Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

Roger P Alexander,Pieter Mestdagh,Klaas E A Max,Brian S White,Rongshan Yu,Bojan Losic,Bogdan Mateescu,Matthew Roth,Aleksander Milosavljevic,Robert R Kitchen,Leonora Balaj,Juan Pablo Tosar,Joel Rozowsky,Eric L Van Nostrand,Ryan M Spengler,Karolina Elżbieta Kaczor-Urbanowicz,Emily Laplante,Gustavo Stolovitzky,Justin Chang

doi:10.3389/fgene.2021.778416

Abstract

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19–20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems.

Highlights

In 2013, the NIH Common Fund launched the Extracellular RNA Communication Consortium to stimulate research into the fundamental biology of extracellular RNA (exRNA) and its clinical applications in disease diagnosis and treatment
Isolating and purifying exRNA and extracellular vesicles (EVs) from experimental samples is itself difficult, and RNA isolation kits used for these tasks are known to be a major source of variability in the resulting exRNA data
It is important to be mindful that standard RNA-seq methods are blind to many RNA base modifications, and mis-annotated RNAs can lead to misinterpretation

Summary

INTRODUCTION

In 2013, the NIH Common Fund launched the Extracellular RNA Communication Consortium to stimulate research into the fundamental biology of exRNA and its clinical applications in disease diagnosis and treatment. Isolating and purifying exRNA and extracellular vesicles (EVs) from experimental samples is itself difficult, and RNA isolation kits used for these tasks are known to be a major source of variability in the resulting exRNA data Compensating for this variation is a central challenge, as each kit and RNA sequencing method has different sequence biases that must accounted for when doing larger analyses (Murillo et al, 2019; Srinivasan et al, 2019). Two major open problems in the field are identifying tissue of origin of the exRNAs in a biofluid and associating them with their molecular carrier, whether that be an RNA binding protein, a lipid like HDL or LDL, or a variety of classes of extracellular vesicle. A beneficial outcome of such a challenge would be the creation of better gold standards of cell-, tissue-, and molecular carrier-associated exRNA profiles for use with reference-based deconvolution methods. Finding that proxy environment was beyond the scope of the workshop but will be necessary to make possible a non-synthetic exRNA deconvolution challenge

DISCUSSION

DATA AVAILABILITY STATEMENT