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111,247 results for “cells”
H3K36 methylation maintains cell identity by regulating opposing lineage programs
<p>This upload contains cellranger count outputs and RDS objects of preprocessed scRNAseq data from the publication "H3K36 Methylation Maintains Cell Identity by Regulating Opposing Lineage Programs" for new data published with the paper.</p> <p>These RDS objects are produced by code available here <a href="https://github.com/Michorlab/H3K36_methylation_scRNAseq">https://github.com/Michorlab/H3K36_methylation_scRNAseq</a>. The same GitHub repo contains plots and tables generated from data in these objects.</p>
Data for "Brain cell-type shifts in Alzheimer's disease, autism and schizophrenia interrogated using methylomics and genetics"
<p>Data for "Genetic and methylomic interrogation of brain cell-type shifts in autism, schizophrenia, and Alzheimer’s disease" (Yap et al. 2023).</p> <p>Source data from ROSMAP, LIBD and UCLA_ASD post-mortem brain datasets.</p> <p>This data repository contains 3 files:</p> <p><strong>220819_Supplementary_Tables.xlsx</strong></p> <p>Supplementary Tables for the manuscript:</p> <ol> <li>Supplementary Table 1: Comparison of CTP deconvolution methods in the ROSMAP dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 2: Comparison of CTP deconvolution methods in the LIBD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 3: Comparison of CTP deconvolution methods in the UCLA_ASD dataset. mcc* denotes methylCC deconvolution; sSV* denotes smartSVA; h* indicates Houseman using array reference data; hseq* indicates Houseman with sequencing reference data; celfie* indicates CelFIE (and includes an output titled "unknown1"); VAEe* indicates variational autoencoder embeddings.</li> <li>Supplementary Table 4: Deconvolved brain CTPs (raw), with covariates.</li> <li>Supplementary Table 5: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and raw PGS for all ancestries. Comp* indicates compositionally-aware principal components of the CTP data; *pgs_raw indicates PGS calculated for using genotyping across all ancestries. This table has a total of n=1,098 across all ancestries, including n=885 EUR. After applying a rel<0.05 threshold on the n=885 EUR, there were n=878 EUR which were used in the PGS analysis so that population stratification PCs did not simply capture family structure.</li> <li>Supplementary Table 6: Deconvolved brain CTPs (clr-transform, offset 1e-3), with covariates and standardised PGS for n=878 Europeans. *pgs_raw indicates unstandardised PGS, PC* indicates genotyping PCs within the European dataset, *_PGS indicates standardised PGS within the European subset.</li> <li>Supplementary Table 7: Deconvolved brain CTPs (clr-transform, offset 1e-3), adjusted for oligodendrocyte proportions, with covariates.</li> <li>Supplementary Table 8: Deconvolved brain CTPs (raw), adjusted for oligodendrocyte proportions, with covariates.</li> </ol> <p><strong>20220108_maf05_gwas_ctp.tar.gz</strong></p> <p>GWAS summary statistics for the 7 brainCTPs (clr-transformed): Exc, Inh, Astro, Endo, Micro, Oligo, OPC</p> <p>METAL output format:<br> MarkerName: SNP<br> Allele1<br> Allele2<br> Freq1: Allele1 frequency<br> FreqSE: frequency standard error<br> MinFreq: minimum Allele1 frequency in meta-analysis<br> MaxFreq: maximum Allele1 frequency in meta-analysis<br> Effect: effect size<br> StdErr: standard error of effect size<br> P-value: calculated in inverse variance weighted meta-analysis<br> Direction: directions of effects across the 3 datasets<br> HetISq: heterozygosity I-squared<br> HetChiSq: heterozygosity chi-squared<br> HetDf: heterozygosity degress of freedom<br> HetPVal: heterozygosity test p-value</p> <p><strong>20220108_maf05_gwas_ctp_pc.tar.gz</strong></p> <p>GWAS summary statistics for the 5 CTP_PCs</p> <p>METAL output format (see above)</p> <p> </p> <p><strong>Source data</strong>:</p> <p>ROSMAP: Raw methylation .idat files were obtained from Synapse accession syn7357283. Whole genome sequencing .vcf files (variants jointly called with MSBB and Mayo studies) were obtained from Synapse accession syn11707420.</p> <p>LIBD: Raw methylation .idat files were obtained from GEO accession GSE74193. SNP genotypes were downloaded from dbGaP accession phs000417.v2.p1.</p> <p>UCLA-ASD: The processed methylation beta matrix was downloaded from Synapse accession syn8263588. SNP genotypes were downloaded from Synapse accession syn10537134.</p> <p>GWAS summary statistics are available at: 10.5281/zenodo.7604231</p> <p>Code is available on GitHub: gandallab/brain_CTP_deconv</p>
Non-targeted metabolome profiling in splenic monocyte-derived dendritic cells from Plasmodium chabaudi-infecetd mice
<p>Spleens from mice infected with Plasmodium chabaudi were processed and stained for localization of monocyte-derived dendritic cells (MODCs) by flow cytometry. The markers utilized were: Live/Dead, F4/80, CD11b, DCSign, MHCII, CD11c and CD3. After sorted out, MODCs were frozen in liquid nitrogen, followed by metabolite extraction, as recommended by The Metabolomics Facility at MD Anderson. Metabolites were extracted using ice-cold 0.1% Ammonium hydroxide in 80/20 (v/v) methanol/water. Extracts were centrifuged at 17,000 g for 5 min at 4°C, and supernatants were transferred to clean tubes, followed by evaporation to dryness under nitrogen. Dried extracts were reconstituted in deionized water, and 5 μL was injected for analysis by ion chromatography (IC)-MS. IC mobile phase A (MPA; weak) was water, and mobile phase B (MPB; strong) was water containing 100 mM KOH. A Thermo Scientific Dionex ICS-5000+ system included a Thermo IonPac AS11 column (4 µm particle size, 250 x 2 mm) with column compartment kept at 30°C. The autosampler tray was chilled to 4°C. The mobile phase flow rate was 350 µL/min and gradient from 1mM to 100mM KOH was used. The total run time was 60 min. To assist the desolvation for better sensitivity, methanol was delivered by an external pump and combined with the eluent via a low dead volume mixing tee. Data were acquired using a Thermo Orbitrap Fusion Tribrid Mass Spectrometer under ESI negative ionization mode at a resolution of 240,000.</p>
MinION sequencing data of mtDNA from BH10 cells
<p><span>Mitochondrial DNA (mtDNA) recombination in animals has remained enigmatic because of its uniparental inheritance and subsequent homoplasmic state, which excludes the biological need for genetic recombination, as well as limits tools to study it. However, molecular recombination is an important genome maintenance mechanism for all organisms, most notably being required for double-strand break repair. To demonstrate the existence of mtDNA recombination, we have taken advantage of a cell model with two different types of mitochondrial genomes and impaired ability to turn over broken mtDNA. The resulting excess of linear DNA fragments caused increased formation of cruciform mtDNA, appearance of heterodimeric mtDNA complexes and recombinant mtDNA genomes, detectable by Southern blot. Combining our observations with previously published work, we propose that the mitochondrial replisome can catalyze microhomology-mediated recombination of linear mtDNA ends, thus rendering a specialized mitochondrial recombinase unnecessary. The error-proneness of this system is likely to contribute to the formation of pathological mtDNA rearrangements.</span></p>
Raw Data for the article: High-Resolution Secretome Analysis of Chemical Hypoxia Treated Cells Identifies Putative Biomarkers of Chondrosarcoma
<p>Chondrosarcoma is the second most common bone tumor, accounting for 20% of all cases. Little is known about the pathology and molecular mechanisms involved in the development and in the metastatic process of chondrosarcoma. As a consequence, there are no approved therapies for this tumor and surgical resection is the only treatment currently available. Moreover, there are no available biomarkers for this type of tumor, and chondrosarcoma classification relies on operator-dependent histopathological assessment. Reliable biomarkers of chondrosarcoma are urgently needed, as well as greater understanding of the molecular mechanisms of its development for translational purposes. Hypoxia is a central feature of chondrosarcoma progression. The hypoxic tumor microenvironment of chondrosarcoma triggers a number of cellular events, culminating in increased invasiveness and migratory capability. Herein, we analyzed the effects of chemically-induced hypoxia on the secretome of SW 1353, a human chondrosarcoma cell line, using high-resolution quantitative proteomics. We found that hypoxia induced unconventional protein secretion and the release of proteins associated to exosomes. Among these proteins, which may be used to monitor chondrosarcoma development, we validated the increased secretion in response to hypoxia of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), a glycolytic enzyme well-known for its different functional roles in a wide range of tumors. In conclusion, by analyzing the changes induced by hypoxia in the secretome of chondrosarcoma cells, we identified molecular mechanisms that can play a role in chondrosarcoma progression and pinpointed proteins, including GAPDH, that may be developed as potential biomarkers for the diagnosis and therapeutic management of chondrosarcoma.</p>
The EGFR signaling modulates in mesenchymal stem cells the expression of miRNAs involved in the interaction with breast cancer cells
<p>We previously demonstrated that the epidermal growth factor receptor (EGFR) modulates in mesenchymal stem cells (MSCs) the expression of a number of genes coding for secreted proteins that promote breast cancer progression. However, the role of the EGFR in modulating in MSCs the expression of miRNAs potentially involved in the progression of breast cancer remains largely unexplored. Following small RNA-sequencing, we identified 36 miRNAs differentially expressed between MSCs untreated or treated with the EGFR ligand transforming growth factor α (TGFα), with a fold change (FC) <0.56 or FC ≥1.90 (CI, 95%). KEGG analysis revealed a significant enrichment in signaling pathways involved in cancer development and progression. EGFR activation in MSCs downregulated the expression of different miRNAs, including miR-23c. EGFR signaling also reduced the secretion of miR-23c in conditioned medium from MSCs. Functional assays demonstrated that miR-23c acts as tumor suppressor in basal/claudin-low MDA-MB-231 and MDA-MB-468 cells, through the repression of IL-6R. MiR-23c downregulation promoted cell proliferation, migration and invasion of these breast cancer cell lines. Collectively, our data suggested that the EGFR signaling regulates in MSCs the expression of miRNAs that might be involved in breast cancer progression, providing novel information on the mechanisms that regulate the MSC-tumor cell cross-talk.We previously demonstrated that the epidermal growth factor receptor (EGFR) modulates in mesenchymal stem cells (MSCs) the expression of a number of genes coding for secreted proteins that promote breast cancer progression. However, the role of the EGFR in modulating in MSCs the expression of miRNAs potentially involved in the progression of breast cancer remains largely unexplored. Following small RNA-sequencing, we identified 36 miRNAs differentially expressed between MSCs untreated or treated with the EGFR ligand transforming growth factor α (TGFα), with a fold change (FC) <0.56 or FC ≥1.90 (CI, 95%). KEGG analysis revealed a significant enrichment in signaling pathways involved in cancer development and progression. EGFR activation in MSCs downregulated the expression of different miRNAs, including miR-23c. EGFR signaling also reduced the secretion of miR-23c in conditioned medium from MSCs. Functional assays demonstrated that miR-23c acts as tumor suppressor in basal/claudin-low MDA-MB-231 and MDA-MB-468 cells, through the repression of IL-6R. MiR-23c downregulation promoted cell proliferation, migration and invasion of these breast cancer cell lines. Collectively, our data suggested that the EGFR signaling regulates in MSCs the expression of miRNAs that might be involved in breast cancer progression, providing novel information on the mechanisms that regulate the MSC-tumor cell cross-talk.</p>
Efficient isolation of rare B cells using next-generation antigen barcoding
<p><strong>ABSTRACT:</strong> The ability to efficiently isolate antigen-specific B cells in high throughput will greatly accelerate the discovery of therapeutic monoclonal antibodies (<strong>mAbs</strong>) and catalyze rational vaccine development. Traditional mAb discovery is a costly and labor-intensive process, although recent advances in single-cell genomics using emulsion microfluidics allow simultaneous processing of thousands of individual cells. Here we present a streamlined method for isolation and analysis of large numbers of antigen-specific B cells, including next generation antigen barcoding and an integrated computational framework for B cell multi-omics. We demonstrate the power of this approach by recovering thousands of antigen-specific mAbs, including the efficient isolation of extremely rare precursors of VRC01-class and IOMA-class broadly neutralizing HIV mAbs.</p> <p> </p>
Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source II
<p>Source data underlying MS data set (SFig.2). Datafile comprises MS raw files + MaxQuant output files.</p>
Dynamic interactome of the MHC I peptide loading complex in human dendritic cells - Source III
<p>Source data underlying Raji cell data set (SFig.3). Datafile comprises MS raw data + MaxQuant output files.</p>
Cell-free Synthesis of Proteins with Selectively 13C-Labelled Methyl Groups from Inexpensive Precursors- Dataset
<p>NMR Spectra relating to the publication:<br> <br> Cell-free Synthesis of Proteins with Selectively 13C-Labelled Methyl Groups from Inexpensive Precursors.</p> <p>Spectra are labelled as such in the folder name and in the description.</p>
STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images
<p>The datasets consist of time-lapse images underlying the research article "STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images". Please visit the STrack github page for instructions on how to install and use STrack to track cells in images containing segmented cell masks: https://github.com/Helena-todd/STrack</p> <p>The data was generated at the Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland, by Tania Miguel Trabajo. The datasets are organised in four folders, one per bacterial species (<em>Pseudomonas putida, Pseudomonas veronii, Rahnella and Lysobacter</em>), that each contain 5 time-lapse datasets. Each of the 20 folders is organised in two subfolders, containing:</p> <p>- the raw, phase contrast, timelapse images (taken with a Nikon ECLIPSE Ti Series inverted microscope coupled with a Hamamatsu C11440 22CU camera and a Nikon CFI Plan Apo Lambda 100X Oil objective)</p> <p>- the manually segmented masks (manually generated using the QuPath open-source software for bioimage analysis )</p> <p>Dowload and unzip to view the contents.</p>
Dataset for Cell aggregation is associated with enzyme secretion strategies in marine polysaccharide-degrading bacteria
<p>Dataset includes: </p> <p>1. Analysed source data for figures</p> <p>2. Raw time-lapse images and tracking data that were used to generate the analysis</p>
Images of H3K9me3 Immunostain in IMR90 Cells
<p>Files are two-channel images at 49 unique xy positions showing the fluorescence intensity of a DRAQ5 DNA stain (c1) and H3K9me3 immunostain with an Alexa488 secondary (c2). Imaging was performed using a Nikon Ti2-E inverted microscope equipped with a Yokogawa CSU-W1 confocal spinning disk unit and a Zyla 2.3 PLUS sCMOS camera. We used 488 nm and 647 nm laser lines paired with 525/36 (MVI, 77074803), and 705/72 (MVI, 77074329) emission filters, respectively. Samples were imaged through a 1.15 NA CFI Apo LWD Lambda S 40X water immersion objective lens (Nikon MRD77410). The imagining system was controlled by NIS-Elements AR software.</p>
Images of H4K20me Immunostain in IMR90 Cells
<p>Files are two-channel images at 25 unique xy positions showing the fluorescence intensity of a DRAQ5 DNA stain (c1) and H4K20me immunostain with an Alexa488 secondary (c2). Imaging was performed using a Nikon Ti2-E inverted microscope equipped with a Yokogawa CSU-W1 confocal spinning disk unit and a Zyla 2.3 PLUS sCMOS camera. We used 488 nm and 647 nm laser lines paired with 525/36 (MVI, 77074803), and 705/72 (MVI, 77074329) emission filters, respectively. Samples were imaged through a 1.15 NA CFI Apo LWD Lambda S 40X water immersion objective lens (Nikon MRD77410). The imagining system was controlled by NIS-Elements AR software.</p>
Images of H4K8ac Immunostain in IMR90 Cells
<p>Files are two-channel images at 64 unique xy positions showing the fluorescence intensity of a DRAQ5 DNA stain (c1) and H4K8ac immunostain with an Alexa488 secondary (c2). Imaging was performed using a Nikon Ti2-E inverted microscope equipped with a Yokogawa CSU-W1 confocal spinning disk unit and a Zyla 2.3 PLUS sCMOS camera. We used 488 nm and 647 nm laser lines paired with 525/36 (MVI, 77074803), and 705/72 (MVI, 77074329) emission filters, respectively. Samples were imaged through a 1.15 NA CFI Apo LWD Lambda S 40X water immersion objective lens (Nikon MRD77410). The imagining system was controlled by NIS-Elements AR software.</p>
Targeted Illumination in HeLa Cells
<p>Files are a time series of showing targeted illumination of a subset of HeLa cell nuclei containing a photolabile ATAC-seq library. Time points are 300 ms apart. The single-channel images were acquired using a spinning disk confocal with a 40X objective (Nikon MRD77410) using a 561 nm laser line and 525/36 emission filter (MVI, 77074803). Images were captured using a Zyla 2.3 PLUS sCMOS camera. The pixel value represents the fluorescence intensity of the photocleavable fluorophore attached to the fragment library. Images are acquired concurrently with targeted illumination using a 405 nm laser line.</p>
Prochlorococcus Cell Concentrations During the BiG-RAPA Expedition (Cruise MV1015) in the Peru Current and Eastern South Pacific Subtropical Gyre Between November and December of 2010
<p>"These data include Prochlorococcus cell concentrations (total cell densities by flow cytometry and cell densities for specific ecotypes/clades determined by quantitative PCR). Samples were collected during the C-MORE Biogeochemical Gradients Role in Arranging Planktonic Assemblages (BiG-RAPA) expedition (Cruise MV1015) in the Peru Current and Eastern South Pacific Subtropical Gyre between dates 2010-11-19 and 2010-12-10 along a zonal transect from the northern coast of Chile to the island of Rapa Nui. Prochlorococcus is an important primary producer in the oligotrophic South Pacific Gyre and these data facilitate studies examining Prochlorococcus' ecology." https://www.bco-dmo.org/dataset/886299 Time is in UTC. Additional cruise information and methods can be found here: http://dmoserv3.bco-dmo.org/jg/info/BCO-DMO/C-MORE/prochlorococcus_data%7Bdir=dmoserv3.whoi.edu/jg/dir/BCO-DMO/C-MORE/,data=dmoserv3.bco-dmo.org:80/jg/serv/BCO-DMO/C-MORE/prochlorococcus_data.html0%7D?</p>
Data Availability for "The RNA-binding protein landscapes differ between mammalian organs and cultured cells"
<p>Data Availability for "The RNA-binding protein landscapes differ between mammalian organs and cultured cells" Joel I. Perez-Perri, Dunja Ferring-Appel, Ina Huppertz, Thomas Schwarzl, Sudeep Sahadevan, Frank Stein, Mandy Rettel, Bruno Galy, and Matthias W. Hentze</p>
10k 1:1 Mixture of Jurkat and MM1S Cells, 5' v2.0, CRISPR Screening, Chromium X - BESCA
<p>This dataset is a BESCA processed version of the example resources found at https://www.10xgenomics.com/resources/datasets/10k-1-1-mixture-of-jurkat-and-mm1s-cells-5-v2-0-chromium-x-2-standard</p>
T-cell activity - Imaging flow cytometry experiment data
<p>Raw image data from the imaging flow cytometry experiments for correlating receptor localization and cytokine production.</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.