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11,105 results for “single cells”
Discretized bulk data by the discretization step of rFASTCORMICS used in in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>Bulk data RNAseq data were downloaded from GEO, GTEX, and other sources (see below) and discretized by the discretization step of rFASTCORMICS (Pacheco et al, 2019) used in the optimization step in scFASTCORMICS:</p> <p>CRC bulk RNAseq data were obtained from Lee et al(2020) <br> CRC control (NM) was downloaded from GSE81861 (GTEX, Healthy colon from)</p> <p>Pancreatic Human islet bulk RNAseq data was downloaded from EBI Expression Atlas (Pancreatic islet cells)</p> <p>Immune cells in pancreatic carcinoma bulk data were obtained from GEO (GSE156278)</p> <p>liver and breast cancer bulk RNAseq data were obtained from the TCGA (GSE62944)</p> <p> </p> <p>rFASTCORMICS and tutorial on rFASTCORMICS can be found: https://github.com/sysbiolux</p> <p> </p> <p> </p> <p> </p> <p><br> </p>
single-cell RNAseq data (data set 5) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset5) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor3 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 15) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset15) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from CD8 T-cells in PACA samples downloaded from the GEO website (GSE156728)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 4) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset4) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from pancreas donor2 downloaded from the GEO website (<strong>GSE114297). </strong></p> <p> </p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p>
single-cell RNAseq data (data set 3) in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>The present dataset (dataset3) was used as input to build scFASTCORMICS models. The files correspond to the clusters identified by Seurat in the single-cell data from normal Pancreas donor1 downloaded from the GEO website (GSE114297)<strong>. </strong></p> <p>see the protocol: scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data</p> <p>and github: https://github.com/sysbiolux/scFASTCORMICS</p> <p>For more information, version updates of the scFASTCORMICS. </p> <pre> </pre>
Uncovering bacterial hosts of class 1 integrons in an urban coastal aquatic environment with a single-cell fusion-polymerase chain reaction technology
<p>Horizontal gene transfer (HGT) is a key driver of bacterial evolution via transmission of genetic materials across taxa. Class 1 integrons are genetic elements that correlate strongly with anthropogenic pollution and contribute to the spread of antimicrobial resistance (AMR) genes via HGT. Despite their significance to human health, there is a shortage of robust, culture-free surveillance technologies for identifying uncultivated environmental taxa that harbour class 1 integrons. We developed a modified version of epicPCR (emulsion, paired isolation and concatenation PCR) that links class 1 integrons amplified from single bacterial cells to taxonomic markers from the same cells in emulsified aqueous droplets. Using this single-cell genomic approach and Nanopore sequencing, we successfully assigned class 1 integron gene cassette arrays containing mostly AMR genes to their hosts in coastal water samples that were affected by pollution. Our work presents the first application of epicPCR for targeting variable, multi-gene loci of interest. We also identified the <em>Rhizobacter</em> genus as novel hosts of class 1 integrons. These findings establish epicPCR as a powerful tool for linking taxa to class 1 integrons in environmental bacterial communities and offer the potential to direct mitigation efforts towards hotspots of class 1 integron-mediated dissemination of AMR.</p>
Single-cell burst size estimates
<p>Bacteriophage burst size is the average number of phage virions released from infected bacterial cells, and its magnitude depends on the duration of an intracellular progeny accumulation phase. Burst size is often measured at the population, not single-cell, level, and consequently, statistical moments are not commonly available. In this study, we estimated the bacteriophage lambda (λ) single-cell burst size mean and variance following different length intracellular accumulation periods by employing E. coli lysogens bearing lysis-deficient λ prophages. Single lysogens can be isolated and chemically lysed at desired times following prophage induction to quantify progeny intracellular accumulation within individual cells. Our data show that λ phage burst size initially increased exponentially with increased lysis time (i.e., period between induction and chemical lysis), and then saturated at longer lysis times. We also demonstrated that cell-to-cell variation or "noise" in lysis timing did not contribute significantly to burst size noise. The burst size noise remained constant with increasing mean burst size. The most likely explanation for the experimentally observed constant burst size noise was that cell-to-cell differences in burst size originated from intercellular heterogeneity in cellular capacities to produce phages. The mean burst size measured at different lysis times was positively correlated to cell volume, which may determine the cellular phage production capacity. However, experiments controlling for cell size indicated that there are other factors in addition to cell size that determine this cellular capacity.</p>
Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference [Datasets]
<p>This repository contains processed single-cell dataset files for DELAY.</p>
Optical trapping of micro-particles and bacterial cells in single channel and flow-focusing microfluidic devices
<p><strong>Video 1</strong> - The video shows the flow-focusing and trapping of 1.84 μm bacteria-sized particles flowing at a sample flow rate of 0.1 μL/min. The horizontal sheath flow rate 1 μL/min and the vertical sheath flow rate is 0.5 μL/min. Trapping is achieved using a maximum laser power of 250mW. </p> <p><strong>Video 2</strong> - The video shows the flow and fluorescence trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 3</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 1 μL/min. Increased flow rate results in continuous transient trapping of the cells is achieved at a trapping power of 250mW. The microchannel surface is not treated with pluronic F-127, therefore lot of particles stick to the channel surface. </p> <p><strong>Video 4</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 5</strong> - The video shows the flow and trapping of <em>E. coli</em> MG1655 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 6</strong> - The video shows the flow and trapping of <em>S. aureus</em> 6538 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p>
Cancer-Associated Fibroblast Classification in Single-Cell and Spatial Proteomics Data
<p>ometiff: Imaging Data</p> <p>Cell Masks: Masks generated with cellprofiler from ilastik segmentation training</p> <p>cp-output_config: All relevant cellprofiler output and additional configuration files (for example clinical data) necessary to generate the single cell experiments.</p> <p>IMC Data Objects: Single cell experiment RDS files.</p> <p> </p> <p>scRNA-seq_dataobjects: .Rds files containing the clustered breast cancer, colon cancer, HNSCC, NSCLC and PDAC datasets as well as the integrated validation dataset.</p>
Single-cell RNA sequencing of mouse embryonic cells from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages
<p>STRT-N is a newly optimized single-cell RNA sequencing method for studies of early genome activation in mammalian preimplantation development. Single embryos from the oocyte, 2-cell, 4-cell, 8-cell, blastocyst, and morula stages were sampled for experiments and were sequenced using STRT-N method. Here is the raw data from STRTN-seq. FASTQ files are available in <a href="https://www.ebi.ac.uk/biostudies/studies/S-BSST976">BioStudies database</a>.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 26 as continuation of dataset <strong>10.5281/zenodo.7494413.</strong> The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of those parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p> <p>Additionally, this repository contains the metadata of the patients analyzed in this study, the panel information and the single-cell data that was extracted from the multiplexed images together with the associated metadata in SingleCellExperiment format for analysis in R.</p> <p>The analysis.zip folder contains files that were written out during the analysis according to the scripts in https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The single-cell and other data outputs from CellProfiler can be found in the cpout.zip file.</p> <p>The IF_whole_sections.zip file contains the IF images of the primary breast cancer sections (czi files) and the extracted single-cell data.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the raw IMC data of ZTMA 21 and 25 of the matched primary breast cancer and lymph node metastasis study presented in Fischer and Jackson et al., 2023. The code that was used to process and analyze this data can be found at https://github.com/BodenmillerGroup/BC_LN_metastses.</p> <p>The zip files starting with ZTMA contain the raw IMC measurements (mcd and txt) of the respective parts of the TMA. The TMA measurements are split up into parts in order to avoid huge files.</p>
Data for Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response
<p>Data from simulations used to generate the figures in the paper <em>Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in the antibiotic response</em>.</p> <p>To reproduce analyses, extract <em>colony_data.zip</em> in the <em>data</em> folder after cloning the <em>vivarium-ecoli</em> repository.</p> <p>The extracted folder contains the following items:</p> <ul> <li><em>sim_dfs</em>: a folder containing the CSV files that represent a subset of the raw simulation data used for downstream analyses.</li> <li><em>glc_10000_fluxome.csv</em>: Each row represents a reaction in central carbon metabolism (in same order as listed in <em>validation/ecoli/flat/toya_2010_central_carbon_fluxes.tsv</em>). Each column represents a single time point for a single cell in a baseline glucose simulation (seed 10000). Each value is a flux (mmol/L/hr). Provided as input to <em>ecoli/analysis/centralCarbonMetabolism.py </em>script to reproduce fluxome validation plot.</li> <li><em>glc_10000_proteome_avgs.csv</em>: Each row represents a protein monomer (in same order as <em>sim_data.translation.monomer_data["id"]</em> where <em>sim_data</em> is <em>reconstruction/sim_data/kb/validationData.cPickle</em>). Each column represents a cell in a baseline glucose simulation (seed 10000). Each row represents a protein monomer. Each value represents the average count of a given protein monomer for a given cell. Provided as input to <em>ecoli/analysis/proteinCountsValidation.py</em> script to reproduce proteome validation plot.</li> <li><em>glc_10000_expressome.csv</em>: Each column represents a gene (with the exception of the final two metadata columns: "Time" and "Agent ID"). Each row represents a specific cell (agent) at a specific time in a baseline glucose simulation (seed 10000). Each value represents the number of new RNA transcripts for a given gene in a given cell at a given time. Provided as input to <em>ecoli/analysis/antibiotics_colony/subgen_gene_plots/count_subgen.py</em> script to calculate number of sub-generational genes among all genes and antibiotic response genes.</li> <li><em>glc_10000_total_mrna.json</em>: Mapping of agent IDs for all cells in a baseline glucose simulation (seed 10000) to their average total mRNA count. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. 2C,D.</li> <li><em>jenner_2013.csv</em>: Data extracted from Fig. 2C of <a href="https://doi.org/10.1073/pnas.1216691110">10.1073/pnas.1216691110</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. S6A.</li> <li><em>olson_2006.csv</em>: Data extracted from Fig. 2D of <a href="https://doi.org/10.1128%2FAAC.01499-05">10.1128/AAC.01499-05</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. S6A.</li> <li><em>lysis_ratios.csv</em>: Data extracted from Fig. 2 of <a href="https://doi.org/10.1099/00221287-31-3-339">10.1099/00221287-31-3-339</a>. Used by <em>ecoli/analysis/antibiotics_colony/plot.py </em>to generate Fig. 4N.</li> </ul>
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics
<p>Datasets associated with https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009797</p>
Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability
<p>Additional files for "Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability".</p> <p><strong>Additional file 1 - list of benchmarks with consolidate survey answers (CSV)</strong></p> <p><strong>Additional file 2 - review form (CSV) </strong></p> <p><strong>Additional file 3 - unedited (anonymized) survey responses (TXT)</strong></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>
Extended Data for Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation
<p>The dataset includes supporting information for the study titled "Single-cell Transcriptional Uncertainty Landscape of Cell Differentiation".</p>
Supporting data for "Morphological profiling by high-throughput single-cell biophysical fractometry"
<p>No specific description. For more information, please contact: iriszqzh@connect.hku.hk.</p>
Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution
<p>Supplementary Tables for article "Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single cell resolution"<br> </p>
ScienceDex guides
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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.