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24 results for “Single Cell Experiments”
Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience
<p>Dataset of the paper "Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience"</p>
Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
<p><strong>Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation</strong></p> <p>We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing <strong><em>C. glutamicum</em></strong> cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue.</p> <p><strong>Data Acquisition</strong></p> <p><strong><em>Corynebacterium glutamicum</em></strong> ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir.</p> <p>The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.</p>
Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.
<p>Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.</p> <p> </p> <p>List of files used for each dataset.</p> <p> </p> <p>Dataset 0 : MS2-CY5_Cyto543_560_woStim</p> <p> Images in the dataset :</p> <p> ROI001_XY1657814108_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657815441_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657814110_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI004_XY1657814111_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI005_XY1657814112_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI006_XY1657814113_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI007_XY1657814114_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI008_XY1657814115_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI009_XY1657814116_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI010_XY1657814117_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI011_XY1657814118_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI012_XY1657814119_Z00_T0_merged.tif - Image Id Number: 11</p> <p> </p> <p>Datset 1 : MS2-CY5_Cyto543_560_18minTPL_5uM</p> <p> Images in the dataset :</p> <p> ROI001 - Position 1_XY1657818948_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI001 - Position 2_XY1657818949_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI001 - Position 4_XY1657818951_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI001 - Position 5_XY1657818952_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI001 - Position 6_XY1657818953_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI001 - Position 7_XY1657818954_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI001 - Position 8_XY1657818955_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI001 - Position 9_XY1657818956_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI001 - Position 10_XY1657818957_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI001 - Position 11_XY1657818958_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI001 - Position 12_XY1657818959_Z00_T0_merged.tif - Image Id Number: 10</p> <p> </p> <p>Dataset 2: MS2-CY5_Cyto543_560_5hTPL_5uM</p> <p> Images in the datset :</p> <p> ROI001_XY1657822809_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657822933_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657822934_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI005_XY1657822936_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI006_XY1657822937_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI007_XY1657822938_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI008_XY1657822939_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI010_XY1657822941_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI013_XY1657822944_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI014_XY1657822945_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI015_XY1657822946_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI016_XY1657822947_Z00_T0_merged.tif - Image Id Number: 11</p> <p> ROI017_XY1657822948_Z00_T0_merged.tif - Image Id Number: 12</p> <p> ROI018_XY1657822949_Z00_T0_merged.tif - Image Id Number: 13</p> <p> </p> <p> </p>
Single-cell ATAC-seq control of cross-contaminations (experiment 2)
<p>On the Fluidigm C1 platform for single-cell analysis, the cells are captured in 96 chambers arranged serially, and then washed before further processing. Thus, debris present from the loading medium or released by captured cells upstream are a possible source of contamination. We generated a control datasets using the single-cell ATAC-seq protocol available from Fluidigm's ScriptHub. We cultivated human Hep G2 and mouse Hepa 1-6 (both are liver cancer cell lines), stained them with green and red calceins (respectively), and loaded them at equal concentration in a Fluidigm medium flow cell (old design), before running the C1 single-cell ATAC-seq program. To evaluate damage and carry-over of debris from FACS-sorting, two IFCs were run in two C1 machines in parallel. In the first (flowcell ID 1772-123-155), the cells not washed and in the second, they were washed (ID 1772-123-158).</p> <p>The data deposited here is a sequencing run (Illumina MiSeq) of these ATAC-seq libraries. The metadata indicating the contents of each well is being uploaded separately and this record will be updated once the DOIs are available.</p>
Single-cell ATAC-seq control of cross-contaminations (experiment 1)
<p>On the Fluidigm C1 platform for single-cell analysis, the cells are captured in 96 chambers arranged serially, and then washed before further processing. Thus, debris present from the loading medium or released by captured cells upstream are a possible source of contamination. We generated a control datasets using the single-cell ATAC-seq protocol available from Fluidigm's ScriptHub. We cultivated human Hep G2 and mouse Hepa 1-6 (both are liver cancer cell lines), stained them with green and red calceins (respectively), and loaded them at equal concentration in a Fluidigm medium flow cell (old design), before running the C1 single-cell ATAC-seq program.</p> <p>The data deposited here is a sequencing run (Illumina MiSeq) of these ATAC-seq libraries. The metadata indicating the contents of each well is being uploaded separately and this record will be updated once the DOIs are available.</p>
Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise
<p>Numerical simulation outputs and model schematic figures for the Python portion (main text 3.1 and Supporting Information) in the preprint <em>Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise</em> Huy D. Vo, Linda Forero, Luis Aguilera, Brian Munsky bioRxiv doi: https://doi.org/10.1101/2021.05.11.443611</p>
utility: Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR
<p><strong>Introduction</strong></p> <p>The original intent of assembling a data set of publicly-available tumor-infiltrating T cells (TILs) with paired TCR sequencing was to expand and improve the <a href="https://github.com/ncborcherding/scRepertoire">scRepertoire</a> R package. However, after some discussion, we decided to release the data set for everyone, a complete summary of the sequencing runs and the sample information can be found in the meta data of the Seurat object. This repository is the 4th version of the data, with addition of cells and changes to the workflow. </p> <p><strong>Methods</strong></p> <p><em>Single-Cell Data Processing</em></p> <p>The filtered gene matrices output from Cell Ranger align function from individual sequencing runs (10x Genomics, Pleasanton, CA) loaded into the R global environment. For each sequencing run cell barcodes were appended to contain a unique prefix to prevent issues with duplicate barcodes. The results were then ported into individual Seurat objects (<a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a>), where the cells with > 10% mitochondrial genes and/or 2.5x natural log distribution of counts were excluded for quality control purposes. At the individual sequencing run level, doublets were estimated using the scDblFinder (v1.4.0) R package.</p> <p><em>Annotation of Cells</em></p> <p>Automatic annotation was performed using the singler (v1.4.1) R package (<a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a>) with the HPCA (<a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a>) and Monaco (<a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a>) data sets as references and the fine label discriminators. Individual sequencing runs were subsetted to run through the singleR algorithm in order to reduce memory demands. The output of all the singleR analyses were collated and appended to the meta data of the seurat object. Likewise, the ProjecTILs (v0.4.1) R Package (<a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a>) was used for automatic annotation as a partially orthogonal approach. </p> <p><em>Addition of TCR data</em></p> <p>The filtered contig annotation T cell receptor (TCR) data for available sequencing runs were loaded into the R global environment. Individual contigs were combined using the combineTCR() function of scRepertoire (v1.3.5) R Package (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a>). Clonotypes were assigned to barcodes and were multiple duplicate chains for individual cells were filtered to select for the top expressing contig by read count. The clonotype data was then added to the Seurat Object with proportion across individual patients being used to calculate frequency.</p> <p><strong>Citations</strong></p> <p>As of right now, there is no citation associated with the assembled data set. However if using the data, please find the corresponding manuscript for each data set in the meta.data of the single-cell object. In addition, if using the processed data, feel free to modify the language in the methods section (above) and please cite the appropriate manuscripts of the software or references that were used.</p> <p><em>Itemized List of the Software Used</em></p> <ul> <li>Seurat v4.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a></li> <li>harmony v1.0 - <a href="https://pubmed.ncbi.nlm.nih.gov/31740819/">citation</a></li> <li>singler v1.4.1 - <a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a></li> <li>ProjecTILs v2.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a></li> <li>UCell v1.0.0 - <a href="https://www.biorxiv.org/content/10.1101/2021.04.13.439670v1">citation</a></li> <li>scRepertoire v1.3.5 - <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a></li> </ul> <p><em>Itemized List of Reference Data Used</em></p> <ul> <li>Human Primary Cell Atlas (HPCA) - <a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a></li> <li>Monaco Data Set - <a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a></li> </ul> <p><strong>Future Directions</strong></p> <ul> <li>Data Hosting for Interactive Analysis</li> <li>Easy Submission Portal for Researchers to Add Data</li> <li>Using the Data to Build a Reference Atlas</li> </ul> <p>There are areas in which we are actively hoping to develop to further facilitate the usage of the data set - if you have other suggestions, please reach out using the contact information below.</p> <p><strong>Contact</strong></p> <p>Questions, comments, and suggestions, please feel free to contact Nick Borcherding via this repository, <a href="mailto:ncborch@gmail.com">email</a>, or using <a href="https://twitter.com/theHumanBorch">twitter</a>.</p>
Single cell RNA-seq data of human hESCs comparing experiments with cDNA library equalization
GEO Series GSE156494. Homo sapiens. 288 samples. Type: Expression profiling by high throughput sequencing.
A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity
GEO Series GSE243665. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Single-Cell Analysis of Sensory Experience Regulated Gene Expression in Mouse Visual Cortex
GEO Series GSE102827. Mus musculus. 40 samples. Type: Expression profiling by high throughput sequencing.
Evaluating Capture Sequence Performance for Single-cell CRISPR-activation Experiments
GEO Series GSE164393. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Single-cell transcriptomic profiling of mouse lung tumor in response to CDK7 inhibitor, anti-PD-1 and combination treatment (Experiment 2)
GEO Series GSE129298. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Single cell transcriptome profiling of the bone marrow niche at steady state and under stress conditions (validation experiment)
GEO Series GSE123078. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
single-cell RNA-seq experiment to study mouse stomach metaplastic organoids
GEO Series GSE269839. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
GMM-Demux: sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing.
GEO Series GSE152981. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Glyoxal as alternative fixative for single cell transcriptome experiments
GEO Series GSE163736. Drosophila melanogaster; Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Targeting T cell plasticity by pooled single cell CRISPR-screening in preclinical models of kidney and gut inflammation - Colitis experiment
GEO Series GSE298377. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Multiplexed engineering and analysis of endogenous enhancer activity in single cells: Human/mouse mixing Drop-Seq experiments for K562 and MEFs
GEO Series GSE81848. Mus musculus; Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Single-cell transcriptomic profiling of mouse lung tumor in response to CDK7 inhibitor, anti-PD-1 and combination treatment (Experiment 1)
GEO Series GSE129297. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
A contamination focused approach for optimizing the single-cell RNA-seq experiment
GEO Series GSE234620. Homo sapiens; Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing.
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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.