Extracellular matrix gene expression signatures as cell type and cell state identifiers

Analysis of cancer cell line and tissue RNA sequencing data reveals an essential and dark matrisome

Extracellular matrix remodeling is a hallmark of tissue development, homeostasis, and disease. The processes that mediate remodeling, and the consequences of such, are the topic of extensive focus in biomedical research. Cell culture methods represent a crucial tool utilized by those interested in matrisome function, the easiest of which are implemented with immortalized/cancer cell lines. These cell lines often form the foundations of a research proposal, or serve as vehicles of validation for other model systems. For these reasons, it is important to understand the complement of matrisome genes that are expressed when identifying appropriate cell culture models for hypothesis testing. To this end, we harvested bulk RNA sequencing data from the Cancer Cell Line Encyclopedia (CCLE) to assess matrisome gene expression in 1019 human cell lines. Our examination reveals that a large proportion of the matrisome is poorly represented in human cancer cell lines, with approximately 10% not expressed above threshold in any of the cell lines assayed. Conversely, we identify clusters of essential/common matrisome genes that are abundantly expressed in cell lines. To validate these observations against tissue data, we compared our findings with bulk RNA sequencing data from the Genotype-Tissue Expression (GTEx) portal and The Cancer Genome Atlas (TCGA) program. This comparison demonstrates general agreement between the "essential/common" and "dark/uncommon" matrisome across the three datasets, albeit with discordance observed in 59 matrisome genes between cell lines and tissues. Notably, all of the discordant genes are essential/common in tissues yet minimally expressed in cell lines, underscoring critical considerations for matrix biology researchers employing immortalized cell lines for their investigations.

Longevity interventions modulate mechanotransduction and extracellular matrix homeostasis in C. elegans

Dysfunctional extracellular matrices (ECM) contribute to aging and disease. Repairing dysfunctional ECM could potentially prevent age-related pathologies. Interventions promoting longevity also impact ECM gene expression. However, the role of ECM composition changes in healthy aging remains unclear. Here we perform proteomics and in-vivo monitoring to systematically investigate ECM composition (matreotype) during aging in C. elegans revealing three distinct collagen dynamics. Longevity interventions slow age-related collagen stiffening and prolong the expression of collagens that are turned over. These prolonged collagen dynamics are mediated by a mechanical feedback loop of hemidesmosome-containing structures that span from the exoskeletal ECM through the hypodermis, basement membrane ECM, to the muscles, coupling mechanical forces to adjust ECM gene expression and longevity via the transcriptional co-activator YAP-1 across tissues. Our results provide in-vivo evidence that coordinated ECM remodeling through mechanotransduction is required and sufficient to promote longevity, offering potential avenues for interventions targeting ECM dynamics.

FICTURE: Scalable segmentation-free analysis of submicron resolution spatial transcriptomics

Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.

Mechanotransduction through hemidesmosomes during aging and longevity

Hemidesmosomes are structural protein complexes localized at the interface of tissues with high mechanical demand and shear forces. Beyond tissue anchoring, hemidesmosomes have emerged as force-modulating structures important for translating mechanical cues into biochemical and transcriptional adaptation (i.e. mechanotransduction) across tissues. Here, we discuss the recent insights into the roles of hemidesmosomes in age-related tissue regeneration and aging in C. elegans, mice and humans. We highlight the emerging concept of preserved dynamic mechanoregulation of hemidesmosomes in tissue maintenance and healthy aging.

Strategic outline of interventions targeting extracellular matrix for promoting healthy longevity

The extracellular matrix (ECM), composed of interlinked proteins outside of cells, is an important component of the human body that helps maintain tissue architecture and cellular homeostasis. As people age, the ECM undergoes changes that can lead to age-related morbidity and mortality. Despite its importance, ECM aging remains understudied in the field of geroscience. In this review, we discuss the core concepts of ECM integrity, outline the age-related challenges and subsequent pathologies and diseases, summarize diagnostic methods detecting a faulty ECM, and provide strategies targeting ECM homeostasis. To conceptualize this, we built a technology research tree to hierarchically visualize possible research sequences for studying ECM aging. This strategic framework will hopefully facilitate the development of future research on interventions to restore ECM integrity, which could potentially lead to the development of new drugs or therapeutic interventions promoting health during aging.

Extracellular Matrix Dynamics as an Emerging yet Understudied Hallmark of Aging and Longevity

The biomechanical properties of extracellular matrices (ECM) and their consequences for cellular homeostasis have recently emerged as a driver of aging. Here we review the age-dependent deterioration of ECM in the context of our current understanding of the aging processes. We discuss the reciprocal interactions of longevity interventions with ECM remodeling. And the relevance of ECM dynamics captured by the matrisome and the matreotypes associated with health, disease, and longevity. Furthermore, we highlight that many established longevity compounds promote ECM homeostasis. A large body of evidence for the ECM to qualify as a hallmark of aging is emerging, and the data in invertebrates is promising. However, direct experimental proof that activating ECM homeostasis is sufficient to slow aging in mammals is lacking. We conclude that further research is required and anticipate that a conceptual framework for ECM biomechanics and homeostasis will provide new strategies to promote health during aging.

ClonoCluster: A method for using clonal origin to inform transcriptome clustering

Clustering cells based on their high-dimensional profiles is an important data reduction process by which researchers infer distinct cellular states. The advent of cellular barcoding, however, provides an alternative means by which to group cells: by their clonal origin. We developed ClonoCluster, a computational method that combines both clone and transcriptome information to create hybrid clusters that weight both kinds of data with a tunable parameter. We generated hybrid clusters across six independent datasets and found that ClonoCluster generated qualitatively different clusters in all cases. The markers of these hybrid clusters were different but had equivalent fidelity to transcriptome-only clusters. The genes most strongly associated with the rearrangements in hybrid clusters were ribosomal function and extracellular matrix genes. We also developed the complementary tool Warp Factor that incorporates clone information in popular 2D visualization techniques like UMAP. Integrating ClonoCluster and Warp Factor revealed biologically relevant markers of cell identity.

Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma

BACKGROUND

Squamous cell carcinoma (SqCC) is a subtype of non-small cell lung cancer for which patient prognosis remains poor. The extracellular matrix (ECM) is critical in regulating cell behavior; however, its importance in tumor aggressiveness remains to be comprehensively characterized.

METHODS

Multi-omics data of SqCC human tumor specimens was combined to characterize ECM features associated with initiation and recurrence. Penalized logistic regression was used to define a matrix risk signature for SqCC tumors and its performance across a panel of tumor types and in SqCC premalignant lesions was evaluated. Consensus clustering was used to define prognostic matreotypes for SqCC tumors. Matreotype-specific tumor biology was defined by integration of bulk RNAseq with scRNAseq data, cell type deconvolution, analysis of ligand-receptor interactions and enriched biological pathways, and through cross comparison of matreotype expression profiles with aging and idiopathic pulmonary fibrosis lung profiles.

RESULTS

This analysis revealed subtype-specific ECM signatures associated with tumor initiation that were predictive of premalignant progression. We identified an ECM-enriched tumor subtype associated with the poorest prognosis. In silico analysis indicates that matrix remodeling programs differentially activate intracellular signaling in tumor and stromal cells to reinforce matrix remodeling associated with resistance and progression. The matrix subtype with the poorest prognosis resembles ECM remodeling in idiopathic pulmonary fibrosis and may represent a field of cancerization associated with elevated cancer risk.

CONCLUSIONS

Collectively, this analysis defines matrix-driven features of poor prognosis to inform precision medicine prevention and treatment strategies towards improving SqCC patient outcome.

Cell types as species: Exploring a metaphor

The concept of "cell type," though fundamental to cell biology, is controversial. Cells have historically been classified into types based on morphology, physiology, or location. More recently, single cell transcriptomic studies have revealed fine-scale differences among cells with similar gross phenotypes. Transcriptomic snapshots of cells at various stages of differentiation, and of cells under different physiological conditions, have shown that in many cases variation is more continuous than discrete, raising questions about the relationship between cell type and cell state. Some researchers have rejected the notion of fixed types altogether. Throughout the history of discussions on cell type, cell biologists have compared the problem of defining cell type with the interminable and often contentious debate over the definition of arguably the most important concept in systematics and evolutionary biology, "species." In the last decades, systematics, like cell biology, has been transformed by the increasing availability of molecular data, and the fine-grained resolution of genetic relationships have generated new ideas about how that variation should be classified. There are numerous parallels between the two fields that make exploration of the "cell types as species" metaphor timely. These parallels begin with philosophy, with discussion of both cell types and species as being either individuals, groups, or something in between (e.g., homeostatic property clusters). In each field there are various different types of lineages that form trees or networks that can (and in some cases do) provide criteria for grouping. Developing and refining models for evolutionary divergence of species and for cell type differentiation are parallel goals of the two fields. The goal of this essay is to highlight such parallels with the hope of inspiring biologists in both fields to look for new solutions to similar problems outside of their own field.

Longevity-Promoting Pathways and Transcription Factors Respond to and Control Extracellular Matrix Dynamics During Aging and Disease

Aging is one of the largest risk factors for cancer, type 2 diabetes, osteoarthritis, cardiovascular diseases, and other age-related pathologies. Here, we give a detailed description of the interplay of chronic age-related pathologies with the remodeling of the extracellular matrix during disease development and progression. Longevity-promoting signaling pathways slow or prevent age-related diseases. In particular, we focus on the mTOR signaling pathway, sirtuins, and canonical longevity-promoting transcription factors, such as FOXO, NF-κB, and Nrf2. We extend our analysis using chromatin immunoprecipitation (ChIP) sequencing and transcriptomic data and report that many established and emerging longevity-promoting transcription factors, such as CREB1, FOXO1,3, GATA1,2,3,4, HIF1A, JUN, KLF4, MYC, NFE2L2/Nrf2, RELA/NF-κB, REST, STAT3,5A, and TP53/p53, directly regulate many extracellular matrix genes and remodelers. We propose that modulation of these pathways increases lifespan and protects from age-related diseases in part due to their effects on extracellular matrix remodeling. Therefore, to successfully treat age-related diseases, it is necessary to better understand the connection between extracellular matrix components and longevity pathways.

Systematic Analysis of Actively Transcribed Core Matrisome Genes Across Tissues and Cell Phenotypes

The extracellular matrix (ECM) is a highly dynamic, well-organized acellular network of tissue-specific biomolecules, that can be divided into structural or core ECM proteins and ECM-associated proteins. The ECM serves as a blueprint for organ development and function and, when structurally altered through mutation, altered expression, or degradation, can lead to debilitating syndromes that often affect one tissue more than another. Cross-referencing the FANTOM5 SSTAR (Semantic catalog of Samples, Transcription initiation And Regulators) and the defined catalog of core matrisome ECM (glyco)proteins, we conducted a comprehensive analysis of 511 different human samples to annotate the context-specific transcription of the individual components of the defined matrisome. Relative log expression normalized SSTAR cap analysis gene expression peak data files were downloaded from the FANTOM5 online database and filtered to exclude all cell lines and diseased tissues. Promoter-level expression values were categorized further into eight core tissue systems and three major ECM categories: proteoglycans, glycoproteins, and collagens. Hierarchical clustering and correlation analyses were conducted to identify complex relationships in promoter-driven gene expression activity. Integration of the core matrisome and curated FANTOM5 SSTAR data creates a unique tool that provides insight into the promoter-level expression of ECM-encoding genes in a tissue- and cell-specific manner. Unbiased clustering of cap analysis gene expression peak data reveals unique ECM signatures within defined tissue systems. Correlation analysis among tissue systems exposes both positive and negative correlation of ECM promoters with varying levels of significance. This tool can be used to provide new insight into the relationships between ECM components and tissues and can inform future research on the ECM in human disease and development. We invite the matrix biology community to continue to explore and discuss this dataset as part of a larger and continuing conversation about the human ECM. An interactive web tool can be found at matrixpromoterome.github.io along with additional resources that can be found at dx.doi.org/10.6084/m9.figshare.19794481 (figures) and https://figshare.com/s/e18ecbc3ae5aaf919b78 (python notebook).