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111,247 results for “cells”
Human breast cancer PDTX models bulk and single cell RNA sequencing
<p>This dataset includes information relevant to the following manuscript from the labs of Prof. Carlos Caldas (University of Cambridge), and Dr. Long V. Nguyen (Princess Margaret Cancer Centre, University Health Network):</p> <p>Nguyen LV et al. Dynamics and plasticity of human breast cancer single cell-derived clones. Under consideration for publication.</p> <p>Bulk RNA sequencing raw count matrices are provided (RawCounts.csv) along with the normalized count matrices (LogCPMNormCounts.csv).</p> <p>Single cell RNA sequencing count matrix processed from R package metacell is provided (mat.pdx_LN_v2_filt.Rda), along with the mc and mc2d files with information on metacell partitions (mc.pdx_LN_v2_filt.Rda and mc2d.pdx_LN_v2_filt.Rda).</p> <p>Single cell RNA sequencing count matrices processed using Seurat are also provided separately for each PDTX model analysed (STG139.rds, STG201.rds, AB040.rds and IC07.rds).</p> <p>Code and information on data analysis is provided for reviewers in our unpublished manuscript and on Github (https://github.com/cclab-brca/clone-dynamics).</p>
Computer code accompanying Schraivogel, D. et al. "High-speed fluorescence image-enabled cell sorting" Science, 2022. doi: 10.1126/science.abj3013
<p>Computer code accompanying Schraivogel et al. "High-speed fluorescence image-enabled cell sorting". Details are provided in the manuscript's data and materials availability section and table 3.</p> <p> </p> <p>We provide three directories:</p> <p>(1) R code to reproduce figures (ICS2021_0.1.0.tar.gz)</p> <p>(2) Python code to reproduce figures (ICS_Fiji_Plugin.zip)</p> <p>(3) Code for ICS/CellView Fiji plugins (ICSPython.zip)</p> <p> </p> <p>Code for (1) and (3) has also been shared via Github:</p> <p>https://github.com/benediktrauscher/ICS</p> <p>https://github.com/embl-cba/ICS</p> <p> </p> <p>We recommend downloading the ICS Fiji plugins via Github or to install them using the Fiji update site to ensure you're using the most recent version.</p>
BarWare Cell Hashing Pipeline Demonstration
<p>Data and code used to demonstrate the BarWare cell hashing deconvolution pipeline.</p>
Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
<p> </p> <p><strong>Abstract</strong></p> <p><strong>Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.</strong></p> <p><em><strong>SourceCode</strong></em></p> <p><strong><em>Main.R</em></strong><br> Please execute code along with the guide in this script</p> <p><em><strong>CreateBASiCS.R</strong></em><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes ~48 hours. Use of HPC is recommended</p> <p><em><strong>DifferenceTestAll.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from all cells. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>DifferenceTestCluster.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from cells belonging to the same cluster. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>STP12_detail_analysis.Rmd</strong></em><br> Output results mentioned in the discussion. Code is placed within the chunk.</p> <p><em><strong>CreateSupFig2.R</strong></em><br> Generate supplement figure 2</p> <p><em><strong>CreateFigure4AndS6.R</strong></em><br> Generate figure 4 and supplement figure 6</p> <p><strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A)</p> <p><br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2.</p> <p><strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.</p>
Radiation-Induced Stem Cell Competition and Dose-Rate Effect
<p>A radiation biological effect of a given dose generally decreases with decreasing radiation dose rate, which is known as a “dose-rate effect”. The dose-rate effect demonstrated by many cellular and animal studies. Additionally, recent epidemiological study in high background radiation area in Kerala, India showed that cancer incidence did not increase with increasing cumulative dose (Jayalekshmi et al. Radiat Environ Med 2021). Tissue stem cells have been considered as a target of radiation-induced carcinogenesis. Radiation biological effect could be reduced if damaged stem cells are eliminated by stem cell competition. ICRP described that stem cell competition at the tissue level leaves an ample possibility for a dose-rate effective factor (DREF) value larger than unity, as in the case of the current dose and dose-rate effective factor (DDREF) value (ICRP Publication 131).</p> <p>Cells expressing Lgr5 are one of the major components of intestinal stem cells. Intestinal organoids are three-dimensional cultured tissue model generated from intestinal stem cells. To evaluate a radiation-induced stem cell competition, we established a quantitative method using mixed-organoid derived from two independent fluorescent protein-expressing Lgr5 stem cells, which one of stem cells were irradiated, for mimicking heterogeneous exposure under low-dose-rate irradiation. The organoid-forming potential (OFP) is one of the indices of the abilities of self-renewal, proliferation, and differentiation of stem cells. We found that irradiated stem cells exhibited a growth disadvantage in the mixed organoid, whereas the OFP of irradiated cells per se did not decrease significantly from that of non-irradiated cells.</p> <p>Additionally, we constructed a mathematical model to assess stem cell competition under low-dose-rate irradiation condition. In our model, a stem cell pool, containing a constant number of cells, was assumed, and changed through transition and turnover event. The intact cells turned into damaged cells through transition event which was assumed as the effect of radiation exposure. In the turnover event, a single cell was divided, and a single cell was eliminated from the stem cell pool. The probability of cell division and elimination depended on the properties of cells. The properties of damaged cells were different from that of intact cells. Under very low-dose-rate conditions, the radiation damage was suppressed when the damaged cells were less reproductive and tended to be eliminated compared to the intact cells.</p> <p>These results suggest the radiation-induced stem cell competition can be occurred in the intestine, and the stem-cell competition plays an important role in suppress carcinogenesis under low-dose-rate irradiation condition.</p>
Data for: Molecular mechanisms behind safranal's toxicity to liver cancer cells from dual omics
<p>The spice saffron (<em>Crocus sativus</em>) has anticancer activity in several human tissues, but the molecular mechanisms underlying potential therapeutic effects are poorly understood. We investigated the impact of safranal, a small molecule secondary metabolite from saffron, on the HCC cell line HEP-G2 using untargeted metabolomics (HPLC-MS) and transcriptomics (RNAseq). Increases in glutathione disulfide and other biomarkers for oxidative damage contrasted with lower levels of the antioxidants biliverdin IX (139-fold decrease, p=5.3E-5), the ubiquinol precursor 3-4-dihydroxy-5-all-trans-decaprenylbenzoate (3-fold decrease, p=1.9E-5), and resolvin E1 (-3,282-fold decrease, p=4E-5), which indicates sensitization to reactive oxygen species. We observed a significant increase in intracellular hypoxanthine (538-fold increase, p=7.7E-6) that may be primarily responsible for oxidative damage in HCC after safranal treatment. The accumulation of free fatty acids and other biomarkers, such as S-methyl-5'-thioadenosine, are consistent with safranal-induced mitochondrial de-uncoupling and explain the sharp increase in hypoxanthine we observed. Overall, the dual omics datasets describe routes to widespread protein destabilization and DNA damage from safranal-induced oxidative stress in HCC cells.</p>
Datasets corresponding to publication: Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes
<p>This repository contains all dataset that correspond to the publication "Machine learning assisted Real-time deformability cytometry of CD34+ cells allows to identify patients with Myelodysplastic Syndromes". Furthermore, Python scripts are provided which allow to reproduce all analyses shown in the manuscript. Execution of the scripts requires a Python environment with packages as stated in the Methods section of the manuscript, or by using PyBox 0.1.0. PyBox is a readily installed Python environment containing all packages at the required version. PyBox is publicly available on GitHub: <a href="https://github.com/maikherbig/PyBox">https://github.com/maikherbig/PyBox</a>.</p>
Live-cell fluorescence microscopy data for: Structural Dynamics of the Functional Nonameric Type III Translocase Export Gate
<p><strong>yuan_t3ss_raw_data.zip:</strong></p> <p>Microscopy data types:<br> <strong>- </strong>Brightfield images<br> - Fluorescence images (514 nm excitation)</p> <p>Microscope:<br> Olympus IX-83 with 1.49NA oil-immersion objective (Olympus UAPON 100x)<br> ET442/514/561 Laser triple band set filtercube (69904, Chroma)</p> <p>Camera:<br> Hamamatsu C9100-13 EM-CCD Camera<br> 80 nm pixel size</p> <p><strong>yuan_t3ss_cell_objects.zip:</strong></p> <p>EPEC and C41 ColiCoords cell objects derived from the raw data. The set contains a total of 24979 individual cells (brightfield, binary, fluorescence and localization datasets for each cell) with corresponding coordinate systems.</p> <p>Code to generate these cell objects from the raw data: https://github.com/Jhsmit/T3SS-paper</p> <p>The data format is .hdf5 and can be read with any HDF5 reader or directly with ColiCoords: https://github.com/Jhsmit/ColiCoords</p> <p><strong>Contact:</strong><br> Jochem Smit</p>
Code and data for 'Bacillus subtilis histidine kinase KinC activates biofilm formation by controlling heterogeneity of single-cell responses'
<p>Code and data used in the paper 'Bacillus subtilis histidine kinase KinC activates biofilm formation by controlling heterogeneity of single-cell responses' https://doi.org/10.1128/mBio.01694-21</p>
Blood Cell Transcriptomics and Proteomics of Axial Spondyloarthritis patients undergoing adalimumab treatment
<p>This study aims at identifying molecular biomarkers differentiating good responders and non-responders to treatment with TNF inhibitors (TNFi), among patients with axial spondyloarthritis (axSpA). Publication: <em>Biomolecules</em> <strong>2024</strong>, <em>14</em>(3), 382; <a href="https://doi.org/10.3390/biom14030382">https://doi.org/10.3390/biom14030382</a></p>
Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies
<p>The pcYeast7.6 model and files, required to reproduce the figures, provided in the publication "Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies", accepted in <em>Nat Commun</em>. The Zenodo upload was created by Pranas Grigaitis, p.grigaitis [at] vu.nl.</p> <p>Abstract</p> <p>When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.</p>
Raw data to: "Vectored antibody gene delivery restores host B and T cell control of persistent viral infection"
<p>Raw data underlying the publication by Ertuna et al. entitled "Vectored antibody gene delivery restores host B and T cell control of persistent viral infection"</p>
Host cell maturation modulates parasite invasion and sexual differentiation in Plasmodium.
<p>Raw expression counts data is provided along with code needed to analyse the scRNA-seq data presented in the publication "Host cell maturation modulates parasite invasion and sexual differentiation in Plasmodium."</p>
Molecular dynamics of solids at constant pressure and stress using anisotropic stochastic cell rescaling - dataset
<p>Supporting data related to manuscript "Molecular dynamics at constant pressure and stress using anisotropic stochastic cell rescaling"</p>
Expression and function of Twitch-2B in the granulosa cells of mouse ovarian follicles
<p>Briefly, antral follicles were dissected from 23- to 26-d-old <em>RR8; Bact_Cre mice</em>. Follicles were cultured for 24–30 h on organotypic membranes (Millipore; cat. No. PICMORG50) in the presence of follicle-stimulating hormone. The follicle was held in a perfusion slide consisting of a plastic slide (ibidi) and a glass coverslip and assembled using silicon grease. The slide was constructed such that medium containing ovine LH (National Hormone and Peptide Program; 10 μg/mL) could be perfused through a 200-μm-deep channel holding the follicle. Temperature was maintained at 30–34 °C, by use of a warm air blower (Nevtek). Preovulatory Follicles were imaged using a Zeiss Pascal confocal microscope with a 40X/1.2 NA objective. Images were collected every 10 seconds. Measurements were corrected for autofluorescence and for spectral bleed-through of CFP into the YFP channel. Ratios were calculated by dividing the mean CFP intensity in each region of interest by the mean YFP intensity. Data analysis was done using ImageJ and Excel software. Data is representative of 4 follicles.</p>
Supporting data for "Towards reliable three-electrode cells for lithium–sulfur batteries"
<p>This is the dataset of electrochemical measurements for our publication "Towards reliable three-electrode cells for lithium–sulfur batteries". This archive contains the raw data and scripts written in R used in the analysis and presentation of the results in this manuscript.</p> <p>Abstract of the manuscript:</p> <p>Three-electrode measurements are valuable to the understanding of the electrochemical processes in a battery system. However, their application in lithium–sulfur chemistry is difficult due to the complexity of the system and thus rarely reported. Here, we present a simple three-electrode cell format with relatively good life time and minimum interference with the cell operation. </p>
Effect of local topography on cell division of Staphylococci sp.
<p><strong>Dataset.zip</strong></p> <p>This dataset includes the raw and annotated images used to train a Stardist 2D deep learning model for segmentation of surface attached <em>S.aureus</em> as described in <em>Effect of local topography on cell division of Staphylococci sp.</em></p> <p> </p> <p><strong>Stardist2d_Model.zip</strong></p> <p>Stardist 2D deep learning model for segmentation of surface attached <em>S.aureus, </em>obtained using the StarDist 2D ZeroCostDL4Mic notebook (v 1.12.3).</p>
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: validation cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the validation cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) SNP genotypes for the two SNPs which overlap with the discovery cohort<br> - (nicaragua_snp_genotypes_ints.tsv) -- SNP genotypes as integers<br> - (nicaragua_snp_genotypes_strings.tsv) -- SNP genotypes as allele strings <br> (2) the ancestry PCs for each individual in the validation cohort (nicaragua_snp_ancestry_PCA.tsv)<br> (3) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (4) a file including IMGT genes used for parsing TCRA repertoire data (human_vj_alleles_alpha.tsv)<br> (5) Parsed TCRA repertoire data (nicaragua_parsed_TCRA.tgz)<br> (6) Parsed TCRB repertoire data (nicaragua_parsed_TCRB.tgz) </p> <p><strong>Corresponding raw validation cohort TCR repertoire data is available here:</strong> https://www. ncbi.nlm.nih.gov/bioproject/PRJNA762269 (The BioProject database, accession number: PRJNA762269)</p> <p><strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
scPretrain: Multi-task self-supervised learning for cell type classification
<p>The dataset and code for paper, scPretrain: Multi-task self-supervised learning for cell type classification.</p>
RSV subgroup A direct RNA seq of infected MRC5 cells and mock infected MRC5 cells mRNA on nanopore
<p>Fastq files of mRNA sequenced by direct RNAseq on Oxford nanopore device and associated data for the paper</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)
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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.