News from… Part II

Medical University of Vienna: Core Facility Proteomics, Department of Pathology, Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, Austria

Spatial proteomics to reveal intratumoral heterogeneity in breast cancer subtypes induced by the tumor microenvironment
Breast cancer (BC) remains a global health challenge, impacting a significant portion of the female population. Understanding proteomic heterogeneity within BC is crucial for improving diagnostic accuracy and therapeutic efficacy. Imaging Mass Cytometry (IMC) of tumor xenograft models provides high spatial resolution and targeted protein analysis within BC tissue architecture but is limited in multiplexing. In contrast, Imaging Mass Spectrometry (IMS) enables broader molecular profiling with higher multiplexing capabilities, but limited in spatial resolution and confidence of identification.
Human BC cell lines (MCF-7, SKBR-3, MDA-MB-231) were inoculated into female athymic BALB/c-nude mice. Excised tumors were embedded in gelatin or paraffin, or were snap-frozen in liquid nitrogen. One tissue was allocated for IMC and an adjacent one for IMS. An optimized IMC panel, selected based on literature review and a bulk proteomics experiment, revealed clear heterogeneity between BC subtypes and within tissues, demonstrating inter- and intra-tumoral heterogeneity. IMS explored the broader proteomic landscape and identified more candidate proteins. Bulk tissue LC-MS/MS identified approximately 17,000 proteins, enabling validation of IMS signals.
Integration of IMC and IMS datasets allowed spatial and molecular correlation. Segmentation maps aligned with histological assessments and distinguished tumor, stroma, and necrotic regions. Unsupervised U-MAP clustering revealed distinct molecular regions with unique proteomic profiles.
Combining IMC and IMS provided a comprehensive characterization of BC heterogeneity, identifying spatial proteomic variation not detectable by conventional methods. As next steps we aim to combine these translational research results with in vivo PET/MRI data to develop non-invasive diagnostic tools and enhance personalized therapeutic strategies.

Center for Regenerative Therapies (CRTD), TU Dresden, Germany 

Epi-CyTOF-based investigation of the epigenetic state of the human developing neocortex
The neocortex is the brain structure attributed to higher cognitive functions in humans. Its development is governed by spatiotemporal gene expression programs regulated by epigenetic mechanisms, such as post-translational modifications of histones. Specific histone modifications act on genomic loci to repress or promote gene expression, thereby contributing to the tuning of proliferation and differentiation of neural progenitor cells. Given the large number of epigenetic modifications, we currently lack an understanding of the epigenetic state of neural cell populations beyond a few well-studied histone modifications. Moreover, the interplay of these modifications and their temporal changes in the developing human neocortex remain largely uncharacterised. To unravel the complexity of the epigenome during neurogenesis at single-cell resolution, we employed cytometry by time of flight with a comprehensive epigenetic panel spanning 30 different epigenetic markers, referred to as Epi-CyTOF. In human primary tissue and human cortical organoids, we were able to dissect different cell populations. Moreover, our data reveals distinct epigenetic marking in neural progenitor cells and neurons in both tissues. Among the most differentially enriched epigenetic marks, we find H3K27me3, a repressive epigenetic modification deposited by the Polycomb repressive complex 2 (PRC2), to be strongly enriched in postmitotic neurons, whereas the activating epigenetic mark H3K4me2/3 is less abundant in intermediate progenitor cells compared to other cell types. Altogether, Epi-CyTOF presents a powerful technology to decipher the complexity and dynamics of histone modifications during brain development. In the future, it will be applied to elucidate epigenetic changes in human neurodevelopmental disorders caused by mutations in epigenetic modifiers.

German Cancer Research Center (DKFZ), Heidelberg, Systems Immunology & Single-Cell Biology, Germany
German Cancer Consortium (DKTK), DKFZ Core Center, Heidelberg, Germany

Spatial quantification of cellular metabolism identifies metabolic niches predictive of response to immune checkpoint inhibition in metastatic melanoma patients

Metastatic melanoma remains challenging to treat despite advances in immune checkpoint inhibition (ICI). Clinically-relevant immune features such as T cell exhaustion and immunosuppressive polarization of myeloid cells are influenced by the cellular metabolic state. However, the metabolic landscape of the human tumor microenvironment and its implication on ICI response remain poorly understood. Here, we integrate single-cell metabolic regulome profiling (scMEP) with multiplexed ion beam imaging (MIBI) to dissect the spatial metabolic heterogeneity in the tumor microenvironment of ICI-naïve metastatic melanoma patients. At the single-cell level, we found that CD8⁺ T cells in responders display unique metabolic states marked by balanced mitochondrial and glycolytic activity, contrasting with terminally exhausted states prevalent in non-responders. Leveraging the spatial context, we identified conserved metabolic niches in human melanoma that transcend lineage boundaries and stratify patients into distinct response groups. In addition, presence of these metabolic niches was associated with immunosuppressive myeloid cell polarization. Together, our findings reveal a novel axis of therapeutic vulnerability by linking spatial metabolic organization to ICI response. These insights pave the way for metabolic interventions that could synergize with existing immunotherapies, potentially transforming treatment paradigms for metastatic melanoma.

Max-Planck-Institut für Molekulare Genetik, Berlin, Germany

Quantifying IMC data analysis step by step: a comparative evaluation of MICCRA and traditionally used approaches
Imaging Mass Cytometry (IMC) enables the spatial co-detection of 40+ proteins, which makes it a technology that has high application potential, enabling deep insights into tissue architecture and disease states. Simultaneously, IMC data is often very challenging to analyse. In recent years, several end-to-end data analysis pipelines have been published: SIMPLI, MCMICRO, imcRtools in conjunction with steinbock, and SPEX. Those pipelines are made for batch-processing of multiplexed images in general, and come at different levels of interactiveness. We introduce MICCRA (Modular IMC Cell Characterization with automatic Region Assembly), a modular end-to-end pipeline which in contrast to those pipelines focuses only on the analysis of IMC data, allowing a higher degree of automation. MICCRA also introduces novel contributions to IMC data analysis: automated stitching and normalisation of regions of interest (ROIs), the implementation of a distance-based denoising method by Keren et al., and performance-improving adaptations to the commonly used clustering package FlowSOM.

Our work focuses on the quantitative evaluation of the results of the steps of IMC data analysis, which is a challenging and often overlooked aspect of IMC related research. We compare MICCRA to other approaches, create groundtruth data or devise approximative quantification metrics. We show that Mesmer and the IMC Segmentation pipeline produce the best segmentation results compared to three other tools, while the output of the IMC Segmentation pipeline corresponds better to biological expectations and exhibits no reduced performance when dense tissue is segmented. Using groundtruth data of spatial T cell location, we observe the surprising result that even with the best segmentation approach, only 85% of T cells can correctly be phenotyped in our example superROI. Finally, we present adaptations to traditionally used clustering strategies. Firstly, we employ a flexible cofactor arcsinh data transformation, which succeeds in integrating clustering features from markers of all different intensity scales. Secondly, we present evidence of reduced reproducibility of clustering results based on a single SOM fitting, and that calculating a consensus result of more than 500 SOMs stabilises the result.

Department of Internal Medicine 3 – Rheumatology and Clinical Immunology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Bayern, Germany
Deutsches Zentrum Immuntherapie (DZI), Erlangen University Hospital, Erlangen, Bayern, Germany

Imaging mass cytometry-based characterisation of fibroblast subsets and their cellular niches in systemic sclerosis

Systemic sclerosis (SSc) is an autoimmune fibrotic diesease affecting the skin and internal organs. Fibroblasts are recognized as the key effector cells in the fibrosis. Over the last decade, have been shown that fibroblasts are a heterogeneous cell population with phenotypical and functional differences. However, a complete comprehensive picture is missing. Moreover, only approaches requiring tissue disaggregation have been used, losing the spatial information.

We used Imaging Mass Cytometry as a spatial proteomic technique to characterize fibroblasts subsets in skin biopsies of SSc patients and healthy controls. We have identified 13 distinct fibroblast subsets, of which five were increased in SSc (Myofibroblasts, S1PR⁺, FAP^high, THY1⁺ADAM12^highPU.1^high and ADAM12⁺GLI1⁺) and three were decreased (THY1⁺ADAM12^low, TFAM^high and PI16⁺FAP⁺). The spatial localization within the dermis differs between subsets: TFAM^high and ADAM12⁺GLI1⁺ fibroblasts are mainly located in the upper dermis, being the first increased in healthy donors and the second in SSc patients; myofibroblasts and FAP^high are exclusively found in the lower dermis; S1PR⁺ and THY1⁺ADAM12^highPU.1^high are increased in both dermal layers in SSc. Subsequently, an interaction analysis was conducted. The subepithelial space consists of TFAM^high fibroblasts in healthy and ADAM12⁺GLI1⁺ fibroblasts in SSc. Furthermore, the S1PR⁺ fibroblasts exhibits a complete shift in their neighbourhood network in SSc compared to healthy. Lastly, the percentage of S1PR⁺ and their neighbouring ADAM12⁺GLI1⁺ fibroblasts are significantly associated with clinical outcomes of SSc. This indicates that not only are the cell subsets of significance in the disease development, but also their spatial localization and relationships.

Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel and Department of Internal Medicine I, Medical Center-University of Freiburg, Freiburg, Germany

CytoVI: Deep generative modeling of cytometry data across technologies

Flow cytometry was historically the first single cell technology to measure millions of cellular states within minutes. Due to its robustness and scalability flow cytometry and related antibody-based single cell technologies have become an irreplaceable part of routine clinics and evolved to a powerful tool for exploratory research. Opposed to the intrinsically noisy and sparse data characteristics of most genomic single cell technologies, antibody-based cytometry technologies offer high-resolution measurements of millions of cells across a wide dynamic range facilitating the analysis of large patient cohorts. However, the analysis of multi-cohort studies is often obstructed by batch effects and differences in antibody panels or technology platforms utilized to analyze samples. Here, we present CytoVI, a deep generative model designed for the integration across antibody-based technologies. CytoVI removes technical variation in flow cytometry, CyTOF or CITE-seq data and embeds cells into a meaningful low-dimensional representation corresponding to a cells intrinsic state. CytoVI performs favourable compared to existing tools in data integration tasks, imputes missing markers in experiments with overlapping antibody panels and predicts a cells transcriptome if paired with CITE-seq data. We utilized CytoVI to generate an integrated B cell maturation atlas across 350 proteins from conventional mass cytometry data and automatically detect T cell states associated with disease in a large cohort of Non-hodgkin B cell lymphoma patient measured by flow cytometry. Beyond its applicability for preclinical research, we showcased that CytoVI can automatically identify tumor cells in chronic lymphatic leukemia patients via transfer learning and predict a patient’s diagnosis in a fully automated fashion. Therefore, CytoVI represents a powerful deep learning tool for preclinical research and enables an accurate automated analysis of immunophenotypes in patient samples in clinical settings.