Single-cell RNA sequencing of mononuclear phagocytes (PMNs), including monocytes (Mo), macrophages (Mø), and dendritic cells (DC) isolated from blood and tracheobronchial aspirates (BAS) from infants with severe RSV infection and controls.

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Authors: Klasina Chappin
Cited by: 0

Abstract

Mononuclear phagocytes (PMNs) were isolated of infants and young children who were hospitalized with a confirmed RSV infection or control patients. Cells were obtained from blood and/or broncheal aspirate (BAS). Cells were fixed using fixation kits (Evercode™ WT V2, Parse Biosciences) and stored according to the manufacturer’s protocol. Libraries were generated with the Single Cell Whole Transcriptome Kit (V2 chemistry, Parse Biosciences) to produce eight sub-libraries covering 100,000 cells. Samples were processed following the Evercode WT Barcoding Plate protocol. In total, 31 samples were barcoded, with each receiving an equal library fraction (~3.22%); two low–cell count samples were loaded multiple times and merged as technical replicates during downstream analysis, resulting in 27 unique samples. FASTQ files (R1 and R2) for each of the eight sublibraries were processed using the Parse Biosciences Pipeline (v1.5.0, Parse Biosciences) for sample demultiplexing, library merging, and alignment to the human reference genome (GRCh38). The resulting unfiltered count matrices were analyzed in R (v4.5.1) using Seurat (v5.3.1) for downstream single-cell analysis. Quality control filtering excluded cells with >12% mitochondrial gene content or fewer than 500 or more than 5,000 detected features. Doublets were removed using DoubletFinder (2.0.6) with a 3% expected doublet rate (multiplet_rate of 0.03) per Parse Biosciences’ recommendations and removal of non-MNP cells.

Fields of Study & Subjects

Respiratory viral infections research Single-cell and spatial transcriptomics Inflammation biomarkers and pathways

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
27 February 2026
Language
EN
Document type
Dataset