This dataset contains all the Seurat objects that were used for generating all the figures in Pal et al. 2021 (https://doi.org/10.15252/embj.2020107333). All the Seurat objects were created under R v3.6.1 using the Seurat package v3.1.1. The detailed information of each object is listed in a table in Chen et al. 2021.

Scripts used for analysis of V1 and V2 Datasets.seurat_v1.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, PCA analysis, clustering, tSNE visualization. Used for v1 datasets. merge_seurat.R - merge two or more seurat objects into one seurat object. Perform linear regression to remove batch effects from separate objects. Used for v1 datasets. subcluster_seurat_v1.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA. Used for v1 datasets.seurat_v2.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, and PCA analysis. Used for v2 datasets. clustering_markers_v2.R - clustering and tSNE visualization for v2 datasets. subcluster_seurat_v2.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA analysis. Used for v2 datasets.seurat_object_analysis_v1_and_v2.R - downstream analysis and plotting functions for seurat object created by seurat_v1.R or seurat_v2.R. merge_clusters.R - merge clusters that do not meet gene threshold. Used for both v1 and v2 datasets. prepare_for_monocle_v1.R - subcluster cells of interest and perform linear regression, but not scaling in order to input normalized, regressed values into monocle with monocle_seurat_input_v1.R monocle_seurat_input_v1.R - monocle script using seurat batch corrected values as input for v1 merged timecourse datasets. monocle_lineage_trace.R - monocle script using nUMI as input for v2 lineage traced dataset. monocle_object_analysis.R - downstream analysis for monocle object - BEAM and plotting. CCA_merging_v2.R - script for merging v2 endocrine datasets with canonical correlation analysis and determining the number of CCs to include in downstream analysis. CCA_alignment_v2.R - script for downstream alignment, clustering, tSNE visualization, and differential gene expression analysis.

Single cell RNA-sequencing dataset of peripheral blood mononuclear cells (pbmc: T, B, NK and monocytes) extracted from two healthy donors.

Cells labeled as C26 come from a 30 years old female and cells labeled as C27 come from a 53 years old male. Cells have been isolated from blood using ficoll. Samples were sequenced using standard 3' v3 chemistry protocols by 10x genomics. Cellranger v4.0.0 was used for the processing, and reads were aligned to the ensembl GRCg38 human genome (GRCg38_r98-ensembl_Sept2019). QC metrics were calculated on the count matrix generated by cellranger (filtered_feature_bc_matrix). Cells with less than 3 genes per cells, less than 500 reads per cell and more than 20% of mithocondrial genes were discarded.

The processing steps was performed with the R package Seurat (https://satijalab.org/seurat/), including sample integration, data normalisation and scaling, dimensional reduction, and clustering. SCTransform method was adopted for the normalisation and scaling steps. The clustered cells were manually annotated using known cell type markers.

Files content:

- raw_dataset.csv: raw gene counts

- normalized_dataset.csv: normalized gene counts (single cell matrix)

- cell_types.csv: cell types identified from annotated cell clusters

- cell_types_macro.csv: cell macro types

- UMAP_coordinates.csv: 2d cell coordinates computed with UMAP algorithm in Seurat

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Methods Mice. The Cornell University Institutional Animal Care and Use Committee (IACUC) approved all animal protocols, and experiments were performed in compliance with its institutional guidelines. Adult C57BL/6J mice (mus musculus) were obtained from Jackson Laboratories (#000664; Bar Harbor, ME) and were used at 4-7 months of age. Aged C57BL/6J mice were obtained from the National Institute of Aging (NIA) Rodent Aging Colony and were used at 20 months of age. For new scRNAseq experiments, female mice were used in each experiment.

Mouse injuries and single-cell isolation. To induce muscle injury, both tibialis anterior (TA) muscles of old (20 months) C57BL/6J mice were injected with 10 µl of notexin (10 µg/ml; Latoxan; France). At 0, 1, 2, 3.5, 5, or 7 days post-injury (dpi), mice were sacrificed and TA muscles were collected and processed independently to generate single-cell suspensions. Muscles were digested with 8 mg/ml Collagenase D (Roche; Switzerland) and 10 U/ml Dispase II (Roche; Switzerland), followed by manual dissociation to generate cell suspensions. Cell suspensions were sequentially filtered through 100 and 40 μm filters (Corning Cellgro #431752 and #431750) to remove debris. Erythrocytes were removed through incubation in erythrocyte lysis buffer (IBI Scientific #89135-030).

Single-cell RNA-sequencing library preparation. After digestion, single-cell suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of 106 cells/ml. Cells were counted manually with a hemocytometer to determine their concentration. Single-cell RNA-sequencing libraries were prepared using the Chromium Single Cell 3’ reagent kit v3 (10x Genomics, PN-1000075; Pleasanton, CA) following the manufacturer’s protocol. Cells were diluted into the Chromium Single Cell A Chip to yield a recovery of 6,000 single-cell transcriptomes. After preparation, libraries were sequenced using on a NextSeq 500 (Illumina; San Diego, CA) using 75 cycle high output kits (Index 1 = 8, Read 1 = 26, and Read 2 = 58). Details on estimated sequencing saturation and the number of reads per sample are shown in Sup. Data 1.

Spatial RNA sequencing library preparation. Tibialis anterior muscles of adult (5 mo) C57BL6/J mice were injected with 10µl notexin (10 µg/ml) at 2, 5, and 7 days prior to collection. Upon collection, tibialis anterior muscles were isolated, embedded in OCT, and frozen fresh in liquid nitrogen. Spatially tagged cDNA libraries were built using the Visium Spatial Gene Expression 3’ Library Construction v1 Kit (10x Genomics, PN-1000187; Pleasanton, CA) (Fig. S7). Optimal tissue permeabilization time for 10 µm thick sections was found to be 15 minutes using the 10x Genomics Visium Tissue Optimization Kit (PN-1000193). H&E stained tissue sections were imaged using Zeiss PALM MicroBeam laser capture microdissection system and the images were stitched and processed using Fiji ImageJ software. cDNA libraries were sequenced on an Illumina NextSeq 500 using 150 cycle high output kits (Read 1=28bp, Read 2=120bp, Index 1=10bp, and Index 2=10bp). Frames around the capture area on the Visium slide were aligned manually and spots covering the tissue were selected using Loop Browser v4.0.0 software (10x Genomics). Sequencing data was then aligned to the mouse reference genome (mm10) using the spaceranger v1.0.0 pipeline to generate a feature-by-spot-barcode expression matrix (10x Genomics).

Download and alignment of single-cell RNA sequencing data. For all samples available via SRA, parallel-fastq-dump (github.com/rvalieris/parallel-fastq-dump) was used to download raw .fastq files. Samples which were only available as .bam files were converted to .fastq format using bamtofastq from 10x Genomics (github.com/10XGenomics/bamtofastq). Raw reads were aligned to the mm10 reference using cellranger (v3.1.0).

Preprocessing and batch correction of single-cell RNA sequencing datasets. First, ambient RNA signal was removed using the default SoupX (v1.4.5) workflow (autoEstCounts and adjustCounts; github.com/constantAmateur/SoupX). Samples were then preprocessed using the standard Seurat (v3.2.1) workflow (NormalizeData, ScaleData, FindVariableFeatures, RunPCA, FindNeighbors, FindClusters, and RunUMAP; github.com/satijalab/seurat). Cells with fewer than 750 features, fewer than 1000 transcripts, or more than 30% of unique transcripts derived from mitochondrial genes were removed. After preprocessing, DoubletFinder (v2.0) was used to identify putative doublets in each dataset, individually. BCmvn optimization was used for PK parameterization. Estimated doublet rates were computed by fitting the total number of cells after quality filtering to a linear regression of the expected doublet rates published in the 10x Chromium handbook. Estimated homotypic doublet rates were also accounted for using the modelHomotypic function. The default PN value (0.25) was used. Putative doublets were then removed from each individual dataset. After preprocessing and quality filtering, we merged the datasets and performed batch-correction with three tools, independently- Harmony (github.com/immunogenomics/harmony) (v1.0), Scanorama (github.com/brianhie/scanorama) (v1.3), and BBKNN (github.com/Teichlab/bbknn) (v1.3.12). We then used Seurat to process the integrated data. After initial integration, we removed the noisy cluster and re-integrated the data using each of the three batch-correction tools.

Cell type annotation. Cell types were determined for each integration method independently. For Harmony and Scanorama, dimensions accounting for 95% of the total variance were used to generate SNN graphs (Seurat::FindNeighbors). Louvain clustering was then performed on the output graphs (including the corrected graph output by BBKNN) using Seurat::FindClusters. A clustering resolution of 1.2 was used for Harmony (25 initial clusters), BBKNN (28 initial clusters), and Scanorama (38 initial clusters). Cell types were determined based on expression of canonical genes (Fig. S3). Clusters which had similar canonical marker gene expression patterns were merged.

Pseudotime workflow. Cells were subset based on the consensus cell types between all three integration methods. Harmony embedding values from the dimensions accounting for 95% of the total variance were used for further dimensional reduction with PHATE, using phateR (v1.0.4) (github.com/KrishnaswamyLab/phateR).

Deconvolution of spatial RNA sequencing spots. Spot deconvolution was performed using the deconvolution module in BayesPrism (previously known as “Tumor microEnvironment Deconvolution”, TED, v1.0; github.com/Danko-Lab/TED). First, myogenic cells were re-labeled, according to binning along the first PHATE dimension, as “Quiescent MuSCs” (bins 4-5), “Activated MuSCs” (bins 6-7), “Committed Myoblasts” (bins 8-10), and “Fusing Myoctes” (bins 11-18). Culture-associated muscle stem cells were ignored and myonuclei labels were retained as “Myonuclei (Type IIb)” and “Myonuclei (Type IIx)”. Next, highly and differentially expressed genes across the 25 groups of cells were identified with differential gene expression analysis using Seurat (FindAllMarkers, using Wilcoxon Rank Sum Test; results in Sup. Data 2). The resulting genes were filtered based on average log2-fold change (avg_logFC > 1) and the percentage of cells within the cluster which express each gene (pct.expressed > 0.5), yielding 1,069 genes. Mitochondrial and ribosomal protein genes were also removed from this list, in line with recommendations in the BayesPrism vignette. For each of the cell types, mean raw counts were calculated across the 1,069 genes to generate a gene expression profile for BayesPrism. Raw counts for each spot were then passed to the run.Ted function, using

While human inflammatory skin diseases' cellular and molecular features are well-characterized, their tissue context and systemic impact remain poorly understood. We thus profiled human psoriasis (PsO) as a prototypic immune-mediated condition with a high preference for extra-cutaneous involvement. Spatial transcriptomics (ST) analyses of 25 healthy, active, and clinically uninvolved skin biopsies, and integration with public single-cell transcriptomics data revealed striking differences in immune microniches between healthy and inflamed skin. Tissue scale-cartography further identified core disease features across all active lesions, including the emergence of an inflamed suprabasal epidermal state and the presence of B lymphocytes in lesional skin. Notably, both lesional and distal non-lesional samples were stratified by skin disease severity, and not by the presence of systemic disease. This segregation was driven by macrophage-, fibroblast- and lymphatic-enriched spatial regions with gene signatures associated with metabolic dysfunction. Taken together, these findings suggest that mild and severe forms of PsO have distinct molecular features and that severe PsO may profoundly alter the cellular and metabolic make up of distal unaffected skin sites. Additionally, our study provides an unprecedented resource for the research community to study spatial gene organization of healthy and inflamed human skin.

Seurat object stored in RDS file format. Three fresh tumor samples (respectively from INI254, INI255 and INI267) were prepared and loaded in 10x Chromium instrument (10x Genomics). Libraries were prepared using a Single Cell 3’ Reagent Kit (V2 chemistry, 10X Genomics) and sequenced on an Illumina HiSeq2500 using paired-end 26x98 bp as sequencing mode, targeting at least 50 000 reads par cell. Mapping and UMI counting per gene were performed using cellranger tool (version 3.1.0) and the hg19 reference genome version. Cells with both a low number of genes and a high proportion of mitochondrial RNA were discarded. The threshold of the minimum number of detected genes was set as the 5th percentile of the distribution of the number of detected genes in all cells while the maximum proportion of mitochondrial genes were set by visual inspection of the plot of the number of detected genes versus the percentage of mitochondrial gene of each sample. scRNA-seq data integration was performed using the CCA-based implemented in Seurat version 3. The clustering was conducted using the graph-based modularity optimization Louvain algorithm implemented in Seurat v3. The resolution 0.2 (integrated_snn_res.0.2) was choosen for the final result.

This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have >2 TB of disk space and >64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable.

R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html

Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘test-all-new.bash’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘test-all.log’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.

Seurat object stored in RDS file format.Three fresh primary mouse RT tumor samples were prepared and loaded in 10x Chromium instrument (10x Genomics). Libraries were prepared using a Single Cell 3’ Reagent Kit (V2 chemistry, 10X Genomics) and sequenced on an Illumina HiSeq2500 using paired-end 26x98 bp as sequencing mode, targeting at least 50 000 reads par cell. Mapping and UMI counting per gene were performed using cellranger tool (version 3.1.0) and the hg19 reference genome version.Cells with both a low number of genes and a high proportion of mitochondrial RNA were discarded. The threshold of the minimum number of detected genes was set as the 5th percentile of the distribution of the number of detected genes in all cells while the maximum proportion of mitochondrial genes were set by visual inspection of the plot of the number of detected genes versus the percentage of mitochondrial gene of each sample.scRNA-seq data integration was performed using the CCA-based implemented in Seurat version 3. The clustering was conducted using the graph-based modularity optimization Louvain algorithm implemented in Seurat v3. The resolution 0.4 (integrated_snn_res.0.4) was choosen for the final result.

The attached R Scripts supplement our protocol paper currently under editorial review at the Journal of Visualized Experiments.Scope of the article:This protocol describes the general processes and quality control checks necessary for preparing healthy adult single cells in preparation for droplet-based, high-throughput single cell RNA-Seq analysis using the 10X Genomics' Chromium System. We also describe sequencing parameters, alignment and downstream single-cell bioinformatic analysis.

Annotated Seurat object of scRNA-seq from female and male murine neutrophils

This record includes training materials associated with the Australian BioCommons workshop 'Single cell RNAseq analysis in R'. This workshop took place over two, 3.5 hour sessions on 26 and 27 October 2023. Event description Analysis and interpretation of single cell RNAseq (scRNAseq) data requires dedicated workflows. In this hands-on workshop we will show you how to perform single cell analysis using Seurat - an R package for QC, analysis, and exploration of single-cell RNAseq data. We will discuss the 'why' behind each step and cover reading in the count data, quality control, filtering, normalisation, clustering, UMAP layout and identification of cluster markers. We will also explore various ways of visualising single cell expression data. This workshop is presented by the Australian BioCommons, Queensland Cyber Infrastructure Foundation (QCIF) and the Monash Genomics and Bioinformatics Platform with the assistance of a network of facilitators from the national Bioinformatics Training Cooperative. Lead trainers: Sarah Williams, Adele Barugahare, Paul Harrison, Laura Perlaza Jimenez Facilitators: Nick Matigan, Valentine Murigneux, Magdalena (Magda) Antczak Infrastructure provision: Uwe Winter Coordinator: Melissa Burke Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. scRNAseq_Schedule (PDF): A breakdown of the topics and timings for the workshop Materials shared elsewhere: This workshop follows the tutorial 'scRNAseq Analysis in R with Seurat' https://swbioinf.github.io/scRNAseqInR_Doco/index.html Slides used to introduce key topics are available via GitHub https://github.com/swbioinf/scRNAseqInR_Doco/tree/main/slides This material is based on the introductory Guided Clustering Tutorial tutorial from Seurat. It is also drawing from a similar workshop held by Monash Bioinformatics Platform Single-Cell-Workshop, with material here.

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection. Methods Study participants, blood draws and processing Participants were recruited as described previously from adults who had a positive SARS-COV-2 RT-PCR test at Stanford Health Care (NCT04373148). Collection of Covid samples occurred between May to December 2020. The cohort used in this study consisted of asymptomatic (n=2), mild (n=17), and moderate (n=3) COVID-19 infections, some of whom developed long term COVID-19 (n=15). The clinical case severities at the time of diagnosis were defined as asymptomatic, moderate or mild according to the guidelines released by NIH. Long term (LT) COVID was defined as symptoms occurring 30 or more days after infection, consistent with CDC guidelines. Some participants in our study continued to have LT COVID symptoms 90 days after diagnosis (n=12). Exclusion criteria for COVID sample study were NIH severity diagnosis of severe or critical at the time of positive covid test. Samples selected for this study were obtained within 76 days of positive PCR COVID-19 test date. Healthy controls were selected who had sample collection before 2020. Informed consent was obtained from all participants. All protocols were approved by the Stanford Administrative Panel on Human Subjects in Medical Research. Peripheral blood was drawn by venipuncture and using validated and published procedures, peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll-based density gradient centrifugation, frozen in aliquots and stored in liquid nitrogen at -80°C , until thawing. A summary of participant demographics is presented in Supp. Table 1.
ILC Enrichment, single cell captures for Abseq and targeted mRNAseq Participant PBMCs were thawed, and each sample stained with Sample Tag (BD #633781) at room temperature for 20 minutes. Samples were combined in healthy control or COVID-19 tubes. Cells were surface stained with a panel of fluorochrome-conjugated antibodies (Supp. Table 2) in buffer (PBS with 0.25% BSA and 1mM EDTA) for 20 minutes at room temperature prior to immunomagnetic negative selection for ILCs. Following ILC enrichment using the EasySep human Pan-ILC enrichment kit (StemCell Technologies #17975), cells from healthy and COVID-19 recovered participants were counted and normalized before combining. ILCs were sorted using a BD FACS Aria at the Stanford FACS facility prior to incubation with AbSeq oligo-linked mAbs (Supp. Table 3). Sorted cells were processed by the Stanford Human Immune Monitoring Center (HIMC) using the BD Rhapsody platform. Library was prepared using the BD Immune Response Targeting Panel (BD Kit #633750) with addition of custom gene panel reagents (Supp. Table 4) and sequenced on Illumina NovaSeq 6000 at Stanford Genomics Sequencing Center (SGSC). ILCs were identified as Lineageneg (CD3neg, CD14neg, CD34neg, CD19neg), NKG2Aneg, CD45+ and ILCs further defined as CD127+CD161+ and as subsets: ILC1 (CD117negCRTH2neg), ILC2 (CRTH2+) and ILCp (CD117+CRTH2neg) (Supp. Fig. 1). Computational data analysis The above multi-modal setup allowed paired measurements of cellular transcriptome and cell surface protein abundance. The ILC1, ILC2 and ILCp cells were manually gated based on the abundance profile of CD127, CD117, CD161 and CRTH2 (Supp. Fig. 1). Before the integrative analysis, the complete multi-modal single cell dataset containing ILC subsets was converted into single Seurat object. All the subsequent protein-level and gene-level analyses were performed using multimodal data analysis pipeline of Seurat R package version 4.0. The normalized and scaled protein abundance profile was used for estimating the integrated harmony dimensions using runHarmony function in Seurat R package (reduction= ‘apca’ and group.by.vars = ‘batch’) . The batch corrected harmony embeddings were then used for computing the Uniform Manifold Approximation and Projection (UMAP) dimensions to visualize the clusters of ILC subsets. Differential marker analysis of surface proteins, between two groups of cells (COVID-19 and Healthy cohort), from abseq panels was computed with normalized and scaled expression values using FindMarkers function from Seurat R package (test.use=’wilcox’). Similarly, differential gene expression was performed on normalized and scaled gene expression values from between two groups of cells (COVID-19 and Healthy cohort) using the FindMarkers function from Seurat R package (test.use=’MAST’ and latent.vars=’batch’). Genes with log-fold change > 0.5 and adjusted p-value < 0.05 (method: Benjamini-Hochberg) (were considered as significant for further evaluation. The resulting adjusted p-values box-plots were plotted using ggplot2 R package (version 3.4.2) after computing the number of cells expressing a given protein or gene in each sample. Pathway enrichment analysis of DE genes was performed using web-server metascape (version 3.5). The AUCells score and gene regulatory network analysis was performed using pySCENIC pipeline (version 0.12.1). Gene regulatory network was reconstructed using GRNBoost2 algorithm and the list of TFs in humans (genome version: hg38) were obtained from cisTarget database. (https://resources.aertslab.org/cistarget). Cellular enrichment (aka AUCell) analysis that measures the activity of TF or gene signatures across all single cells was performed using aucell function in pySCENIC python library. The ggplot2 R package (version 3.4.2) was used for boxplot visualization. The differential gene co-expression analysis was performed using scSFMnet R package. Circular plots were generated using the R package circlize (version 0.4.15).

This repository contains proteomics data and 10x Genomics single-cell RNA sequencing data for "Competitive binding of E3 ligases TRIM26 and WWP2 controls SOX2 in glioblastoma." Proteomics data for three samples (one IgG control and two replicates of the SOX2 IP) is stored in the file ProteomicsData.zip. scRNAseq for four samples (from three tumors) is stored in a Seurat object (Cycling.SCT.PCA.UMAP.TSNE.CLUST.200522.rds). Raw fastq files have been deposited in Annotare. As of 9/18/20, they are still in the curation stage.

Single-Cell Sequencing and Data Analysis Tumor growth, digestion, and isolation of cell suspensions were prepared as previously described for tumor digests and tumoroids were dissociated as previously described. Then, 2 subsequent washes in sterile PBS + 0.04% non-acetylated BSA were performed to further remove debris from final suspension. Cell pellets were resuspended in PBS with 0.04% non-acetylated BSA prior to single-cell sequencing preparation using Chromium Single-cell 3ʹ GEM, Library & Gel Bead Kit v3 (10× Genomics, Catalog no. 1000075) on a 10× Genomics Chromium Controller following manufacturers protocol. Sequencing data was demultiplexed, quality controlled, and analyzed using Cell Ranger (10× Genomics) and Seurat [42]. Data analysis, expression values, and representative plots were generated using Loupe Cell Browser (10× Genomics) and Seurat (26). The Cell Ranger Single-Cell Software Suite was used to perform sample demultiplexing, barcode processing, and single-cell 3′gene counting. Samples were first demultiplexed and then aligned to the mouse genome (mm10) using “cellranger mkfastq” with default parameters. Unique molecular identifier (UMI) counts were generated using “cellranger count”. Further analysis was performed in R using the Seurat package. For in vivo and ex vivo samples, we performed an integrated analysis to identify and compare common cell types. Cells with fewer than 500 detected genes per cell and genes that were expressed by fewer than 5 cells were not included in the analysis. Prior to data integration, we performed a log-normalization and identified the 2000 most variable genes in each dataset. Subsequently, integration anchors were identified and both datasets were integrated to generate a new integrated matrix. The integrated matrix was then scaled to a mean of 0 and variance of 1 and the dimensionality of the data was reduced by principal component analysis (PCA) (30 principle components). Subsequently, a non-linear dimensional reduction was performed via uniform manifold approximation and projection (UMAP) using the first 20 principle components. Then, we used a graph-based clustering approach to cluster cells. We constructed a K-nearest-neighbor (KNN) graph based on the euclidean distance in PCA space using the “FindNeighbors” function and applied Louvain algorithm to iteratively group cells together by “FindClusters” function (resolution = 0.5). A total of 14 clusters were identified in the integrated dataset. Cell type identification based on high gene expression of the following genes relative to all cells: Cancer: Epcam; Proliferating Cancer: Epcam/Mki67; Fibroblast: Thy1/Dcn; Myofibroblasts: Thy1/Dcn/Acta2; Endothelial: Pecam1/Cdh5; Neutrophils: S100a8/Retnlg; Myeloid: Ptprc/CD14; M2-like Macrophages: Ptprc/CD14/Mrc1/Cd163; Inflammatory macrophages: Ptprc/CD14/Il1b; Proliferating myeloid: Ptprc/CD14/Mki67; T-Cell/NK Cells: Ptprc/Thy1/CD3e/Nkg7; B Cells: Ptprc/CD19/CD79a. Current pre-clinical models of cancer fail to recapitulate the cancer cell behavior in primary tumors primarily because of the lack of a deeper understanding of the effects that the microenvironment has on cancer cell phenotype. Transcriptomic profiling of 4T1 murine mammary carcinoma cells from 2D and 3D cultures, subcutaneous or orthotopic allografts (from immunocompetent or immunodeficient mice), as well as ex vivo tumoroids, revealed differences in molecular signatures including altered expression of genes involved in cell cycle progression, cell signaling and extracellular matrix remodeling. The 3D culture platforms had more in vivo-like transcriptional profiles than 2D cultures. In vivo tumors had more cells undergoing epithelial-to-mesenchymal transition (EMT) while in vitro cultures had cells residing primarily in an epithelial or mesenchymal state. Ex vivo tumoroids incorporated aspects of in vivo and in vitro culturing, retaining higher abundance of cells undergoing EMT while shifting cancer cell fate towards a more mesenchymal state. Cellular heterogeneity surveyed by scRNA-seq revealed that ex vivo tumoroids, while rapidly expanding cancer and fibroblast populations, lose a significant proportion of immune components. This study emphasizes the need to improve in vitro culture systems and preserve syngeneic-like tumor composition by maintaining similar EMT heterogeneity as well as inclusion of stromal subpopulations.

This dataset details the scRNASeq and TCR-Seq analysis of sorted PD-1+ CD8+ T cells from patients with melanoma treated with checkpoint therapy (anti-PD-1 monotherapy and anti-PD-1 & anti-CTLA-4 combination therapy) at baseline and after the first cycle of therapy. A major publication using this dataset is accessible here: (reference)

*experimental design

Single-cell RNA sequencing was performed using 10x Genomics with feature barcoding technology to multiplex cell samples from different patients undergoing mono or dual therapy so that they can be loaded on one well to reduce costs and minimize technical variability. Hashtag oligomers (oligos) were obtained as purified and already oligo-conjugated in TotalSeq-C format from BioLegend. Cells were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.

*extract protocol

PBMCs were thawed, counted and 20 million cells per patient and time point were used for staining. Cells were stained with barcoded antibodies together with a staining solution containing antibodies against CD3, CD4, CD8, PD-1/IgG4 and fixable viability dye (eBioscience) prior to FACS sorting. Barcoded antibody concentrations used were 0.5 µg per million cells, as recommended by the manufacturer (BioLegend) for flow cytometry applications. After staining, cells were washed twice in PBS containing 2% BSA and 0.01% Tween 20, followed by centrifugation (300 xg 5 min at 4 °C) and supernatant exchange. After the final wash, cells were resuspended in PBS and filtered through 40 µm cell strainers and proceeded for sorting. Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions.

*library construction protocol

Sorted cells were counted and approximately 75,000 cells were processed through 10x Genomics single-cell V(D)J workflow according to the manufacturer’s instructions. Gene expression, hashing and TCR libraries were pooled to desired quantities to obtain the sequencing depths of 15,000 reads per cell for gene expression libraries and 5,000 reads per cell for hashing and TCR libraries. Libraries were sequenced on a NovaSeq 6000 flow cell in a 2X100 paired-end format.

*library strategy

scRNA-seq and scTCR-seq

*data processing step

Pre-processing of sequencing results to generate count matrices (gene expression and HTO barcode counts) was performed using the 10x genomics Cell Ranger pipeline.

Further processing was done with Seurat (cell and gene filtering, hashtag identification, clustering, differential gene expression analysis based on gene expression).

*genome build/assembly

Alignment was performed using prebuilt Cell Ranger human reference GRCh38.

*processed data files format and content

RNA counts and HTO counts are in sparse matrix format and TCR clonotypes are in csv format.

Datasets were merged and analyzed by Seurat and the analyzed objects are in rds format.

The adult Hydra polyp continually renews all of its cells using three separate stem cell populations, but the genetic pathways enabling this homeostatic tissue maintenance are not well understood. We sequenced 24,985 Hydra single-cell transcriptomes and identified the molecular signatures of a broad spectrum of cell states, from stem cells to terminally differentiated cells. We constructed differentiation trajectories for each cell lineage and identified gene modules and putative regulators expressed along these trajectories, thus creating a comprehensive molecular map of all developmental lineages in the adult animal. In addition, we built a gene expression map of the Hydra nervous system. Our work constitutes a resource for addressing questions regarding the evolution of metazoan developmental processes and nervous system function.

Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.

This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).

##### CD40 activation and the effect on Neutrophils

# Load necessary libraries for data manipulation, analysis, and visualization

library(dplyr)

library(Seurat)

library(patchwork)

library(plyr)

# Set the working directory to the folder containing the data

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/938-1_cellranger_count/outs")

# Read the M0 dataset from the 10X Genomics format

pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/")

RNA <- pbmc.data$`Gene Expression`

ADT <- pbmc.data$`Antibody Capture`

HST <- pbmc.data$`Multiplexing Capture`

# Load the Matrix package

library(Matrix)

# Hashtag 1, 2 and 3 are marking the organs (heart, blood, spleen)

# Subset the rows based on row names

subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")

animals_data <- HST[subsetted_rows, , drop = FALSE]

# Hashtag 4, 5, 6, 7 are representing IgG_1, IgG_1, FGK45_1 and FGK45_1

subsetted_rows <- c("TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")

treatment_data <- HST[subsetted_rows, , drop = FALSE]

#Create a Seurat obeject and more assays to combine later

RNA <- CreateSeuratObject(counts = RNA)

ADT <- CreateAssayObject(counts = ADT)

Organ <- CreateAssayObject(counts = animals_data)

Treatment <- CreateAssayObject(counts = treatment_data)

seurat <- RNA

#Add the Assays

seurat[["ADT"]] <- ADT

seurat[["HST_Mice"]] <- Organ

seurat[["HST_Treatment"]] <- Treatment

#Check for AK Names

rownames(seurat[["ADT"]])

#Cluster cells on the basis of their scRNA-seq profiles

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

FeaturePlot(seurat, features = "S100a9", order = T)

# Normalize ADT data,

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

#Demultiplex cells based on Mouse_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.99)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Mice_classification.global)

DimPlot(seurat, group.by = "HST_Mice_classification")

#Demultiplex cells based on Treatment_Hashtag Enrichment

seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")

seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.99)

#Visualize demultiplexing results

# Global classification results

table(seurat$HST_Treatment_classification.global)

DimPlot(seurat, group.by = "HST_Treatment_classification")

Idents(seurat) <- seurat$HST_Treatment_classification

pbmc.singlet <- subset(seurat, idents = "Negative", invert = T)

Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification

pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

#Redo the clssification to remove the doublettes

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)

table(pbmc.singlet$HST_Treatment_classification.global)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification")

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99)

table(pbmc.singlet$HST_Mice_classification.global)

pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)

pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)

DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")

DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")

seurat <- pbmc.singlet

seurat$organ <- seurat$HST_Mice_maxID

seurat$mouse <- seurat$HST_Treatment_maxID

seurat$treatment <- seurat$HST_Treatment_maxID

library(plyr)

seurat$treatment <- revalue(seurat$treatment, c(

"TotalSeq-B0304" = "IgG",

"TotalSeq-B0305" = "IgG",

"TotalSeq-B0306" = "FGK45",

"TotalSeq-B0307" = "FGK45"

))

library(plyr)

seurat$organ <- revalue(seurat$organ, c(

"TotalSeq-B0301" = "heart",

"TotalSeq-B0302" = "blood",

"TotalSeq-B0303" = "spleen"

))

seurat$mouse <- revalue(seurat$mouse, c(

"TotalSeq-B0304" = "1",

"TotalSeq-B0305" = "2",

"TotalSeq-B0306" = "3",

"TotalSeq-B0307" = "4"

))

#Cluster cells on the basis of their scRNA-seq profiles without doublettes

# perform visualization and clustering steps

DefaultAssay(seurat) <- "RNA"

seurat <- NormalizeData(seurat)

seurat <- FindVariableFeatures(seurat)

seurat <- ScaleData(seurat)

seurat <- RunPCA(seurat, verbose = FALSE)

seurat <- FindNeighbors(seurat, dims = 1:30)

seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)

seurat <- RunUMAP(seurat, dims = 1:30)

DimPlot(seurat, label = TRUE)

DefaultAssay(seurat) <- "ADT"

seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2)

setwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/Analyse")

saveRDS(seurat, file = "FGK45_heart_blood_spleen.v0.1.RDS")

Introduction

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 scRepertoire 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 contains the code for the initial processing and annotating of the data set (we are calling this version 0.0.1). This involves several steps 1) loading the respective GE data, 2) harmonizing the data by sample and cohort information, 3) iterating through automatic annotation, 4) unifying annotation via manual inspection and enrichment analysis, and 5) adding the TCR information.

Methods

Single-Cell Data Processing

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 (citation), 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. All the sequencing runs across experiments were merged into a single Seurat Object using the merge() function. All the data was then normalized using the default settings and 2,000 variable genes were identified using the "vst" method. Next the data was scaled with the default settings and principal components were calculated for 40 components. Data was integrated using the harmony (v1.0.0) R package (citation) using both cohort and sample information to correct for batch effect with up to 20 iterations. The UMAP was created using the runUMAP() function in Seurat, using 20 dimensions of the harmony calculations.

Annotation of Cells

Automatic annotation was performed using the singler (v1.4.1) R package (citation) with the HPCA (citation) and DICE (citation) 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 (citation) was used for automatic annotation as a partially orthogonal approach. Consensus annotation was derived from all 3 databases (HPCA, DICE, ProjecTILs) using a majority approach. No annotation designation was assigned to cells that returned NA for both singleR and ProjecTILs. Mixed annotations were designated with SingleR identified non-Tcells and ProjecTILs identified T cells. Cell type designations with less than 100 cells in the entire cohort were reduced to "other". Automated annotations were checked manually using canonical marker genes and gene enrichment analysis performed using UCell (v1.0.0) R package (citation).

Addition of TCR data

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 (v1.3.2) R Package (citation). 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.

Citations

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.

Itemized List of the Software Used

• Seurat v4.0.3 - citation
• harmony v1.0 - citation
• singler v1.4.1 - citation
• ProjecTILs v0.4.1 - citation
• UCell v1.0.0 - citation
• scRepertoire v1.3.2 - citation

Itemized List of Reference Data Used

• Human Primary Cell Atlas (HPCA) - citation
• Database Immune Cell Expression (DICE) - citation
• Immune-related Gene Sets - citation

Future Directions

• Data Hosting for Interactive Analysis
• Easy Submission Portal for Researchers to Add Data
• Using the Data to Build a Reference Atlas

There are areas in which we are actively hoping to develop to further facilitate the usefulness of the data set - if you have other suggestions, please reach out using the contact information below.

Contact

Questions, comments, suggestions, please feel free to contact Nick Borcherding via this repository, email, or using twitter.

Publication version of the Single-Cell Tumor Immune Atlas

This upload contains:

• TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas
• TICAtlas.h5ad: an h5ad file with the whole Atlas
• TICAtlas_downsampled.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas
• TICAtlas_downsampled.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas
• TICAtlas_metadata.csv: a comma-separated text file with the metadata for each of the cells

All the files contain the following patient/sample metadata variables:

• patient: assigned patient identifiers
• nCountRNA and nFeatureRNA: number of UMIs and genes per cell
• percent.mt: percentage of mitochondrial genes
• gender: the patient's gender (male/female/unknown)
• source: dataset of origin
• subtype: cancer type (abbreviations as indicated in the preprint)
• kmeans_cluster: patients clusters, NA if filtered out before clustering
• lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)

If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the TICAtlas_metadata.csv file.

For more information, read our paper, check our GitHub and our ShinyApp.

h5ad files can be read with Python using Scanpy, rds files can be read in R using Seurat. For format conversion between AnnData and Seurat we recommend SeuratDisk. For other single-cell data formats you can use sceasy.