The emergence of single-cell sequencing technology enables people to observe cells with unprecedented precision. However, it is difficult to capture the information on all cells and genes in one single-cell RNA sequencing (scRNA-seq) experiment. Single-cell data of a single modality cannot explain cell state and system changes in detail. The integrative analysis of single-cell data aims to address these two types of problems. Integrating multiple scRNA-seq data can collect complete cell types and provide a powerful boost for the construction of cell atlases. Integrating single-cell multimodal data can be used to study the causal relationship and gene regulation mechanism across modalities. The development and application of data integration methods helps fully explore the richness and relevance of single-cell data and discover meaningful biological changes. Based on this, this article reviews the basic principles, methods and applications of multiple scRNA-seq data integration and single-cell multimodal data integration. Moreover, the advantages and disadvantages of existing methods are discussed. Finally, the future development is prospected.
Base Sequence ; Gene Expression Profiling ; Gene Expression Regulation ; Humans ; Sequence Analysis, RNA ; Single-Cell Analysis

Single-cell RNA sequencing (scRNA-seq) is a powerful technique to analyze the transcriptomic heterogeneities at the single cell level. It is an important step for studying cell sub-populations and lineages, with an effective low-dimensional representation and visualization of the original scRNA-Seq data. At the single cell level, the transcriptional fluctuations are much larger than the average of a cell population, and the low amount of RNA transcripts will increase the rate of technical dropout events. Therefore, scRNA-seq data are much noisier than traditional bulk RNA-seq data. In this study, we proposed the deep variational autoencoder for scRNA-seq data (VASC), a deep multi-layer generative model, for the unsupervised dimension reduction and visualization of scRNA-seq data. VASC can explicitly model the dropout events and find the nonlinear hierarchical feature representations of the original data. Tested on over 20 datasets, VASC shows superior performances in most cases and exhibits broader dataset compatibility compared to four state-of-the-art dimension reduction and visualization methods. In addition, VASC provides better representations for very rare cell populations in the 2D visualization. As a case study, VASC successfully re-establishes the cell dynamics in pre-implantation embryos and identifies several candidate marker genes associated with early embryo development. Moreover, VASC also performs well on a 10× Genomics dataset with more cells and higher dropout rate.
Computer Graphics ; Gene Expression Profiling ; methods ; Humans ; Sequence Analysis, RNA ; methods ; Single-Cell Analysis

Single-cell transcriptome sequencing(scRNA-seq) can be used to analyze the expression characteristics of the transcriptome at the level of individual cell, and discover the heterogeneity of gene expression in individual cell that is "diluted" or averaged in study of group organization. The scRNA-seq, with the characteristics of standardization, high-throughput, and high integration, can greatly simplify the experimental operation and significantly reduce the consumption of reagents. At the same time, a variety of cells are screened and the gene expression patterns are analyzed at the single-cell level to provide a more efficient detection technique and more rich and accurate information for drug research. In the field of traditional Chinese medicine(TCM), the scRNA-seq is still a new technology, but the individual and precision concepts embodied by scRNA-seq and the theory of TCM syndrome differentiation and treatment have reached the same effect between the micro and macro aspects. This study tried to broaden the thinking for the modernization of TCM by introducing the development of scRNA-seq technology and its application in modern drug research and discussing the application prospects of scRNA-seq in TCM research.
Gene Expression Profiling ; Medicine, Chinese Traditional ; Reference Standards ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome

Traditional bulk RNA sequencing assesses the average expression level of genes in tissues rather than the differences in cellular responses. Accordingly, it is hard to differentiate sensitive responding cells, leading to inaccurate identification of toxicity pathways. Single-cell RNA sequencing (scRNA-seq) isolated single cells from tissue and subjected them to cell subtypes-specific transcriptome analysis. This technique in toxicological studies realizes the heterogeneous cellular responses in the tissue microenvironment upon chemical exposure. Thus it helps to identify sensitive responding cells and key molecular events, providing a powerful tool and a new perspective for exploring the mechanisms of toxicity and the modes of action. This review summarizes the development, principle, method, application and limitations of scRNA-seq in mechanistic toxicological researches, and discusses the prospect of multi-directional applications.
Base Sequence ; Gene Expression Profiling ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome

Aging/metabolism* ; Animals ; Gene Expression Regulation ; Macaca fascicularis ; Retina/metabolism* ; Single-Cell Analysis

Postnatal heart maturation is the basis of normal cardiac function and provides critical insights into heart repair and regenerative medicine. While static snapshots of the maturing heart have provided much insight into its molecular signatures, few key events during postnatal cardiomyocyte maturation have been uncovered. Here, we report that cardiomyocytes (CMs) experience epigenetic and transcriptional decline of cardiac gene expression immediately after birth, leading to a transition state of CMs at postnatal day 7 (P7) that was essential for CM subtype specification during heart maturation. Large-scale single-cell analysis and genetic lineage tracing confirm the presence of transition state CMs at P7 bridging immature state and mature states. Silencing of key transcription factor JUN in P1-hearts significantly repressed CM transition, resulting in perturbed CM subtype proportions and reduced cardiac function in mature hearts. In addition, transplantation of P7-CMs into infarcted hearts exhibited cardiac repair potential superior to P1-CMs. Collectively, our data uncover CM state transition as a key event in postnatal heart maturation, which not only provides insights into molecular foundations of heart maturation, but also opens an avenue for manipulation of cardiomyocyte fate in disease and regenerative medicine.
Gene Expression Regulation ; Heart ; Myocytes, Cardiac/metabolism* ; Single-Cell Analysis ; Transcription Factors/metabolism*

Efforts have been made to establish various human pluripotent stem cell lines. However, such methods have not yet been duplicated in non-human primate cells. Here, we introduce a multiplexed single-cell sequencing technique to profile the molecular features of monkey pluripotent stem cells in published culture conditions. The results demonstrate suboptimized maintenance of pluripotency and show that the selected signaling pathways for resetting human stem cells can also be interpreted for establishing monkey cell lines. Overall, this work legitimates the translation of novel human cell line culture conditions to monkey cells and provides guidance for exploring chemical cocktails for monkey stem cell line derivation.
Animals ; Haplorhini ; Single-Cell Gene Expression Analysis ; Pluripotent Stem Cells/metabolism* ; Cell Line ; Signal Transduction ; Cell Differentiation ; Transcriptome

Tumors are complex ecosystems in which heterogeneous cancer cells interact with their microenvironment composed of diverse immune, endothelial, and stromal cells. Cancer biology had been studied using bulk genomic and gene expression profiling, which however mask the cellular diversity and average the variability among individual molecular programs. Recent advances in single-cell transcriptomic sequencing have enabled a detailed dissection of tumor ecosystems and promoted our understanding of tumorigenesis at single-cell resolution. In the present review, we discuss the main topics of recent cancer studies that have implemented single-cell RNA sequencing (scRNA-seq). To study cancer cells, scRNA-seq has provided novel insights into the cancer stem-cell model, treatment resistance, and cancer metastasis. To study the tumor microenvironment, scRNA-seq has portrayed the diverse cell types and complex cellular states of both immune and non-immune cells interacting with cancer cells, with the promise to discover novel targets for future immunotherapy.
Ecosystem ; Gene Expression Profiling ; Genomics ; Humans ; Neoplasms/pathology* ; Sequence Analysis, RNA ; Single-Cell Analysis ; Transcriptome ; Tumor Microenvironment/genetics*

AIMTo investigate the method of detecting gene expression in colon tissue at a single cell level.

METHODSIndividual cell(s) were picked up from colon frozen section using laser microdissection. RNA was extracted, reverse transcribed to complementary DNA (cDNA). cDNA was then analyzed by nested reverse transcription polymerase chain reaction (nested RT-PCR) using two pairs of primers.

RESULTSSingle cell(s) were selectively picked up using an ultraviolet laser micromanipulator. RNA was extracted, reverse transcribed and used for nested RT-PCR. Amplification products of cDNA from down to a single cell could be clearly visualized in the agarose gel.

CONCLUSIONThe combined utilization of laser microdissection and nested RT-PCR provides an opportunity to analyze gene expression at single cell(s) level in colon tissue.

Colon ; cytology ; Gene Expression ; Gene Expression Profiling ; methods ; Humans ; Laser Capture Microdissection ; Reverse Transcriptase Polymerase Chain Reaction ; methods ; Single-Cell Analysis

Studies of cancer heterogeneity have received considerable attention recently, because the presence or absence of resistant sub-clones may determine whether or not certain therapeutic treatments are effective. Previously, we have reported G64, a co-regulated gene module composed of 64 different genes, can differentiate tumor intra- or inter-subpopulations in lung adenocarcinomas (LADCs). Here, we investigated whether the G64 module genes were also expressed distinctively in different subpopulations of other cancers. RNA sequencing-based transcriptome data derived from 22 cancers, except LADC, were downloaded from The Cancer Genome Atlas (TCGA). Interestingly, the 22 cancers also expressed the G64 genes in a correlated manner, as observed previously in an LADC study. Considering that gene expression levels were continuous among different tumor samples, tumor subpopulations were investigated using extreme expressional ranges of G64-i.e., tumor subpopulation with the lowest 15% of G64 expression, tumor subpopulation with the highest 15% of G64 expression, and tumor subpopulation with intermediate expression. In each of the 22 cancers, we examined whether patient survival was different among the three different subgroups and found that G64 could differentiate tumor subpopulations in six other cancers, including sarcoma, kidney, brain, liver, and esophageal cancers.
Adenocarcinoma ; Brain ; Esophageal Neoplasms ; Gene Expression ; Gene Regulatory Networks ; Genome ; Humans ; Kidney ; Liver ; Lung ; Population Characteristics ; RNA ; Sarcoma ; Single-Cell Analysis ; Survival Analysis ; Transcriptome