Advancements in detection of SARS-CoV-2 infection for confronting COVID-19 pandemics.

As one of the major approaches in combating the COVID-19 pandemics, the availability of specific and reliable assays for the SARS-CoV-2 viral genome and its proteins is essential to identify the infection in suspected populations, make diagnoses in symptomatic or asymptomatic individuals, and determine clearance of the virus after the infection. For these purposes, use of the quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) for detection of the viral nucleic acid remains the most valuable in terms of its specificity, fast turn-around, high-throughput capacity, and reliability. It is critical to update the sequences of primers and probes to ensure the detection of newly emerged variants. Various assays for increased levels of IgG or IgM antibodies are available for detecting ongoing or past infection, vaccination responses, and persistence and for identifying high titers of neutralizing antibodies in recovered individuals. Viral genome sequencing is increasingly used for tracing infectious sources, monitoring mutations, and subtype classification and is less valuable in diagnosis because of its capacity and high cost. Nanopore target sequencing with portable options is available for a quick process for sequencing data. Emerging CRISPR-Cas-based assays, such as SHERLOCK and AIOD-CRISPR, for viral genome detection may offer options for prompt and point-of-care detection. Moreover, aptamer-based probes may be multifaceted for developing portable and high-throughput assays with fluorescent or chemiluminescent probes for viral proteins. In conclusion, assays are available for viral genome and protein detection, and the selection of specific assays depends on the purposes of prevention, diagnosis and pandemic control, or monitoring of vaccination efficacy.

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.

Neuroepigenetics of psychiatric disorders: Focus on lncRNA.

Understanding the pathology of psychiatric disorders is challenging due to their complexity and multifactorial origin. However, development of high-throughput technologies has allowed for better insight into their molecular signatures. Advancement of sequencing methodologies have made it possible to study not only the protein-coding but also the noncoding genome. It is now clear that besides the genetic component, different epigenetic mechanisms play major roles in the onset and development of psychiatric disorders. Among them, examining the role of long noncoding RNAs (lncRNAs) is a relatively new field. Here, we present an overview of what is currently known about the involvement of lncRNAs in schizophrenia, major depressive and bipolar disorders, as well as suicide. The diagnosis of psychiatric disorders mainly relies on clinical evaluation without using measurable biomarkers. In this regard, lncRNA may open new opportunities for development of molecular tests. However, so far only a small set of known lncRNAs have been characterized at molecular level, which means they have a long way to go before clinical implementation. Understanding how changes in lncRNAs affect the appearance and development of psychiatric disorders may lead to a more classified and objective diagnostic system, but also open up new therapeutic targets for these patients.

[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.
.

Quantifying the effect of experimental perturbations at single-cell resolution.

Current methods for comparing single-cell RNA sequencing datasets collected in multiple conditions focus on discrete regions of the transcriptional state space, such as clusters of cells. Here we quantify the effects of perturbations at the single-cell level using a continuous measure of the effect of a perturbation across the transcriptomic space. We describe this space as a manifold and develop a relative likelihood estimate of observing each cell in each of the experimental conditions using graph signal processing. This likelihood estimate can be used to identify cell populations specifically affected by a perturbation. We also develop vertex frequency clustering to extract populations of affected cells at the level of granularity that matches the perturbation response. The accuracy of our algorithm at identifying clusters of cells that are enriched or depleted in each condition is, on average, 57% higher than the next-best-performing algorithm tested. Gene signatures derived from these clusters are more accurate than those of six alternative algorithms in ground truth comparisons.

Patch-seq: Past, Present, and Future.

Single-cell transcriptomic approaches are revolutionizing neuroscience. Integrating this wealth of data with morphology and physiology, for the comprehensive study of neuronal biology, requires multiplexing gene expression data with complementary techniques. To meet this need, multiple groups in parallel have developed "Patch-seq," a modification of whole-cell patch-clamp protocols that enables mRNA sequencing of cell contents after electrophysiological recordings from individual neurons and morphologic reconstruction of the same cells. In this review, we first outline the critical technical developments that enabled robust Patch-seq experimental efforts and analytical solutions to interpret the rich multimodal data generated. We then review recent applications of Patch-seq that address novel and long-standing questions in neuroscience. These include the following: (1) targeted study of specific neuronal populations based on their anatomic location, functional properties, lineage, or a combination of these factors; (2) the compilation and integration of multimodal cell type atlases; and (3) the investigation of the molecular basis of morphologic and functional diversity. Finally, we highlight potential opportunities for further technical development and lines of research that may benefit from implementing the Patch-seq technique. As a multimodal approach at the intersection of molecular neurobiology and physiology, Patch-seq is uniquely positioned to directly link gene expression to brain function.

New Twists in Detecting mRNA Modification Dynamics.

Modified nucleotides in mRNA are an essential addition to the standard genetic code of four nucleotides in animals, plants, and their viruses. The emerging field of epitranscriptomics examines nucleotide modifications in mRNA and their impact on gene expression. The low abundance of nucleotide modifications and technical limitations, however, have hampered systematic analysis of their occurrence and functions. Selective chemical and immunological identification of modified nucleotides has revealed global candidate topology maps for many modifications in mRNA, but further technical advances to increase confidence will be necessary. Single-molecule sequencing introduced by Oxford Nanopore now promises to overcome such limitations, and we summarize current progress with a particular focus on the bioinformatic challenges of this novel sequencing technology.

Review of Single-Cell RNA Sequencing in the Heart.

Single-cell RNA sequencing (scRNA-seq) technology is a powerful, rapidly developing tool for characterizing individual cells and elucidating biological mechanisms at the cellular level. Cardiovascular disease is one of the major causes of death worldwide and its precise pathology remains unclear. scRNA-seq has provided many novel insights into both healthy and pathological hearts. In this review, we summarize the various scRNA-seq platforms and describe the molecular mechanisms of cardiovascular development and disease revealed by scRNA-seq analysis. We then describe the latest technological advances in scRNA-seq. Finally, we discuss how to translate basic research into clinical medicine using scRNA-seq technology.

Development and comparison of RNA-sequencing pipelines for more accurate SNP identification: practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle.

BACKGROUND: Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n = 6 low-RFI, n = 6 high-RFI). Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. RESULTS: On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group (2886 for low-RFI, 3075 for high-RFI), which were significantly (P < 0.05) associated with immune and metabolic pathways. CONCLUSION: The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle.

Single-cell sequencing of miRNAs: A modified technology.

The recent development of next-generation sequencing technologies has offered valuable insights into individual cells. This technology is centered on the characterization of single cells for epigenomics, genomics, and transcriptomics. Ever since the first report appeared in 2009, the single-cell RNA-sequencing saga started to explore deeper into the mechanics intrigued within a single cell. microRNA (miRNA) has been increasingly recognized as an essential molecule triggering an additional layer for gene regulation. Therefore, single-cell sequencing of miRNAs is crucial to explore the logical riddles surrounding the epigenomics, genomics, and transcriptomics of an individual cell. Scientists from the Vienna Biocenter Campus have lately performed single-cell sequencing of miRNAs in the fly, Drosophila, and nematode, Caenorhabditis elegans. In this review, we present the latest scientific explorations supported by all-inclusive data on this novel subject matter of next-generation sequencing.

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches.

BACKGROUND: The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population. METHODS: Our dataset consisted of 50 samples, 23 from children in remission and 27 from children with an active disease on therapy. Nine of these samples were from children with mixed European/Native American ancestry. We used 4 different machine learning methods to create predictive models in 2 populations: the whole dataset and then the samples from children with exclusively European ancestry. RESULTS: In both populations, models were able to predict JIA status well, with training accuracies > 74% and testing accuracies > 78%. Performance was better in the whole dataset model. We note a high degree of overlap between genes identified in both populations. Using ingenuity pathway analysis, genes from the whole dataset associated with cell-to-cell signaling and interactions, cell morphology, organismal injury and abnormalities, and protein synthesis. CONCLUSIONS: This study demonstrates it is feasible to use machine learning in conjunction with RNA sequencing of PBMCs to predict JIA stage. Thus, developing objective biomarkers from easy to obtain clinical samples remains an achievable goal.

Evolving methods for rational de novo design of functional RNA molecules.

Artificial RNA molecules with novel functionality have many applications in synthetic biology, pharmacy and white biotechnology. The de novo design of such devices using computational methods and prediction tools is a resource-efficient alternative to experimental screening and selection pipelines. In this review, we describe methods common to many such computational approaches, thoroughly dissect these methods and highlight open questions for the individual steps. Initially, it is essential to investigate the biological target system, the regulatory mechanism that will be exploited, as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality. This process can usually be split into constrained sequence sampling, the formulation of an optimization problem and an in silico analysis to narrow down the number of candidates with respect to secondary goals. Finally, experimental analysis is important to check whether the defined design objectives are indeed met in the target environment and detailed characterization experiments should be performed to improve the mechanistic models and detect missing design requirements.

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.

Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

As a fundamental unit of life, the cell has rightfully been the subject of intense investigation throughout the history of biology. Technical innovations now make it possible to assay cellular features at genomic scale, yielding breakthroughs in our understanding of the molecular organization of tissues, and even whole organisms. As these data accumulate we will soon be faced with a new challenge: making sense of the plethora of results. Early investigations into the replicability of cell type profiles inferred from single-cell RNA sequencing data have indicated that this is likely to be surprisingly straightforward due to consistent gene co-expression. In this opinion article we discuss the evidence for this claim and its implications for interpreting cell type-specific gene expression.

Cell type discovery using single-cell transcriptomics: implications for ontological representation.

Cells are fundamental function units of multicellular organisms, with different cell types playing distinct physiological roles in the body. The recent advent of single-cell transcriptional profiling using RNA sequencing is producing 'big data', enabling the identification of novel human cell types at an unprecedented rate. In this review, we summarize recent work characterizing cell types in the human central nervous and immune systems using single-cell and single-nuclei RNA sequencing, and discuss the implications that these discoveries are having on the representation of cell types in the reference Cell Ontology (CL). We propose a method, based on random forest machine learning, for identifying sets of necessary and sufficient marker genes, which can be used to assemble consistent and reproducible cell type definitions for incorporation into the CL. The representation of defined cell type classes and their relationships in the CL using this strategy will make the cell type classes being identified by high-throughput/high-content technologies findable, accessible, interoperable and reusable (FAIR), allowing the CL to serve as a reference knowledgebase of information about the role that distinct cellular phenotypes play in human health and disease.