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

Sexual dimorphisms, which vary depending on age group and pubertal status, have been described across both the innate and adaptive immune system. We explored the influence of sex hormones on Treg differential gene expression in the context of adolescent health (cisgender and transgender individuals) and autoimmunity (juvenile-onset SLE).

In the post-genomic era, our understanding of the molecular regulators of physiologic and pathologic processes in pregnancy is expanding at the whole-genome level. Longitudinal changes in the known protein-coding transcriptome during normal pregnancy, which we recently reported (Gomez-Lopez et al., 2019), have improved our definition of the major operant networks, yet pregnancy-related functions of the non-coding RNA transcriptome remain poorly understood. A key finding of the ENCODE (Encyclopedia of DNA Elements) Consortium, the successor of the Human Genome Project, was that the human genome contains approximately 60,000 genes, the majority of which do not encode proteins. The total transcriptional output of non-protein-coding RNA genes, collectively referred to as the non-coding transcriptome, is comprised mainly of long non-coding RNA (lncRNA) transcripts (Derrien et al., 2012). Although the ncRNA transcriptome eclipses its protein-coding counterpart in abundance, it has until recently lacked a comprehensive, unbiased, genome-scale characterization over the timecourse of normal human pregnancy. Here, we annotated, characterized, and selectively validated the longitudinal changes in the non-coding transcriptome of maternal whole blood during normal pregnancy to term. We identified nine long non-coding RNAs (lncRNAs), including long intergenic non-coding RNAs (lincRNAs) as well as lncRNAs antisense to or otherwise in the immediate vicinity of protein-coding genes, that were differentially expressed with advancing gestation in normal pregnancy: AL355711, BC039551 (expressed mainly in the placenta), JHDM1D-AS1, A2M-AS1, MANEA-AS1, NR_034004, LINC00649, LINC00861, and LINC01094. By cross-referencing our dataset against major public pseudogene catalogs, we also identified six transcribed pseudogenes that were differentially expressed over time during normal pregnancy in maternal blood: UBBP4, FOXO3B, two Makorin (MKRN) pseudogenes (MKRN9P and LOC441455), PSME2P2, and YBX3P1. We also identified three non-coding RNAs belonging to other classes that were modulated during gestation: the microRNA MIR4439, the small nucleolar RNA (snoRNA) SNORD41, and the small Cajal-body specific ncRNA SCARNA2. The expression profiles of most hits were broadly suggestive of functions in pregnancy. These time-dependent changes of the non-coding transcriptome during normal pregnancy, which may confer specific regulatory impacts on their protein-coding gene targets, will facilitate a deeper molecular understanding of pregnancy and lncRNA-mediated molecular pathways at the maternal-fetal interface and of how these pathways impact maternal and fetal health.

The Human Developmental Biology Resource (HDBR) expression resource is a new resource for studying prenatal human brain development. It consists of two parts which have been uploaded as two submissions: HDBR expression resource- RNAseq (E-MTAB-4840, this submission) and HDBR expression resource- SNP data (E-MTAB-4843). It is unique in the age range (4 post conception weeks [PCW] to 17PCW) and number of brains studied, particularly those under 8PCW. It is also unique in that both the large-scale data sets and the corresponding RNA and DNA samples are available, the latter via the MRC-Wellcome Trust Human Developmental Biology Resource (HDBR; http://www.hdbr.org). There are 628 RNA-seq datasets from different regions of the brains studied. The number of regions depends on the stage and size of the sample and/or the tissue available. Embryos (4-8PCW) have been staged using a modified Carnegie staging system, details can be found at http://database.hudsen.eu/. All the RNAseq datasets from the same brain have the same embryo/fetus number (e.g. RNAseq datasets HDBR251-HDBR254 are from different tissues from embryo number 1406). The embryo/fetus ID number is recorded in the individual column. The majority of the brains studied are between 4 and 12PCW. During this time the major brain regions are established and there are the early stages of cortex development. DNA was prepared from all the tissues used for RNA-seq analysis and SNP genotype data generated from one tissue from each brain. RNA-seq data and SNP-data from the same tissue and brain have the same name (e.g. HDBR469). There are also SNP genotype data from 229 additional specimens in paraffin wax blocks available for individual gene expression studies. These data are in the accompanying HDBR expression resource- SNP data (E-MTAB-4843).

BackgroundMechanistic understanding of transient exposures that lead to adverse health outcomes will enhance our ability to recognize biological signatures of disease. Here, we measured the transcriptomic and epigenomic alterations due to exposure to the metabolic reprogramming agent, dichloroacetic acid (DCA). Previously, we showed that exposure to DCA increased liver tumor incidence in B6C3F1 mice after continuous or early life exposures significantly over background level.MethodsUsing archived formalin-fixed liver samples, we utilized modern methodologies to measure gene expression and DNA methylation levels to link to previously generated phenotypic measures. Gene expression was measured by targeted RNA sequencing (TempO-seq 1500+ toxicity panel: 2754 total genes) in liver samples collected from 10-, 32-, 57-, and 78-week old mice exposed to deionized water (controls), 3.5 g/L DCA continuously in drinking water (“Direct” group), or DCA for 10-, 32-, or 57-weeks followed by deionized water until sample collection (“Stop” groups). Genome-scaled alterations in DNA methylation were measured by Reduced Representation Bisulfite Sequencing (RRBS) in 78-week liver samples for control, Direct, 10-week Stop DCA exposed mice.ResultsTranscriptomic changes were most robust with concurrent or adjacent timepoints after exposure was withdrawn. We observed a similar pattern with DNA methylation alterations where we noted attenuated differentially methylated regions (DMRs) in the 10-week Stop DCA exposure groups compared to the Direct group at 78-weeks. Gene pathway analysis indicated cellular effects linked to increased oxidative metabolism, a primary mechanism of action for DCA, closer to exposure windows especially early in life. Conversely, many gene signatures and pathways reversed patterns later in life and reflected more pro-tumorigenic patterns for both current and prior DCA exposures. DNA methylation patterns correlated to early gene pathway perturbations, such as cellular signaling, regulation and metabolism, suggesting persistence in the epigenome and possible regulatory effects.ConclusionLiver metabolic reprogramming effects of DCA interacted with normal age mechanisms, increasing tumor burden with both continuous and prior DCA exposure in the male B6C3F1 rodent model.

ObjectiveTo guide animal experiments, we investigated the similarities and differences between humans and mice in aging and Alzheimer’s disease (AD) at the single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing (scRNA-seq) level.MethodsMicroglia cells were extracted from dataset GSE198323 of human post-mortem hippocampus. The distributions and proportions of microglia subpopulation cell numbers related to AD or age were compared. This comparison was done between GSE198323 for humans and GSE127892 for mice, respectively. The Seurat R package and harmony R package were used for data analysis and batch effect correction. Differentially expressed genes (DEGs) were identified by FindMarkers function with MAST test. Comparative analyses were conducted on shared genes in DEGs associated with age and AD. The analyses were done between human and mouse using various bioinformatics techniques. The analysis of genes in DEGs related to age was conducted. Similarly, the analysis of genes in DEGs related to AD was performed. Cross-species analyses were conducted using orthologous genes. Comparative analyses of pseudotime between humans and mice were performed using Monocle2.Results(1) Similarities: The proportion of microglial subpopulation Cell_APOE/Apoe shows consistent trends, whether in AD or normal control (NC) groups in both humans and mice. The proportion of Cell_CX3CR1/Cx3cr1, representing homeostatic microglia, remains stable with age in NC groups across species. Tuberculosis and Fc gamma R-mediated phagocytosis pathways are shared in microglia responses to age and AD across species, respectively. (2) Differences: IL1RAPL1 and SPP1 as marker genes are more identifiable in human microglia compared to their mouse counterparts. Most genes of DEGs associated with age or AD exhibit different trends between humans and mice. Pseudotime analyses demonstrate varying cell density trends in microglial subpopulations, depending on age or AD across species.ConclusionsMouse Apoe and Cell_Apoe maybe serve as proxies for studying human AD, while Cx3cr1 and Cell_Cx3cr1 are suitable for human aging studies. However, AD mouse models (App_NL_G_F) have limitations in studying human genes like IL1RAPL1 and SPP1 related to AD. Thus, mouse models cannot fully replace human samples for AD and aging research.

ObjectiveTo guide animal experiments, we investigated the similarities and differences between humans and mice in aging and Alzheimer’s disease (AD) at the single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing (scRNA-seq) level.MethodsMicroglia cells were extracted from dataset GSE198323 of human post-mortem hippocampus. The distributions and proportions of microglia subpopulation cell numbers related to AD or age were compared. This comparison was done between GSE198323 for humans and GSE127892 for mice, respectively. The Seurat R package and harmony R package were used for data analysis and batch effect correction. Differentially expressed genes (DEGs) were identified by FindMarkers function with MAST test. Comparative analyses were conducted on shared genes in DEGs associated with age and AD. The analyses were done between human and mouse using various bioinformatics techniques. The analysis of genes in DEGs related to age was conducted. Similarly, the analysis of genes in DEGs related to AD was performed. Cross-species analyses were conducted using orthologous genes. Comparative analyses of pseudotime between humans and mice were performed using Monocle2.Results(1) Similarities: The proportion of microglial subpopulation Cell_APOE/Apoe shows consistent trends, whether in AD or normal control (NC) groups in both humans and mice. The proportion of Cell_CX3CR1/Cx3cr1, representing homeostatic microglia, remains stable with age in NC groups across species. Tuberculosis and Fc gamma R-mediated phagocytosis pathways are shared in microglia responses to age and AD across species, respectively. (2) Differences: IL1RAPL1 and SPP1 as marker genes are more identifiable in human microglia compared to their mouse counterparts. Most genes of DEGs associated with age or AD exhibit different trends between humans and mice. Pseudotime analyses demonstrate varying cell density trends in microglial subpopulations, depending on age or AD across species.ConclusionsMouse Apoe and Cell_Apoe maybe serve as proxies for studying human AD, while Cx3cr1 and Cell_Cx3cr1 are suitable for human aging studies. However, AD mouse models (App_NL_G_F) have limitations in studying human genes like IL1RAPL1 and SPP1 related to AD. Thus, mouse models cannot fully replace human samples for AD and aging research.

IntroductionIndividuals with Down syndrome (DS) exhibit neurological deficits throughout life including the development of in Alzheimer’s disease (AD) pathology and cognitive impairment. At the cellular level, dysregulation in neuronal gene expression is observed in postmortem human brain and mouse models of DS/AD. To date, RNA-sequencing (RNA-seq) analysis of hippocampal neuronal gene expression including the characterization of discrete circuit-based connectivity in DS remains a major knowledge gap. We postulate that spatially characterized hippocampal neurons display unique gene expression patterns due, in part, to dysfunction of the integrity of intrinsic circuitry.MethodsWe combined laser capture microdissection to microisolate individual neuron populations with single population RNA-seq analysis to determine gene expression analysis of CA1 and CA3 pyramidal neurons and dentate gyrus granule cells located in the hippocampus, a region critical for learning, memory, and synaptic activity.ResultsThe hippocampus exhibits age-dependent neurodegeneration beginning at ~6 months of age in the Ts65Dn mouse model of DS/AD. Each population of excitatory hippocampal neurons exhibited unique gene expression alterations in Ts65Dn mice. Bioinformatic inquiry revealed unique vulnerabilities and differences with mechanistic implications coinciding with onset of degeneration in this model of DS/AD.ConclusionsThese cell-type specific vulnerabilities may underlie degenerative endophenotypes suggesting precision medicine targeting of individual populations of neurons for rational therapeutic development.

IntroductionIdiopathic pulmonary fibrosis (IPF) is characterized by progressive lung dysfunction due to excessive collagen production and tissue scarring. Despite recent advancements, the molecular mechanisms remain unclear.MethodsRNA sequencing identified 475 differentially expressed genes (DEGs) in the TGF-β1-induced primary lung fibrosis model. Gene expression chips GSE101286 and GSE110147 from NCBI gene expression omnibus (GEO) database were analyzed using GEO2R, revealing 94 DEGs in IPF lung tissue samples. The gene ontology (GO) and pathway enrichment, Protein-protein interaction (PPI) network construction, and Maximal Clique Centrality (MCC) scoring were performed. Experimental validation included RT-qPCR, Immunohistochemistry (IHC), and Western Blot, with siRNA used for gene knockdown. A co-expression network was constructed by GeneMANIA.ResultsGO enrichment highlighted significant enrichment of DEGs in TGF-β cellular response, connective tissue development, extracellular matrix components, and signaling pathways such as the AGE-RAGE signaling pathway and ECM-receptor interaction. PPI network analysis identified hub genes, including FN1, COL1A1, POSTN, KIF11, and ECT2. CALD1 (Caldesmon 1), CDH2 (Cadherin 2), and POSTN (Periostin) were identified as dysregulated hub genes in both the RNA sequencing and GEO datasets. Validation experiments confirmed the upregulation of CALD1, CDH2, and POSTN in TGF-β1-treated fibroblasts and IPF lung tissue samples. IHC experiments probed tissue-level expression patterns of these three molecules. Knockdown of CALD1, CDH2, and POSTN attenuated the expression of fibrotic markers (collagen I and α-SMA) in response to TGF-β1 stimulation in primary fibroblasts. Co-expression analysis revealed interactions between hub genes and predicted genes involved in actin cytoskeleton regulation and cell-cell junction organization.ConclusionsCALD1, CDH2, and POSTN, identified as potential contributors to pulmonary fibrosis, present promising therapeutic targets for IPF patients.

Bovine tuberculosis (bTB), caused by infection with Mycobacterium bovis, continues to cause significant issues for the global agriculture industry as well as for human health. An incomplete understanding of the host immune response contributes to the challenges of control and eradication of this zoonotic disease. In this study, high-throughput bulk RNA sequencing (RNA-seq) was used to characterise differential gene expression in γδ T cells – a subgroup of T cells that bridge innate and adaptive immunity and have known anti-mycobacterial response mechanisms. γδ T cell subsets are classified based on expression of a pathogen-recognition receptor known as Workshop Cluster 1 (WC1) and we hypothesised that bTB disease may alter the phenotype and function of specific γδ T cell subsets. Peripheral blood was collected from naturally M. bovis-infected (positive for single intradermal comparative tuberculin test (SICTT) and IFN-γ ELISA) and age- and sex-matched, non-infected control Holstein-Friesian cattle. γδ T subsets were isolated using fluorescence activated cell sorting (n = 10–12 per group) and high-quality RNA extracted from each purified lymphocyte subset (WC1.1+, WC1.2+, WC1- and γδ-) was used to generate transcriptomes using bulk RNA-seq (n = 6 per group, representing a total of 48 RNA-seq libraries). Relatively low numbers of differentially expressed genes (DEGs) were observed between most cell subsets; however, 189 genes were significantly differentially expressed in the M. bovis-infected compared to the control groups for the WC1.1+ γδ T cell compartment (absolute log2 FC ≥ 1.5 and FDR Padj. ≤ 0.1). The majority of these DEGs (168) were significantly increased in expression in cells from the bTB+ cattle and included genes encoding transcription factors (TBX21 and EOMES), chemokine receptors (CCR5 and CCR7), granzymes (GZMA, GZMM, and GZMH) and multiple killer cell immunoglobulin-like receptor (KIR) proteins indicating cytotoxic functions. Biological pathway overrepresentation analysis revealed enrichment of genes with multiple immune functions including cell activation, proliferation, chemotaxis, and cytotoxicity of lymphocytes. In conclusion, γδ T cells have important inflammatory and regulatory functions in cattle, and we provide evidence for preferential differential activation of the WC1.1+ specific subset in cattle naturally infected with M. bovis.