Sample processing and single cell RNA-sequencing of peripheral blood immune cells from COVID-19 patients

Abstract

Single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) allows in-depth assessment of transcriptional changes in immune cells of patients with COVID-19. However, collecting, processing, and analyzing samples from patients with COVID-19 pose many challenges because blood samples may contain infectious virus, identification of immune cell subtypes can be difficult, and biological interpretation of analytical results is complex. To address these issues, we describe a protocol for sample processing, sorting, methanol fixation, and scRNA-seq analysis of PBMCs from frozen buffy coat samples from patients with COVID-19. For complete details on the use and execution of this protocol, please refer to (Yao et al., 2021).

Keywords: Bioinformatics; Immunology; Sequence analysis.

Graphical abstract

Figure 1

Gating strategy for FACS sorting Sequential gating is utilized to obtain live PBMCs for subsequent scRNA-seq.

Figure 2

Overview of scRNA-seq data analysis A step-wise approach for systematic data processing, cell type identification, differential gene expression and pathway analysis is shown, as well as recommended software programs for each step.

Figure 3

Single cell demultiplex, quality control, and batch correction (A and B) HBA2 and HBB expression before and after ambient RNA clean up with SoupX. (C) Before and after quality control for nFeature_RNA (gene number per cell), nCount_RNA (UMI) and percent.mt (percentage of mitochondrial genes).

Figure 4

Identification of major blood cell subsets UMAP of all cells. (A–E) Major immune cell subsets were identified as: A) CD4 and CD8 T cells (CD3G and either CD4 or CD8B); (B) NK cells (cells expressing TYROBP and FCGR3A that cluster with CD8 T cells), monocytes (cells expressing CD14 and/or FCGR3A that cluster together), and cDCs (express high HLA genes and cluster near monocytes); (C) proliferating lymphocytes (MKI67 clustering with lymphocytes); (D) B cells (MS4A1), plasma cells (express JCHAIN but lack MS4A1 and SERPINF1), and pDCs (JCHAIN and SERPINF1); and E) erythrocytes (HBA2) and platelets (PF4) cells. (F) Identified immune cell subsets are indicated. Undefined and undesired subsets such as erythrocytes and platelets can be excluded from subsequent analysis. UMAPs before and after exclusion are shown. Figure modified from (Yao et al., 2021).

Figure 5

Identification of B and plasma cell subsets UMAP of B and plasma cells only. (A) B and plasma cell clusters were identified as immature B cells (IL7R), naïve and activated B cells (IGHM, IGHD, IL4R), activated B cells (CD69), plasma cells (CD27, CD38, JCHAIN) and memory B cells (AIM2). (B) Identified subsets are indicated.

Figure 6

Identification of monocyte and DC subsets UMAP of monocytes and DCs only. (A) Monocyte clusters and pDCs were identified as classical monocytes (CD14), non-classical monocytes (FCGR3A), and pDCs (SERPINF1, LILRA4). (B) cDCs are predominantly DC2 cells (HLA-DRB1, CD1C, FCER1A, CLEC10 and low or no monocyte gene expression). (C) Identified subsets are indicated.

Figure 7

Canonical pathway analysis For each immune cell type, differentially expressed genes between Severe vs. Moderate and Severe vs. Recovering COVID-19 patients were imported into IPA for core analysis. Significantly enriched canonical pathways between disease group comparisons across cell types were identified using FDR < 0.01. The activation/inhibition state of a given pathway was determined using z-scores. The bubble chart depicts a select number of canonical pathways that were significantly enriched in most cell types between patient groups. Figure modified from (Yao et al., 2021).

Figure 8

Upstream regulator analysis (A) Differentially expressed gene patterns for immune cells were leveraged to identify IRF7 as a putative master regulator in Severe vs. Moderate COVID-19. The regulatory network, with IRF7 as the key orchestrator, was constructed based on the overlap between the patterns of differential gene expression and IPA’s knowledgebase across cell types as assessed by Fisher's exact test P-value and a z-score, with a positive z-score indicating activation and negative z-score indicating inhibition. Note that each member of this network is itself a regulator of other gene targets in each cell type. (B) Heatmap highlighting whether a given member of the regulatory network is expected to be activated or inhibited in each immune cell population in Severe versus Moderate groups. This analysis implies that the IRF7 network is inhibited in monocytes, but activated in other immune cells. Figure modified from (Yao et al., 2021).

Figure 9

Causal network analysis IPA causal network analysis was applied to B cell transcriptional profiles of Recovering vs. Severe groups to identify putative mechanistic relationships between regulators. One of the most significantly enriched “master regulators” was SYK, which was itself differentially upregulated, and orchestrated a causal network comprised of 40 other regulators. This analysis suggests that activation of SYK-regulated pathways is a key driver of humoral responses in patients recovering from severe SARS-Cov2 infection. Figure modified from (Yao et al., 2021).