Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks

TL;DR

This paper introduces a novel deep learning approach using self-supervised edge features and graph neural networks to analyze single-cell RNA sequencing data, revealing molecular and cellular factors linked to SARS-CoV-2 infection and COVID-19 severity.

Contribution

It presents a new graph neural network model with self-supervised edge features for predicting infection status and severity from single-cell data, advancing understanding of COVID-19 biology.

Findings

Achieved state-of-the-art prediction of disease state from single-cell transcriptomes.

Identified key genes and cell types associated with infection and severity.

First deep learning application to single-cell omics for SARS-CoV-2 research.

Abstract

A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell types associated with SARS-CoV-2 infection and COVID-19 severity. To do this, we developed a new approach to generating self-supervised edge features. We propose a model that builds on Graph Attention Networks (GAT), creates edge features using self-supervised learning, and ingests these edge features via a Set Transformer. This model achieves significant improvements in predicting the disease state of individual cells, given their transcriptome. We apply our model to single-cell RNA sequencing datasets of SARS-CoV-2 infected lung organoids and bronchoalveolar lavage fluid samples of patients with COVID-19, achieving state-of-the-art performance on both datasets with our model. We then borrow from the field of explainable AI (XAI) to identify the features (genes) and cell types that discriminate bystander vs. infected cells across time and moderate vs. severe COVID-19 disease. To the best of our knowledge, this represents the first application of deep learning to identifying the molecular and cellular determinants of SARS-CoV-2 infection and COVID-19 severity using single-cell omics data.