SIDEseq: A Cell Similarity Measure Defined by Shared Identified Differentially Expressed Genes for Single-Cell RNA sequencing Data

One goal of single-cell RNA sequencing (scRNA seq) is to expose possible heterogeneity within cell populations due to meaningful, biological variation. Examining cell-to-cell heterogeneity, and further, identifying subpopulations of cells based on scRNA seq data has been of common interest in life science research. A key component to successfully identifying cell subpopulations (or clustering cells) is the (dis)similarity measure used to group the cells. In this paper, we introduce a novel measure, named SIDEseq, to assess cell-to-cell similarity using scRNA seq data. SIDEseq first identifies a list of putative differentially expressed (DE) genes for each pair of cells. SIDEseq then integrates the information from all the DE gene lists (corresponding to all pairs of cells) to build a similarity measure between two cells. SIDEseq can be implemented in any clustering algorithm that requires a (dis)similarity matrix. This new measure incorporates information from all cells when evaluating the similarity between any two cells, a characteristic not commonly found in existing (dis)similarity measures. This property is advantageous for two reasons: (a) borrowing information from cells of different subpopulations allows for the investigation of pairwise cell relationships from a global perspective and (b) information from other cells of the same subpopulation could help to ensure a robust relationship assessment. We applied SIDEseq to a newly generated human ovarian cancer scRNA seq dataset, a public human embryo scRNA seq dataset, and several simulated datasets. The clustering results suggest that the SIDEseq measure is capable of uncovering important relationships between cells, and outperforms or at least does as well as several popular (dis)similarity measures when used on these datasets.     

RNA sequencing reveals two major classes of gene expression levels in metazoan cells

The expression level of a gene is often used as a proxy for determining whether the protein or RNA product is functional in a cell or tissue. Therefore, it is of fundamental importance to understand the global distribution of gene expression levels, and to be able to interpret it mechanistically and functionally. Here we use RNA sequencing (RNA‐seq) of mouse Th2 cells, coupled with a range of other techniques, to show that all genes can be separated, based on their expression abundance, into two distinct groups: one group comprised of lowly expressed and putatively non‐functional mRNAs, and the other of highly expressed mRNAs with active chromatin marks at their promoters. These observations are confirmed in many other microarray and RNA‐seq data sets of metazoan cell types.     

The ACE2 expression in Sertoli cells and germ cells may cause male reproductive disorder after SARS‐CoV‐2 infection

The serious coronavirus disease‐2019 (COVID‐19) was first reported in December 2019 in Wuhan, China. COVID‐19 is an infectious disease caused by severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). Angiotensin converting enzyme 2(ACE2) is the cellular receptor for SARS‐CoV‐2. Considering the critical roles of testicular cells for the transmission of genetic information between generations, we analyzed single‐cell RNA‐sequencing (scRNA‐seq) data of adult human testis. The mRNA expression of ACE2 was expressed in both germ cells and somatic cells. Moreover, the positive rate of ACE2 in testes of infertile men was higher than normal, which indicates that SARS‐CoV‐2 may cause reproductive disorders through pathway activated by ACE2 and the men with reproductive disorder may easily to be infected by SARS‐CoV‐2. The expression level of ACE2 was related to the age, and the mid‐aged with higher positive rate than young men testicular cells. Taken together, this research provides a biological background of the potential route for infection of SARS‐CoV‐2 and may enable rapid deciphering male‐related reproductive disorders induced by COVID‐19.     

RNA‐sequencing profiles hippocampal gene expression in a validated model of cancer‐induced depression

To investigate the pathophysiology of cancer‐induced depression (CID), we have recently developed a validated CID mouse model. Given that the efficacy of antidepressants in cancer patients is controversial, it remains unclear whether CID is a biologically distinct form of depression. We used RNA‐sequencing (RNA‐seq) to investigate differentially expressed genes (DEGs) in hippocampi of animals from our CID model relative a positive control model of depressive‐like behavior induced with chronic corticosterone (CORT). To validate RNA‐seq results, we performed quantitative real‐time RT‐PCR (qRT‐PCR) on a subset of DEGs. Enrichment analysis using DAVID was performed on DEGs to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and biological process gene ontologies (GO:BP). qRT‐PCR results significantly predicted RNA‐seq results. RNA‐seq revealed that most DEGs identified in the CORT model overlapped with the CID model. Enrichment analyses identified KEGG pathways and GO:BP terms associated with ion homeostasis and neuronal communication for both the CORT and CID model. In addition, CID DEGs were enriched in pathways and terms relating to neuronal development, intracellular signaling, learning and memory. This study is the first to investigate CID at the mRNA level. We have shown that most hippocampal mRNA changes that are associated with a depressive‐like state are also associated with cancer. Several other changes occur at the mRNA level in cancer, suggesting that the CID model may represent a biologically distinct form of a depressive‐like state.     

Differential expression analyses for single-cell RNA-Seq: old questions on new data

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.     

LncRNA‐mRNA expression profiles and functional networks in osteoclast differentiation

Human osteoclasts are differentiated from CD14⁺ monocytes and are responsible for bone resorption. Long non‐coding RNAs (lncRNAs) have been proved to be significantly involved in multiple biologic processes, especially in cell differentiation. However, the effect of lncRNAs in osteoclast differentiation is less appreciated. In our study, RNA sequencing (RNA‐seq) was used to identify the expression profiles of lncRNAs and mRNAs in osteoclast differentiation. The results demonstrated that expressions of 1117 lncRNAs and 296 mRNAs were significantly altered after osteoclast differentiation. qRT‐PCR assays were performed to confirm the expression profiles, and the results were almost consistent with the RNA‐seq data. GO and KEGG analyses were used to predict the functions of these differentially expressed mRNA and lncRNAs. The Path‐net analysis demonstrated that MAPK pathway, PI3K‐AKT pathway and NF‐kappa B pathway played important roles in osteoclast differentiation. Co‐expression networks and competing endogenous RNA networks indicated that ENSG00000257764.2‐miR‐106a‐5p‐TIMP2 may play a central role in osteoclast differentiation. Our study provides a foundation to further understand the role and underlying mechanism of lncRNAs in osteoclast differentiation, in which many of them could be potential targets for bone metabolic disease.     

Substantial differences in bias between single‐digest and double‐digest RAD‐seq libraries: A case study

The trade‐offs of using single‐digest vs. double‐digest restriction site‐associated DNA sequencing (RAD‐seq) protocols have been widely discussed. However, no direct empirical comparisons of the two methods have been conducted. Here, we sampled a single population of Gulf pipefish (Syngnathus scovelli) and genotyped 444 individuals using RAD‐seq. Sixty individuals were subjected to single‐digest RAD‐seq (sdRAD‐seq), and the remaining 384 individuals were genotyped using a double‐digest RAD‐seq (ddRAD‐seq) protocol. We analysed the resulting Illumina sequencing data and compared the two genotyping methods when reads were analysed either together or separately. Coverage statistics, observed heterozygosity, and allele frequencies differed significantly between the two protocols, as did the results of selection components analysis. We also performed an in silico digestion of the Gulf pipefish genome and modelled five major sources of bias: PCR duplicates, polymorphic restriction sites, shearing bias, asymmetric sampling (i.e., genotyping fewer individuals with sdRAD‐seq than with ddRAD‐seq) and higher major allele frequencies. This combination of approaches allowed us to determine that polymorphic restriction sites, an asymmetric sampling scheme, mean allele frequencies and to some extent PCR duplicates all contribute to different estimates of allele frequencies between samples genotyped using sdRAD‐seq versus ddRAD‐seq. Our finding that sdRAD‐seq and ddRAD‐seq can result in different allele frequencies has implications for comparisons across studies and techniques that endeavour to identify genomewide signatures of evolutionary processes in natural populations.