Combined Single Cell Transcriptome and Surface Epitope Profiling Identifies Potential Biomarkers of Psoriatic Arthritis and Facilitates Diagnosis via Machine Learning.

Jared Liu,Maria Castillo,Julie Hong,Wilson Liao,Lianne S Gensler,Samuel Yeroushalmi,Mehrdad Matloubian,Marwa Hakimi,Hsin-Wen Chang,Daniel D Cummins,Maria Calvo,Tina Bhutani,Zhi-Ming Huang,Sugandh Kumar,Mimi Chung,Erin Bartholomew,Diana Paez,Chun Jimmie Ye

doi:10.3389/fimmu.2022.835760

Abstract

Early diagnosis of psoriatic arthritis (PSA) is important for successful therapeutic intervention but currently remains challenging due, in part, to the scarcity of non-invasive biomarkers. In this study, we performed single cell profiling of transcriptome and cell surface protein expression to compare the peripheral blood immunocyte populations of individuals with PSA, individuals with cutaneous psoriasis (PSO) alone, and healthy individuals. We identified genes and proteins differentially expressed between PSA, PSO, and healthy subjects across 30 immune cell types and observed that some cell types, as well as specific phenotypic subsets of cells, differed in abundance between these cohorts. Cell type-specific gene and protein expression differences between PSA, PSO, and healthy groups, along with 200 previously published genetic risk factors for PSA, were further used to perform machine learning classification, with the best models achieving AUROC ≥ 0.87 when either classifying subjects among the three groups or specifically distinguishing PSA from PSO. Our findings thus expand the repertoire of gene, protein, and cellular biomarkers relevant to PSA and demonstrate the utility of machine learning-based diagnostics for this disease.

Highlights

Psoriatic arthritis (PSA) is an inflammatory rheumatic disease that can affect the peripheral joints, axial joints, and entheses
All 30 cell types were comparably represented among psoriatic arthritis (PSA), PSO, PSX and healthy subjects (Figure 1B), with the exception of Tregs and dnT cells, which were relatively increased in PSA patients compared to both PSO and healthy subjects (p < 0.03, Figure 1B), and hematopoietic stem precursor cells (HSPCs), which were relatively increased in healthy subjects (p < 0.007)
We found 1 – 135 differentially expressed genes (DEGs) and 1 – 18 DEPs with significant differences between PSA and PSO, PSA and healthy, or PSO and healthy cells (Figures 2A, B), with the most differentially expressed features detected in CD14 monocytes, the most abundant cell type in our dataset

Summary

Introduction

Psoriatic arthritis (PSA) is an inflammatory rheumatic disease that can affect the peripheral joints, axial joints, and entheses. The ongoing effort to develop better molecular diagnostics for PSA has identified genetic polymorphisms, primarily in major histocompatibility complex and IL-17/IL-23 signaling loci that contribute to PSA risk in PSO patients [4, 5], as well as diseaserelevant immune cells within the inflamed synovium of affected joints. These include both adaptive and innate cell types that have a common inflammatory and IL-17-secreting role in pathogenesis and are significantly expanded in the synovium [6]. Some cell types have been reported to be perturbed in PSA patients, and while some studies have reported serum biomarkers for distinguishing PSA from PSO [7, 8], a more recent study found similar serum proteomes among PSO patients with and without PSA [9]

Methods

Results

Conclusion