Dataset Information

Inferring the transcriptional landscape of bovine skeletal muscle by integrating co-expression networks.

ABSTRACT:

Background

Despite modern technologies and novel computational approaches, decoding causal transcriptional regulation remains challenging. This is particularly true for less well studied organisms and when only gene expression data is available. In muscle a small number of well characterised transcription factors are proposed to regulate development. Therefore, muscle appears to be a tractable system for proposing new computational approaches.

Methodology/principal findings

Here we report a simple algorithm that asks "which transcriptional regulator has the highest average absolute co-expression correlation to the genes in a co-expression module?" It correctly infers a number of known causal regulators of fundamental biological processes, including cell cycle activity (E2F1), glycolysis (HLF), mitochondrial transcription (TFB2M), adipogenesis (PIAS1), neuronal development (TLX3), immune function (IRF1) and vasculogenesis (SOX17), within a skeletal muscle context. However, none of the canonical pro-myogenic transcription factors (MYOD1, MYOG, MYF5, MYF6 and MEF2C) were linked to muscle structural gene expression modules. Co-expression values were computed using developing bovine muscle from 60 days post conception (early foetal) to 30 months post natal (adulthood) for two breeds of cattle, in addition to a nutritional comparison with a third breed. A number of transcriptional landscapes were constructed and integrated into an always correlated landscape. One notable feature was a 'metabolic axis' formed from glycolysis genes at one end, nuclear-encoded mitochondrial protein genes at the other, and centrally tethered by mitochondrially-encoded mitochondrial protein genes.

Conclusions/significance

The new module-to-regulator algorithm complements our recently described Regulatory Impact Factor analysis. Together with a simple examination of a co-expression module's contents, these three gene expression approaches are starting to illuminate the in vivo transcriptional regulation of skeletal muscle development.

SUBMITTER: Hudson NJ

PROVIDER: S-EPMC2749936 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Skeletal muscle is a complex heterogeneous tissue and characterizing its cellular heterogeneity and transcriptional and epigenetic signatures are important for understanding the details of its ontogeny. In our study, we applied scRNA-seq and scATAC-seq to investigate the cell types, molecular features, transcriptional and epigenetic regulation, and patterns of developing bovine skeletal muscle from gestational, lactational and adult stages. Detailed molecular analyses were used to dissect cellular heterogeneity, and we deduced the differentiation trajectory of myogenic cells and uncovered their dynamic gene expression profiles. SCENIC analysis was performed to demonstrate key regulons during cell fate decisions. We explored the future expression states of these heterogeneous cells by RNA velocity analysis and found extensive networks of intercellular communication using the toolkit CellChat. Moreover, the transcriptomic and chromatin accessibility modalities were confirmed to be highly concordant, and integrative analysis of chromatin accessibility and gene expression revealed key transcriptional regulators acting during myogenesis. In bovine skeletal muscle, by scRNA-seq and scATAC-seq analysis, different cell types such as adipocytes, endothelial cells, fibroblasts, lymphocytes, monocytes, pericyte cells and eight skeletal myogenic subpopulations were identified at the three developmental stages. The pseudotime trajectory exhibited a distinct sequential ordering for these myogenic subpopulations and eight distinct gene clusters were observed according to their expression pattern. Moreover, specifically expressed TFs (such as MSC, MYF5, MYOD1, FOXP3, ESRRA, BACH1, SIX2 and ATF4) associated with muscle development were predicted, and likely future transcriptional states of individual cells and the developmental dynamics of differentiation among neighbouring cells were predicted. CellChat analysis on the scRNA-seq data set then classified many ligand-receptor pairs among these cell clusters, which were further categorized into significant signalling pathways, including BMP, IGF, WNT, MSTN, ANGPTL, TGFB, TNF, VEGF and FGF. Finally, scRNA-seq and scATAC-seq results were successfully integrated to reveal a series of specifically expressed TFs that are likely to be candidates for the promotion of cell fate transition during bovine skeletal muscle development. Overall, our results outline a single-cell dynamic chromatin/transcriptional landscape for normal bovine skeletal muscle development; these provide an important resource for understanding the structure and function of mammalian skeletal muscle, which will promote research into its biology.

Project description:MotivationSingle cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.ResultsWe developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.Availability and implementationMATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.Supplementary informationSupplementary data are available at Bioinformatics online.