strong> Cube maps for the levels of expression for DC markers after sLA exposure and LPS challenge. DCs were first infected with Ad-shCtrl and Ad-shMCT1 adenoviruses for 24 h, then co-cultured with 50 mM sLA or PBS for 48 h, and finally stimulated with 1 ug/mL LPS for 24 h. Flow cytometry was used to detect the expression of CD1a (A), CD80 (B), CD83 (C), CD86 (D) and MHCII (E) in DCs.</p> <p>Table S1. docx, Statistics for Figure S1.</p>
CCBB congenital CMV cohort immune cell subset RNA seq analysis
<p>CCBB congenital CMV cohort immune cell subset RNA seq analysis includes:</p> <p>1) metadata file with sample cell type and CMV infection status</p> <p>2) read counts for FAC-sorted cord blood immune cell subsets</p> <p>3) sample code for bulk RNA seq analysis and data visualization</p>
Restoration of Immunity to Vaccine Preventable Diseases After CART-T Cell Therapy
ClinicalTrials.gov study NCT06333275. IPD Sharing: UNDECIDED. Countries: 1. Publications: 11.
Transfer of Infection Fighting Immune Cells Generated in the Laboratory to High Risk Patients With COVID-19 Infection
ClinicalTrials.gov study NCT04765449. IPD Sharing: NO. Countries: 1. Publications: 1.
Immune Ablation and Hematopoietic Stem Cell Support in Patients With Systemic Lupus Erythematosus: A Phase II Study
ClinicalTrials.gov study NCT00271934. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Immune Cells in Patients With Granulomatous Mastitis
ClinicalTrials.gov study NCT04543149. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Immune Cells During Disuse and Recovery
ClinicalTrials.gov study NCT04416191. IPD Sharing: NO. Countries: 1. Publications: 0.
Pilot Study in Young Adults to Examine the Kinetics of Changes in the B-cell Repertoire Following TIV Immunization
ClinicalTrials.gov study NCT02987374. IPD Sharing: YES. Countries: 0. Publications: 3.
Immune Responses To Antigen-Bearing Dendritic Cells in Patients With Malignancy
ClinicalTrials.gov study NCT00700167. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Substudy 03A: A Study of Immune and Targeted Combination Therapies in Participants With First Line (1L) Renal Cell Carcinoma (MK-3475-03A)
ClinicalTrials.gov study NCT04626479. IPD Sharing: YES. Countries: 14. Publications: 0.
To Evaluate the Immunogenicity and Safety of Sequential Booster Immunization of Recombinant Novel Coronavirus Vaccine (CHO Cells) for SARS-CoV-2
ClinicalTrials.gov study NCT05895110. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of CMV-Specific Immune Reconstitution at the End of Letermovir Prophylaxis on the Development of Late Cytomegalovirus Infection in Hematopoietic Stem Cell Transplant Recipients (INMUNOEND)
ClinicalTrials.gov study NCT06814301. IPD Sharing: YES. Countries: 1. Publications: 1.
Perioperative Cancer Cell Dissemination and Systemic Immune Suppression in Resectable Ductal Pancreatic Adenocarcinoma
ClinicalTrials.gov study NCT00495924. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Role of Interferon-gamma 1-b (IFN-γ) on Cells of the Innate Immune System: Functional, Biochemical and Gene Expression Studies in Patients With Chronic Granulomatous Disease
ClinicalTrials.gov study NCT03548818. IPD Sharing: NO. Countries: 1. Publications: 1.
Impact of Probiotic Use on Immune Cell Function in Children
ClinicalTrials.gov study NCT02687503. IPD Sharing: NO. Countries: 1. Publications: 35.
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
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DANDI Archive for NWB datasets
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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.