Identification of estrogen response-associated STRA6+ granulosa cells within high-grade serous ovarian carcinoma by single-cell sequencing

Background

High-grade serous ovarian carcinoma (HGSOC) is a pathologic subtype of ovarian cancer (OC) with a more lethal prognosis. Extensive heterogeneity results in HGSOC being more susceptible to treatment resistance and adverse treatment effects. Revealing the heterogeneity involved is crucial.

Methods

We downloaded the single-cell RNA-seq (scRNA) data from GEO database and performed a scRNA analysis for cell landscape of HGSOC by using the Seurat package. The highly expressed genes were uploaded into the DAVID and KEGG database for enrichment analysis, and the AUCell package was used to calculate cancer-associated hallmark score. The SCENIC analysis was used for key regulons, the estrogen response enrichment scores in TCGA-OV RNA-seq dataset were calculated by using the GSVA package. Besides, the expression of STRA6 and IRF1 and the cell invasion and migration in si-STRA6 OC cells were detected by using the quantitative reverse transcription (qRT)-PCR method and Transwell assay respectively.

Results

We successfully constructed a single-cell atlas of HGSOC and delineated the heterogeneity of epithelial cells therein. There were five epithelial cell subpopulations, GLDC + Epithelial cells, PEG3+ leydig cells, STRA6+ granulosa cells, POLE2+ Epithelial cells, and AURKA + Epithelial cells. STRA6+ granulosa cells have the potential to promote tumor growth as well as the highest estrogen response early activity through the biological pathways analysis of highly expressed genes and estrogen response score of ssGSEA. We found that IRF1 and STRA6 expression was remarkably upregulated in the OC cancer cell line HEY. Silencing of STRA6 markedly decreased the invasion and migration ability of the OC cancer cell line HEY.

Conclusion

There is extreme heterogeneity of epithelial cells in HGSOC, and STRA6+ granulosa cells may be able to promote cancer progression. Our findings are benefit to the heterogeneity identification of HGSOC and develop targeted therapy strategy for HGSOC patients.

Single-cell transcriptome analysis dissects lncRNA-associated gene networks in Arabidopsis

The plant genome produces an extremely large collection of long noncoding RNAs (lncRNAs) that are generally expressed in a context-specific manner and have pivotal roles in regulation of diverse biological processes. Here, we mapped the transcriptional heterogeneity of lncRNAs and their associated gene regulatory networks at single-cell resolution. We generated a comprehensive cell atlas at the whole-organism level by integrative analysis of 28 published single-cell RNA sequencing (scRNA-seq) datasets from juvenile Arabidopsis seedlings. We then provided an in-depth analysis of cell-type-related lncRNA signatures that show expression patterns consistent with canonical protein-coding gene markers. We further demonstrated that the cell-type-specific expression of lncRNAs largely explains their tissue specificity. In addition, we predicted gene regulatory networks on the basis of motif enrichment and co-expression analysis of lncRNAs and mRNAs, and we identified putative transcription factors orchestrating cell-type-specific expression of lncRNAs. The analysis results are available at the single-cell-based plant lncRNA atlas database (scPLAD; https://biobigdata.nju.edu.cn/scPLAD/). Overall, this work demonstrates the power of integrative single-cell data analysis applied to plant lncRNA biology and provides fundamental insights into lncRNA expression specificity and associated gene regulation.

Pan-cancer analyses of classical protein tyrosine phosphatases and phosphatase-targeted therapy in cancer

Protein tyrosine phosphatases function in dephosphorylating target proteins to regulate signaling pathways that control a broad spectrum of fundamental physiological and pathological processes. Detailed knowledge concerning the roles of classical PTPs in human cancer merits in-depth investigation. We comprehensively analyzed the regulatory mechanisms and clinical relevance of classical PTPs in more than 9000 tumor patients across 33 types of cancer. The independent datasets and functional experiments were employed to validate our findings. We exhibited the extensive dysregulation of classical PTPs and constructed the gene regulatory network in human cancer. Moreover, we characterized the correlation of classical PTPs with both drug-resistant and drug-sensitive responses to anti-cancer drugs. To evaluate the PTP activity in cancer prognosis, we generated a PTPscore based on the expression and hazard ratio of classical PTPs. Our study highlights the notable role of classical PTPs in cancer biology and provides novel intelligence to improve potential therapeutic strategies based on pTyr regulation.

Techniques for Small Non-Coding RNA Analysis in Seeds of Forest Tree Species

In recent years, the scientific community has become aware that epigenetic mechanisms play a more important role in gene regulatory networks (GRNs) than was hitherto thought, as accumulating evidence has shown that changes in epigenetics without genetic variation can affect complex traits over multiple generations. Within the epigenetic machinery, small non-coding RNAs (sRNAs, 18-24 nucleotides in length) are evolutionarily conserved RNA molecules that target mRNAs for deregulation or translational repression. They commonly have high-level regulatory functions in GRNs by mediating DNA and/or histone methylation and gene silencing essential for plant developmental programs and adaptability. Local adaptation enables plants to acquire a high fitness by, for example, properly timing developmental transitions to match plant growth stages with organism's favorable seasons. In particular, the seed represents a key evolutionary adaptation of seed plants that facilitates dispersal and reinitiates the development coupled in time with suitable environmental conditions. With the advent of high-throughput sequencing for sRNAs and computational approaches for sRNA detection and categorization, it is now feasible to unravel how sRNAs contribute to the fitness of tree species that can survive hundreds of years (e.g., conifers). Of particular interest is to disentangle the roles of sRNAs from complex genomic information in tree species with intimidating genomic sizes (commonly 20-30 Gb in conifers) and abundant nongenic components (e.g., >60% transposable elements). In this chapter, we use seeds of the conifer Picea glauca as a study system to describe the methods and protocols we used or have recently updated, from high-quality RNA isolation to sRNA identification, sequence conservation, abundance comparison, and functional analysis.

RACIPE: a computational tool for modeling gene regulatory circuits using randomization

Background

One of the major challenges in traditional mathematical modeling of gene regulatory circuits is the insufficient knowledge of kinetic parameters. These parameters are often inferred from existing experimental data and/or educated guesses, which can be time-consuming and error-prone, especially for large networks.

Results

We present a user-friendly computational tool for the community to use our newly developed method named random circuit perturbation (RACIPE), to explore the robust dynamical features of gene regulatory circuits without the requirement of detailed kinetic parameters. Taking the network topology as the only input, RACIPE generates an ensemble of circuit models with distinct randomized parameters and uniquely identifies robust dynamical properties by statistical analysis. Here, we discuss the implementation of the software and the statistical analysis methods of RACIPE-generated data to identify robust gene expression patterns and the functions of genes and regulatory links. Finally, we apply the tool on coupled toggle-switch circuits and a published circuit of B-lymphopoiesis.

Conclusions

We expect our new computational tool to contribute to a more comprehensive and unbiased understanding of mechanisms underlying gene regulatory networks. RACIPE is a free open source software distributed under (Apache 2.0) license and can be downloaded from GitHub (https://github.com/simonhb1990/RACIPE-1.0).

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0594-6) contains supplementary material, which is available to authorized users.