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Review
. 2022 Mar 16;118(4):1004-1019.
doi: 10.1093/cvr/cvab117.

Dissecting the transcriptome in cardiovascular disease

Emma L Robinson  1   2 , Andrew H Baker  3 , Mairi Brittan  3 , Ian McCracken  3 , G Condorelli  4 , C Emanueli  5 , P K Srivastava  5 , C Gaetano  6 , T Thum  7 , M Vanhaverbeke  8 , C Angione  9 , S Heymans  1 , Y Devaux  10 , T Pedrazzini  11 , F Martelli  12 ; EU-CardioRNA COST Action CA17129
Affiliations
Review

Dissecting the transcriptome in cardiovascular disease

Emma L Robinson et al. Cardiovasc Res. .

Abstract

The human transcriptome comprises a complex network of coding and non-coding RNAs implicated in a myriad of biological functions. Non-coding RNAs exhibit highly organized spatial and temporal expression patterns and are emerging as critical regulators of differentiation, homeostasis, and pathological states, including in the cardiovascular system. This review defines the current knowledge gaps, unmet methodological needs, and describes the challenges in dissecting and understanding the role and regulation of the non-coding transcriptome in cardiovascular disease. These challenges include poor annotation of the non-coding genome, determination of the cellular distribution of transcripts, assessment of the role of RNA processing and identification of cell-type specific changes in cardiovascular physiology and disease. We highlight similarities and differences in the hurdles associated with the analysis of the non-coding and protein-coding transcriptomes. In addition, we discuss how the lack of consensus and absence of standardized methods affect reproducibility of data. These shortcomings should be defeated in order to make significant scientific progress and foster the development of clinically applicable non-coding RNA-based therapeutic strategies to lessen the burden of cardiovascular disease.

Keywords: Methodology standardisation; Non-coding RNAs; Transcriptomics; Translational cardiovascular research.

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Figures

Figure 1
Figure 1
Depiction of the major cell types of the mature mammalian heart along with examples of ways cellular morphology and function can change in disease. Numbered arrows refer to the processes numbered in the text. Text and numbering is colour-coded according to cell type. From far-right clockwise: endothelial cells, cardiomyocytes, immune cells, smooth muscle cells, pericytes, and cardiac fibroblasts.
Figure 2
Figure 2
Schematic of a typical scRNA-seq workflow: single cells are separated and sequencing libraries generated using either droplet or plate-based scRNA-seq platforms. Raw sequencing data are processed and aligned to the reference genome to generate count matrices. Following quality control (QC) and normalisation, dimension reduction is performed to facilitate visualisation in low dimensional space. Subsequent clustering is performed to group cells by their similarity in transcriptional profile. Frequently used bioinformatic tools for each of these steps are indicated on the right side.
Figure 3
Figure 3
Advantages (Pros) and challenges (Cons) of single-cell sequencing technology.
Figure 4
Figure 4
Schematic of typical alternative splicing (AS) analysis workflow using from RNA-seq data. Frequently used bioinformatic tools for each are indicated on the right side of each step.
Figure 5
Figure 5
Challenges and opportunities in transcriptomic studies in peripheral blood.
Figure 6
Figure 6
Potential clinical application of circulating non-coding RNAs in personalized cardiovascular medicine.
See this image and copyright information in PMC

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