Clinical Perspectives of Single-Cell RNA Sequencing.

The ability of single-cell genomics to resolve cellular heterogeneity is highly appreciated in cancer and is being exploited for precision medicine. In the recent decade, we have witnessed the incorporation of cancer genomics into the clinical decision-making process for molecular-targeted therapies. Compared with conventional genomics, which primarily focuses on the specific and sensitive detection of the molecular targets, single-cell genomics addresses intratumoral heterogeneity and the microenvironmental components impacting the treatment response and resistance. As an exploratory tool, single-cell genomics provides an unprecedented opportunity to improve the diagnosis, monitoring, and treatment of cancer. The results obtained upon employing bulk cancer genomics indicate that single-cell genomics is at an early stage with respect to exploration of clinical relevance and requires further innovations to become a widely utilized technology in the clinic.

[Progress in Single-cell RNA Sequencing of Lung Adenocarcinoma].

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and one of the main causes of cancer-related deaths. In the past decade, with the widespread use of computed tomography (CT) in routine screening for lung cancer, the incidence of LUAD presenting as small pulmonary nodules radiologically, has increased remarkably. The mechanisms of the occurrence and progression of LUADs are complex, and the prognoses of patients with LUAD vary significantly. Although significant progress has been made in targeted therapy and immunotherapy for LUADs in recent years, the drug resistance of tumor cells has not been effectively overcome, which limits the benefits of patients. With the accomplishment of the Human Genome Project, sequencing-based genomic and transcriptomics have come into the field of clinical and scientific researches. Single-cell sequencing, as a new type of sequencing method that has captured increasing attention recently, can perform specific analysis of cell populations at single-cell level, which can reveal the unique changes of each cell type. Single-cell sequencing can also provide accurate assessment on heterogeneous stromal cells and cancer cells, which is helpful to reveal the complexity of molecular compositions and differences between non- and malignant tissues. To sum up, it is an urgent need for clinicians and basic scientists to deeply understand the pathogenesis and development of LUAD, the heterogeneity of tumor microenvironment (TME) and the mechanism of drug resistance formation through single-cell sequencing, so as to discover new therapeutic targets. In this paper, we reviewed and summarized the application and progress in single-cell sequencing of LUADs.
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Whole blood RNA sequencing reveals a unique transcriptomic profile in patients with ARDS following hematopoietic stem cell transplantation.

BACKGROUND: The acute respiratory distress syndrome (ARDS) is characterized by the acute onset of hypoxemia and bilateral lung infiltrates in response to an inciting event, and is associated with high morbidity and mortality. Patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT) are at increased risk for ARDS. We hypothesized that HSCT patients with ARDS would have a unique transcriptomic profile identifiable in peripheral blood compared to those that did not undergo HSCT. METHODS: We isolated RNA from banked peripheral blood samples from a biorepository of critically ill ICU patients. RNA-Seq was performed on 11 patients with ARDS (5 that had undergone HSCT and 6 that had not) and 12 patients with sepsis without ARDS (5 that that had undergone HCST and 7 that had not). RESULTS: We identified 687 differentially expressed genes between ARDS and ARDS-HSCT (adjusted p-value < 0.01), including IFI44L, OAS3, LY6E, and SPATS2L that had increased expression in ARDS vs. ARDS-HSCT; these genes were not differentially expressed in sepsis vs sepsis-HSCT. Gene ontology enrichment analysis revealed that many differentially expressed genes were related to response to type I interferon. CONCLUSIONS: Our findings reveal significant differences in whole blood transcriptomic profiles of patients with non-HSCT ARDS compared to ARDS-HSCT patients and point toward different immune responses underlying ARDS and ARDS-HSCT that contribute to lung injury.

Technology in MicroRNA Profiling: Circulating MicroRNAs as Noninvasive Cancer Biomarkers in Breast Cancer.

This report describes technologies to identify and quantify microRNAs (miRNAs) as potential cancer biomarkers, using breast cancer as an example. Most breast cancer patients are not diagnosed until the disease has advanced to later stages, which decreases overall survival rates. Specific miRNAs are up- or downregulated in breast cancer patients at various stages, can be detected in plasma and serum, and have shown promising preliminary clinical sensitivity and specificity for early cancer diagnosis or staging. Nucleic acid testing methods to determine relative concentrations of selected miRNAs include reverse transcription, followed by quantitative PCR (RT-qPCR), microarrays, and next-generation sequencing (NGS). Of these methods, NGS is the most powerful approach for miRNA biomarker discovery, whereas RT-qPCR shows the most promise for eventual clinical diagnostic applications.

Bayesian inference of MicroRNA targets from sequence and expression data.

MicroRNAs (miRNAs) regulate a large proportion of mammalian genes by hybridizing to targeted messenger RNAs (mRNAs) and down-regulating their translation into protein. Although much work has been done in the genome-wide computational prediction of miRNA genes and their target mRNAs, an open question is how to efficiently obtain functional miRNA targets from a large number of candidate miRNA targets predicted by existing computational algorithms. In this paper, we propose a novel Bayesian model and learning algorithm, GenMiR++ (Generative model for miRNA regulation), that accounts for patterns of gene expression using miRNA expression data and a set of candidate miRNA targets. A set of high-confidence functional miRNA targets are then obtained from the data using a Bayesian learning algorithm. Our model scores 467 high-confidence miRNA targets out of 1,770 targets obtained from TargetScanS in mouse at a false detection rate of 2.5%: several confirmed miRNA targets appear in our high-confidence set, such as the interactions between miR-92 and the signal transduction gene MAP2K4, as well as the relationship between miR-16 and BCL2, an anti-apoptotic gene which has been implicated in chronic lymphocytic leukemia. We present results on the robustness of our model showing that our learning algorithm is not sensitive to various perturbations of the data. Our high-confidence targets represent a significant increase in the number of miRNA targets and represent a starting point for a global understanding of gene regulation.