Carsten Krieg

Spatial omics is transforming our ability to interrogate local tissue microenvironments by enabling spatially resolved measurement of biomolecules such as transcripts, proteins, and metabolites. However, capturing the full biological complexity of tissues often requires combining multiple modalities, which introduces both experimental as well as computational challenges. To address computational difficulties due to differences in resolution, noise levels, and available channels, we present MIMIC: a reproducible, semi-automated workflow that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry for joint downstream analysis. MIMIC incorporates rigorous quality control, including registration error assessment, and supports pixel-level modeling to delineate analyte-cell type associations. We demonstrate the power of our approach with a proof-of-concept study on artificial tissue and apply it to human liver tissue affected by metabolic dysfunction-associated steatotic liver disease. Despite integration challenges, MIMIC provides a robust framework that successfully recovers known molecular associations and reveals novel spatial relationships across modalities.

Integrating antibody-based imaging with mass spectrometry imaging (MSI) on the same formalin-fixed paraffin-embedded (FFPE) tissue section offers powerful opportunities for multimodal spatial analysis but remains analytically challenging due to cross-platform chemical and physical interference. In particular, chemically aggressive on-tissue derivatization strategies required for isomer-resolved glycan MSI may compromise downstream antibody detection. Here, we systematically evaluate the analytical compatibility and acquisition order of imaging mass cytometry (IMC) and sialic-acid-linkage-resolving N-glycan MALDI-MSI using an Amidation–Activation–X-Linkage (AAXL) derivatization strategy on the same FFPE tissue section. Two same-section workflows were compared: AAXL–MALDI MSI followed by IMC (MALDI-first) and IMC followed by AAXL–MALDI MSI (IMC-first). We find that AAXL-first processing results in severe and widespread loss of IMC anti-body signal across epithelial, immune, and nuclear markers, rendering subsequent antibody-based analysis unreliable. In contrast, IMC-first acquisition preserves quantitative antibody performance while maintaining spatial glycan distributions, relative abundance structure, and isomer-specific signal integrity in downstream AAXL–MALDI MSI. Using high-precision co-registration, we further demonstrate that IMC-first sequencing enables analytically robust integration of IMC and MSI data at both domain and pixel levels. These results establish IMC-first acquisition as the preferred same-section strategy for workflows combining antibody imaging with chemically intensive, isomer-resolved glycan MSI and provide generalizable guidance for the design of multimodal spatial mass spectrometry experiments.

Accurate cell type annotation in imaging mass cytometry (IMC) and related technologies critically depends on preprocessing steps such as normalization, segmentation, and marker aggregation. More distinct separation between negative and positive signals enables more precise cell boundary inference and more robust marker assignment to single cells. However, inherent limitations in spatial resolution and uncertain cell boundaries can lead to spillover, where signal leaks from one cell to a neighboring cell, distorting marker intensities and leading to incorrect cell type annotation. To address these challenges, we first systematically investigated the impact of spatial resolution and segmentation variability on per-cell marker aggregation using simulated IMC datasets, establishing upper limits for reliable marker separation in cell type annotation. We further analyzed technical biases in large scale studies, demonstrating that appropriate normalization approaches can significantly reduce batch effects without compromising biological variability. Finally, we benchmarked multiple spillover correction strategies across both (semi-)simulated and real IMC datasets. Our results revealed two simple methods: spatial smoothing of intensity images followed by mean marker aggregation, or resampling of cell masks followed by mean marker aggregation and median calculation, both of which improved annotation performance. Other methods often performed worse than the baseline of simple mean aggregation. Together, these findings underscore the central importance of spatial resolution, normalization, and marker aggregation in IMC data preprocessing for accurate single-cell annotation.

The complement system has emerged as a critical regulator of intestinal homeostasis, inflammation, and cancer. In this Review, we explore the multifaceted roles of complement in the gastrointestinal tract, highlighting its canonical and noncanonical functions across intestinal epithelial and immune cells. Under homeostatic conditions, intestinal cells produce complement that maintains barrier integrity and modulates local immune responses, but complement dysregulation contributes to intestinal inflammation and promotes colon cancer. We discuss recent clinical and preclinical studies to provide a cohesive overview of how complement-mediated modulation of immune and nonimmune cell functions can protect or exacerbate inflammation and colon cancer development. The complement system plays a dual role in the intestine, with certain components supporting tissue protection and repair and others exacerbating inflammation. Intriguingly, distinct complement pathways modulate colon cancer progression and response to therapy, with novel findings suggesting that the C3a/C3aR axis constrains early tumor development but may limit antitumor immunity. The recent discovery of intracellular complement activation and tissue-specific complement remains vastly underexplored in the context of intestinal inflammation and colon cancer. Collectively, complement functions are context- and cell-type-dependent, acting both as a shield and a sword in intestinal diseases. Future studies dissecting the temporal and spatial dynamics of complement are essential for leveraging its potential as a biomarker and therapeutic in colon cancer.

The advent of combination immune checkpoint inhibitors (cICI), specifically ipilimumab and nivolumab, has transformed the treatment landscape for metastatic renal cell carcinoma (mRCC), offering durable remissions and improved outcomes for intermediate- and poor-risk patients. However, intrinsic resistance remains a significant challenge, with 40-60% of patients failing to achieve meaningful responses. Here, we conducted a comprehensive, multi-omic analysis of systemic and tumor-associated immune responses in mRCC patients enrolled in a clinical trial (CA209-980, SAKK07/17, NCT03297593 ) testing a novel response-adapted cICI regimen. Our study aimed to identify immune correlates of response to cICI therapy. High-dimensional mass cytometry and single-cell proteomic and bulk RNA sequencing of the tumor revealed an enrichment of Th17 CD4+ T cells in responders. These cells exhibited upregulated IL-21-driven pathways, IL-17 signaling, and inflammasome-associated processes, highlighting their central role in therapeutic efficacy. Our integrative analysis underscores the importance of Th17 cells in mediating effective responses to cICI and provides a framework for developing predictive biomarkers and therapeutic strategies to overcome resistance. These findings highlight the translational potential of targeting Th17-centric pathways to enhance immunotherapy outcomes in mRCC and potentially other cancers. This work represents a significant step toward advancing personalized oncology through integrated immune profiling.

Spatial omics is transforming our ability to interrogate local tissue microenvironments by enabling spatially resolved measurement of biomolecules such as transcripts, proteins, and metabolites. However, capturing the full biological complexity of tissues often requires combining multiple modalities, which introduces both experimental as well as computational challenges. To address computational difficulties due to differences in resolution, noise levels, and available channels, we present MIMIC: a reproducible, semi-automated workflow that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry for joint downstream analysis. MIMIC incorporates rigorous quality control, including registration error assessment, and supports pixel-level modeling to delineate analyte–cell type associations. We demonstrate the power of our approach with a proof-of-concept study on artificial tissue and apply it to human liver tissue affected by metabolic dysfunction-associated steatotic liver disease. Despite integration challenges, MIMIC provides a robust framework that successfully recovers known molecular associations and reveals novel spatial relationships across modalities.

BioStudies – one package for all the data supporting a study

Immune checkpoint inhibitors (ICI) have improved patient outcomes in hepatocellular carcinoma (HCC); however, most patients do not experience durable benefit. The non-T cell-inflamed tumor microenvironment, characterized by limited CD8⁺ T-cell infiltration, reduced dendritic cell function, and low interferon-γ-associated gene expression, is associated with a lower likelihood of response to ICI. To nominate new therapeutic targets for overcoming ICI resistance in HCC, we conducted a large-scale multiomic analysis on 900+human specimens (RNA sequencing (RNA-seq), proteomics) and 31 tumor single-cell (sc) RNA-seq samples, with tissue validation through imaging mass cytometry (IMC) and spatial lipidomics by matrix-assisted laser desorption/ionization (MALDI), with experimental investigation by in vitro CD8⁺ T-cell recruitment and macrophage polarization functional assays using three-dimensional (3D) co-culture models. We discovered 32 oncogenic pathways associated with immune exclusion, with sterol regulatory element binding protein 1 (SREBP1, encoded by SREBF1) as a hub regulator. scRNA-seq analysis showed that SREBP1 signaling is associated with enriched lipid biogenesis pathways in tumor cells, elevated immunosuppressive markers in macrophages, and diminished CD8⁺ T-cell infiltration. Integration of IMC and MALDI images revealed distinct lipid species differentially abundant in tumor regions with low versus high CD8⁺ T cell infiltration. Functional studies in a 3D in vitro tumor-immune co-culture system demonstrated that CRISPR-mediated SREBF1 knockout (KO) in HepG2 cells reduced monocyte recruitment, decreased expression of the protumorigenic CD206 marker in macrophages, and enhanced CD8⁺ T-cell migration compared with wild-type (WT) (pSREBF1 KO versus WT tumor cells confirmed suppression of lipid biosynthesis genes. Our findings nominate an atlas of tumor-intrinsic drivers of immune exclusion, particularly SREBP1 via pro-tumorigenic macrophage (M2-like) reprogramming. These pathways may represent novel therapeutic targets to enhance antitumor immunity and deserve further study as targeted therapy candidates to enhance ICI in HCC.

Pancreatic ductal adenocarcinoma (PDAC) has one of the lowest 5-year survival rates of all cancers, and limited treatment options exist. Immunotherapy is effective in some cancer types, but the immunosuppressive tumor microenvironment (TME) of PDAC is a barrier to effective immunotherapy. CXCR2+ myeloid-derived suppressor cells (MDSCs) are abundant in PDAC tumors in humans and in mouse models. MDSCs suppress effector cell function, making them attractive targets for restoring anti-tumor immunity. In this study, we show that the most abundant soluble factors released from a genetically diverse set of human and mouse PDAC cells are CXCR2 ligands, including CXCL8, CXCL5, and CXCL1. Expression of CXCR2 ligands is at least partially dependent on mutant KRAS and NFκB signaling, which are two of the most commonly dysregulated pathways in PDAC. We show that MDSCs are the most prevalent immune cells in PDAC tumors. MDSCs expressed high levels of CXCR2, and we found that myeloid cells readily migrate toward conditioned media (CM) prepared from PDAC cultures. We designed CXCR2 ligand-Fc fusion proteins to modulate the CXCR2 chemotactic signaling axis. Unexpectedly, these fusion proteins were superior to native chemokines in binding and activation of CXCR2 on myeloid cells. These “superkines” were potent inhibitors of PDAC CM-induced myeloid cell migration and were superior to CXCR2 small-molecule inhibitors and neutralizing antibodies. Our findings suggest that CXCR2 superkines may disrupt myeloid cell recruitment to PDAC tumors, ultimately improving immunotherapy outcomes in patients with PDAC.