# IMmuneCite: an integrated workflow for analysis of immune enriched spatial proteomic data

## TL;DR

IMmuneCite is a new computational tool that improves the analysis of immune cell types in spatial proteomic data from tissues like the liver.

## Contribution

IMmuneCite introduces a novel workflow for reliable immune cell segmentation and phenotype assignment in spatial proteomics.

## Key findings

- IMmuneCite identified 32 distinct immune cell phenotypes in human liver samples.
- The tool reduces nonbiological cell clusters caused by overlapping marker signals.
- It successfully analyzed immune landscapes in liver transplantation and hepatocellular carcinoma.

## Abstract

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.