Dataset Information

Highly Multiplexed Mass Cytometry Identifies the Immunophenotype in the Skin of Dermatomyositis.

ABSTRACT: Dermatomyositis (DM) is a rare, systemic autoimmune disease that most frequently affects the skin, muscles, and lungs. The inflammatory infiltrate in the skin has not been fully characterized, and, in this study, we took a single-cell, unbiased approach using imaging mass cytometry. Substantial monocyte‒macrophage diversity was observed, with the CD14+ population correlating positively with Cutaneous Dermatomyositis Disease Area and Severity Index scores (P = 0.031). The T-cell compartment revealed CD4+ T, CD8+ T, and FOXP3+ T cells. Activated (CD69+) circulating memory T cells correlated positively with Cutaneous Dermatomyositis Disease Area and Severity Index scores (P = 0.0268). IFN-β protein was highly upregulated in the T-cell, macrophage, dendritic cell, and endothelial cell populations of DM skin. Myeloid dendritic cells expressed phosphorylated peroxisome proliferator‒activated receptor γ, phosphorylated IRF3, IL-4, and IL-31, and their quantity correlated with itch as measured in Skindex-29. Plasmacytoid dendritic cells colocalized with IFN-γ in addition to the known colocalization with IFN-β. Nuclear phosphorylated peroxisome proliferator‒activated receptor γ expression was found in the DM endothelium. Imaging mass cytometry allows us to characterize single cells in the immune cell population and identify upregulated cytokines and inflammatory pathways in DM. These findings have important implications for the development of future targeted therapies for DM.

SUBMITTER: Patel J

PROVIDER: S-EPMC8384654 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Highly Multiplexed Mass Cytometry Identifies the Immunophenotype in the Skin of Dermatomyositis.

Patel Jay J Maddukuri Spandana S Li Yubin Y Bax Christina C Werth Victoria P VP

The Journal of investigative dermatology 20210322 9

Dermatomyositis (DM) is a rare, systemic autoimmune disease that most frequently affects the skin, muscles, and lungs. The inflammatory infiltrate in the skin has not been fully characterized, and, in this study, we took a single-cell, unbiased approach using imaging mass cytometry. Substantial monocyte‒macrophage diversity was observed, with the CD14+ population correlating positively with Cutaneous Dermatomyositis Disease Area and Severity Index scores (P = 0.031). The T-cell compartment revea ...[more]

PMID: 33766508

Similar Datasets

Project description:Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous protein targets with 1μm resolution. Each slide is a spatial assay consisting of high-dimensional multivariate observations (m-dimensional feature space) collected at different spatial positions and capturing data from a single biological sample or even representative spots from multiple samples when using tissue microarrays. Often, each of these spatial assays could be characterized by several regions of interest (ROIs). To extract meaningful information from the multi-dimensional observations recorded at different ROIs across different assays, we propose to analyze such datasets using a two-step graph-based approach. We first construct for each ROI a graph representing the interactions between the m covariates and compute an m dimensional vector characterizing the steady state distribution among features. We then use all these m-dimensional vectors to construct a graph between the ROIs from all assays. This second graph is subjected to a nonlinear dimension reduction analysis, retrieving the intrinsic geometric representation of the ROIs. Such a representation provides the foundation for efficient and accurate organization of the different ROIs that correlates with their phenotypes. Theoretically, we show that when the ROIs have a particular bi-modal distribution, the new representation gives rise to a better distinction between the two modalities compared to the maximum a posteriori (MAP) estimator. We applied our method to predict the sensitivity to PD-1 axis blockers treatment of lung cancer subjects based on IMC data, achieving 97.3% average accuracy on two IMC datasets. This serves as empirical evidence that the graph of graphs approach enables us to integrate multiple ROIs and the intra-relationships between the features at each ROI, giving rise to an informative representation that is strongly associated with the phenotypic state of the entire image.

Dataset Information

Highly Multiplexed Mass Cytometry Identifies the Immunophenotype in the Skin of Dermatomyositis.

Publications

Highly Multiplexed Mass Cytometry Identifies the Immunophenotype in the Skin of Dermatomyositis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets