
==== Front
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ResearchSquare
Research Square
2693-5015
American Journal Experts

10.21203/rs.3.rs-4791069/v1
10.21203/rs.3.rs-4791069
preprint
1
Article
Cellular indexing of transcriptomes and epitopes (CITE-Seq) in hidradenitis suppurativa identifies dysregulated cell types in peripheral blood and facilitates diagnosis via machine learning
Liao Wilson
Kumar Sugandh https://orcid.org/0000-0001-7000-4718

Orcales Faye
Shih Bobby
Fang Xiaohui
Yin Congcong https://orcid.org/0000-0003-0865-2823

Yates Ashley
Dimitrion Peter
Neuhaus Isaac
Johnson Chandler
Adrianto Indra
Wiala Antonia
Hamzavi Iltefat H.
Zhou Li
Naik Haley
Posch Christian
Mi Qing-Sheng https://orcid.org/0000-0002-1411-6827

09 9 2024
rs.3.rs-4791069https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
https://www.researchsquare.com/article/rs-4791069/v1
nihpp-rs4791069v1.pdf
Abstract

Hidradenitis suppurativa (HS) is a chronic inflammatory skin condition characterized by painful nodules, abscesses, and scarring, predominantly affecting intertriginous regions. This study aimed to utilize single cell RNA and cell-surface protein sequencing (CITE-Seq) to delineate the immune composition of circulating cells in Hidradenitis suppurativa (HS) peripheral blood compared to healthy controls. CITE-Seq was used to analyze the gene and protein expression profiles of peripheral blood mononuclear cells (PBMCs) from 9 HS and 29 healthy controls. The study identified significant differences cell composition between HS patients and healthy controls, including increased proportions of CD14+ and CD16+ monocytes, cDC2, plasmablasts, and proliferating CD4+ T cells in HS patients. Differential expression analysis revealed upregulation of inflammatory markers such as TNF, IL1B, and NF-κB in monocytes, as well as chemokines and cell adhesion molecules involved in immune cell recruitment and tissue infiltration. Pathway enrichment analysis highlighted the involvement of IL-17, IL-26 and TNF signaling pathways in HS pathogenesis. Machine learning identified key markers for diagnostics and therapeutic development. The findings also support the potential for machine learning models to aid in the diagnosis of HS based on immune cell markers. These insights may inform future therapeutic strategies targeting specific immune pathways in HS.
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