Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

High-Quality Chromosome-Level Genome Assembly of the Corsac Fox (Vulpes corsac) Reveals Adaptation to Semiarid and Harsh Environments

The Corsac fox (Vulpes corsac) is a species of fox distributed in the arid prairie regions of Central and Northern Asia, with distinct adaptations to dry environments. Here, we applied Oxford-Nanopore sequencing and a chromosome structure capture technique to assemble the first Corsac fox genome, which was then assembled into chromosome fragments. The genome assembly has a total length of 2.2 Gb with a contig N50 of 41.62 Mb and a scaffold N50 of 132.2 Mb over 18 pseudo-chromosomal scaffolds. The genome contained approximately 32.67% of repeat sequences. A total of 20,511 protein-coding genes were predicted, of which 88.9% were functionally annotated. Phylogenetic analyses indicated a close relation to the Red fox (Vulpes vulpes) with an estimated divergence time of ~3.7 million years ago (MYA). We performed separate enrichment analyses of species-unique genes, the expanded and contracted gene families, and positively selected genes. The results suggest an enrichment of pathways related to protein synthesis and response and an evolutionary mechanism by which cells respond to protein denaturation in response to heat stress. The enrichment of pathways related to lipid and glucose metabolism, potentially preventing stress from dehydration, and positive selection of genes related to vision, as well as stress responses in harsh environments, may reveal adaptive evolutionary mechanisms in the Corsac fox under harsh drought conditions. Additional detection of positive selection for genes associated with gustatory receptors may reveal a unique desert diet strategy for the species. This high-quality genome provides a valuable resource for studying mammalian drought adaptation and evolution in the genus Vulpes.

The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives

As an emerging sequencing technology, single-cell RNA sequencing (scRNA-Seq) has become a powerful tool for describing cell subpopulation classification and cell heterogeneity by achieving high-throughput and multidimensional analysis of individual cells and circumventing the shortcomings of traditional sequencing for detecting the average transcript level of cell populations. It has been applied to life science and medicine research fields such as tracking dynamic cell differentiation, revealing sensitive effector cells, and key molecular events of diseases. This review focuses on the recent technological innovations in scRNA-Seq, highlighting the latest research results with scRNA-Seq as the core technology in frontier research areas such as embryology, histology, oncology, and immunology. In addition, this review outlines the prospects for its innovative application in traditional Chinese medicine (TCM) research and discusses the key issues currently being addressed by scRNA-Seq and its great potential for exploring disease diagnostic targets and uncovering drug therapeutic targets in combination with multiomics technologies.

Recent advances and application of whole genome amplification in molecular diagnosis and medicine

Whole genome amplification (WGA) is a technology for non-selective amplification of the whole genome sequence, first appearing in 1992. Its primary purpose is to amplify and reflect the whole genome of trace tissues and single cells without sequence bias and to provide sufficient DNA template for subsequent multigene and multilocus analysis, along with comprehensive genome research. WGA provides a method to obtain a large amount of genetic information from a small amount of DNA and provides a valuable tool for preserving limited samples in molecular biology. WGA technology is especially suitable for forensic identification and genetic disease research, along with new technologies such as next-generation sequencing (NGS). In addition, WGA is also widely used in single-cell sequencing. Due to the small amount of DNA in a single cell, it is often unable to meet the amount of samples needed for sequencing, so WGA is generally used to achieve the amplification of trace samples. This paper reviews WGA methods based on different principles, summarizes both amplification principle and amplification quality, and discusses the application prospects and challenges of WGA technology in molecular diagnosis and medicine.

Single-Cell Sequencing to Unveil the Mystery of Embryonic Development

Embryonic development is a fundamental physiological process that can provide tremendous insights into stem cell biology and regenerative medicine. In this process, cell fate decision is highly heterogeneous and dynamic, and investigations at the single-cell level can greatly facilitate the understanding of the molecular roadmap of embryonic development. Rapid advances in the technology of single-cell sequencing offer a perfectly useful tool to fulfill this purpose. Despite its great promise, single-cell sequencing is highly interdisciplinary, and successful applications in specific biological contexts require a general understanding of its diversity as well as the advantage versus limitations for each of its variants. Here, the technological principles of single-cell sequencing are consolidated and its applications in the study of embryonic development are summarized. First, the technology basics are presented and the available tools for each step including cell isolation, library construction, sequencing, and data analysis are discussed. Then, the works that employed single-cell sequencing are reviewed to investigate the specific processes of embryonic development, including preimplantation, peri-implantation, gastrulation, and organogenesis. Further, insights are provided on existing challenges and future research directions.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

Single-Cell Sequencing and Organoids: A Powerful Combination for Modelling Organ Development and Diseases

The development and function of a particular organ and the pathogenesis of various diseases remain intimately linked to the features of each cell type in the organ. Conventional messenger RNA- or protein-based methodologies often fail to elucidate the contribution of rare cell types, including some subpopulations of stem cells, short-lived progenitors and circulating tumour cells, thus hampering their applications in studies regarding organ development and diseases. The scRNA-seq technique represents a new approach for determining gene expression variability at the single-cell level. Organoids are new preclinical models that recapitulate complete or partial features of their original organ and are thought to be superior to cell models in mimicking the sophisticated spatiotemporal processes of the development and regeneration and diseases. In this review, we highlight recent advances in the field of scRNA-seq, organoids and their current applications and summarize the advantages of using a combination of scRNA-seq and organoid technology to model diseases and organ development.

SCDevDB: A Database for Insights Into Single-Cell Gene Expression Profiles During Human Developmental Processes

Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org

Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis

Single-cell RNA sequencing (scRNA-seq) has been tremendously developed in the past decade owing to overcoming challenges associated with isolation of massive quantities of single cells. Previously, cell heterogeneity and low quantities of available biological material posed significant difficulties to scRNA-seq. Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of normal and malignant hematopoietic cells; this heterogeneity has often been ignored in omics studies. The application of scRNA-seq has profoundly changed our comprehension of many biological phenomena, including organ development and carcinogenesis. Hematopoiesis, is actually a maturation process for more than ten distinct blood and immune cells, and is thought to be critically involved in hematological homeostasis and in sustaining the physiological functions. However, aberrant hematopoiesis directly leads to hematological malignancy, and a deeper understanding of malignant hematopoiesis will provide deeper insights into diagnosis and prognosis for patients with hematological malignancies. Here, we aim to review the recent technical progress and future prospects for scRNA-seq, as applied in physiological and malignant hematopoiesis, in efforts to further understand the hematopoietic hierarchy and to illuminate personalized therapy and precision medicine approaches used in the clinical treatment of hematological malignancies.

Single-cell sequencing and tumorigenesis: improved understanding of tumor evolution and metastasis

Extensive genomic and transcriptomic heterogeneity in human cancer often negatively impacts treatment efficacy and survival, thus posing a significant ongoing challenge for modern treatment regimens. State-of-the-art DNA- and RNA-sequencing methods now provide high-resolution genomic and gene expression portraits of individual cells, facilitating the study of complex molecular heterogeneity in cancer. Important developments in single-cell sequencing (SCS) technologies over the past 5 years provide numerous advantages over traditional sequencing methods for understanding the complexity of carcinogenesis, but significant hurdles must be overcome before SCS can be clinically useful. In this review, we: (1) highlight current methodologies and recent technological advances for isolating single cells, single-cell whole-genome and whole-transcriptome amplification using minute amounts of nucleic acids, and SCS, (2) summarize research investigating molecular heterogeneity at the genomic and transcriptomic levels and how this heterogeneity affects clonal evolution and metastasis, and (3) discuss the promise for integrating SCS in the clinical care arena for improved patient care.

Cross-Tissue Identification of Somatic Stem and Progenitor Cells Using a Single-Cell RNA-Sequencing Derived Gene Signature

A long-standing question in biology is whether multipotent somatic stem and progenitor cells (SSPCs) feature molecular properties that could guide their system-independent identification. Population-based transcriptomic studies have so far not been able to provide a definite answer, given the rarity and heterogeneous nature of these cells. Here, we exploited the resolving power of single-cell RNA-sequencing to develop a computational model that is able to accurately distinguish SSPCs from differentiated cells across tissues. The resulting classifier is based on the combined expression of 23 genes including known players in multipotency, proliferation, and tumorigenesis, as well as novel ones, such as Lcp1 and Vgll4 that we functionally validate in intestinal organoids. We show how this approach enables the identification of stem-like cells in still ambiguous systems such as the pancreas and the epidermis as well as the exploration of lineage commitment hierarchies, thus facilitating the study of biological processes such as cellular differentiation, tissue regeneration, and cancer. Stem Cells 2017;35:2390-2402.

High resolution temporal transcriptomics of mouse embryoid body development reveals complex expression dynamics of coding and noncoding loci

Cellular responses to stimuli are rapid and continuous and yet the vast majority of investigations of transcriptional responses during developmental transitions typically use long interval time courses; limiting the available interpretive power. Moreover, such experiments typically focus on protein-coding transcripts, ignoring the important impact of long noncoding RNAs. We therefore evaluated coding and noncoding expression dynamics at unprecedented temporal resolution (6-hourly) in differentiating mouse embryonic stem cells and report new insight into molecular processes and genome organization. We present a highly resolved differentiation cascade that exhibits coding and noncoding transcriptional alterations, transcription factor network interactions and alternative splicing events, little of which can be resolved by long-interval developmental time-courses. We describe novel short lived and cycling patterns of gene expression and dissect temporally ordered gene expression changes in response to transcription factors. We elucidate patterns in gene co-expression across the genome, describe asynchronous transcription at bidirectional promoters and functionally annotate known and novel regulatory lncRNAs. These findings highlight the complex and dynamic molecular events underlying mammalian differentiation that can only be observed though a temporally resolved time course.

Single-cell RNA-sequencing of the brain

Single-cell RNA-sequencing (scRNA-seq) is revolutionizing our understanding of the genomic, transcriptomic and epigenomic landscapes of cells within organs. The mammalian brain is composed of a complex network of millions to billions of diverse cells with either highly specialized functions or support functions. With scRNA-seq it is possible to comprehensively dissect the cellular heterogeneity of brain cells, and elucidate their specific functions and state. In this review, we describe the current experimental methods used for scRNA-seq. We also review bioinformatic tools and algorithms for data analyses and discuss critical challenges. Additionally, we summarized recent mouse brain scRNA-seq studies and systematically compared their main experimental approaches, computational tools implemented, and important findings. scRNA-seq has allowed researchers to identify diverse cell subpopulations within many brain regions, pinpointing gene signatures and novel cell markers, as well as addressing functional differences. Due to the complexity of the brain, a great deal of work remains to be accomplished. Defining specific brain cell types and functions is critical for understanding brain function as a whole in development, health, and diseases.

Metagenomics and Single-Cell Omics Data Analysis for Human Microbiome Research

Microbes are ubiquitous on our planet, and it is well known that the total number of microbial cells on earth is huge. These organisms usually live in communities, and each of these communities has a different taxonomical structure. As such, microbial communities would serve as the largest reservoir of genes and genetic functions for a vast number of applications in "bio"-related disciplines, especially in biomedicine. Human microbiome is the area in which the relationships between ourselves as hosts and our microbiomes have been examined.In this chapter, we have first reviewed the researches in microbes on community, population and single-cell levels in general. Then we have focused on the effects of recent metagenomics and single-cell advances on human microbiome research, as well as their effects on translational biomedical research. We have also foreseen that with the advancement of big-data analysis techniques, deeper understanding of human microbiome, as well as its broader applications, could be realized.

Single Cell Isolation and Analysis

Individual cell heterogeneity within a population can be critical to its peculiar function and fate. Subpopulations studies with mixed mutants and wild types may not be as informative regarding which cell responds to which drugs or clinical treatments. Cell to cell differences in RNA transcripts and protein expression can be key to answering questions in cancer, neurobiology, stem cell biology, immunology, and developmental biology. Conventional cell-based assays mainly analyze the average responses from a population of cells, without regarding individual cell phenotypes. To better understand the variations from cell to cell, scientists need to use single cell analyses to provide more detailed information for therapeutic decision making in precision medicine. In this review, we focus on the recent developments in single cell isolation and analysis, which include technologies, analyses and main applications. Here, we summarize the historical background, limitations, applications, and potential of single cell isolation technologies.

Single-Cell Sequencing for Precise Cancer Research: Progress and Prospects

Advances in genomic technology have enabled the faithful detection and measurement of mutations and the gene expression profile of cancer cells at the single-cell level. Recently, several single-cell sequencing methods have been developed that permit the comprehensive and precise analysis of the cancer-cell genome, transcriptome, and epigenome. The use of these methods to analyze cancer cells has led to a series of unanticipated discoveries, such as the high heterogeneity and stochastic changes in cancer-cell populations, the new driver mutations and the complicated clonal evolution mechanisms, and the novel identification of biomarkers of variant tumors. These methods and the knowledge gained from their utilization could potentially improve the early detection and monitoring of rare cancer cells, such as circulating tumor cells and disseminated tumor cells, and promote the development of personalized and highly precise cancer therapy. Here, we discuss the current methods for single cancer-cell sequencing, with a strong focus on those practically used or potentially valuable in cancer research, including single-cell isolation, whole genome and transcriptome amplification, epigenome profiling, multi-dimensional sequencing, and next-generation sequencing and analysis. We also examine the current applications, challenges, and prospects of single cancer-cell sequencing.

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.

Bioinformatics approaches to single-cell analysis in developmental biology

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology.

Recent advances and current issues in single-cell sequencing of tumors

Intratumoral heterogeneity is a recently recognized but important feature of cancer that underlies the various biocharacteristics of cancer tissues. The advent of next-generation sequencing technologies has facilitated large scale capture of genomic data, while the recent development of single-cell sequencing has allowed for more in-depth studies into the complex molecular mechanisms of intratumoral heterogeneity. In this review, the recent advances and current challenges in single-cell sequencing methodologies are discussed, highlighting the potential power of these data to provide insights into oncological processes, from tumorigenesis through progression to metastasis and therapy resistance.

Aberrant expression of maternal Plk1 and Dctn3 results in the developmental failure of human in-vivo- and in-vitro-matured oocytes

Fertilisation is the first step in embryonic development, and dynamic changes of key genes may potentially improve assisted reproduction techniques efficiency during this process. Here, we analysed genes that were differentially expressed between oocytes and zygotes and focused on cytokinesis-related genes. Plk1 and Dctn3 were identified as showing dramatic changes in expression during fertilisation and were suggested to play a key role in inducing aneuploidy in zygotes and 8-cell embryos. Moreover, we found that maternal Plk1 and Dctn3 were expressed at lower levels in in vitro matured oocytes, which may have contributed to the high ratio of resulting embryos with abnormal Plk1 and Dctn3 expression levels, thereby reducing the developmental competence of the resulting embryos. Furthermore, the overexpression of Dctn3 can silence Plk1 expression, which suggests a potential regulation mechanism. In conclusion, our present study showed that aberrant expression of Plk1 and Dctn3 increases embryo aneuploidy and developmental failure, particularly in in vitro matured oocytes. Our results facilitate a better understanding of the effects of oocyte maternal gene expression on embryonic development and can be used to improve the outcome of assisted reproduction techniques.

Mass spectrometry-based characterization of endogenous peptides and metabolites in small volume samples

Technologies to assay single cells and their extracellular microenvironments are valuable in elucidating biological function, but there are challenges. Sample volumes are low, the physicochemical parameters of the analytes vary widely, and the cellular environment is chemically complex. In addition, the inherent difficulty of isolating individual cells and handling small volume samples complicates many experimental protocols. Here we highlight a number of mass spectrometry (MS)-based measurement approaches for characterizing the chemical content of small volume analytes, with a focus on methods used to detect intracellular and extracellular metabolites and peptides from samples as small as individual cells. MS has become one of the most effective means for analyzing small biological samples due to its high sensitivity, low analyte consumption, compatibility with a wide array of sampling approaches, and ability to detect a large number of analytes with different properties without preselection. Having access to a flexible portfolio of MS-based methods allows quantitative, qualitative, untargeted, targeted, multiplexed, and spatially resolved investigations of single cells and their similarly scaled extracellular environments. Combining MS with on-line and off-line sample conditioning tools, such as microfluidic and capillary electrophoresis systems, significantly increases the analytical coverage of the sample's metabolome and peptidome, and improves individual analyte characterization/identification. Small volume assays help to reveal the causes and manifestations of biological and pathological variability, as well as the functional heterogeneity of individual cells within their microenvironments and within cellular populations. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

Single Cell Analysis: From Technology to Biology and Medicine

Single-cell analysis heralds a new era that allows “omics” analysis, notably genomics, transcriptomics, epigenomics and proteomics at the single-cell level. It enables the identification of the minor subpopulations that may play a critical role in a biological process of a population of cells, which conventionally are regarded as homogeneous. It provides an ultra-sensitive tool to clarify specific molecular mechanisms and pathways and reveal the nature of cell heterogeneity. It also facilitates the clinical investigation of patients when a very low quantity or a single cell is available for analysis, such as noninvasive prenatal diagnosis and cancer screening, and genetic evaluation for in vitro fertilization. Within a few short years, single-cell analysis, especially whole genomic sequencing and transcriptomic sequencing, is becoming robust and broadly accessible, although not yet a routine practice. Here, with single cell RNA-seq emphasized, an overview of the discipline, progresses, and prospects of single-cell analysis and its applications in biology and medicine are given with a series of logic and theoretical considerations.