Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Dermatology, Huashan Hospital, Fudan University, Shanghai, China.
Front Immunol. 2023 May 2;14:1188745. doi: 10.3389/fimmu.2023.1188745. eCollection 2023.
The hyperproliferation featured with upregulated glycolysis is a hallmark of psoriasis. However, molecular difference of keratinocyte glycolysis amongst varied pathologic states in psoriasis remain elusive.
To characterize glycolysis status of psoriatic skin and assess the potential of glycolysis score for therapeutic decision.
We analyzed 345414 cells collected from different cohorts of single-cell RNA seq database. A new method, , was used to integrate the phenotypes in GSE11903 to guide single-cell data analysis, allowing identification of responder subpopulations. algorithm was performed to evaluate the glycolysis status of single cell. Glycolysis signature was used for further ordering in trajectory analysis. The signature model was built with logistic regression analysis and validated using external datasets.
Keratinocytes (KCs) expressing and were identified as a novel glycolysis-related subpopulation. Scissor cells and Scissor cells were defined as response and non-response phenotypes. In Scissor KCs, ATP synthesis pathway was activated, especially, the glycolysis pathway being intriguing. Based on the glycolysis signature, keratinocyte differentiation was decomposed into a three-phase trajectory of normal, non-lesional, and lesional psoriatic cells. The area under the curve (AUC) and Brier score (BS) were used to estimate the performance of the glycolysis signature in distinguishing response and non-response samples in GSE69967 (AUC =0.786, BS =17.7) and GSE85034 (AUC=0.849, BS=11.1). Furthermore, Decision Curve Analysis suggested that the glycolysis score was clinically practicable.
We demonstrated a novel glycolysis-related subpopulation of KCs, identified 12-glycolysis signature, and validated its promising predictive efficacy of treatment effectiveness.
过度增殖伴有糖酵解上调是银屑病的一个标志。然而，银屑病不同病理状态下角质形成细胞糖酵解的分子差异仍不清楚。
描述银屑病皮肤的糖酵解状态，并评估糖酵解评分在治疗决策中的潜力。
我们分析了来自单细胞 RNA seq 数据库不同队列的 345414 个细胞。一种新方法， ，用于整合 GSE11903 中的表型，指导单细胞数据分析，允许识别应答亚群。 算法用于评估单细胞的糖酵解状态。糖酵解特征用于轨迹分析中的进一步排序。该特征模型通过逻辑回归分析构建，并使用外部数据集进行验证。
表达 和 的角质形成细胞（KCs）被鉴定为一种新的糖酵解相关亚群。剪刀细胞和剪刀细胞被定义为应答和非应答表型。在剪刀 KCs 中，ATP 合成途径被激活，特别是糖酵解途径引人注目。基于糖酵解特征，角质形成细胞分化被分解为正常、非病变和病变银屑病细胞的三阶段轨迹。曲线下面积（AUC）和 Brier 评分（BS）用于估计糖酵解特征在区分 GSE69967（AUC=0.786，BS=17.7）和 GSE85034（AUC=0.849，BS=11.1）中应答和非应答样本的性能。此外，决策曲线分析表明，糖酵解评分具有临床实用性。
我们展示了一种新的角质形成细胞糖酵解相关亚群，鉴定了 12-糖酵解特征，并验证了其对治疗效果的有前途的预测效果。
