A resource of single-cell gene expression profiles in a planarian Dugesia japonica

The freshwater planarian Dugesia japonica maintains an abundant heterogeneous cell population called neoblasts, which include adult pluripotent stem cells. Thus, it is an excellent model organism for stem cell and regeneration research. Recently, many single-cell RNA sequencing (scRNA-seq) databases of several model organisms, including other planarian species, have become publicly available; these are powerful and useful resources to search for gene expression in various tissues and cells. However, the only scRNA-seq dataset for D. japonica has been limited by the number of genes detected. Herein, we collected D. japonica cells, and conducted an scRNA-seq analysis. A novel, automatic, iterative cell clustering strategy produced a dataset of 3,404 cells, which could be classified into 63 cell types based on gene expression profiles. We introduced two examples for utilizing the scRNA-seq dataset in this study using D. japonica. First, the dataset provided results consistent with previous studies as well as novel functionally relevant insights, that is, the expression of DjMTA and DjP2X-A genes in neoblasts that give rise to differentiated cells. Second, we conducted an integrative analysis of the scRNA-seq dataset and time-course bulk RNA-seq of irradiated animals, demonstrating that the dataset can help interpret differentially expressed genes captured via bulk RNA-seq. Using the R package “Seurat” and GSE223927, researchers can easily access and utilize this dataset.     

Methods to analyze cell type-specific gene expression profiles from heterogeneous cell populations

Recent technical progress in DNA and protein identification has made genome-wide survey of gene expression at tissue and animal levels a routine approach, such as microarray and RNA sequencing technologies to measure mRNA abundance and mass spectrometry to measure protein abundance. A key limitation in applying these genome-wide gene expression profiling methods at tissue and animal levels is that the contribution of a specific cell type to the total amount of measured gene expression cannot be determined. Here, we review currently available approaches to resolve this and discuss future directions of study to solve questions not addressable by state-of-the-art techniques.     

Stage-dependent gene expression profiles during natural killer cell development

Natural killer (NK) cells develop from hematopoietic stem cells (HSCs) in the bone marrow. To understand the molecular regulation of NK cell development, serial analysis of gene expression (SAGE) was applied to HSCs, NK precursor (pNK) cells, and mature NK cells (mNK) cultured without or with OP9 stromal cells. From 170,464 total individual tags from four SAGE libraries, 35,385 unique genes were identified. A set of genes was expressed in a stage-specific manner: 15 genes in HSCs, 30 genes in pNK cells, and 27 genes in mNK cells. Among them, lipoprotein lipase induced NK cell maturation and cytotoxic activity. Identification of genome-wide profiles of gene expression in different stages of NK cell development affords us a fundamental basis for defining the molecular network during NK cell development.     

Revealing allele-specific gene expression by single-cell transcriptomics

Single-cell sequencing has emerged as a revolutionary method that reveals biological processes with unprecedented resolution and scale, and has already greatly impacted biology and medicine. To investigate processes such as alternative splicing, novel exon detection and allele-specific expression (ASE), full-length based single-cell RNA-seq methods are required for broad sequence coverage and single nucleotide polymorphism (SNP) identification. In this review, we revisit recent achievements from studies that used single-cell RNA-seq to advance our understanding of ASE in the context of both autosomal and X-chromosome genes. We also recapitulate useful bioinformatic tools developed to identify haplotype phase.     

Whole-body integration of gene expression and single-cell morphology

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Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tissue differences revealed by gene expression profiles of various cell lines

Mechanisms through which tissues are formed and maintained remain unknown but are fundamental aspects in biology. Tissue-specific gene expression is a valuable tool to study such mechanisms. But in many biomedical studies, cell lines, rather than human body tissues, are used to investigate biological mechanisms Whether or not cell lines maintain their tissue-specific characteristics after they are isolated and cultured outside the human body remains to be explored. In this study, we applied a novel computational method to identify core genes that contribute to the differentiation of cell lines from various tissues. Several advanced computational techniques, such as Monte Carlo feature selection method, incremental feature selection method, and support vector machine (SVM) algorithm, were incorporated in the proposed method, which extensively analyzed the gene expression profiles of cell lines from different tissues. As a result, we extracted a group of functional genes that can indicate the differences of cell lines in different tissues and built an optimal SVM classifier for identifying cell lines in different tissues. In addition, a set of rules for classifying cell lines were also reported, which can give a clearer picture of cell lines in different issues although its performance was not better than the optimal SVM classifier. Finally, we compared such genes with the tissue-specific genes identified by the Genotype-tissue Expression project. Results showed that most expression patterns between tissues remained in the derived cell lines despite some uniqueness that some genes show tissue specificity.     

Challenges of single-cell diagnostics: analysis of gene expression

Analysis of single-cell gene expression promises a more precise understanding of human disease pathogenesis and important diagnostic applications. Here, we review the rationale for the study of gene expression at the single-cell level, practical methods to isolate homogeneous or single-cell samples, and current approaches to the analysis of single-cell gene expression. Finally, we highlight applications of laser microdissection-based gene expression analysis to the study of human disease and clinical diagnosis.     

Recursive regularization for inferring gene networks from time-course gene expression profiles

Background  

Inferring gene networks from time-course microarray experiments with vector autoregressive (VAR) model is the process of identifying functional associations between genes through multivariate time series. This problem can be cast as a variable selection problem in Statistics. One of the promising methods for variable selection is the elastic net proposed by Zou and Hastie (2005). However, VAR modeling with the elastic net succeeds in increasing the number of true positives while it also results in increasing the number of false positives.     

Identification of circadian-related gene expression profiles in entrained breast cancer cell lines

Cancer cells have broken circadian clocks when compared to their normal tissue counterparts. Moreover, it has been shown in breast cancer that disruption of common circadian oscillations is associated with a more negative prognosis. Numerous studies, focused on canonical circadian genes in breast cancer cell lines, have suggested that there are no mRNA circadian-like oscillations. Nevertheless, cancer cell lines have not been extensively characterized and it is unknown to what extent the circadian oscillations are disrupted. We have chosen representative non-cancerous and cancerous breast cell lines (MCF-10A, MCF-7, ZR-75-30, MDA-MB-231 and HCC-1954) in order to determine the degree to which the circadian clock is damaged. We used serum shock to synchronize the circadian clocks in culture. Our aim was to initially observe the time course of gene expression using cDNA microarrays in the non-cancerous MCF-10A and the cancerous MCF-7 cells for screening and then to characterize specific genes in other cell lines. We used a cosine function to select highly correlated profiles. Some of the identified genes were validated by quantitative polymerase chain reaction (qPCR) and further evaluated in the other breast cancer cell lines. Interestingly, we observed that breast cancer and non-cancerous cultured cells are able to generate specific circadian expression profiles in response to the serum shock. The rhythmic genes, suggested via microarray and measured in each particular subtype, suggest that each breast cancer cell type responds differently to the circadian synchronization. Future results could identify circadian-like genes that are altered in breast cancer and non-cancerous cells, which can be used to propose novel treatments. Breast cell lines are potential models for in vitro studies of circadian clocks and clock-controlled pathways.     

Variability of gene expression profiles in human blood and lymphoblastoid cell lines

Background

Infection of plants by pathogens and the subsequent disease development involves substantial changes in the biochemistry and physiology of both partners. Analysis of genes that are expressed during these interactions represents a powerful strategy to obtain insights into the molecular events underlying these changes. We have employed expressed sequence tag (EST) analysis to identify rice genes involved in defense responses against infection by the blast fungus Magnaporthe oryzae and fungal genes involved in infectious growth within the host during a compatible interaction.

Results

A cDNA library was constructed with RNA from rice leaves (Oryza sativa cv. Hwacheong) infected with M. oryzae strain KJ201. To enrich for fungal genes, subtraction library using PCR-based suppression subtractive hybridization was constructed with RNA from infected rice leaves as a tester and that from uninfected rice leaves as the driver. A total of 4,148 clones from two libraries were sequenced to generate 2,302 non-redundant ESTs. Of these, 712 and 1,562 ESTs could be identified to encode fungal and rice genes, respectively. To predict gene function, Gene Ontology (GO) analysis was applied, with 31% and 32% of rice and fungal ESTs being assigned to GO terms, respectively. One hundred uniESTs were found to be specific to fungal infection EST. More than 80 full-length fungal cDNA sequences were used to validate ab initio annotated gene model of M. oryzae genome sequence.

Conclusion

This study shows the power of ESTs to refine genome annotation and functional characterization. Results of this work have advanced our understanding of the molecular mechanisms underpinning fungal-plant interactions and formed the basis for new hypothesis.