Analysis of transcriptional modules during human fibroblast ageing

High-throughput mechanical phenotyping and transcriptomics of single cells

The molecular system regulating cellular mechanical properties remains unexplored at single-cell resolution mainly due to a limited ability to combine mechanophenotyping with unbiased transcriptional screening. Here, we describe an electroporation-based lipid-bilayer assay for cell surface tension and transcriptomics (ELASTomics), a method in which oligonucleotide-labelled macromolecules are imported into cells via nanopore electroporation to assess the mechanical state of the cell surface and are enumerated by sequencing. ELASTomics can be readily integrated with existing single-cell sequencing approaches and enables the joint study of cell surface mechanics and underlying transcriptional regulation at an unprecedented resolution. We validate ELASTomics via analysis of cancer cell lines from various malignancies and show that the method can accurately identify cell types and assess cell surface tension. ELASTomics enables exploration of the relationships between cell surface tension, surface proteins, and transcripts along cell lineages differentiating from the haematopoietic progenitor cells of mice. We study the surface mechanics of cellular senescence and demonstrate that RRAD regulates cell surface tension in senescent TIG-1 cells. ELASTomics provides a unique opportunity to profile the mechanical and molecular phenotypes of single cells and can dissect the interplay among these in a range of biological contexts.

Transcriptional changes are tightly coupled to chromatin reorganization during cellular aging

Human life expectancy is constantly increasing and aging has become a major risk factor for many diseases, although the underlying gene regulatory mechanisms are still unclear. Using transcriptomic and chromosomal conformation capture (Hi-C) data from human skin fibroblasts from individuals across different age groups, we identified a tight coupling between the changes in co-regulation and co-localization of genes. We obtained transcription factors, cofactors, and chromatin regulators that could drive the cellular aging process by developing a time-course prize-collecting Steiner tree algorithm. In particular, by combining RNA-Seq data from different age groups and protein-protein interaction data we determined the key transcription regulators and gene regulatory changes at different life stage transitions. We then mapped these transcription regulators to the 3D reorganization of chromatin in young and old skin fibroblasts. Collectively, we identified key transcription regulators whose target genes are spatially rearranged and correlate with changes in their expression, thereby providing potential targets for reverting cellular aging.

Single-cell analysis of human dermal fibroblasts isolated from a single male donor over 35 years

The aim of this study is to examine the effects of ageing on dermal fibroblast heterogeneity based on samples obtained from the same donor. We used a dermal fibroblast lineage (named ASF-4 cell lines) isolated from the inner side of the upper arm of a healthy male donor over a 35-year period, beginning at 36 years of age. Because clonal analysis of ASF-4 cell lines demonstrated a donor age-dependent loss of proliferative capacity and acquisition of senescent traits at the single-cell level, cultured cells frozen at passage 10 at ages 36 and 72 years were subjected to single-cell RNA sequencing. Transcriptome analysis revealed an increase in senescent fibroblasts and downregulation of genes associated with extracellular matrix remodelling with ageing. In addition, two putative differentiation pathways, with one endpoint consisting of senescent fibroblasts and the other without, were speculated using a pseudo-time analysis. Knockdown of the characteristic gene of the non-senescent fibroblast cluster endpoint, EFEMP2, accelerated cellular senescence. This was also confirmed in two other normal human dermal fibroblast cell lines. The detection of a common cellular senescence-related gene from single-donor analysis is notable. This study provides new insights into the behaviour of dermal fibroblasts during skin ageing.

Skin fibroblast functional heterogeneity in health and disease

Fibroblasts are the major cell population of connective tissue, including the skin dermis, and are best known for their function in depositing and remodelling the extracellular matrix. Besides their role in extracellular matrix homeostasis, fibroblasts have emerged as key players in many biological processes ranging from tissue immunity and wound healing to hair follicle development. Recent advances in single-cell RNA-sequencing technologies have revealed an astonishing transcriptional fibroblast heterogeneity in the skin and other organs. A key challenge in the field is to understand the functional relevance and significance of the identified new cell clusters in health and disease. Here, we discuss the functionally distinct fibroblast subtypes identified in skin homeostasis and repair and how they evolve in fibrotic disease conditions, in particular keloid scars and cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Transcriptome analysis reveals differences in cell cycle, growth and migration related genes that distinguish fibroblasts derived from pre-invasive and invasive breast cancer

Background/Introduction

As the most common form of pre-invasive breast cancer, ductal carcinoma in situ (DCIS) affects over 50,000 women in the US annually. Despite standardized treatment involving lumpectomy and radiation therapy, up to 25% of patients with DCIS experience disease recurrence often with invasive ductal carcinoma (IDC), indicating that a subset of patients may be under-treated. As most DCIS cases will not progress to invasion, many patients may experience over-treatment. By understanding the underlying processes associated with DCIS to IDC progression, we can identify new biomarkers to determine which DCIS cases may become invasive and improve treatment for patients. Accumulation of fibroblasts in IDC is associated with disease progression and reduced survival. While fibroblasts have been detected in DCIS, little is understood about their role in DCIS progression.

Goals

We sought to determine 1) whether DCIS fibroblasts were similar or distinct from normal and IDC fibroblasts at the transcriptome level, and 2) the contributions of DCIS fibroblasts to breast cancer progression.

Methods

Fibroblasts underwent transcriptome profiling and pathway analysis. Significant DCIS fibroblast-associated genes were further analyzed in existing breast cancer mRNA databases and through tissue array immunostaining. Using the sub-renal capsule graft model, fibroblasts from normal breast, DCIS and IDC tissues were co-transplanted with DCIS.com breast cancer cells.

Results

Through transcriptome profiling, we found that DCIS fibroblasts were characterized by unique alterations in cell cycle and motility related genes such as PKMYT1, TGF-α, SFRP1 and SFRP2, which predicted increased cell growth and invasion by Ingenuity Pathway Analysis. Immunostaining analysis revealed corresponding increases in expression of stromal derived PKMYT1, TGF-α and corresponding decreases in expression of SFRP1 and SFRP2 in DCIS and IDC tissues. Grafting studies in mice revealed that DCIS fibroblasts enhanced breast cancer growth and invasion associated with arginase-1+ cell recruitment.

Conclusion

DCIS fibroblasts are phenotypically distinct from normal breast and IDC fibroblasts, and play an important role in breast cancer growth, invasion, and recruitment of myeloid cells. These studies provide novel insight into the role of DCIS fibroblasts in breast cancer progression and identify some key biomarkers associated with DCIS progression to IDC, with important clinical implications.

Activated fibroblasts in cancer: Perspectives and challenges

Activated fibroblasts in tumors, or cancer-associated fibroblasts (CAFs), have become a popular research area over the past decade. As important players in many aspects of tumor biology, with functions ranging from collagen deposition to immunosuppression, CAFs have been the target of clinical and pre-clinical studies that have revealed their potential pro- and anti-tumorigenic dichotomy. In this review, we describe the important role of CAFs in the tumor microenvironment and the technological advances that made these discoveries possible, and we detail the models that are currently available for CAF investigation. Additionally, we present evidence to support the value of encompassing CAF investigation as a future therapeutic avenue alongside immune and cancer cells while highlighting the challenges that must be addressed for successful clinical translation of new findings.

Comprehensive comparison of gene expression diversity among a variety of human stem cells

Several factors, including tissue origins and culture conditions, affect the gene expression of undifferentiated stem cells. However, understanding the basic identity across different stem cells has not been pursued well despite its importance in stem cell biology. Thus, we aimed to rank the relative importance of multiple factors to gene expression profile among undifferentiated human stem cells by analyzing publicly available RNA-seq datasets. We first conducted batch effect correction to avoid undefined variance in the dataset as possible. Then, we highlighted the relative impact of biological and technical factors among undifferentiated stem cell types: a more influence on tissue origins in induced pluripotent stem cells than in other stem cell types; a stronger impact of culture condition in embryonic stem cells and somatic stem cell types, including mesenchymal stem cells and hematopoietic stem cells. In addition, we found that a characteristic gene module, enriched in histones, exhibits higher expression across different stem cell types that were annotated by specific culture conditions. This tendency was also observed in mouse stem cell RNA-seq data. Our findings would help to obtain general insights into stem cell quality, such as the balance of differentiation potentials that undifferentiated stem cells possess.

Progeria and Aging-Omics Based Comparative Analysis

Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare "normal aging" (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.

Single-cell analysis of menstrual endometrial tissues defines phenotypes associated with endometriosis

BACKGROUND

Endometriosis is a common, complex disorder which is underrecognized and subject to prolonged delays in diagnosis. It is accompanied by significant changes in the eutopic endometrial lining.

METHODS

We have undertaken the first single-cell RNA-sequencing (scRNA-Seq) comparison of endometrial tissues in freshly collected menstrual effluent (ME) from 33 subjects, including confirmed endometriosis patients (cases) and controls as well as symptomatic subjects (who have chronic symptoms suggestive of endometriosis but have not been diagnosed).

RESULTS

We identify a unique subcluster of proliferating uterine natural killer (uNK) cells in ME-tissues from controls that is almost absent from endometriosis cases, along with a striking reduction of total uNK cells in the ME of cases (p < 10^-16). In addition, an IGFBP1+ decidualized subset of endometrial stromal cells are abundant in the shed endometrium of controls when compared to cases (p < 10^-16) confirming findings of compromised decidualization of cultured stromal cells from cases. By contrast, endometrial stromal cells from cases are enriched in cells expressing pro-inflammatory and senescent phenotypes. An enrichment of B cells in the cases (p = 5.8 × 10^-6) raises the possibility that some may have chronic endometritis, a disorder which predisposes to endometriosis.

CONCLUSIONS

We propose that characterization of endometrial tissues in ME will provide an effective screening tool for identifying endometriosis in patients with chronic symptoms suggestive of this disorder. This constitutes a major advance, since delayed diagnosis for many years is a major clinical problem in the evaluation of these patients. Comprehensive analysis of ME is expected to lead to new diagnostic and therapeutic approaches to endometriosis and other associated reproductive disorders such as female infertility.

Identification of 5 microRNA biomarkers associated with the prognosis of uveal melanoma

To uncover the role of microRNAs in the occurrence and development of uveal melanoma (UM), we used R language packages in this study to analyze the correlations between the expression of microRNA isoforms, their target genes, and the clinical data for UM patients retrieved from The Cancer Genome Atlas (TCGA). We used Weighted Correlation Network Analysis (WGCNA) to divide the expression profiles of different microRNAs into 10 modules, among which blue and yellow modules were associated with UM survival. Hsa-miR-513a-5p, miR-506-3p, miR-508-3p, miR-140-3p, and miR-103a-2-5p were further identified as the top 5 node microRNAs based on the risk scores in both modules using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. After combining these 5 microRNAs into an integrated risk signature, the prognostic performance of the risk signature was evaluated by area under the receiver operating characteristic (AUROC) curve, and their association with UM clinical characteristics was further analyzed using multiple Cox regression. Our results showed that this risk signature was sensitivity and specificity, and could serve as an independent prognostic factor. In addition, Spearman correlation analysis showed that expression of almost all target mRNAs were significantly positively or negatively correlated with the associated microRNAs. The gene ontology (GO), pathways, and disease enrichment analyses also showed that these 5 microRNAs were closely related to the incidence and progression of tumor, indicating their potential for predicting the outcome of UM.