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1,921 results for “single cell analysis”
Single-cell analysis of megakaryopoiesis in peripheral CD34+ cells: insights into ETV6-related thrombocytopenia
<p>This repository contains necessary files for reproducing the analysis in Bigot et al, 2023. The instructions for reproducing the analysis are given in github (https://github.com/poggiteam/ETV6_2020).</p>
Genome alignments for the project "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Genome alignments for data generated in the project "<em>Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol – Optimization of the protocol.</em>" Files names indicate unique identifiers of MOIRAI workflow runs, with the following structure: library name, dot, workflow ID (OP-WORKFLOW-CAGEscan-short-reads-v2.0.), dot, timestamp. The raw (FASTQ) data of each library is also deposited in Zenodo (<a href="https://doi.org/10.5281/zenodo.250156">10.5281/zenodo.250156</a>). Library names correspond to the following runs:</p> <ul> <li> NC33: 151007_M00528_0161_000000000-AEBDC</li> <li> NC37: 151204_M00528_0173_000000000-AEBEF</li> <li> NC38: 151211_M00528_0175_000000000-AE9PJ</li> <li> NC39: 160122_M00528_0185_000000000-AEB18</li> <li> NC42: 160302_M00528_0192_000000000-AELYK</li> </ul> <p>This data can be analysed using the "CAGEr" software package available from Bioconductor. The "multiplex_files.zip" file contains tables indicating which samples are biological replicates of each other or negative controls.</p>
Mammary single-cell RNA-seq analysis and prostate cancer survival as a function of H2AFJ expression for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial cells
<p>H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.</p>
Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).
<p>This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis.</p> <p>CyTOF data:</p> <ul> <li>The debarcoded fcs files (before batch-correction) can be found in <a href="https://flowrepository.org/id/FR-FCM-Z4P5">https://flowrepository.org/id/FR-FCM-Z4P5</a>. \</li> <li>Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html): <ul> <li>data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).</li> <li>data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.</li> <li>phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (<a href="https://github.com/jacoblevine/PhenoGraph">https://github.com/jacoblevine/PhenoGraph</a>) per T cell compartment.</li> <li>clusterannotation.csv: annotation for each cluster and metacluster</li> </ul> </li> </ul> <p>scRNA-seq data:</p> <ul> <li>The raw data can be found in <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450</a></li> <li>Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd): <ul> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_Sawitzki_RECAST_09_2021.xlsx">scRNAseq_Sawitzki_RECAST_09_2021.xlsx</a>: Single-cell metadata.</li> <li>scRNAseq_samples.tsv: Samples metadata.</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_genelist_annotation.xlsx">scRNAseq_genelist_annotation.xlsx</a>: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450">G</a>ene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt">scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt</a>, <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt">scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt</a>, , <a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt">scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt</a>: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).</li> <li><a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_traj18_trav10.txt">scRNAseq_traj18_trav10.txt</a>,<a href="https://zenodo.org/api/files/76286c93-628d-4251-9118-52130d4a75c6/scRNAseq_trbv25.txt">scRNAseq_trbv25.txt</a>: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.</li> </ul> </li> </ul>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
EpiScanpy: integrated single-cell epigenomic analysis
<p>All files below are in "h5ad" format, which can be opened by the Python package AnnData (see https://anndata.readthedocs.io/en/latest). The files are organized as:</p> <p><br> 1. Single-nucleus methylcytosine sequencing (snmC-seq) of neurons from the frontal cortex of young adult mouse brains from Luo et al., 2017. The files are the preprocessed input for EpiScanpy for multiple CG imputed feature spaces, 100k base pair windows, enhancers, gene bodies, promoters and CH gene bodies.<br> - processed_enhancers_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CH_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_promoter_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_windows_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> </p> <p>2. Single cell ATAC sequencing (scATAC-seq) from 10X Genomics, preprocessed peak count matrix of Next GEM v1.1 10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor. Peak matrices of Next GEM v1.1 10k PBMCs and whole blood fresh data (GEO:GSE129785) from Satpathy et al. 2019 (Greenleaf's lab). Concatenated 5000bp matrix of Fresh cortex from adult mouse brain (P50) from 10X Genomics and CEMBA180312_3B mouse brain sample from Fang et al. 2019.<br> - atac_pbmc_10k_nextgem_fragments_macs2_peaks_outter_all_chrom.h5ad<br> - atac_pbmc_10k_nextgem_fragments_merged_peaks_for_integration_greenleaf_outter_all_chrom.h5ad<br> - raw_greenleaf_pre_integration_oct_2020.h5ad (Satpathy et al. 2019)<br> - preprocessed_10x_genomics_5k_adulte_mouse_brain_Fang_et_al_2019_CEMBA180312_3B_5kb_windows.h5ad</p>
Supplementary Material: Fluorescent Protein‐Tagged Sindbis Virus E2 Glycoprotein Allows Single Particle Analysis of Virus Budding from Live Cells
<p>Supplementary Videos for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(12), 6182-6199; doi:10.3390/v7122926, http://www.mdpi.com/1999-4915/7/12/2926:</p> <p><strong>Video S1A</strong> BHK cells infected with mCherry-E2 virus at 3 h p.i. Glycoprotein containing vesicles are transported to the PM from where individual virions bud out. White arrow point to budding virions. Overall amount of glycoproteins present on the PM and the number of virus particles budding out are relatively reduced compared to the late stage of infection. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S1B </strong>Enlarged area of video S1A showing budding virus particles from PM. White arrow indicates single particle post-budding moving away from the cell. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S2A</strong> Virus budding and single particle movement associated with filopodial extensions observed from mCherry-E2 virus-infected BHK cells at 6 h p.i. Glycoprotein containing vesicle transport to the PM is also observed. Budded virions travel along the periphery of filopodia and are released from filopodial extensions to the surrounding media. Image acquisition was at a rate of 1 fps and 285 frames were acquired. Movie was generated using these images and played at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S2B</strong> Enlarged area of video S2A showing budding virus particles from filopodia. White arrow indicates virus budding from filopodial extensions. Images were acquired at a rate of 1 fps and the acquired 285 frames were used to generate the video at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S3</strong> BHK cells transfected with RNA from a non-budding cdE2 mutant <sub>416</sub>CC<sub>417</sub>/A2 mCherry-E2 virus. This non-budding mutant is unable to release fluorescent virus particles from the infected cells. The video shows the absence of fluorescent virus particle budding from the PM at 6 h post transfection even though the PM and filopodial extensions contain mCherry-E2. Despite the transport of glycoproteins to the PM, no fluorescent particles were released into the media. Yellow arrows point toward filopodial extensions. For the video, 304 images were acquired at a rate of 0.98 fps and the video was generated using the acquired images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S4</strong> BHK cells transfected with RNA from an E1 Fusion loop (G91D) mutant of mCherry-E2 virus at 6 h post transfection. This non-fusing mutant produces fluorescent virus particles at a slower rate compared to WT that are unable to fuse after entering a new cell. White arrow points to fluorescent particles that are releasing into the media from filopodial extensions. Yellow arrow represents a fluorescent particle that had entered an adjacent un-transfected cell. A total of 149 images were acquired at a rate of 0.98 fps. Video was generated using these images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p><strong>Video S5A</strong> Glycoprotein E2 (mCherry-E2; red) colocalizing with Golgi stain (green) in BHK cells infected with mCherry-E2 virus and stained with BODIPY FL C5 ceramide at 5 h p.i. and imaged at 6 h p.i. Glycoprotein-containing red vesicles originate from Golgi as evidenced from the colocalization of red and green and these vesicles display anterograde transport to the PM and the virus particles are released by budding from the PM. Fluorescent particles are also seen budding from filopodial extensions (white arrows). Images were acquired at a rate of 0.13 fps for 295 seconds. Video was generated using these acquired images at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S5B</strong> An enlarged area of the video S5A near the white arrow showing movement of particles on filopodial extensions between two cells. Movie was played at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p> </p>
Single cell analysis by Quantitative image-based cytometry (QIBC)
<p>Quantitative image-based cytometry (QIBC): Employing automated multichannel wild-field microscopy using the Olympus ScanR screening system. This system includes an inverted motorized Olympus IX83 microscope, a motorized stage, IR-laser hardware autofocus, a fast emission filter wheel with single band emission filters. </p> <p>Images were analyzed and processed using ScanR analysis software and TIBCOSpotfire software was used to plot total nuclear pixel intensities and mean (total pixel intensities divided by nuclear area) nuclear intensities.</p>
Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics
<p>This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package.</p> <p>First, generate a new folder and then download all the files into the folder.</p> <p>Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder.</p> <p>Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets.</p> <p>Finally, run the following scripts to generate the figures and tables in our manuscript.</p> <p>Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh </p> <p>Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh </p> <p>Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh </p> <p>Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh </p> <p>Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh </p> <p>Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh </p> <p> </p>
Image data for bioRxiv article named: mtFociCounter - Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci
<p>Raw imaging data to reproduce and test the findings of the bioRxiv article: <strong>mtFociCounter </strong>- Reproducible, open source and quantitative single-cell analysis of mitochondrial nucleoids and other foci. It contains data from three imaging days and 2 or three technical replicates on each day.</p> <p> </p>
[Demo Input Data] for SCAFE: a software suite for analysis of transcribed cis-regulatory elements in single cells
<p>This archive (input.tar.gz) contains the demo data for SCAFE v1.0.0 (on <a href="https://doi.org/10.5281/zenodo.7023163">Zenodo</a> or <a href="https://github.com/chung-lab/SCAFE/releases/tag/v1.0.0">Github</a>)</p> <p><em>SCAFE</em> (Single Cell Analysis of Five-prime Ends) provides an end-to-end solution for processing of single cell 5’end RNA-seq data. It takes a read alignment file (*.bam) from single-cell RNA-5’end-sequencing (e.g. 10xGenomics Chromimum®), precisely maps the cDNA 5'ends (i.e. transcription start sites, TSS), filters for the artefacts and identifies genuine TSS clusters using logistic regression. Based on the TSS clusters, it defines transcribed cis-regulatory elements (tCRE) and annotated them to gene models. It then counts the UMI in tCRE in single cells and returns a tCRE UMI/cellbarcode matrix ready for downstream analyses, e.g. cell-type clustering, linking promoters to enhancers by co-activity <em>etc</em>.</p> <p>For details on installation, usage and test run on demo data, visit <a href="https://github.com/chung-lab/SCAFE">https://github.com/chung-lab/SCAFE</a></p>
Optimized summary-statistic-based single-cell meta-analysis. Input files
<p>This dataset contains information about the input files used in the Optimized summary-statistic-based single-cell meta-analysis research project. </p> <p> </p>
Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"
<p>Data related to the publication Murschhauser <em>et al.</em>: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details.</p> <p>The format of the data is described below.</p> <p>The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity.</p> <p>Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers.</p> <p>The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below.</p> <p>The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory.</p> <p>The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following:</p> <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul> <p> </p>
Text-fig. 13. Scanning electron microscope (SEM) images of pollen or spore clump with pollen grains or spores of unknown affinity that occur separately or adhering together in dyads, triads and tetrads; Torres Vedras locality, Portugal. a) Clump of pollen or spores that yielded the pollen or spores in this Text-figure; b–f) Grains adhering together in twos, threes or fours (b–e) or occurring singly and apparently with a proximal trilete mark (f); note that the adhering grains are connected by a smooth bandlike covering, perhaps remains of the microspore mother cell; note also abundant orbicules of various sizes among and over the grains. Specimen, TV44-S148149. Scale bars 300 Μm (a), 30 Μm (b–f). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 13. Scanning electron microscope (SEM) images of pollen or spore clump with pollen grains or spores of unknown affinity that occur separately or adhering together in dyads, triads and tetrads; Torres Vedras locality, Portugal. a) Clump of pollen or spores that yielded the pollen or spores in this Text-figure; b–f) Grains adhering together in twos, threes or fours (b–e) or occurring singly and apparently with a proximal trilete mark (f); note that the adhering grains are connected by a smooth bandlike covering, perhaps remains of the microspore mother cell; note also abundant orbicules of various sizes among and over the grains. Specimen, TV44-S148149. Scale bars 300 Μm (a), 30 Μm (b–f).
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>
Comparative Analysis of Droplet- vs. Microwell-based Whole Transcriptome Single-Cell Sequencing Technologies in Complex Human Tissues
<p>In the past decade, high-dimensional single-cell omics tools have enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, recent investigations suggest that each technique has its unique strengths but also technology-inherent limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium <em>vs.</em> microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.</p> <p>Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of detectable cell populations differed. This could in part be ascribed to differences in the performance to recover cells with low-mRNA content. Hence, immune cells such as neutrophils are underrepresented in data generated with the widely used droplet-based scRNA-seq protocol, highlighting the importance of considering platform limitations in low mRNA content cell recovery. In contrast, droplet-based scRNA-seq demonstrated superiority in terms of recovering cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation, affecting the composition of identified tissue profiles and the exploratory value of the generated datasets. Overall, our study emphasizes the importance of carefully selecting the appropriate scRNA-seq platform to improve cell type representation and obtain a more comprehensive and accurate understanding of the TME.</p>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency
<p>This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen <em>et al</em>. Please refer to the READMEs in the directories, which are summarized below.</p> <p>The record contains the following files:<br> <br> `clusters.tsv`: <strong> </strong>contains the cluster id, name and colour of clusters in the paper</p> <p><strong>scATAC.zip</strong></p> <p>Analysis products for the single-cell ATAC-seq data. Contains:</p> <p>- `cells.tsv`: list of barcodes that pass QC. Columns include:<br> - `barcode`<br> - `sample`: (time point)<br> - `umap1`<br> - `umap2`<br> - `cluster`<br> - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root<br> - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root<br> - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `features.tsv`: 50 dimensional representation of each cell <br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed` </p> <p><strong>scATAC_clusters.zip</strong></p> <p>Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data. </p> <p>- `clusters.tsv`: contains the cluster id, name and colour used in the paper<br> - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format.<br> - `fragments`: contains per cluster fragment files </p> <p><strong>scATAC_scRNA_integration.zip</strong></p> <p>Analysis products from the integration of scATAC with scRNA. Contains:</p> <p>- `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45.<br> - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type.<br> - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode.</p> <p><strong>scRNA.zip</strong></p> <p>Analysis products for the single-cell RNA-seq data. Contains:</p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC)<br> - `genes.txt`: list of all genes<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC) <br> - `sample`: sample name (D0, D2, .., D14, iPSC)<br> - `umap1`<br> - `umap2`<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `cluster`<br> - `percent.mt`: percent of mitochondrial transcripts in cell<br> - `percent.oskm`: percent of OSKM transcripts in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` <br> - `pca.tsv`: first 50 PC of each cell<br> - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv`</p> <p><strong>multiome.zip</strong></p> <p><em>multiome/snATAC:</em></p> <p>These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2). </p> <p>- `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:<br> - `barcode`<br> - `umap1`: These are the coordinates used for the figures involving multiome in the paper.<br> - `umap2`: ^^^ <br> - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq<br> - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels. <br> - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA.<br> - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`.<br> - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`.<br> - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`.</p> <p><em>multiome/snRNA:</em></p> <p>- `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency.<br> - `genes.txt`: list of all genes (this is different from the list in scRNA analysis)<br> - `cells.tsv`: list of barcodes that pass QC across samples. Contains:<br> - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively) <br> - `sample`: sample name (D1M, D2M)<br> - `nCount_RNA`<br> - `nFeature_RNA`<br> - `percent.oskm`: percent of OSKM genes in cell<br> - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt` </p>
Vizgen MERFISH files for Single-cell analysis reveals M. tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs
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