The Analysis of the Human Megakaryocyte and Platelet Coding Transcriptome in Healthy and Diseased Subjects

Abstract

Platelets are generated and released into the bloodstream from their precursor cells, megakaryocytes that reside in the bone marrow. Though platelets have no nucleus or DNA, they contain a full transcriptome that, during platelet formation, is transported from the megakaryocyte to the platelet. It has been described that transcripts in platelets can be translated into proteins that influence platelet response. The platelet transcriptome is highly dynamic and has been extensively studied using microarrays and, more recently, RNA sequencing (RNA-seq) in relation to diverse conditions (inflammation, obesity, cancer, pathogens and others). In this review, we focus on bulk and single-cell RNA-seq studies that have aimed to characterize the coding transcriptome of healthy megakaryocytes and platelets in humans. It has been noted that bulk RNA-seq has limitations when studying in vitro-generated megakaryocyte cultures that are highly heterogeneous, while single-cell RNA-seq has not yet been applied to platelets due to their very limited RNA content. Next, we illustrate how these methods can be applied in the field of inherited platelet disorders for gene discovery and for unraveling novel disease mechanisms using RNA from platelets and megakaryocytes and rare disease bioinformatics. Next, future perspectives are discussed on how this field of coding transcriptomics can be integrated with other next-generation technologies to decipher unexplained inherited platelet disorders in a multiomics approach.

Keywords: bulk RNA sequencing; inherited platelet disorders; megakaryocytes; megakaryopoiesis; platelets; rare disease bioinformatics; single-cell RNA sequencing; thrombopoiesis; transcriptomics.

Figure 1

(A) Stages in megakaryopoiesis and thrombopoiesis; (B) RNA metabolism in maturing megakaryocytes and transfer to platelets during thrombopoiesis; (C) different cell markers associated with stages in MK and platelet differentiation (adapted from Davizon-Castillo et al. [5]).

Figure 2

Schematic of information gained by combining RNA-seq data and Whole Genome Sequencing (WGS) data. (A) RNA-seq gene expression outliers offer insight into the transcriptomic effect of coding and non-coding patient mutations in WGS data. (B) The probability of a change in DNA sequence causing alternative splicing can be predicted by deep learning. Predictions made for patient mutations seen in WGS data can be verified through splicing outlier detection in RNA-seq data. (C) The effect of heterogeneous patient mutations from WGS data is also dependent on monoallelic expression, in which one of the two alleles is (partially) unused. This can be quantified using RNA-seq data.