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Single-cell RNA sequencing generates an atlas of normal tibia cartilage under mechanical loading conditions.
Chondrocytes in articular cartilage can secrete extracellular matrix to maintain cartilage homeostasis. It is well known that articular cartilage chondrocytes are sensitive to mechanical loading and that mechanical stimuli can be translated to biological processes. This study provides deep insight into the impact of mechanical loading on chondrocytes via single-cell RNA sequencing (scRNA-seq). Five cartilage tissue samples from the high-loading region of medial cartilage from the upper tibia (the TL group) and six cartilage tissue samples from the low-loading region of lateral cartilage from the upper tibia (the TN group) were obtained from six donors and subjected to scRNA-seq. TL and TN cartilage tissues from another donor were subjected to immunohistochemical staining. In total, 132,685 cells were analyzed and assigned to 11 cell types. The functions, developmental relationships and interactions of these cell types were determined, and gene transcription data were also evaluated. In addition, differentially expressed genes between the TL and TN groups and their functions were identified. The hub genes for the TL group were identified as GAPDH, FN1, VEGFA, LDHA, SOD1, CTGF, DCN, SERPINE1, ENO1 and CAV1, whereas the hub genes for the TN group included ACTB, CD44, MMP2, COL1A1, COL1A2, SPP1, CTGF, MYC, CCL2, and IGF1. The different enrichment terms indicated that physiological mechanical loading may induce reactive oxygen species accumulation and thus cause ferroptosis in chondrocytes.
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Single-cell DNA methylation and 3D genome architecture in the human brain.
Delineating the gene-regulatory programs underlying complex cell types is fundamental for understanding brain function in health and disease. Here, we comprehensively examined human brain cell epigenomes by probing DNA methylation and chromatin conformation at single-cell resolution in 517 thousand cells (399 thousand neurons and 118 thousand non-neurons) from 46 regions of three adult male brains. We identified 188 cell types and characterized their molecular signatures. Integrative analyses revealed concordant changes in DNA methylation, chromatin accessibility, chromatin organization, and gene expression across cell types, cortical areas, and basal ganglia structures. We further developed single-cell methylation barcodes that reliably predict brain cell types using the methylation status of select genomic sites. This multimodal epigenomic brain cell atlas provides new insights into the complexity of cell-type-specific gene regulation in adult human brains.
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scGen predicts single-cell perturbation responses.
Accurately modeling cellular response to perturbations is a central goal of computational biology. While such modeling has been based on statistical, mechanistic and machine learning models in specific settings, no generalization of predictions to phenomena absent from training data (out-of-sample) has yet been demonstrated. Here, we present scGen (https://github.com/theislab/scgen), a model combining variational autoencoders and latent space vector arithmetics for high-dimensional single-cell gene expression data. We show that scGen accurately models perturbation and infection response of cells across cell types, studies and species. In particular, we demonstrate that scGen learns cell-type and species-specific responses implying that it captures features that distinguish responding from non-responding genes and cells. With the upcoming availability of large-scale atlases of organs in a healthy state, we envision scGen to become a tool for experimental design through in silico screening of perturbation response in the context of disease and drug treatment.
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Spatial, transcriptomic, and epigenomic analyses link dorsal horn neurons to chronic pain genetic predisposition.
Key mechanisms underlying chronic pain occur within the dorsal horn. Genome-wide association studies (GWASs) have identified genetic variants predisposed to chronic pain. However, most of these variants lie within regulatory non-coding regions that have not been linked to spinal cord biology. Here, we take a multi-species approach to determine whether chronic pain variants impact the regulatory genomics of dorsal horn neurons. First, we generate a large rhesus macaque single-nucleus RNA sequencing (snRNA-seq) atlas and integrate it with available human and mouse datasets to produce a single unified, species-conserved atlas of neuron subtypes. Cellular-resolution spatial transcriptomics in mouse shows the precise laminar location of these neuron subtypes, consistent with our analysis of neuron-subtype-selective markers in macaque. Using this cross-species framework, we generate a mouse single-nucleus open chromatin atlas of regulatory elements that shows strong and selective relationships between the neuron-subtype-specific chromatin regions and variants from major chronic pain GWASs.
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Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains capture global and local expression variation.
Mapping the impact of genomic variation on gene expression provides insight into the molecular basis of complex phenotypic traits and disease predisposition. Mouse models offer a controlled framework to capture genomic diversity across tissues. As part of the IGVF consortium, we profiled the transcriptomes of eight tissues from each founder strain of the Collaborative Cross using single-nucleus RNA sequencing. The resulting "8-cube" dataset contains 5.2 million nuclei across 106 cell types and cell states. Transcriptome variation correlated with genetic divergence, highest in the wild-derived strains. Heart and brain were relatively similar, whereas gonads, adrenal gland, skeletal muscle, kidney, and liver showed greater divergence. Variation often concentrated in specific cell types and states, especially those linked to immune and metabolic traits. The founder 8-cube dataset provides rich transcriptome signatures to help explain strain-specific traits and disease states and serves as a foundation for deeper analysis of these tissues across diverse mouse genotypes.
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scMAGIC: accurately annotating single cells using two rounds of reference-based classification.
Here, we introduce scMAGIC (Single Cell annotation using MArker Genes Identification and two rounds of reference-based Classification [RBC]), a novel method that uses well-annotated single-cell RNA sequencing (scRNA-seq) data as the reference to assist in the classification of query scRNA-seq data. A key innovation in scMAGIC is the introduction of a second-round RBC in which those query cells whose cell identities are confidently validated in the first round are used as a new reference to again classify query cells, therefore eliminating the batch effects between the reference and the query data. scMAGIC significantly outperforms 13 competing RBC methods with their optimal parameter settings across 86 benchmark tests, especially when the cell types in the query dataset are not completely covered by the reference dataset and when there exist significant batch effects between the reference and the query datasets. Moreover, when no reference dataset is available, scMAGIC can annotate query cells with reasonably high accuracy by using an atlas dataset as the reference.
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Expression Atlas update: insights from sequencing data at both bulk and single cell level.
Expression Atlas (www.ebi.ac.uk/gxa) and its newest counterpart the Single Cell Expression Atlas (www.ebi.ac.uk/gxa/sc) are EMBL-EBI's knowledgebases for gene and protein expression and localisation in bulk and at single cell level. These resources aim to allow users to investigate their expression in normal tissue (baseline) or in response to perturbations such as disease or changes to genotype (differential) across multiple species. Users are invited to search for genes or metadata terms across species or biological conditions in a standardised consistent interface. Alongside these data, new features in Single Cell Expression Atlas allow users to query metadata through our new cell type wheel search. At the experiment level data can be explored through two types of dimensionality reduction plots, t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP), overlaid with either clustering or metadata information to assist users' understanding. Data are also visualised as marker gene heatmaps identifying genes that help confer cluster identity. For some data, additional visualisations are available as interactive cell level anatomograms and cell type gene expression heatmaps.
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Time-Resolved Single-Cell Atlas Reveals Early Endothelial Activation and Stage-Dependent Immune-Stromal Communication in HFpEF.
BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome associated with metabolic stress, hypertension, systemic inflammation, and microvascular dysfunction. Early cell-type-specific events and intercellular communication programs that accompany disease onset and progression remain poorly defined. METHODS: We performed a longitudinal study of HFpEF progression in high-fat diet (HFD)+L-NAME mice at control/baseline (0 weeks, 0w/Ctrl), early (1w), intermediate (4w), and established (8w) stages. Metabolic, hemodynamic, exercise, echocardiographic, and single-cardiomyocyte function were assessed. Cardiac non-cardiomyocytes (non-CMs) were profiled by single-cell RNA sequencing (scRNA-seq), with bulk RNA-seq for tissue-level comparison. Endothelial remodeling was assessed in an L-NAME-independent HFD plus mild transverse aortic constriction model (HFD+mTAC) and a published human HFpEF single-nucleus RNA-seq cohort. An endothelial-macrophage adhesion assay tested whether HFpEF-mimic stress promotes endothelial activation and macrophage adhesion. RESULTS: In the HFD+L-NAME model, metabolic dysfunction, hypertension, reduced exercise tolerance, abnormal diastolic filling with preserved ejection fraction, and altered cardiomyocyte calcium handling were detected by 1w and persisted through 8w. Bulk RNA-seq showed progressive remodeling, with limited change between 8w and 12w, guiding scRNA-seq timepoint selection. scRNA-seq of 94,848 cardiac non-CMs identified nine major populations with stage-dependent remodeling. Endothelial cells (ECs) were recovered in high proportion and showed an early, pronounced transcriptional response, with inflammatory, adhesion, interferon-response, migratory, and vascular-remodeling programs emerging by 1w. Related EC activation signatures were observed in HFD+mTAC and human HFpEF data. Functionally, HFpEF-mimic stress increased adhesion and chemokine expression in human ECs and enhanced macrophage adhesion. Fibroblast matrix remodeling occurred at later stages, while macrophages progressively shifted toward inflammatory states. CellChat suggested stage-dependent communication remodeling from early endothelial-immune interactions toward later macrophage-fibroblast crosstalk. CONCLUSION: Time-resolved scRNA-seq reveals coordinated, stage-dependent remodeling of the cardiac microvascular and interstitial microenvironment during HFpEF progression. Early endothelial activation emerges before later fibroblast matrix remodeling and inflammatory macrophage remodeling, identifying candidate cell states and signaling pathways for future mechanistic investigation.
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WWOX Loss of Function in Neurodevelopmental and Neurodegenerative Disorders.
The WWOX gene was initially discovered as a putative tumor suppressor. More recently, its association with multiple central nervous system (CNS) pathologies has been recognized. WWOX biallelic germline pathogenic variants have been implicated in spinocerebellar ataxia type 12 (SCAR12; MIM:614322) and in early infantile epileptic encephalopathy (EIEE28; MIM:616211). WWOX germline copy number variants have also been associated with autism spectrum disorder (ASD). All identified germline genomic variants lead to partial or complete loss of WWOX function. Importantly, large-scale genome-wide association studies have also identified WWOX as a risk gene for common neurodegenerative conditions such as Alzheimer's disease (AD) and multiple sclerosis (MS). Thus, the spectrum of CNS disorders associated with WWOX is broad and heterogeneous, and there is little understanding of potential mechanisms at play. Exploration of gene expression databases indicates that WWOX expression is comparatively higher in the human cerebellar cortex than in other CNS structures. However, RNA in-situ hybridization data from the Allen Mouse Brain Atlas show that specific regions of the basolateral amygdala (BLA), the medial entorhinal cortex (EC), and deep layers of the isocortex can be singled out as brain regions with specific higher levels of Wwox expression. These observations are in close agreement with single-cell RNA-seq data which indicate that neurons from the medial entorhinal cortex, Layer 5 from the frontal cortex as well as GABAergic basket cells and granule cells from cerebellar cortex are the specific neuronal subtypes that display the highest Wwox expression levels. Importantly, the brain regions and cell types in which WWOX is most abundantly expressed, such as the EC and BLA, are intimately linked to pathologies and syndromic conditions in turn associated with this gene, such as epilepsy, intellectual disability, ASD, and AD. Higher Wwox expression in interneurons and granule cells from cerebellum points to a direct link to the described cerebellar ataxia in cases of WWOX loss of function. We now know that total or partial impairment of WWOX function results in a wide and heterogeneous variety of neurodegenerative conditions for which the specific molecular mechanisms remain to be deciphered. Nevertheless, these observations indicate an important functional role for WWOX in normal development and function of the CNS. Evidence also indicates that disruption of WWOX expression at the gene or protein level in CNS has significant deleterious consequences.
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Atlas of expression of acyl CoA binding protein/diazepam binding inhibitor (ACBP/DBI) in human and mouse.
Acyl CoA binding protein encoded by diazepam binding inhibitor (ACBP/DBI) is a tissue hormone that stimulates lipo-anabolic responses and inhibits autophagy, thus contributing to aging and age-related diseases. Protein expression profiling of ACBP/DBI was performed on mouse tissues to identify organs in which this major tissue hormone is expressed. Transcriptomic and proteomic data bases corroborated a high level of human-mouse interspecies conservation of ACBP/DBI expression in different organs. Single-cell RNA-seq data confirmed that ACBP/DBI was strongly expressed by parenchymatous cells from specific human and mouse organs (e.g., kidney, large intestine, liver, lung) as well as by myeloid or glial cells from other organs (e.g., adipose tissue, brain, eye) following a pattern that was conserved among the two species. We identified a panel of 44 mRNAs that are strongly co-expressed with ACBP/DBI mRNA in normal and malignant human and normal mouse tissues. Of note, 22 (50%) of these co-expressed mRNAs encode proteins localized at mitochondria, and mRNAs with metabolism-related functions are strongly overrepresented (66%). Systematic data mining was performed to identify transcription factors that regulate ACBP/DBI expression in human and mouse. Several transcription factors, including growth response 1 (EGR1), E2F Transcription Factor 1 (E2F1, which interacts with retinoblastoma, RB) and transformation-related protein 53 (TRP53, best known as p53), which are endowed with oncosuppressive effects, consistently repress ACBP/DBI expression as well as its co-expressed mRNAs across multiple datasets, suggesting a mechanistic basis for a coregulation network. Furthermore, we identified multiple transcription factors that transactivate ACBP/DBI gene expression together with its coregulation network. Altogether, this study indicates the existence of conserved mechanisms determining the expression of ACBP/DBI in specific cell types of the mammalian organism.