CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

Comparative Transcriptome and WGCNA RevealKey Genes Involved in Lignocellulose Degradation in Sarcomyxa Edulis

In order to explore the molecular mechanism of Sarcomyxa edulis response to lignocelluloses degradation, the developmental transcriptomes was analyzed for six stages covering the whole developmental process, including mycelium growing to half bag (B1), mycelium in cold stimulation after full bag (B2), mycelium in primordia appearing (B3), primordia (B4), mycelium at the harvest stage (B5) and mature fruiting body (B6). A total of 6 samples were used for transcriptome sequencing, with three biological replicates. Based on the above transcriptome data, we constructed a co-expression network of weighted genes associated with extracellular enzyme physiological traits by WGCNA, and obtained 19 gene co-expression modules closely related to lignocelluloses degradation. In addition, a number of key genes involved in lignocelluloses degradation pathways were discovered from the four modules with the highest correlation with target traits. These results provide clues for further study on the molecular genetic mechanisms of Sarcomyxa edulis lignocelluloses degradation.

Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.

Response to comment on ‘SARS-CoV-2 suppresses anticoagulant and fibrinolytic gene expression in the lung’

Early in the SARS-CoV-2 pandemic, we compared transcriptome data from hospitalized COVID-19 patients and control patients without COVID-19. We found changes in procoagulant and fibrinolytic gene expression in the lungs of COVID-19 patients (Mast et al., 2021). These findings have been challenged based on issues with the samples (Fitzgerald and Jamieson, 2022). We have revisited our previous analyses in the light of this challenge and find that these new analyses support our original conclusions.

Molecular Evolution of Tubulins in Diatoms

Microtubules are formed by α- and β-tubulin heterodimers nucleated with γ-tubulin. Tubulins are conserved eukaryotic proteins. Previously, it was shown that microtubules are involved in diatom silica frustule morphogenesis. Diatom frustules are varied, and their morphology is species-specific. Despite the attractiveness of the problem of elucidating the molecular mechanisms of genetically programmed morphogenesis, the structure and evolution of diatom tubulins have not been studied previously. Based on available genomic and transcriptome data, we analyzed the phylogeny of the predicted amino acid sequences of diatom α-, β- and γ-tubulins and identified five groups for α-tubulins, six for β-tubulins and four for γ-tubulins. We identified characteristic amino acids of each of these groups and also analyzed possible posttranslational modification sites of diatom tubulins. According to our results, we assumed what changes occurred in the diatom tubulin structures during their evolution. We also identified which tubulin groups are inherent in large diatom taxa. The similarity between the evolution of diatom tubulins and the evolution of diatoms suggests that molecular changes in α-, β- and γ-tubulins could be one of the factors in the formation of a high morphological diversity of diatoms.

C-Reactive Protein Is an Indicator of the Immunosuppressive Microenvironment Fostered by Myeloid Cells in Hepatocellular Carcinoma

BackgroundC-reactive protein (CRP) is a widely used marker of systemic inflammation and predicts poor clinical outcomes in patients with hepatocellular carcinoma (HCC); however, its significance in the local immune response at the tumor site is not clear.MethodsSerum CRP levels of 329 HCC patients were detected before resection. Paired paraffin-embedded tumor samples were used to quantify immune cell populations, such as CD11b+ myeloid cells, CD68+ macrophages (Mφs), CD15+ neutrophils, CD8+ T cells, and CD206+, CD204+, CD163+ and CD169+ Mφs, by immunohistochemistry. Enrichment scores for 34 types of immune cells based on transcriptome data from 24 HCC samples were calculated by xCell. Overall survival of patients was analyzed using the Kaplan-Meier method.ResultsSerum CRP levels were correlated with liver functions and tumor stages in patients with HCC. The densities of CD68+ tumor-associated macrophages (TAMs) and CD15+ tumor-associated neutrophils (TANs) were significantly higher in patients with elevated serum CRP levels than in those with low CRP levels (both p < 0.0001). Further analysis of TAM subtypes revealed that serum CRP levels were associated with CD204+ and CD163+ Mφ densities (p < 0.0001 and p = 0.0003, respectively). Moreover, transcriptome data showed that CRP expression was associated with the expression of myeloid cell infiltration-related genes in HCC tumors. The combination of serum CRP with TAMs or TANs in both the nontumor and intratumor regions could represent a powerful criterion for predicting patient prognoses.ConclusionSerum CRP could serve as an indicator of an immunosuppressive TME in HCC, which could be of potential clinical application for treatment strategies targeting the TME.

Towards Tabula Gallus

The Tabula Gallus is a proposed project that aims to create a map of every cell type in the chicken body and chick embryos. Chickens (Gallus gallus) are one of the most recognized model animals that recapitulate the development and physiology of mammals. The Tabula Gallus will generate a compendium of single-cell transcriptome data from Gallus gallus, characterize each cell type, and provide tools for the study of the biology of this species, similar to other ongoing cell atlas projects (Tabula Muris and Tabula Sapiens/Human Cell Atlas for mice and humans, respectively). The Tabula Gallus will potentially become an international collaboration between many researchers. This project will be useful for the basic scientific study of Gallus gallus and other birds (e.g., cell biology, molecular biology, developmental biology, neuroscience, physiology, oncology, virology, behavior, ecology, and evolution). It will eventually be beneficial for a better understanding of human health and diseases.

RNA-combine: a toolkit for comprehensive analyses on transcriptome data from different sequencing platforms

Background Understanding the transcriptome has become an essential step towards the full interpretation of the biological function of a cell, a tissue or even an organ. Many tools are available for either processing, analysing transcriptome data, or visualizing analysis results. However, most existing tools are limited to data from a single sequencing platform and only several of them could handle more than one analysis module, which are far from enough to meet the requirements of users, especially those without advanced programming skills. Hence, we still lack an open-source toolkit that enables both bioinformatician and non-bioinformatician users to process and analyze the large transcriptome data from different sequencing platforms and visualize the results. Results We present a Linux-based toolkit, RNA-combine, to automatically perform the quality assessment, downstream analysis of the transcriptome data generated from different sequencing platforms, including bulk RNA-seq (Illumina platform), single cell RNA-seq (10x Genomics) and Iso-Seq (PacBio) and visualization of the results. Besides, this toolkit is implemented with at least 10 analysis modules more than other toolkits examined in this study. Source codes of RNA-combine are available on GitHub: https://github.com/dongxuemin666/RNA-combine. Conclusion Our results suggest that RNA-combine is a reliable tool for transcriptome data processing and result interpretation for both bioinformaticians and non-bioinformaticians.

Discovery of Sex-Determining Genes in Portunus trituberculatus: A Comparison of Male and Female Transcriptomes During Early Developmental Stages

Portunus trituberculatus is one of the main mariculture crabs of high economic value. To identify genes involved in sex determination, we first performed sex-specific transcriptome sequencing at six larval development stages using a DNA/RNA co-extraction method. A total of 907,952,938 and 828,774,880 reads were obtained from female and male crabs, respectively. 2,379 differentially expressed genes (DEGs) were found between females and males, and were mainly enriched in DNA replication, folate biosynthesis, and retinol metabolism pathways. Furthermore, transcription patterns of genes in the sex-determining region (SD) were analyzed based on the transcriptome data, and one Dmrt gene (PtDMY) was found to be exclusively expressed in males during early developmental stages. Notably, some known sex-related genes, including IAG, Dmrt11E, DmrtB1, and DmrtC2 were significantly down-regulated after knocking down PtDMY. Our results suggested that PtDMY is involved in sex determination and may be one of the key upstream regulators of the sex determination pathway. In addition, the massive volume of transcriptome data obtained in this study provided an important basis for the systematic study of sex determination mechanisms in P. trituberculatus.

Identification of Phosphorus Stress Related Proteins in the Seedlings of Dongxiang Wild Rice (Oryza Rufipogon Griff.) Using Label-Free Quantitative Proteomic Analysis

Phosphorus (P) deficiency tolerance in rice is a complex character controlled by polygenes. Through proteomics analysis, we could find more low P tolerance related proteins in unique P-deficiency tolerance germplasm Dongxiang wild rice (Oryza Rufipogon, DXWR), which will provide the basis for the research of its regulation mechanism. In this study, a proteomic approach as well as joint analysis with transcriptome data were conducted to identify potential unique low P response genes in DXWR during seedlings. The results showed that 3589 significant differential accumulation proteins were identified between the low P and the normal P treated root samples of DXWR. The degree of change was more than 1.5 times, including 60 up-regulated and 15 downregulated proteins, 24 of which also detected expression changes of more than 1.5-fold in the transcriptome data. Through quantitative trait locus (QTLs) matching analysis, seven genes corresponding to the significantly different expression proteins identified in this study were found to be uncharacterized and distributed in the QTLs interval related to low P tolerance, two of which (LOC_Os12g09620 and LOC_Os03g40670) were detected at both transcriptome and proteome levels. Based on the comprehensive analysis, it was found that DXWR could increase the expression of purple acid phosphatases (PAPs), membrane location of P transporters (PTs), rhizosphere area, and alternative splicing, and it could decrease reactive oxygen species (ROS) activity to deal with low P stress. This study would provide some useful insights in cloning the P-deficiency tolerance genes from wild rice, as well as elucidating the molecular mechanism of low P resistance in DXWR.