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. 2024 Jan 16;5(1):101373.
doi: 10.1016/j.xcrm.2023.101373.

Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation

Luyen Tien Vu  1 , Faraz Ahmed  2 , Hongya Zhu  1 , David Shing Huk Iu  1 , Elizabeth A Fogarty  1 , Yeonui Kwak  1 , Weizhong Chen  1 , Carl J Franconi  1 , Paul R Munn  2 , Ann E Tate  2 , Susan M Levine  3 , Jared Stevens  4 , Xiangling Mao  5 , Dikoma C Shungu  5 , Geoffrey E Moore  6 , Betsy A Keller  6 , Maureen R Hanson  1 , Jennifer K Grenier  7 , Andrew Grimson  8
Affiliations

Single-cell transcriptomics of the immune system in ME/CFS at baseline and following symptom provocation

Luyen Tien Vu et al. Cell Rep Med. .

Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a serious and poorly understood disease. To understand immune dysregulation in ME/CFS, we use single-cell RNA sequencing (scRNA-seq) to examine immune cells in patient and control cohorts. Postexertional malaise (PEM), an exacerbation of symptoms following strenuous exercise, is a characteristic symptom of ME/CFS. To detect changes coincident with PEM, we applied scRNA-seq on the same cohorts following exercise. At baseline, ME/CFS patients display classical monocyte dysregulation suggestive of inappropriate differentiation and migration to tissue. We identify both diseased and more normal monocytes within patients, and the fraction of diseased cells correlates with disease severity. Comparing the transcriptome at baseline and postexercise challenge, we discover patterns indicative of improper platelet activation in patients, with minimal changes elsewhere in the immune system. Taken together, these data identify immunological defects present at baseline in patients and an additional layer of dysregulation in platelets.

Keywords: CPET; exercise challenge; long COVID; monocytes; myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS); platelets; scRNA-seq.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
Single-cell transcriptomics of the ME/CFS immune system (A) Study design. PBMCs were collected at BL and 24 h PC for sedentary controls and ME/CFS subjects and used for single-cell gene expression profiling (scRNA-seq). (B) Demographic and clinical parameters for both cohorts. Oxygen consumption was measured during CPET. The change in maximal oxygen consumptions (VO2 at peak) between the VO2 peaks at BL and PC is indicated. Using the SF-36 Version 2 Health Survey, the general health score was self-evaluated, with 100 as perfect health and 0 as worst health. Graphs represent mean ± SEM. ∗∗p < 0.01, ∗∗∗ p < 0.001. (C) Quality control metrics, showing genes and transcripts per cell (left and center, respectively) and percentage of mitochondrial reads per cell (right), compared between indicated cohorts. (D) Integrated uniform manifold approximation and projection (UMAP). Clusters are labeled in order of decreasing number of cells. (E) Relative expression of marker genes for immune cells (x axis) across clusters (y axis); dots indicate average expression and percentage of cells with detected expression (color and size, respectively). (F) Cell types with significant differences in relative cell numbers between cohorts. ∗p < 0.05. Panels represent data from 28 healthy controls and 30 ME/CFS cases.
Figure 2
Figure 2
Dysregulation of immune cells in ME/CFS (A) Counts of differentially expressed (DE) genes (y axis) per immune cell cluster, comparing case and control cells at BL and PC, and compared between control cells at BL and PC. (B) Counts of the strongest significantly enriched gene sets (excluding those related to translation and with an absolute normalized enrichment score [NES] <2) across the largest cell clusters. (C) Representative GSEA gene set (GOBP cytoplasmic translation) showing lower detection of ribosomal proteins in major clusters (NES <0) at both BL and PC in ME/CFS (red and purple, respectively). Dots are sized to denote significance (q values); x axis indicates NES. (D) GSEA results for classical monocytes (cluster 2) comparing patient and control cohorts at BL and PC (red and purple, respectively), focusing on gene sets related to chemokine/cytokine signaling. (E) Single sample scores generated using GSEA of leading-edge genes from (D). ∗∗p < 0.01. (F) Differential expression of genes associated with monocyte migration and differentiation at BL and PC in classical monocytes. ∗p < 0.05; ∗∗p < 0.01. Panels represent data from 28 healthy controls and 30 ME/CFS cases.
Figure 3
Figure 3
Complete transcriptomics of classical monocytes (A) PCA of 4 patient and 4 control transcriptomes from classical monocytes. (B) Significantly enriched gene sets between cohorts by GSEA. Dots are sized to denote significance (adjusted p values); x axis indicates NES. (C) Differentially expressed genes between cohorts. Dots are sized to denote significance (p values). (D) PCA of 3 patient and 4 control proteomes from classical monocytes. (E) Significantly enriched gene sets between cohorts by GSEA comparing proteome profiles of cases and controls; otherwise as in (B). (F) Volcano plot of differentially expressed proteins between cohorts. Cohorts of 4 healthy controls and 4 ME/CFS cases (all females) at BL and PC were chosen for proteome (D–F) and transcriptome (A–C) profiling, respectively.
Figure 4
Figure 4
Heterogeneity in classical monocyte cells from ME/CFS patients Data represent BL female samples, unless described otherwise. (A) Schema describing positive unlabeled learning strategy to stratify single-cell patient transcriptomes. (B) UMAP for classical monocyte cells, tiled and colored by pD state. (C) Percentage of pD cells per individual. (D) Correlation (Spearman) between pD cells per individual, compared between BL and PC. (E) CH index comparing clustering performance across different stratifications of the single-cell dataset (y axis). (F) Correlation (Spearman) between MFI-20 score and percentage of pD cells. (G) Gene sets most differentially enriched between pD and pN cells from cases. Dots are color-coded to indicate enrichment in pD (blue) or pN (yellow) cells; sizes indicate corrected p values. (H) Genes (y axis) most differentially expressed (x axis) between pD and pN cells in an intrasample paired analysis. (I) Expression of CCL4 (y axis) from individual samples, aggregating expression over pD and pN cells from cases and over all cells from controls (x axis and color-coded). ∗∗ p < 0.01, ∗∗∗ p < 0.001. (J) Top gene sets differentially enriched between pD and pN cells, based on GSEA between each subset of cells paired by sample. (K) Pseudobulk PCA from aggregated cells from control samples (yellow); pN and pD cells from case samples (green and blue, respectively), partitioned by sex. (L) Genes contributing to negative values for PC2 (top) and PC3 (bottom) in (K). (E, I, K, and L) Data from 28 healthy controls and 30 ME/CFS cases at BL, with partition based on sex in (I) and (K). (B–D) and (F–H) Data from the female cohort with 20 healthy controls and 20 ME/CFS cases at BL.
Figure 5
Figure 5
Intercellular signaling in the ME/CFS immune system (A) Schema depicting CellChat strategy. (B) Circle plot showing differential number of interactions (case minus control), aggregating clusters of similar cell types. Blue indicates case cells exhibit more interactions than control cells; orange indicates control cells exhibit more interactions. (C) Scatterplot of differential incoming versus outgoing interaction strength in classical monocytes (cluster 2). Positive values indicate increased signaling strength in patients and vice versa. (D) Heatmap of overall signaling for pathways dysregulated (y axis) for classical monocytes receiving signaling from different cells (y axis; cluster identifiers from Figure 1). Top bar plot indicates aggregate interaction strength of incoming signals; right bar plot indicates aggregate interaction strength of outgoing signals. (E) Communication probabilities between specific ligand-receptor pairs in the CCL pathway, for case (blue) and control (orange) cells. Panels represent data from the female cohort, with 20 healthy controls and 20 ME/CFS cases at BL.
Figure 6
Figure 6
Aberrant platelet transcriptomes coincident with PEM in ME/CFS (A) Schema depicting paired analysis (intraindividual expression) of gene expression altered by strenuous exercise in ME/CFS patients compared to controls. (B) Number of significantly enriched gene sets across clusters (x axis) in a paired analysis comparing case and control ΔRNA measurements with GSEA. (C) GSEA results for significantly enriched gene sets related to platelet function. GSEA analyses included the comparison of paired ΔRNA measurements between cases and controls (dark purple for scRNA-seq and pink for RNA-seq), as well as comparing group averages at BL (red for scRNA-seq and blue for RNA-seq) and PC (none detected). (D) Enrichment plot depicting representative significantly enriched gene sets related to platelet function and translation using the intraindividual paired analysis for platelets (cluster 19) from scRNA-seq. (E) Heatmap of leading-edge genes taken from the gene set “GOBP regulation of platelet activation” between 3 healthy controls (HC) and 3 ME/CFS cases (ME) at BL and PC; expression values row-normalized. (F) Enrichment plot depicting the same gene sets shown in (D) using the intraindividual paired analysis of bulk RNA-seq of platelet-derived plasma particles. (B–D) Data from a cohort of 28 healthy controls and 30 ME/CFS cases. (E and F) Data from 3 healthy controls and 3 ME/CFS cases (all females) at both time points.
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