Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data

The construction of a testis transcriptional cell atlas from embryo to adult reveals various somatic cells and their molecular roles

BACKGROUND

The testis is a complex organ that undergoes extensive developmental changes from the embryonic stage to adulthood. The development of germ cells, which give rise to spermatozoa, is tightly regulated by the surrounding somatic cells.

METHODS

To better understand the dynamics of these changes, we constructed a transcriptional cell atlas of the testis, integrating single-cell RNA sequencing data from over 26,000 cells across five developmental stages: fetal germ cells, infants, childhood, peri-puberty, and adults. We employed various analytical techniques, including clustering, cell type assignments, identification of differentially expressed genes, pseudotime analysis, weighted gene co-expression network analysis, and evaluation of paracrine cell-cell communication, to comprehensively analyze this transcriptional cell atlas of the testis.

RESULTS

Our analysis revealed remarkable heterogeneity in both somatic and germ cell populations, with the highest diversity observed in Sertoli and Myoid somatic cells, as well as in spermatogonia, spermatocyte, and spermatid germ cells. We also identified key somatic cell genes, including RPL39, RPL10, RPL13A, FTH1, RPS2, and RPL18A, which were highly influential in the weighted gene co-expression network of the testis transcriptional cell atlas and have been previously implicated in male infertility. Additionally, our analysis of paracrine cell-cell communication supported specific ligand-receptor interactions involved in neuroactive, cAMP, and estrogen signaling pathways, which support the crucial role of somatic cells in regulating germ cell development.

CONCLUSIONS

Overall, our transcriptional atlas provides a comprehensive view of the cell-to-cell heterogeneity in the testis and identifies key somatic cell genes and pathways that play a central role in male fertility across developmental stages.

Integration and gene co-expression network analysis of scRNA-seq transcriptomes reveal heterogeneity and key functional genes in human spermatogenesis

Spermatogenesis is a complex process of cellular division and differentiation that begins with spermatogonia stem cells and leads to functional spermatozoa production. However, many of the molecular mechanisms underlying this process remain unclear. Single-cell RNA sequencing (scRNA-seq) is used to sequence the entire transcriptome at the single-cell level to assess cell-to-cell variability. In this study, more than 33,000 testicular cells from different scRNA-seq datasets with normal spermatogenesis were integrated to identify single-cell heterogeneity on a more comprehensive scale. Clustering, cell type assignments, differential expressed genes and pseudotime analysis characterized 5 spermatogonia, 4 spermatocyte, and 4 spermatid cell types during the spermatogenesis process. The UTF1 and ID4 genes were introduced as the most specific markers that can differentiate two undifferentiated spermatogonia stem cell sub-cellules. The C7orf61 and TNP can differentiate two round spermatid sub-cellules. The topological analysis of the weighted gene co-expression network along with the integrated scRNA-seq data revealed some bridge genes between spermatogenesis's main stages such as DNAJC5B, C1orf194, HSP90AB1, BST2, EEF1A1, CRISP2, PTMS, NFKBIA, CDKN3, and HLA-DRA. The importance of these key genes is confirmed by their role in male infertility in previous studies. It can be stated that, this integrated scRNA-seq of spermatogenic cells offers novel insights into cell-to-cell heterogeneity and suggests a list of key players with a pivotal role in male infertility from the fertile spermatogenesis datasets. These key functional genes can be introduced as candidates for filtering and prioritizing genotype-to-phenotype association in male infertility.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Integrated Analysis of the Transcriptome Profile Reveals the Potential Roles Played by Long Noncoding RNAs in Immunotherapy for Sarcoma

Background

Long-term survival is still low for high-risk patients with soft tissue sarcoma treated with standard management options, including surgery, radiation, and chemotherapy. Immunotherapy is a promising new potential treatment paradigm. However, the application of immune checkpoint inhibitors for the treatment of patients with sarcoma did not yield promising results in a clinical trial. Therefore, there is a considerable need to identify factors that may lead to immune checkpoint inhibitor resistance.

Methods

In this study, we performed a bioinformatic analysis of The Cancer Genome Atlas (TCGA) to detect key long noncoding RNAs (lncRNAs) that were correlated with immune checkpoint inhibitory molecules in sarcoma. The expression levels of these lncRNAs and their correlation with patient prognosis were explored. The upstream long noncoding RNAs were also examined via 450K array data from the TCGA. The potential roles of these lncRNAs were further examined via KEGG and GO analysis using DAVID online software. Finally, the relationship between these lncRNAs and immune cell infiltration in tumors and their effect on immune checkpoint inhibitors were further explored.

Results

We identified lncRNAs correlated with tumor cell immune evasion in sarcoma. The expression of these lncRNAs was upregulated and correlated with worse prognosis in sarcoma and other human cancer types. Moreover, low DNA methylation occupation of these lncRNA loci was detected. Negative correlations between DNA methylation and lncRNA expression were also found in sarcoma and other human cancer types. KEGG and GO analyses indicated that these lncRNAs correlated with immune evasion and negative regulation of the immune response in sarcoma. Finally, high expression of these lncRNAs correlated with more suppressive immune cell infiltration and reduced sensitivity to immune checkpoint inhibitors in sarcoma and other human cancer types.

Conclusion

Our results suggest that long noncoding RNAs confer immune checkpoint inhibitor resistance in human cancer. Further characterization of these lncRNAs may help to elucidate the mechanisms underlying immune checkpoint inhibitor resistance and uncover a novel therapeutic intervention point for immunotherapy.

Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis

Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics

Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.