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2,598 results for “Single cell sequencing”
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p> </p> <p><strong>Data:</strong></p> <p>1. Immgen_cell_types.cls : Microarray Phase 1 expression</p> <p>2. Immgen_norm_exp_data.gct : Microarray Phase 1 class</p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary: </strong>Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>cDC1_maturation_loom_file.rds : Loom file used for RNA Velocity Analysis</p> <p><br> </p> <p> </p> <p> </p>
Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory
<p><strong>Summary:</strong> Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.<br> To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.</p> <p><strong>Data: </strong></p> <p>MDAlab_cDC1_maturation.tar : Docker image used for the analysis</p>
Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"
<p>Extended data for "The need to reassess single-cell RNA sequencing datasets: the importance of biological sample processing"</p>
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
<p class="MsoNormal">Alzheimer's disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31<sup><span>-</span></sup>CD45<sup><span>-</span></sup>Ter119<sup><span>-</span></sup>CD24<sup><span>-</span></sup> NPCs from Tg-SwDI, WT, and Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup></em> mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/<em>Usp16<sup><span>+/-</span></sup> </em>of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.</p>
Script and single cell RNA sequencing datasets of Biomphalaria glabrata hemocyte
<p>The results of the gene and cell barcode counts (feature-barcode matrices) are available in the file "filtered_feature_bc_matrix_Naive". These data have been processed by cellRanger v3.1.0 and can be used with the script "scRNAseq_Biomphalaria_naive" which gathers all the analyses done for publication.</p> <p> </p>
Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"
<p>Supplementary Data for "The shaky foundations of simulating single-cell RNA sequencing data"</p> <p>See description.txt, Supplementary Text, Methods and https://github.com/HelenaLC/simulation-comparison for further description of the files available here.</p>
Sequence tracks of Ago2 Neural stem cells and differentiated neurons from single-cells- Related to Fig. 5
<p>Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Ago2 transcript which was then sent off for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</p>
Single cell whole genome sequencing from Funnell, O'Flanagan, Williams et al
<p>This repository provides the processed data necessary to reproduce the results from: "Single cell genomic variation induced by mutational processes in cancer<strong> </strong><em>Funnell, O’Flanagan, Williams et al</em>"</p> <p>This includes the following:</p> <ul> <li>Single cell whole genome sequencing <ul> <li>Allele specific copy number profiles</li> <li>SNV counts per cell</li> <li>Structural variant counts per cell</li> <li>QC metrics</li> <li>clone assignments</li> <li>phylogenetic trees computed with sitka</li> <li>benchmarking results vs other methods</li> </ul> </li> <li>bulk whole genome sequencing <ul> <li>copy number profiles</li> <li>SNVs</li> </ul> </li> <li>10X single cell RNA sequencing <ul> <li>count matrices</li> <li>seurat Rdata objects</li> </ul> </li> <li>analysis tables <ul> <li>downstream processed results used to generate figures</li> </ul> </li> <li>oxford nanopore <ul> <li>phasing results</li> </ul> </li> </ul> <p> </p> <p>For further information please feel free to get in touch with Marc Williams (william1 [at] mskcc.org)</p> <p> </p>
Exploring cell diversity and fidelity in crustacean limb regeneration using single-nucleus RNA sequencing
<p>R objects containing datasets generated by snRNA-seq on Parhyale hawaiensis limbs. These datasets have been generated during my Phd thesis.</p>
Datasets accompanying "Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA-sequencing"
<p>Datasets include two seurat objects: one with all unfiltered cells and raw expression data (seu_unfiltered.rds) and one dataset with normalised expression and latest annotations (differentiation_object_latest.rds). Additionally, a single-cell object containing post-(py)SCENIC analysis is added (ipsc_scenic.h5ad).</p>
Datasets for evaluating SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data
<p>This resource contains pre-processed A. thaliana root , the H. sapiens aortic valve datasets, PBMC Covid atlas and public 10x datasetse used in the paper, SCEMENT: Scalable and Memory Efficient Integration of Large-scale Single Cell RNA-sequencing Data. The raw datasets provided in the links below are pre-processed for quality control with respect to both cells and genes. </p> <p>A. thaliana datasets are sourced from the following locations at <a href="https://www.ebi.ac.uk/gxa/sc/home">Single-cell Gene expression Atlas </a>and <a href="https://www.ncbi.nlm.nih.gov/geo/">Gene Expression Omnibus (GEO)</a>:</p> <ol> <li>E-GEOD-121619 : <a title="E-GEOD-121619" href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-121619/results</a></li> <li>E-GEOD-152766 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-152766/results</a></li> <li>E-GEOD-158761 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-158761/results</a></li> <li>E-GEOD-123013 : <a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results">https://www.ebi.ac.uk/gxa/sc/experiments/E-GEOD-123013/results</a></li> </ol> <p>H. sapiens datasets are obtained from the NCBI database : <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA562645/ </a></p> <ol> <li>GSE152766: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152766</a></li> <li>GSE158761: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE158761</a></li> </ol> <p>All COVID atlas datasets are from: <a href="http://covid19.cancer-pku.cn">http://covid19.cancer-pku.cn</a> . covid_atlas_data1.zip contains the h5ad files and covid_atlas_data2.zip contains the Seurat rds files.</p> <p>PBMC datasets are from the following public sources:</p> <table> <tbody> <tr> <td>Dataset Name</td> <td>Chemistry Version</td> <td>Web Link</td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-ht-v3-1-chromium-x-3-1-high</a></td> </tr> <tr> <td>20k Human PBMCs, 3' HT v3.1, Chromium X</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0">https://www.10xgenomics.com/datasets/20-k-human-pbm-cs-3-ht-v-3-1-chromium-x-3-1-high-6-1-0</a></td> </tr> <tr> <td>10k Human PBMCs, 3' v3.1, Chromium Controller</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high">https://www.10xgenomics.com/datasets/10k-human-pbmcs-3-v3-1-chromium-controller-3-1-high</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 1)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-1-3-1-standard-3-1-0</a></td> </tr> <tr> <td>Healthy PBMC Chromium Connect (channel 5)</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0">https://www.10xgenomics.com/datasets/peripheral-blood-mononuclear-cells-pbm-cs-from-a-healthy-donor-chromium-connect-channel-5-3-1-standard-3-1-0</a></td> </tr> <tr> <td>10k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/10-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v2 chemistry)</td> <td>v2.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-2-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>1k PBMCs from a Healthy Donor (v3 chemistry)</td> <td>v3.0</td> <td><a href="https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0">https://www.10xgenomics.com/datasets/1-k-pbm-cs-from-a-healthy-donor-v-3-chemistry-3-standard-3-0-0</a></td> </tr> <tr> <td>Fresh 68k PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/fresh-68-k-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor A)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-a-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor B)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-b-1-standard-1-1-0</a></td> </tr> <tr> <td>Frozen PBMCs (Donor C)</td> <td>v1.0</td> <td><a href="https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0">https://www.10xgenomics.com/datasets/frozen-pbm-cs-donor-c-1-standard-1-1-0</a></td> </tr> <tr> <td>PBMCs from a Healthy Donor: Whole Transcriptome Analysis</td> <td>v3.1</td> <td><a href="https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0">https://www.10xgenomics.com/datasets/pbm-cs-from-a-healthy-donor-whole-transcriptome-analysis-3-1-standard-4-0-0</a></td> </tr> <tr> <td>PBMC 600K</td> <td>v1</td> <td><a href="https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads">https://www.ebi.ac.uk/gxa/sc/experiments/E-HCAD-4/downloads</a></td> </tr> <tr> <td>GSM4560071</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560071</a></td> </tr> <tr> <td>GSM4560074</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560074</a></td> </tr> <tr> <td>GSM4560070</td> <td>v2.0</td> <td><a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM4560070</a></td> </tr> </tbody> </table> <p>References for the Datasets :</p> <ol> <li>H. sapiens dataset: Kang Xu, Shangbo Xie,Yuming Huang,Tingwen Zhou, Ming Liu, Peng Zhu, Chunli Wang, Jiawei Shi, Fei Li,Frank W. Sellke and Nianguo Dong (2020) Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease.</li> <li>E-GEOD-152766: Shahan R, Hsu C, Nolan TM, Cole BJ, Taylor IW et al. (2020) A single cell Arabidopsisroot atlas reveals developmental trajectories in wild type and cell identity mutants.</li> <li>E-GEOD-121619: Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L et al. (2019) Dynamics of Gene Expression in Single Root Cells of Arabidopsis thaliana.</li> <li>E-GEOD-123013: Ryu KH, Huang L, Kang HM, Schiefelbein J. (2019) Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells.</li> <li>E-GEOD-158761: Gala HP, Lanctot A, Jean-Baptiste K, Guiziou S, Chu JC et al. (2020) A single cell view of the transcriptome during lateral root initiation in Arabidopsis thaliana.</li> <li>COVID Atlas Reference: Xianwen Ren, Wen Wen, Xiaoying Fan et.al. (2021) COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas</li> <li>PBMC data are downloaded from respective links</li> </ol>
Analysis of public single-cell sequencing database of COVID lung samples
<p>Lung endothelial cells from three published scRNA-seq datasets (GSE122960, GSE149878, GSE171668) of healthy subjects and COVID-19 patients were collected for further integrative analyses. The endothelial cells were classified into three sub-groups according to their distinguished expression of IL7R, DKK2, and EDNRB. For differential analysis of gene expression, counts per million of aggregated UMIs in each group were adopted in Wilcoxon rank-sum test.</p>
Data for Isotype-aware Inference of B cell Clonal Lineage Trees from Single-cell Sequencing Data
<p>This is the accompanying data to the manuscript titled<em> Isotype-aware Inference of B cell Clonal Lineage Trees from Single-cell Sequencing Data</em>. To reproduce the TRIBAL output please use this <a href="https://doi.org/10.5281/zenodo.12741290">code repository</a> as the arguments and codebase may have changed since release. </p>
Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'
<p>Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods". </p> <p>https://www.ncbi.nlm.nih.gov/pubmed/28212749</p> <p> </p> <p>In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences.</p> <p>MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e</p>
Chronic social defeat stress induces meningeal neutrophilia via type I interferon signaling: single cell RNA sequencing data
<p>Meningeal single cell RNA sequencing data</p> <p>Meningeal samples were collected from both dorsal and ventral skull, avoiding inclusion of choroid plexus. Samples were digested in 2.5 mg/mL Collagenase D (Cat. #11088858001; Roche) and 12.5 μL of 0.5 mg/mL DNAseI (Cat. #L5002139; Worthington), put on a shaker at 370C for 30 m, diluted with cold HBSS + 0.1% BSA, and mashed through a 70 μm cell strainer prior to sorting.</p> <p>Data represent live, nucleated, singlet cells (DAPI-DRAQ5+) sorted on a BD FACS Aria Fusion into HBSS + 10% FBS prior to droplet encapsulation using 10x Genomics’ Drop-seq platform (Chromium v2).</p> <p>10X chip lane is indicate by 'group' column</p> <p>Group 1 = 4 pooled homecage control (unstressed) mice</p> <p>Group 2 = 4 pooled homecage control (unstressed) mice</p> <p>Group 3 = 4 pooled mice exposed to chronic social defeat for 14 days; tissue was collected 2 hours following final defeat</p> <p>See the following repositories for data processing:</p> <p><a href="https://github.com/maryellenlynall/2019_bcell_stress/blob/master/bcellstress20.Rmd">https://github.com/maryellenlynall/2019_bcell_stress/</a> (processing from raw files starts at bcellstress020.Rmd)</p> <p><a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a></p> <p>We also provide a processed dataset (processed.RData) with assays 'counts' and 'logcounts' which is the processed single cell object saved at line "# Save object for upload to Zenodo" in script <a href="https://github.com/staceykigar/meningeal_neut/">https://github.com/staceykigar/meningeal_neut/</a>neutrophilstress01.Rmd </p> <p>Cluster annotations are in sce$Annotation</p> <p>Neutrophil subcluster annotations are in sce$Subcluster</p> <p>Sample condition is in sce$cond, where "HC" indicates homecage control and "SD" indicates chronic social defeat</p> <p>10X chip lane is in sce$group</p>
Improving the efficiency of single cell genome sequencing based on overlapping pooling strategy
Single cell genome sequencing has become a useful tool in medicine and biology studies. However, an independent library is required for each cell in single cell genome sequencing, so that the cost grows in step with the number of cells. In this study, we report a study on efficient single-cell copy number variation (CNV) analysis based on overlapping pooling strategy together with branch and bound (B&B) algorithm. Single cells are overlapped pooled before sequencing, and later are assorted into specific types by estimating their CNV patterns by B&B algorithm. Instead of constructing libraries for each cell, a library is required only for each pool. As long as the number of pools is smaller than the cells, fewer libraries are needed, and a lower cost is spent. Through computer simulations, we overlapping pooled 80 cells into 40 and 27 pools and classified them into cell types based on CNV pattern. The results showed that 84% cells in 40 pools and 76.5% cells in 27 pools were correctly classified on average, while only half or one-third of the sequencing libraries are required. Combining with traditional approaches, our method is expected to significantly improve the efficiency of single cell genome sequencing.
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>
Additional raw data in `Cell-type-specific co-expression inference from single cell RNA-sequencing data'.
<p>This repository holds the additional raw data used to generate figures in the publication "<strong><em>Cell-type-specific co-expression inference from single cell RNA-sequencing data</em></strong>" (preprint version: <a href="http://source%20code%20repo%20for%20%60cell-type-specific%20co-expression%20inference%20from%20single%20cell%20rna-sequencing%20data%27./">https://www.biorxiv.org/content/10.1101/2022.12.13.520181v1</a>).</p> <p>Table of contents:</p> <ul> <li>Figure_1B.rds: <ul> <li>raw data of Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_1B.rds: <ul> <li>raw data of Supplementary Figure 1B </li> <li>co-expression estimates of 500*499/2 gene pairs across 100 replicates for 7 methods under two settings of sequencing detph variations</li> </ul> </li> <li>Supplementary_Figure_2.rds: <ul> <li>raw data of Supplementary Figure 2 </li> <li>empirical power evaluated for 4999 gene pairs and 6 methods</li> </ul> </li> <li>Figure_3B.rds: <ul> <li>raw data of Figure 3B</li> <li>co-expression estimates of a network of 500 genes for 9 methods across 100 replicates</li> </ul> </li> <li>Additional_Raw_Data.xlsx <ul> <li>raw data of Figure 3A: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods</li> <li>raw data of Figure 3C: running times for 11 methods</li> <li>raw data of Supplementary Figure 3: (geometric mean expression levels, co-expression estimates) for 4999 gene pairs and 11 methods under two settings of sequencing detph variations</li> </ul> </li> </ul>
Characterising neutrophil subtypes in cancer using human and murine single-cell RNA sequencing datasets
<p>Single cell RNA sequencing data generated by 10xGenomics for Neutrophils derived from colorectal cancer (CRC) KPN tumours (CRC_KPN_counts.csv) and normalised counts (CRC_KPN_NormalisedCounts.csv) as well as from other mouse models of CRC carrying AKPT, BPN, BP and KP mutations (CRC_other_counts.csv and CRC_other_NormalisedCounts.csv), together with the relevant metadata (CRC_KPN_metadata.csv and CRC_other_metadata.csv).</p>
ScienceDex guides
Understand access before you commit
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.