Defining Patient-Level Molecular Heterogeneity in Psoriasis Vulgaris Based on Single-Cell Transcriptomics


doi: 10.3389/fimmu.2022.842651.


eCollection 2022.

Yale Liu 
1
 
2
 
3
, Hao Wang 
4
, Christopher Cook 
2
 
3
, Mark A Taylor 
3
 
5
, Jeffrey P North 
3
, Ashley Hailer 
2
 
3
, Yanhong Shou 
6
, Arsil Sadik 
2
, Esther Kim 
7
, Elizabeth Purdom 
4
, Jeffrey B Cheng 
2
 
3
, Raymond J Cho 
3

Affiliations

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Yale Liu et al.


Front Immunol.


.

Abstract

Identifying genetic variation underlying human diseases establishes targets for therapeutic development and helps tailor treatments to individual patients. Large-scale transcriptomic profiling has extended the study of such molecular heterogeneity between patients to somatic tissues. However, the lower resolution of bulk RNA profiling, especially in a complex, composite tissue such as the skin, has limited its success. Here we demonstrate approaches to interrogate patient-level molecular variance in a chronic skin inflammatory disease, psoriasis vulgaris, leveraging single-cell RNA-sequencing of CD45+ cells isolated from active lesions. Highly psoriasis-specific transcriptional abnormalities display greater than average inter-individual variance, nominating them as potential sources of clinical heterogeneity. We find that one of these chemokines, CXCL13, demonstrates significant correlation with severity of lesions within our patient series. Our analyses also establish that genes elevated in psoriatic skin-resident memory T cells are enriched for programs orchestrating chromatin and CDC42-dependent cytoskeleton remodeling, specific components of which are distinctly correlated with and against Th17 identity on a single-cell level. Collectively, these analyses describe systematic means to dissect cell type- and patient-level differences in cutaneous psoriasis using high-resolution transcriptional profiles of human inflammatory disease.


Keywords:

chromatin; cytoskeleton; heterogeneity; psoriasis vulgaris; single-cell RNA-sequencing.

Conflict of interest statement

JC and RC receive support (research grants to their institution) from LEO Pharmaceuticals, Sanofi, and Pfizer. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures


Figure 1



Figure 1

CD45+ immune cell types identified from 8 psoriasis vulgaris lesions and 7 normal skin samples. (A) UMAP representation of 11 T cell and 9 APC classes based on scRNA-seq transcriptional data, in which each point represents a single cell. (B) Expression of critical marker transcripts distinguishing immune cell classes. (C) Proportion of each immune class in total CD45+ cell populations.


Figure 2



Figure 2

Elevated patient level variance in psoriasis-specific skin-resident memory T cell (Trm2) DEGs. (A) Volcano plot showing psoriasis DEGs identified using a pseudo-bulk approach charted as a function of logFC difference from normal, uninflamed cells (x-axis) and the log of the dispersion score (a proxy for patient-level variation, y-axis). Significant DEGs are shown in red, non-significant DEGs in grey. Labelled in blue are immune activation genes with relatively high dispersion scores, which may have prevented them from reaching statistical significance. (B) LogFC (x-axis) and dispersion score (y-axis) shown for established pathogenic psoriatic cytokines (red), mitotic cell division transcripts (green), psoriasis-specific abnormalities not elevated in atopic dermatitis (orange), and as in (A), immunologically activating DEGs with high end dispersion scores (blue).


Figure 3



Figure 3

Inter-individual variation in lesional psoriasis severity score parallels that of CXCL13 and CD84. Leftmost graph shows severity scores for biopsied lesion for each patient. Subsequent graphs display deduced number of cells with positive expression for each gene, as a percentage of the maximum number of positive cells in any sample, multiplied by 1 x 103 (i.e. normalized to 10). Significant correlations for CXCL13 and CD84 are denoted by asterisks.


Figure 4



Figure 4

Significant functional associations for the 662 genes significantly elevated in psoriasis samples compared to grouped healthy controls in Trm2. (A) Ten example classifications are shown, with functions such as immune cell activation, mitotic cell division, and cytoskeletal reorganization. (B) Heatmaps visually represent average log2FC between individual psoriasis samples and normal controls using ComplexHeatmap (18). Heterogeneity is detected between patients, most prominently the dampened amplitude of transcript abnormalities in Patient 5, who was on systemic tacrolimus and mycophenolate at time of biopsy.


Figure 5



Figure 5

Single-cell correlations and anti-correlations between functional class transcripts and IL17F expression. (A) T cell activation markers like RORA and CD3G are elevated in the highest IL17F expressing cells, (B) Key cytoskeletal reorganization transcripts (PAK2, APBB1IB) are most elevated in the single skin-resident T cells expressing maximal psoriatic inflammatory mediators. (C) Chromatin remodeling transcripts (EZH2, HIST1H1E), are elevated in the lowest IL17F expression cells, suggesting a distinct, pathologic cell population in psoriatic Trm. Density plots show imputed single cell expression of T cell activation, chromatin remodeling, or cytoskeletal transcripts (y-axis) vs. IL17F (x-axis). Dots represent single Trm2 cells (psoriasis samples).

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