Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape.

Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. Since 2016, the scRNA-tools database has cataloged software tools for analyzing scRNA-seq data. With the number of tools in the database passing 1000, we provide an update on the state of the project and the field. This data shows the evolution of the field and a change of focus from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find that open science practices reward developers with increased recognition and help accelerate the field.

Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine.

When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardless of their culture medium and subgroup. For example, when using mesenchymal stem cells (MSCs) in cell therapy, researchers do not consider their source and culture method nor their application angle and function (soft tissue regeneration, hard tissue regeneration, suppression of immune function, or promotion of immune function). By combining machine learning methods (such as deep learning) with data sets obtained through single-cell RNA sequencing (scRNA-seq) technology, we can discover the hidden structure of these cells, predict their effects more accurately, and effectively use subpopulations with differentiation potential for stem cell therapy. scRNA-seq technology has changed the study of transcription, because it can express single-cell genes with single-cell anatomical resolution. However, this powerful technology is sensitive to biological and technical noise. The subsequent data analysis can be computationally difficult for a variety of reasons, such as denoising single cell data, reducing dimensionality, imputing missing values, and accounting for the zero-inflated nature. In this review, we discussed how deep learning methods combined with scRNA-seq data for research, how to interpret scRNA-seq data in more depth, improve the follow-up analysis of stem cells, identify potential subgroups, and promote the implementation of cell therapy and regenerative medicine measures.

Single-Cell RNA Sequencing to Disentangle the Blood System.

The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.

Advances and challenges in single-cell RNA-seq of microbial communities.

Microbes have developed complex strategies to respond to their environment and escape the immune system by individualizing their behavior. While single-cell RNA sequencing has become instrumental for studying mammalian cells, its use with fungi, protozoa and bacteria is still in its infancy. In this review, we highlight the major progress towards mapping the molecular states of microbes at the single-cell level using genome-wide transcriptomics and describe how these technologies can be extended to probe thousands of species at high throughput. We envision that mammalian and microbial single-cell profiling could soon be integrated for the study of microbial communities in health and disease.

Application of single-cell RNA sequencing on human skin: Technical evolution and challenges.

The bulk tissue RNA sequencing technique measures the average gene expression of potentially heterogeneous cellular subsets of human skin. However, single-cell RNA sequencing (scRNA-seq) enables both profiling of gene expression measurements at a single-cell resolution and identification of cellular heterogeneity. This recent technical advance has broadened the understanding of many aspects of skin biology, such as development, oncogenesis, and immunopathogenesis. However, due to the low number of mRNAs detectable in an individual cell and the alteration of transcriptomes during sample preparation, scRNA-seq data are often extremely noisy. Moreover, unstandardized methodologies for sample preparation, capturing, and bioinformatic analysis (e.g., batch correction or integration) hamper reliable inter-study comparisons. Nevertheless, sophisticated bioinformatic analysis and integrative omics-based approaches are making up for these limitations. Here, we discuss both the advantages and technical challenges of scRNA-seq, a promising tool opening new horizons in dermatological research.

Inflammatory Bowel Disease Through the Lens of Single-cell RNA-seq Technologies.

The intestinal mucosa represents a unique environment where the coordinated function of diverse epithelial, mesenchymal, and immune cells maintains a physiologically balanced environment in the presence of gut microbiota. The intestinal mucosa plays a central role in the pathogenesis of inflammatory bowel disease (IBD), yet the molecular and cellular composition of this diverse environment is poorly understood. However, the recent advent of multimodal single-cell technologies, including single-cell RNA sequencing (scRNA-seq), now provides an opportunity to accurately map the tissue architecture, characterize rare cell types that were previously overlooked, and define function at a single-cell level. In this review, we summarize key advances in single-cell technology and provide an overview of important aspects of computational analysis. We describe emerging data in the field of IBD and discuss how the characterization of novel intestinal mucosa cell populations is reshaping our understanding of this complex disease. We conclude by considering the potential clinical applications, including the definition of novel drug targets and the opportunity for personalization of care in this exciting new era of precision medicine.

Molecular subtyping in pancreatic neuroendocrine neoplasms: New insights into clinical, pathological unmet needs and challenges.

Pancreatic neuroendocrine neoplasms (PanNENs) contain two primary subtypes with distinct molecular features and associated clinical outcomes: well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). PanNENs are a group of clinically heterogeneous tumors, whose diagnosis is based on tumor morphologic features and proliferation indices. However, these standards incompletely meet clinical needs by failing to adequately assess the likelihood of tumor recurrence and the potential for therapeutic response. We therefore focused on discussing molecular advances that facilitate the understanding of heterogeneity and exploration of reliable recurrence/treatment predictors. Taking advantage of high-throughput technologies, emerging methods of molecular subtyping in PanNETs include classifications based on co-existing multi-gene mutations, a large-scale loss of heterozygosity or copy number variation, and islet cell type-specific signatures. PanNEC molecular updates were discussed as well. This review aims to help the field classify PanNEN molecular subtypes, gain insights to aid in the solving of clinical, pathological unmet needs, and detect challenges and concerns of genetically-driven trials.

Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies.

Effective cancer treatment has been precluded by the presence of various forms of intratumoral complexity that drive treatment resistance and metastasis. Recent single-cell sequencing technologies are significantly facilitating the characterization of tumor internal architecture during disease progression. New applications and advances occurring at a fast pace predict an imminent broad application of these technologies in many research areas. As occurred with next-generation sequencing (NGS) technologies, once applied to clinical samples across tumor types, single-cell sequencing technologies could trigger an exponential increase in knowledge of the molecular pathways involved in cancer progression and contribute to the improvement of cancer treatment.

A new era of long-read sequencing for cancer genomics.

Cancer is a disease largely caused by genomic aberrations. Utilizing many rapidly emerging sequencing technologies, researchers have studied cancer genomes to understand the molecular statuses of cancer cells and to reveal their vulnerabilities, such as driver mutations or gene expression. Long-read technologies enable us to identify and characterize novel types of cancerous mutations, including complicated structural variants in haplotype resolution. In this review, we introduce three representative platforms for long-read sequencing and research trends of cancer genomics with long-read data. Further, we describe that aberrant transcriptome and epigenome statuses, namely, fusion transcripts, as well as aberrant transcript isoforms and the phase information of DNA methylation, are able to be elucidated by long-read sequencers. Long-read sequencing may shed light on novel types of aberrations in cancer genomics that are being missed by conventional short-read sequencing analyses.

iDog: an integrated resource for domestic dogs and wild canids.

The domestic dog (Canis lupus familiaris) is indisputably one of man's best friends. It is also a fundamental model for many heritable human diseases. Here, we present iDog (http://bigd.big.ac.cn/idog), the first integrated resource dedicated to domestic dogs and wild canids. It incorporates a variety of omics data, including genome sequences assemblies for dhole and wolf, genomic variations extracted from hundreds of dog/wolf whole genomes, phenotype/disease traits curated from dog research communities and public resources, gene expression profiles derived from published RNA-Seq data, gene ontology for functional annotation, homolog gene information for multiple organisms and disease-related literature. Additionally, iDog integrates sequence alignment tools for data analyses and a genome browser for data visualization. iDog will not only benefit the global dog research community, but also provide access to a user-friendly consolidation of dog information to a large number of dog enthusiasts.