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99 results for “cell count”
KSR inhibitor APS-2-79 sensitivity test in JURKAT and ALL-SIL T-cell acute lymphoblastic leukemia cell lines measured by Cell Counting Kit 8
<p>APS-2-79 compound was purchased from MedChemExpress (Monmoutyh Junction, NJ, USA). The 20 mg/ml stock solution was prepared in DMSO. To calculate the IC50, JURKAT and ALL-SIL cells were cultured for 72h with a range of APS-2-79 concentrations (5-15 µM) added as equal volumes. Cells treated with 0.5% DMSO (vehicle) were used as negative control. Cells treated with 10% DMSO were used as positive control. The viability of cells was measured using Cell Counting Kit 8 (Sigma Aldrich) and GloMax Microplate Reader system (Promega) with 450 nm wavelength and 600 nm as reference wavelength. The relevant reads are made from following wells: 2A-2D (15 µM APS-2-79), 3A-3D (12.5 µM APS-2-79), 4A-4D (10 µM APS-2-79), 5A-5D (7.5 µM APS-2-79), 6A-6D (5 µM APS-2-79), 7A-7D (vehicle), 8A-8D (positive control).</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"
<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>
Gene expression count matrix for 4 T cell subtypes from ROSMAP participants
<p><span>Peripheral blood mononuclear cells (PBMCs) from participants in the Rush Religious Orders Study/Memory and Aging Project (ROSMAP) were isolated by Ficoll gradient centrifugation, then sorted by high-speed flow cytometry into the following T cell subtypes:<span> </span>CD4+CD45RO-, CD4+CD45RO+, CD8+CD45RO-, and CD8+CD45RO+.<span> </span>Total RNA was extracted using buffer TCL (Qiagen), then RNA-seq libraries were prepared according to the Single Cell RNA Barcoding and Sequencing method originally developed for single-cell RNA-seq</span><span>, adapted for extracted total RNA.<span> </span>RNA libraries were collected on a single 384-well plate and sequenced on the Illumina HiSeq </span><span>using the High-throughput 3<span>’</span> Digital Gene Expression (DGE) library</span><span>.<span> The "RNA count matrix" file is the raw counts from the 384-well plate, while the "ROSMAP_Tcell_DGE_PlateMap" file contains metadata for the wells on the plate, by well position.</span></span></p>
Picophytoplankton and bacteria total carbon estimates from cell counts analyzed with flow cytometry (FCM) from CCE-CalCOFI Augmented cruises in the California Current System, 2004 - 2023(ongoing).
Picophytoplankton populations and non-pigmented prokaryotes are sampled within the California Current Ecosystem (CCE) for abundances from 3 to 8 depths at CalCOFI stations. Seawater is collected from Niskin bottles and cells are fixed in the field aboard the survey cruises (since 2004, ongoing) with paraformaldehyde, and stained with a DNA-specific dye back in the laboratory. The cells are enumerated by an Altra flow cytometer (with a syringe pump for volumetric sample delivery) simultaniously with argon ion lasers, to distinguish three major populations of photoautotrophs (Prochlorococcus, Synechococcus, and pico-eukaryotes) and the assemblage of heterotrophic prokaryotes collectively referred to as H-Bact. FCM abundance estimates for each are converted to carbon biomass equivalents using mixed-layer estimates.
Gene expression counts from induced Pluripotent Stem Cells
<p><strong>File description:</strong></p> <ol> <li> <p>Gene-level counts using the gtf file from the release 34 of GENCODE <a href="https://www.gencodegenes.org/human/release_34">https://www.gencodegenes.org/human/release_34</a></p> </li> <li> <p>Split counts spanning from one exon to another using an annotation-free algorithm, therefore capturing new splice sites</p> </li> <li> <p>Non-split counts covering exon-intron boundaries</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, the workflow DROP can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Maintainer: </strong>Vicente A. Yépez, <a href="mailto:yepez@in.tum.de">yepez@in.tum.de</a></p> <p><strong>URL:</strong> <a href="https://github.com/gagneurlab/drop/">https://github.com/gagneurlab/drop/</a></p> <p> </p> <p><strong>Title: </strong>induced Pluripotent Stem Cells<br> <strong>Number of samples:</strong> 330<br> <strong>Tissue:</strong> iPSCs<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34<br> <strong>Disease:</strong> None<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Marc Bonder, marcj89 at gmail.com</p> <p><strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References</p>
Microcore cell counting
<p>This dataset shows the raw cell countings values that we have done from anatomical cross sections of peatland trees microcores. The first column corresponds to the tree ID, the second to the Date in the format DD.MM.YY hh:mm, the third column corresponds to the type of cell counting: 3EN corresponds to the number of enlarging cells, 2TH corresponds to the number of thickening cells, ans 1MA correpsonds to the number of mature cells. </p>
Processed counts data of cfMeDIP-seq profiles of small cell lung cancer patients
<p>R objects of cfMeDIP-seq profiles of small cell lung cancer patient cfDNA, peripheral blood leukocytes, non-cancer control patients cfDNA, and CDX tumour tissue. The data are whole-genome across 300bp windows after removing ENCODE-blacklisted regions. The data also includes MeDEStrand-converted MeDIP data for peripheral blood leukocytes</p>
Using Romiplostim to Treat Low Platelet Counts Following Chemotherapy and Autologous Hematopoietic Cell Transplantation in People With Blood Cancer
ClinicalTrials.gov study NCT04478123. IPD Sharing: YES. Countries: 1. Publications: 1.
Evaluating somatic cell count, the California mastitis test, and infrared thermography for subclinical mastitis detection in meat ewes
Open the record for dataset details and reuse information.
Gene expression counts from amniotic fluid cells
<p><strong>File description:</strong></p> <ol> <li> <p>Gene-level counts using the gtf file from the release 29 of GENCODE <a href="https://www.gencodegenes.org/human/release_29">https://www.gencodegenes.org/human/release_29</a></p> </li> <li> <p>Split counts spanning from one exon to another using an annotation-free algorithm, therefore capturing new splice sites</p> </li> <li> <p>Non-split counts covering exon-intron boundaries</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired end specifications match. Afterwards, the DROP pipeline can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Maintainer: </strong>Vicente A. Yépez, <a href="mailto:yepez@in.tum.de">yepez@in.tum.de</a></p> <p><strong>Title:</strong> Gene expression and splicing counts from amniotic fluid cells<br> <strong>Number of samples:</strong> 56<br> <strong>Tissue:</strong> Amniotic fluid cells<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Disease ICD-10</strong>: Q89: F2100984A and F2100985, Q23: F1901567 and Q74: F2000845<br> <strong>Strand specific</strong>: False<br> <strong>Paired end</strong>: True<br> <strong>Cite as</strong>: Cite both the resource using Zenodo's citation and the publication under References<br> <strong>Dataset contact</strong>: Brian Chung bhychung@hku.hk<br> <strong>Comments</strong>: A total of 56 RNA-seq samples were used for running pipeline. Out of those, 52 samples were included in the manuscript.</p>
Novel stereological method for estimation of cell counts in 3D collagen scaffolds
<p>Dataset provides all images used for cell number evaluation. The used macros are the part of Supplementary of the article.</p>
Single-cell RNA-seq count data used in differential expression benchmark study
<p>Count matrices and meta data tables from simulated and real world immune cell single-cell RNA-seq experiments.</p> <p>All files are in Rds format and can be read by R using "readRDS()". </p> <ul> <li>10k_*: These files contain a filtered version of the 10k Human PBMCs, 3' v3.1 data <a href="https://www.10xgenomics.com/resources/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">published</a> by 10x Genomics</li> <li>blueprint_data.Rds: This file contains the bulk RNA-seq data downloaded from <a href="http://dcc.blueprint-epigenome.eu">BLUEPRINT</a></li> <li>blueprint_immune_comparisons.Rds: Results from running three bulk RNA-seq differential expression methods</li> <li>sim_data*: These files contain the count matrices and meta data tables for the simulated data. Every file contains a list of 13 replicates.</li> </ul> <p> </p>
Code for: Multi-modal screening for synergistic neuroprotection of mild extremely preterm brain injury: Cell counting code repository
Open the record for dataset details and reuse information.
Data from: Caspar specifies primordial germ cell count and identity in Drosophila melanogaster
Open the record for dataset details and reuse information.
Counts of PLSS Corners by Cell,Taxa and PFT, Upper Midwest: Level 1
EuroAmerican land use and its legacies have transformed forest structure and composition across the United States (US). More accurate reconstructions of historical states are critical to understanding the processes governing past, current, and future forest dynamics. Gridded (8x8km) estimates of pre-settlement (1800s) forests from the upper Midwestern US (Minnesota, Wisconsin, and most of Michigan) using 19th Century Public Land Survey System (PLSS) records provide relative composition, biomass, stem density, and basal area for 26 tree genera. This mapping is more robust than past efforts, using spatially varying correction factors to accommodate sampling design, azimuthal censoring, and biases in tree selection. We compare pre-settlement to modern forests using US Forest Service Forest Inventory and Analysis (FIA) data to show the prevalence of lost forests, pre-settlement forests with no current analogue, and novel forests, modern forests with no past analogs. Differences between PLSS and FIA forests are spatially structured as a result of differences in the underlying ecology and land use impacts in the Upper Midwestern United States. Modern biomass is higher than pre-settlement biomass in northern Minnesota, northwestern and south central Wisconsin along the former prairie-forest border through Minnesota that was largely open savanna and the Big Woods of Minnesota. PLSS biomass was higher than today in northern Wisconsin and upper and lower Michigan due to shifts in species composition and, presumably, average stand age. Modern forests are more homogeneous, and ecotonal gradients are more diffuse today than in the past. Novel forest assemblages represent 29% of all FIA cells, while 25% of pre-settlement forests no longer exist in a modern context. Lost forests are centered around the forests of the Tension Zone, particularly in hemlock dominated forests of north-central Wisconsin, and in oak-elm-basswood forests along the forest-prairie boundary in south central
RAW data: Knockdown of UTX/KDM6A Enriches Precursor Cell Populations in Urothelial Cell Cultures and Cell Lines - single cell RNAseq - Fastq format and UMI counts
<p>This data set of the single cell sequencing experiment of the urothelial cell line HBLAK belongs to the publication: "Knockdown of UTX/KDM6A Enriches Precursor Cell Populations in Urothelial Cell Cultures and Cell Lines" Cancers 2020, 12(4), 1023; https://doi.org/10.3390/cancers12041023.</p> <p> </p>
RNAseq raw counts FLCN positive vs. FLCN negative renal proximal tubular epithelial cells (RPTEC)
<p>Germline inactivating mutations in Folliculin (FLCN) cause Birt–Hogg–Dubé (BHD) syndrome, a rare autosomal dominant disorder predisposing to kidney tumors. FLCN is a conserved, essential gene linked to diverse cellular processes but the mechanisms by which FLCN prevents kidney cancer remain unknown. Here we show that deleting FLCN activates TFE3, upregulating its downstream E-box genes in human renal tubular epithelial cells (RPTEC/TERT1), including RRAGD and GPNMB, without modifying mTORC1 activity. Surprisingly, deletion of FLCN or its binding partners FNIP1/FNIP2 also induces interferon response genes, but independently of interferon. Mechanistically, FLCN loss promotes STAT2 recruitment to chromatin and slows cellular proliferation. Our integrated analysis identifies STAT1/2 signaling as a novel target of FLCN in renal cells and BHD tumors. STAT1/2 activation appears to counterbalance TFE3-directed hyper-proliferation and may influence the immune response. These findings shed light on unique roles of FLCN in human renal tumorigenesis and pinpoint candidate prognostic biomarkers.</p>
CIDACC: Chlorella vulgaris Image Dataset for Automated Cell Counting
<p><span><span>This </span><span>CIDACC dataset</span><span> was created to </span><span>determine</span><span> the cell </span><span>population </span><span>of</span><span> Chlorella vulgaris microalga during cultivation. Chlorella vulgaris has diverse applications, including use as food supplement, </span></span><span><span>biofuel production, and pollutant removal. </span><span>High resolution</span><span> images were collected using a microscope and </span><span>annotated</span><span>,</span><span> focusing on computer vision and </span><span>machine learning </span><span>models </span><span>creation</span><span> for automatic Chlorella cell detection</span><span>, counting</span><span>,</span><span> size </span><span>and geometry </span><span>estimation</span><span>.</span></span></p> <p><span><span><span><span>The dataset </span><span>is organized </span><span>hierarchically </span><span>into multiple folders and subfolders</span><span>, </span><span>containing</span><span> 628 images taken from a microscope and further processed by </span><span>external</span><span> tools.</span> <span>It consists of three</span><span> root folders</span><span>:</span> <span>“</span><span>original_images</span><span>”</span><span>,</span> <span>“</span><span>clusters</span><span>”</span><span>,</span> <span>and </span><span>“</span><span>distinct</span><span>”</span><span>.</span> <span>The </span><span>“</span><span>original_images</span><span>” folder holds</span><span> the </span><span>raw </span><span>microscope </span><span>images </span><span>with </span><span>initial</span><span> dimensions</span><span> of </span><span>2592x1944 pixels</span><span>.</span><span> These </span><span>images </span><span>are further subdivided into </span><span>“</span><span>clusters</span><span>”</span><span> and </span><span>“</span><span>distinct</span><span>”</span><span> folders</span><span>,</span> <span>indicating</span><span> whether </span><span>they </span><span>contain</span> <span>single </span><span>cells</span> <span>or cell clusters</span><span>. The “clusters” and “distinct” </span><span>root folders </span><span>contain</span> <span>annotated </span><span>images </span><span>with reduced dimensions </span><span>(640x640 pixels)</span><span>.</span><span> The </span><span>“</span><span>clusters</span><span>”</span><span> folder </span><span>includes images showing </span></span><span><span>C</span><span>. vulgaris</span></span><span><span> cells forming clusters</span><span>, where counting individual cells is</span><span> not </span><span>possible.</span><span> The </span><span>“</span><span>distinct</span><span>”</span><span> folder </span><span>contains</span><span> images of cells that can be counted with high precision.</span></span></span></span></p>
Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study
<p><strong>Objectives</strong>: to assess survival predictivity of baseline blood cell differential count (BCDC), discretized according to two different methods, in adults visiting the Emergency Room (ER) for illness or trauma over one-year. </p> <p><strong>Design</strong>: Retrospective cohort study of hospital records. </p> <p><strong>Setting</strong>: Tertiary care public hospital in northern Italy. </p> <p><strong>Participants</strong>: 11052 patients aged > 18 years, consecutively admitted to the ER in one year, and for whom BCDC collection was indicated by ER medical staff at first presentation.</p> <p><strong>Primary outcome</strong>: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed hemoglobin, red cell mean volume (MCV) and distribution-width (RDW), platelet distribution-width (PDW), plateletcrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and platelets. Discretization cutoffs were defined by Benchmark and Tailored methods. Benchmark cutoffs were stated on laboratory reference values (CLSI). Tailored cutoffs for linear, sigmoid-shaped and for U-shaped distributed variables were discretized by Maximally Selected Rank Statistics and by Optimal-Equal Hazard Ratio respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges, in-hospital admission) were analyzed using Cox multivariable regression. ROC curves were drawn by sum of Cox-significant variables for each method.</p> <p><strong>Results</strong>: Of 11052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted in hospital. After a 306-day median follow up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73-years (HR=4.6 CI=4.0–5.2), in-hospital admission (HR=2.2 CI=1.9–2.4), ER admission during SARS-CoV2 surges (Wave-I HR=1.7 CI=1.5–1.9); Wave-II HR=1.2 CI=1.0–1.3). Gender, hemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophils counts were significant in overall. Benchmark-BCDC model included basophils and platelet count (AUROC 0.74). Tailored-BCDC model included monocyte counts and plateletcrit (AUROC 0.79).</p> <p><strong>Conclusions</strong>: baseline discretized BCDC provides meaningful insight regarding Emergency Room patients survival.</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.