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Decoding bioactive signals of the RNA secretome: the cell-free messenger RNA catalogue

Published online by Cambridge University Press:  29 April 2024

Rhys E. De Sota
Affiliation:
Superfluid Dx., 259 E Grand Avenue, South San Francisco, CA 94080, USA
Stephen R. Quake
Affiliation:
Department of Bioengineering and Department of Applied Physics, Stanford University, Stanford, CA, USA Chan Zuckerberg Biohub, San Francisco, CA, USA
John J. Sninsky
Affiliation:
Superfluid Dx., 259 E Grand Avenue, South San Francisco, CA 94080, USA
Shusuke Toden*
Affiliation:
Superfluid Dx., 259 E Grand Avenue, South San Francisco, CA 94080, USA
*
Corresponding author: Shusuke Toden; Email: stoden@superfluiddx.com
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Abstract

Despite gene-expression profiling being one of the most common methods to evaluate molecular dysregulation in tissues, the utilization of cell-free messenger RNA (cf-mRNA) as a blood-based non-invasive biomarker analyte has been limited compared to other RNA classes. Recent advancements in low-input RNA-sequencing and normalization techniques, however, have enabled characterization as well as accurate quantification of cf-mRNAs allowing direct pathological insights. The molecular profile of the cell-free transcriptome in multiple diseases has subsequently been characterized including, prenatal diseases, neurological disorders, liver diseases and cancers suggesting this biological compartment may serve as a disease agnostic platform. With mRNAs packaged in a myriad of extracellular vesicles and particles, these signals may be used to develop clinically actionable, non-invasive disease biomarkers. Here, we summarize the recent scientific developments of extracellular mRNA, biology of extracellular mRNA carriers, clinical utility of cf-mRNA as disease biomarkers, as well as proposed functions in cell and tissue pathophysiology.

Information

Type
Review
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press
Figure 0

Figure 1. Chronological timeline of key studies in cf-mRNA liquid biopsy.

Figure 1

Figure 2. Key features of extracellular vesicles (EVs) and extracellular particles (EPs) (EVPs). EVs are shown in the top panel and EPs are shown in the bottom panel. Receptors are shown on the surface of EVPs. Key molecular contents are shown inside EVPs. The EVPs are not shown as scaled size. The sizes of EVPs are summarized in the figure legend.

Figure 2

Table 1. Key characteristics of extracellular vesicles and particles

Figure 3

Table 2. Summary of key cf-mRNA clinical studies

Figure 4

Figure 3. Clinical utility of cf-mRNA in liquid biopsy. Examples of how cf-mRNA could be used in neurodegenerative disease (top right), pregnancy and pre-natal care (bottom right), cancer and tumour subcategorization (bottom left) and liver/other pathologies (top left).

Figure 5

Figure 4. Cf-mRNA-based liquid biopsy biomarker development pipeline. Body fluid collection (bottom left corner). Types of fluids that can extract cf-mRNA are listed in the box. Selection of specific extracellular vesicles and particles (EVPs) via EVP isolation/purification (top left corner). RNA extracted from directly from body fluids or specific EVPs (top middle). cDNA conversion and sequencing library preparation (Top right). Isolated RNA samples are converted to cDNA and subsequently sequencing libraries are generated. These libraries can undergo exome hybridization process to enrich for messenger RNA or depletion of unwanted transcripts (such as ribosomes). Data generation and analysis (bottom right). Bioinformatic analyses are performed on data generated by next-generation sequencing, qPCR or other quantification methods. Analyses such as differential expression analysis and pathway analysis are conducted to identify key genes and pathways that are dysregulated in the targeted disease. Finally, machine learning approaches will be used to combine the key molecular features and develop robust diagnostic classifiers.