COVID-19db linkage maps of cell surface proteins and transcription factors in immune cells

Koushul Ramjattun*, Xiaojun Ma*, Shou-Jiang Gao, Harinder Singh, Hatice Ulku Osmanbeyoglu
University of Pittsburgh, UPMC Hillman Cancer Center, Center for Systems Immunology

Journal of Medical Virology : https://onlinelibrary.wiley.com/doi/10.1002/jmv.28887


ABSTRACT

The highly contagious SARS-CoV-2 and its associated disease (COVID-19) are a threat to global public health and economies. To develop effective treatments for COVID-19, we must understand the host cell types, cell states and regulators associated with infection and pathogenesis such as dysregulated transcription factors (TFs) and surface proteins, including signaling receptors. To link cell surface proteins with TFs, we recently developed SPaRTAN (Single-cell Proteomic and RNA-based Transcription factor Activity Network) by integrating parallel single-cell proteomic and transcriptomic data based on Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), which contains gene cis-regulatory information. We apply SPaRTAN to CITE-seq datasets from patients with varying degrees of COVID-19 severity and healthy controls to identify the associations between surface proteins and TFs in host immune cells. Here, we present COVID-19db of Immune Cell States (https://covid19db.streamlit.app/), a web server containing cell surface protein expression, SPaRTAN-inferred TF activities, and their associations with major host immune cell types. The data includes five high-quality COVID-19 CITE-seq datasets with a toolset for user-friendly data analysis and visualization. We provide interactive surface protein and TF visualizations across major immune cell types for each dataset, allowing comparison between various patient severity groups for the discovery of potential therapeutic targets and diagnostic biomarkers.


CODE

Code and documentation can be found in GitHub link https://github.com/osmanbeyoglulab/covid19_webapp


Origional data download

GSE161918 dataset link to original data

GSE155673 dataset link to original data

GSE155224 dataset link to original data

E-MTAB-10026 dataset link to original data

E-MTAB-9357 dataset link to original data


SPaRTAN module outputs

The SPaRTAN module predicts transcription factor from gene expression and protein profile. We run the SPaRTAN model for each cell type and each patient. The download links below contain predicted TF for each dataset organized by cell type and patient.

GSE155673.zip -- SPaRTAN module output for dataset GSE155673

GSE161918.zip -- SPaRTAN module output for dataset GSE161918

GSE155224.zip -- SPaRTAN module output for dataset GSE155224

E-MTAB-10026.zip -- SPaRTAN module output for dataset E-MTAB-10026

E-MTAB-9357.zip -- SPaRTAN module data output for dataset E-MTAB-9357


We conducted comprehensive analyses for the SPaRTAN results and provide user-friendly web tools for visualization and further exploration, which is available at https://covid19db.streamlit.app
.
We also have a book chapter to explain and demo how to use SPaRTAN framework at https://link.springer.com/protocol/10.1007/978-1-0716-3163-8_11