Seurat object containing a subset of the mouse liver scRNAseq data (Guilliams et al., Cell 2022)

Data used only for demonstration purpose. Namely, to demonstrate the Differential NicheNet pipeline: https://github.com/saeyslab/nichenetr/blob/master/vignettes/differential_nichenet.md

This repository contains the data necessary to reproduce the results from the SpatialMuxSeq vignette (https://rpubs.com/LiranM/SpatialMuxSeq), featured in our paper "Multiplexed Spatial Mapping of Chromatin Features, Transcriptome, and Proteins in Tissues." To ensure full reproducibility of the results, we have provided a Seurat object that includes all omics layers. For further details and access to all relevant code, please visit our GitHub repository: https://github.com/liranmao/Spatial_multi_omics.

BC.Rdata: Seurat Object

ST_expr_smooth_out.RData: Pre-computed smooths spatial transcriptomics gene expression using the weighted mean of neighbouring spots in one compartment.

The muscle stem cell (MuSC) population is recognized as functionally heterogeneous. Cranial muscle stem cells, which originate from head mesoderm, can have greater proliferative capacity in culture and higher regenerative potential in transplantation assays when compared to those in the limb. The existence of such functional differences in phenotypic outputs remain unresolved as a comprehensive understanding of the underlying mechanisms is lacking. We addressed this issue using a combination of clonal analysis, live imaging, and scRNA-seq, identifying critical biological features that distinguish extraocular (EOM) and limb (Tibialis anterior, TA) MuSC populations. Time-lapse studies using a MyogenintdTomato reporter showed that the increased proliferation capacity of EOM MuSCs is accompanied by a differentiation delay in vitro. Unexpectedly, in vitro activated EOM MuSCs expressed a large array of distinct extracellular matrix (ECM) components, growth factors, and signaling molecules that are typically associated with mesenchymal non-muscle cells. These unique features are regulated by a specific set of transcription factors that constitute a coregulating module. This transcription factor network, which includes Foxc1 as one of the major players, appears to be hardwired to EOM identity as it is present in quiescent adult MuSCs, in the activated counterparts during growth and retained upon passages in vitro. These findings provide insights into how high-performing MuSCs regulate myogenic commitment by active remodeling of their local environment. Methods

scRNAseq data generation MuSCs were isolated on BD FACSAriaTM III based on GFP fluorescence and cell viability from Tg:Pax7- nGFP mice (Sambasivan et al., 2009). Quiescent MuSCs were manually counted using a hemocytometer and immediately processed for scRNA-seq. For activated samples, MuSCs were cultured in vitro as described above for four days. Activated MuSCs were subsequently trypsinized and washed in DMEM/F12 2% FBS. Live cells were re-sorted, manually counted using a hemocytometer and processed for scRNA-seq. Prior to scRNAseq, RNA integrity was assessed using Agilent Bioanalyzer 2100 to validate the isolation protocol (RIN>8 was considered acceptable). 10X Genomics Chromium microfluidic chips were loaded with around 9000 cells and cDNA libraries were generated following manufacturer’s protocol. Concentrations and fragment sizes were determined using Agilent Bioanalyzer and Invitrogen Qubit. cDNA libraries were sequenced using NextSeq 500 and High Output v2.5 (75 cycles) kits. Count matrices were subsequently generated following 10X Genomics Cell Ranger pipeline. Following normalisation and quality control, we obtained an average of 5792 ± 1415 cells/condition. Seurat preprocessing scRNAseq datasets were processed using Seurat (https://satijalab.org/seurat/) (Butler et al., 2018). Cells with more than 10% of mitochondrial gene fraction were discarded. 4000-5000 genes were detected on average across all 4 datasets. Dimensionality reduction and UMAPs were generated following Seurat workflow. The top 100 DEGs were determined using Seurat "FindAllMarkers" function with default parameters. When processed independently (scvelo), the datasets were first regressed on cell cycle genes, mitochondrial fraction, number of genes, number of UMI following Seurat dedicated vignette, and doublets were removed using DoubletFinder v3 (McGinnis et al., 2019). A "StressIndex" score was generated for each cell based on the list of stress genes previously reported (Machado et al., 2021) using the “AddModule” Seurat function. 94 out of 98 genes were detected in the combined datasets. UMAPs were generated after 1. StressIndex regression, and 2. after complete removal of the detected stress genes from the gene expression matrix before normalization. In both cases, the overall aspect of the UMAP did not change significantly (Figure S5). Although immeasurable confounding effects of cell stress following isolation cannot be ruled out, we reasoned that our datasets did not show a significant effect of stress with respect to the conclusions of our study. Matrisome analysis After subsetting for the features of the Matrisome database (Naba et al., 2015) present in our single-cell dataset, the matrisome score was calculated by assessing the overall expression of its constituents using the "AddModuleScore" function from Seurat (Butler et al., 2018).

RNA velocity and driver genes Scvelo was used to calculate RNA velocities (Bergen et al., 2020). Unspliced and spliced transcript matrices were generated using velocyto (Manno et al., 2018) command line function. Seurat-generated filtering, annotations and cell-embeddings (UMAP, tSNE, PCA) were then added to the outputted objects. These datasets were then processed following scvelo online guide and documentation. Velocity was calculated based on the dynamical model (using scv.tl.recover_dynamics(adata), and scv.tl.velocity(adata, mode=’dynamical’)) and differential kinetics calculations were added to the model (using scv.tl.velocity(adata, diff_kinetics=True)). Specific driver genes were identified by determining the top likelihood genes in the selected cluster. The lists of the top 100 drivers for EOM and TA progenitors are given in Suppl Tables 10 and 11. Gene regulatory network inference and transcription factor modules Gene regulatory networks were inferred using pySCENIC (Aibar et al., 2017; Sande et al., 2020). This algorithm regroups sets of correlated genes into regulons (i.e. a transcription factor and its targets) based on binding motifs and co-expression patterns. The top 35 regulons for each cluster were determined using scanpy "scanpy.tl.rank_genes_groups" function (method=t-test). Note that this function can yield less than 35 results depending on the cluster. UMAP and heatmap were generated using regulon AUC matrix (Area Under Curve) which refers to the activity level of each regulon in a given cell. Visualizations were performed using scanpy (Wolf et al., 2018). The outputted list of each regulon and their targets was subsequently used to create a transcription factor network. To do so, only genes that are regulons themselves were kept. This results in a visual representation where each node is an active transcription factor and each edge is an inferred regulation between 2 transcription factors. When placed in a force-directed environment, these nodes aggregate based on the number of shared edges. This operation greatly reduced the number of genes involved, while highlighting co-regulating transcriptional modules. Visualization of this network was performed in a force-directed graph using Gephi “Force-Atlas2” algorithm (https://gephi.org/).

Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE2 and PGD2) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (Nos2) and pro-resolution (Arg1) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis. Methods All ultrasound data acquisition and measurements were performed by investigators blinded to intervention condition. Ultrasound images of the back were acquired under isoflurane anesthesia. A high-frequency ultrasound scanner (Vevo 2100, Fujifilm VisualSonics, Toronto, Canada) in B mode with a 21 MHz transducer (MS 250) was used for optimal spatial resolution. A conductive gel was centrifuged for 5 minutes to remove air bubbles and spread over the skin. The transducer was stabilized with a clamp and mounted into an articulated arm to control the distance and the angle between the transducer and the skin surface. the transducer was oriented transversal or sagittal perpendicular to the skin of the back and centered on the lesion area. Total lesion area was calculated by averaging the lesion area measured at transversal and sagittal positions. Flow cytometry Inflammatory lesions were excised and minced in 5% FBS-DMEM, using a scalpel, then the suspension was filtered through a 70mm filter. Isolated cells were counted using an automated cell counter TC20 (Bio-Rad, CA). For surface receptors, cells at 1 × 106/mL were stained with a mix of mouse monoclonal antibodies: To detect neutrophils (N=92) we used the antibodies: APC anti-mouse Ly-6/Ly-6c(Gr1) and FITC anti-mouse CD45; for macrophage populations (N=32): we used the following combination of antibodies: APC cy7 anti-mouse CD45, APC anti-mouse F4/80, FITC anti-mouse Nos2 (iNOS), PE anti-mouse CD206. Stained cells were examined using a FACSCanto II Flow Cytometer (BD Biosciences, San Jose, CA) with FlowJo single cell analysis software. Single cell and bulk library preparation and sequencing Single cell library preparation was performed according to the manufacturer’s instructions for the 10× Chromium single cell kit (10x Genomics). The libraries were then pooled and sequenced on a NextSeq 2000 sequencer (Illumina). Single cell RNA seq data processing and quality control Read processing was performed using the 10x Genomics workflow (Zheng et al. 2017). Briefly, the Cell Ranger Single Cell Software Suite (v3.0.1) was used for demultiplexing, barcode assignment, and unique molecular identifier (UMI) quantification (http://software.10xgenomics.com/single cell/overview/welcome). The reads were aligned to a custom mm10 reference genome (Genome Reference Consortium Mouse Build 38) extended with additional annotation for several frequently used mouse transgenes. Both lanes per sample were merged using the ‘cellranger mkfastq’ function and processed using the ‘cellranger count’ function. In total, the Cell Ranger software detected 7,921 cells per sample, sequenced at 23,326 reads and identifying 1,611 genes derived from 4,375 UMIs per cell on average across all samples. The following metrics were used to flag poor quality cells: number of genes detected, total number of UMIs, and percentage of molecules mapped to mitochondrial genes. Within the Seurat workflow, low quality and artifact cells were excluded by removing any cells that expressed fewer than 200 genes, and removed genes expressed in less than 3 cells. Gene expression matrices were transformed for better interpretability using the Seurat function ‘NormalizeData’. A total of 51,943 cells were included in the subsequent clustering and pseudo time analyses. For cross-condition data integration and batch correction, ‘FindIntegrationAnchors’ and ‘IntegrateData’ were applied to data in Seurat, following the data integration vignette (https://satijalab.org/seurat/archive/v3.2/immune_alignment. html) Targeted LC-MS/MS Supernatants from the inflammatory lesions were placed in ice cold methanol containing deuterated internal standards (d8-5S-hydroxyeicosatetraenoic acid (5-HETE), d4-leukotriene B4 (LTB4), d4-prostaglandin E2 (PGE2) and d5-lipoxin A4 (LXA4); 500pg each) and homogenized using a PTFE Dounce (Kimble Chase). Proteins were allowed to precipitate (4oC), and lipid mediators were extracted using C18 solid-phase cartridges as described before (Dalli et al., 2018) Measurement of lipid mediators was carried out by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a QTrap 5500 (ABSciex, Framingham, MA) equipped with a Shimadzu LC-20AD HPLC and a Shimadzu SIL-20AC autoinjector (Shimadzu, Kyoto, Japan). An Agilent Eclipse Plus C18 column (100mm x 4.6 mm x 1.8 mm) maintained at 50oC was used with a gradient of methanol/water/acetic acid of 55:45:0.01 (v/v/v) to 100:0:0.01 at 0.4 ml/min flow rate. Multiple reaction monitoring (MRM) transitions were used to identify and quantify lipid mediators in samples, as compared with retention times of authentic standards run in parallel. Quantification was achieved using calibration curves constructed with synthetic standards for each mediator, after normalization to extraction recovery based on internal standards and followed by normalization to tissue weight.

This is the dataset supporting the EPI-Clone manuscript: Targeted single cell methylation profiling of bone marrow was performed with the scTAMseq method. BM from seven donors was analyzed Donor characteristics are described in full in Table S1. This seurat object includes CD34+ cells from all donors A.1-A.7, B.1-B.5. For EPI-Clone analysis, only donors with >1000 CD34+ cells covered were included.Dataset is an R Data File (RDS) with an integrated Seurat object across donorsASSAYS:AB: Antibody expression dataDNAm: DNA methylation data, containing binary observations (0: amplicon not observed, i.e. dropout or absence of DNA methylation, 1: amplicon observed, i.e. DNA methylation). See the paper on scTAMseqDIMENSIONALITY REDUCTIONscanorama: scanorama performed on the DNAm+AB dataumap: UMAP computed on scanoramaTo create donor-specific static CpGs UMAPs, follow the vignette at https://github.com/veltenlab/EPI-cloneMETADATAsample: Donor (A.1 - A.7)age: Donor agecelltype: Cell type annotationNonHhaI: Performance of the control amplicons in that celllog_ChrY: Log number of reads on the ChromosomeY ampliconsEPIClone_id: Cluster annotation computed by EPI-Clonemutation: CH mutation called in each cell. N/A means dropout.