Barbetta Arianna, Bangerth Sarah, Lee Jason T C, Rocque Brittany, Roussos Torres Evanthia T, Kohli Rohit, Akbari Omid, Emamaullee Juliet
Division of Abdominal Organ Transplantation and Hepatobiliary Surgery, Department of Surgery, Keck School of Medicine, University of Southern California, 1510 San Pablo Street, Suite 412, Los Angeles, CA, 90033, USA.
Department of Surgery, University of Rochester, Rochester, NY, USA.
Sci Rep. 2025 Mar 19;15(1):9394. doi: 10.1038/s41598-025-93060-y.
Spatial proteomics enable detailed analysis of tissue at single cell resolution. However, creating reliable segmentation masks and assigning accurate cell phenotypes to discrete cellular phenotypes can be challenging. We introduce IMmuneCite, a computational framework for comprehensive image pre-processing and single-cell dataset creation, focused on defining complex immune landscapes when using spatial proteomics platforms. We demonstrate that IMmuneCite facilitates the identification of 32 discrete immune cell phenotypes using data from human liver samples while substantially reducing nonbiological cell clusters arising from co-localization of markers for different cell lineages. We established its versatility and ability to accommodate any antibody panel and different species by applying IMmuneCite to data from murine liver tissue. This approach enabled deep characterization of different functional states in each immune compartment, uncovering key features of the immune microenvironment in clinical liver transplantation and murine hepatocellular carcinoma. In conclusion, we demonstrated that IMmuneCite is a user-friendly, integrated computational platform that facilitates investigation of the immune microenvironment across species, while ensuring the creation of an immune focused, spatially resolved single-cell proteomic dataset to provide high fidelity, biologically relevant analyses.
空间蛋白质组学能够在单细胞分辨率下对组织进行详细分析。然而，创建可靠的分割掩码并为离散的细胞表型分配准确的细胞表型可能具有挑战性。我们引入了IMmuneCite，这是一个用于全面图像预处理和单细胞数据集创建的计算框架，专注于在使用空间蛋白质组学平台时定义复杂的免疫图谱。我们证明，IMmuneCite利用来自人类肝脏样本的数据有助于识别32种离散的免疫细胞表型，同时大幅减少因不同细胞谱系标记物共定位而产生的非生物细胞簇。通过将IMmuneCite应用于来自小鼠肝脏组织的数据，我们确立了其通用性以及容纳任何抗体组合和不同物种的能力。这种方法能够深入表征每个免疫区室中的不同功能状态，揭示临床肝移植和小鼠肝细胞癌中免疫微环境的关键特征。总之，我们证明IMmuneCite是一个用户友好的集成计算平台，它有助于跨物种研究免疫微环境，同时确保创建一个以免疫为重点、空间分辨的单细胞蛋白质组数据集，以提供高保真、生物学相关的分析。
