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Myeloid-derived suppressor cells (MDSCs) are pathologically activated immature myeloid cells with immunosuppressive activity that expand during chronic inflammation, such as cancer and prosthetic joint infection (PJI). Myeloid-derived suppressor cells can be broadly separated into 2 populations based on surface marker expression and function: monocytic myeloid-derived suppressor cells (M-MDSCs) and granulocytic myeloid-derived suppressor cells (G-MDSCs). Granulocytic myeloid-derived suppressor cells are the most abundant leukocyte infiltrate during PJI; however, how this population is maintained in vivo and cellular heterogeneity is currently unknown. In this study, we identified a previously unknown population of Ly6G+Ly6C+F4/80+MHCII+ MDSCs during PJI that displayed immunosuppressive properties ex vivo. We leveraged F4/80 and MHCII expression by these cells for further characterization using cellular indexing of transcriptomes and epitopes by sequencing, which revealed a distinct transcriptomic signature of this population. F4/80+MHCII+ MDSCs displayed gene signatures resembling G-MDSCs, neutrophils, and monocytes but had significantly increased expression of pathways involved in cytokine response/production, inflammatory cell death, and mononuclear cell differentiation. To determine whether F4/80+MHCII+ MDSCs represented an alternate phenotypic state of G-MDSCs, Ly6G+Ly6C+F4/80-MHCII- G-MDSCs from CD45.1 mice were adoptively transferred into CD45.2 recipients using a mouse model of PJI. A small percentage of transferred G-MDSCs acquired F4/80 and MHCII expression in vivo, suggesting some degree of plasticity in this population. Collectively, these results demonstrate a previously unappreciated phenotype of F4/80+MHCII+ MDSCs during PJI, revealing that a granulocytic-to-monocytic transition can occur during biofilm infection., Competing Interests: Conflict of interest: The authors have no conflict of interest to report., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Leukocyte Biology.)

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

The intestinal mucosa represents a unique environment where the coordinated function of diverse epithelial, mesenchymal, and immune cells maintains a physiologically balanced environment in the presence of gut microbiota. The intestinal mucosa plays a central role in the pathogenesis of inflammatory bowel disease (IBD), yet the molecular and cellular composition of this diverse environment is poorly understood. However, the recent advent of multimodal single-cell technologies, including single-cell RNA sequencing (scRNA-seq), now provides an opportunity to accurately map the tissue architecture, characterize rare cell types that were previously overlooked, and define function at a single-cell level. In this review, we summarize key advances in single-cell technology and provide an overview of important aspects of computational analysis. We describe emerging data in the field of IBD and discuss how the characterization of novel intestinal mucosa cell populations is reshaping our understanding of this complex disease. We conclude by considering the potential clinical applications, including the definition of novel drug targets and the opportunity for personalization of care in this exciting new era of precision medicine., (© 2020 Crohn’s & Colitis Foundation. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation.)