Developing a CITE-sequencing analysis pipeline by investigating a COVID-19 dataset

Abstract

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-sequencing) is a multimodal highthroughput single-cell technology that measures contemporaneously gene and surface protein expression levels for each single cell sequenced. By using a CITE-sequencing dataset of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we aimed at developing a robust analysis pipeline that will later serve for more ambitious purposes. Our study included the analysis of peripheral blood mononuclear cells (PBMCs) derived from 6 COVID-19 donors (3 moderate, 3 severe) and 6 healthy donors. Our study design included a panel of 277 Antibody-derived tags (ADTs) including 9 isotype control antibodies. To decrease confounders and sequencing costs, samples were pooled before sequencing after being labeled using the cell hashing technique. Cells were also designed to be demultiplexed and assigned to their donor using vireo. After a deep quality control and extensive assessment of demultiplexing, RNA and Protein matrices were further pre-processed by maintaining only high-quality sequenced cells and doublets removal. These underwent separate normalization and finally integration using the Weighted Nearest Neighbored analysis. A final annotation completed this initial analysis.