Single‐cell nucleic acid sequencing reinforces to reveal signal‐ dependent pathways contributing to cellular heterogeneity
Year: 2022
Type: article
Abstract: The molecular techniques available to probe the genetic makeup of biological organisms have advanced rapidly since the first nucleic acid sequencing technologies. Determining the transcriptomic and epigenetic landscape of organisms, on top of the genetic sequence, is now feasible and becoming routine. Various sequencing techniques are used to screen for variants and mutations and diagnose diseases. Next-generation sequencing (NGS) technologies are powerful tools to fully understand the nuances of gene expression. NGS can sequence regions to whole genomes of DNA and RNA and has quickly advanced to generate very high throughput, scalable data remarkably quickly. To complement NGS, a new tool called single-cell RNA sequencing (scRNA-seq) has been developed to gauge the heterogeneity between discrete cell types and determine shared characteristics (Figure 1).1 Heterogeneity in cellular tissues is essentially due to different levels of cellular communication, gene expression, and signaling, which can vary between healthy and disease states. The heterogeneity within and between cells arises because of internal and external stimuli, genetic elements, and epigenetic landscape that can change over time or become permanent.2 scRNA-seq has made it possible to identify rare subpopulations of cells within a given tissue. Identifying these subpopulations may help determine biological and developmental characteristics that lead to disease progression and thus illuminate novel diagnostic markers and therapeutic approaches to many diseases. As a rapidly evolving technique, scRNA-seq has already helped map out several plant and animal transcriptomes and changes occurring during development, highlighting pathways related to gene expression. scRNA-seq is currently used for developmental, cancer and embryonic research, as well as infertility, neurology, immunology, cardiovascular, and infectious disease studies. In environmental carcinogenesis research, endocrine disruptors (EDCs) and their effects on human health are of great concern. EDCs are hormonally active agents that can mimic, block, or bind hormone receptors, particularly estrogen and androgen, and can thus cause cancerous tumours, congenital disabilities, developmental disorders, and infertility. Though infertility can have other causes, a link has been established between infertility and EDC pesticides, heavy metals, plasticizer alternatives, nonylphenol, polychlorinated biphenyls, triclosan, and parabens.3 The EDC Bisphenol A (BPA) is an industrial product in a wide variety of daily-use plastics and is known to have estrogen-like activity and effects. Xia et al. recently published a study demonstrating the impact of BPA on gene expression dysregulation in mice testes.4 They found BPA exposure in young male mice for 3 weeks causes phenotypic damage in the testes. They also used scRNA-seq to identify ten major cell types in the mouse testis and their cell type-specific gene expression patterns. This methodology can be used in female mice to explore the effects of BPA on fertility through ovary development, ovulation, conception, progeny, embryo development, etc. Xia et al.'s sequencing data also provided insights into ligand-receptor interactions affected by BPA. Ligand-receptor interactions, important for cell-cell communication and cell signalling, can best be determined in the context of cell location. scRNA-seq is still an evolving technology and has some limitations due to technical and biological factors, which can reduce the quality and reproducibility of the sequencing data.5 Because the concentration of RNA from a single cell is necessarily low, the data gathered are noisier and more varied compared to bulk RNA-seq data. However, scRNA-seq methodologies have been developed to counter the low starting concentration problem. For example, droplet-based technologies focus on longer (full-length) or just the 3′-end transcripts and have been found to sufficiently provide cells and their surrounding subpopulations.6 Moreover, data analysis methods require excellent quality control to weed out low-quality sequences; unique and appropriate computational tools designed for accurate mapping of scRNA-seq data are necessary.6 Additionally, the differences between cells attributed to physical location can be well addressed using a recently developed approach called "spatial transcriptomics". Spatial transcriptomics platforms are a powerful tool to study gene expression in situ without losing spatial information about cell location within tissue samples. Spatial transcriptomics platforms can be categorized into NGS-based and imaging-based.7 NGS-based spatial transcriptomics platforms use spatial barcodes to label the spatial organization of tissue samples before library preparation and sequencing. Imaging-based spatial transcriptomics can use an in situ sequencing approach—where transcripts undergo rolling circle amplification and sequencing directly in the tissue—or an in situ hybridization approach, which uses fluorescent or chromogenic probes to hybridize and locate the target transcripts. The latest spatial transcriptomics technologies can detect thousands of genes at single-cellular/sub-cellular resolution.8 Nevertheless, scRNA-seq and related technologies enable researchers to examine cellular heterogeneity at such a level that we can link regulatory and transcriptional factor variations to changes in gene expression. For example, scRNA-seq has identified spermatogonial stem cell regulators associated with human spermatological germ cell dysfunction, which could provide a genetic explanation for some infertility and presents potential avenues to explore treatments for infertility.9 Furthermore, rapidly evolving scRNA-seq technology combined with spatial transcriptomics will allow a high-resolution transcriptomic landscape to identify intra-tissue heterogeneity caused by factors such as EDCs, mutations, and/or abnormal cell populations and a way to gauge the effectiveness of diagnostics and therapeutics.10 Furthermore, single-cell platforms can be applied to proteomic and metabolomic studies to further understand the potential pathways of EDC's effects on fertility and environmentally-induced reproductive disease. Single-cell transcriptome-level analyses in normal and diseased tissues can reveal blueprints of cellular gene regulation networks to better interpret the molecular mechanisms of disease. Combining immunohistochemistry and histology staining can validate these discoveries at the protein and phenotype levels. Ultimately, integrating scRNA-seq and spatial transcriptomics data will combine the strengths of both techniques and offer an unparalleled view into biologic systems. B.Y. and M.J.S contributed to the preparation and collection of original literatures and figures and the writing and editing of manuscript. B.Y., M.J.S. and S.S.G. were responsible for the structural designs, scientific quality and writing. The authors would like to thank members of the Gadad lab for their careful review and helpful suggestions on this work. S.S.G. is a CPRIT scholar in cancer research and is supported by a First-time faculty recruitment award from the Cancer Prevention and Research Institute of Texas (CPRIT; RR170020) and by the Lizanell and Colbert Coldwell Foundation. Not applicable. The authors declare no conflict of interest. The paper was handled by editors and has undergone a rigorous peer-review process. Dr. Shrikanth S. Gadad was not involved in the journal's review of/or decisions related to this manuscript. Not applicable. Data sharing is not applicable to this article as no new data were created or analyzed in this study.
DOI: 10.1002/ctd2.120
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,300
OpenAlex ID: W4288443098