Biology subjects

Metousis, A.

Publications and source records attributed to Metousis, A..

6 recordsLinked to original sources

bioRxiv2025

Spatial proteomics reveals mechanisms of cell-intrinsic tryptophan metabolism controlling ovarian cancer survival

Indole-2,3-dioxygenase (IDO1) depletes tryptophan to dampen anti-tumor T cells, yet IDO1 inhibitors (IDO1i) have failed clinically. Using deep visual proteomics, we isolated IDO1 high, medium and low ovarian tumor cells in situ and found IDO1 tightly linked to interferon-{gamma} (IFN-{gamma}) signaling and heterogeneously expressed. Across orthogonal models with tunable IDO1, IFN-{gamma} killed ovarian cancer via a pathway requiring IFN-{gamma} signaling, IDO1-dependent tryptophan depletion, and a biphasic integrated stress response that initially protects from starvation and later drives death. IDO1i or tryptophan supplementation rescued these effects, promoting tumor survival. These data reveal a context-dependent, tumor-suppressive facet of IDO1 and explain how IDO1i can paradoxically favor cancer viability. Our findings call for re-evaluation of IDO1 as a target and suggest exploiting the tryptophan-starvation/GCN2-ISR axis to enhance therapy.

cancer biology↗

bioRxiv2025

TCR clonality and TCR clonal expansion in the in situ microenvironment of non-small cell lung cancer

T-cell activation and clonal expansion are essential for the efficacy of immunotherapy in non-small cell lung cancer (NSCLC) patients. Since the distribution of T-cell clones might provide insights into immunogenic mechanisms, we determined the /{beta} TCR clonality using RNA-sequencing from frozen tumor tissue of 182 NSCLC patients and paired the results with extensive in situ image and sequence analyses of the immune microenvironment of NSCLC. TCR clonality (Gini index) patterns ranged from high T-cell clone diversity with high evenness (Gini index low) to clonal dominance with low evenness (Gini index high). TCR clonality in cancer tissue was lower than in matched normal lung (p=0.021). High Gini index correlated strongly with distinct mutations (EGFR, P53), tumor mutation burden (p<0.001), and inflamed tumor phenotypes (PRF1, GZMA, GZMB, INFG) with exhaustion signatures (LAG3, TIGIT, IDO1, PD-1, PD-L1). Correspondingly, PD-1+, CD3+, CD8A+, CD163+, and CD138+ immune cells infiltrated cancer tissue with high TCR clonality. In situ sequencing revealed that dominant T-cell clones were more often of CD8-subtype and tended to approximate the tumor cell compartment (p<0.03). In a checkpoint inhibitor-treated NSCLC patient cohort, high TCR clonality was associated with therapy response (p=0.016) and prolonged survival (p=0.003, median survival 13.8 vs 2.9 months). Our robust analysis pipeline revealed diverse TCR repertoires related to genotypes and immune phenotypes. The in situ positioning of expanded T-cell clones indicated functional impact, which was clinically confirmed in NSCLC patients receiving immunotherapy. One sentence summaryT-cell clone expansion in NSCLC is associated with genetic mutations, immune phenotypes, immunotherapy response, and patient survival.

cancer biology↗

bioRxiv2023

Open-source, high-throughput targeted in-situ transcriptomics for developmental biologists

Multiplexed spatial profiling of mRNAs has recently gained traction as a tool to explore the cellular diversity and the architecture of tissues. We propose a sensitive, open-source, simple and flexible method for the generation of in-situ expression maps of hundreds of genes. We exploit direct ligation of padlock probes on mRNAs, coupled with rolling circle amplification and hybridization-based in situ combinatorial barcoding, to achieve high detection efficiency, high throughput and large multiplexing. We validate the method across a number of species, and show its use in combination with orthogonal methods such as antibody staining, highlighting its potential value for developmental biology studies. Finally, we provide an end-to-end computational workflow that covers the steps of probe design, image processing, data extraction, cell segmentation, clustering and annotation of cell types. By enabling easier access to highthroughput spatially resolved transcriptomics, we hope to encourage a diversity of applications and the exploration of a wide range of biological questions.

molecular biology↗

bioRxiv2023

SPARCS, a platform for genome-scale CRISPR screening for spatial cellular phenotypes

Forward genetic screening associates phenotypes with genotypes by randomly inducing mutations and then identifying those that result in phenotypic changes of interest. Here we present spatially resolved CRISPR screening (SPARCS), a platform for microscopy-based genetic screening for spatial cellular phenotypes. SPARCS uses automated high-speed laser microdissection to physically isolate phenotypic variants in situ from virtually unlimited library sizes. We demonstrate the potential of SPARCS in a genome-wide CRISPR-KO screen on autophagosome formation in 40 million cells. Coupled to deep learning image analysis, SPARCS recovered almost all known macroautophagy genes in a single experiment and discovered a role for the ER-resident protein EI24 in autophagosome biogenesis. Harnessing the full power of advanced imaging technologies, SPARCS enables genome-wide forward genetic screening for diverse spatial phenotypes in situ.

systems biology↗

bioRxiv2023

A standardized and reproducible workflow for membrane glass slides in routine histology and spatial proteomics

Defining the molecular phenotype of single cells in-situ is essential for understanding tissue heterogeneity in health and disease. Powerful imaging technologies have recently been joined by spatial omics technologies, promising unparalleled insights into the molecular landscape of biological samples. One approach involves laser microdissection in combination with membrane glass slides for the isolation of single cells from specific anatomical regions for further analysis by spatial omics. However, so far this is not fully compatible with automated staining platforms and routine histology procedures such as heat-induced epitope retrieval, limiting reproducibility, throughput and integration of advanced staining procedures. This study describes a robust workflow for routine use of glass membrane slides, allowing precise extraction of tissue in combination with automated and multicolor immunofluorescence staining. The key advance is the addition of glycerol to standard heat-induced epitope retrieval protocol, preventing membrane distortion while preserving antigen retrieval properties. Importantly, we show that glycerol is fully compatible with mass-spectrometry based proteomics and does not affect proteome depth or quality. Further, we enable single focal plane imaging by removing remaining trapped air pockets with an incision. We demonstrate our workflow using the recently introduced Deep Visual Proteomics technology on the single-cell type analysis of adjacent suprabasal and basal keratinocytes of human skin. Our protocol extends the utility of membrane glass slides and enables much more robust integration with routine histology procedures, high-throughput multiplexed imaging and sophisticated downstream spatial omics technologies.

pathology↗

bioRxiv2022

Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome

Single-cell proteomics by mass spectrometry (MS) is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed MS. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a slice of a cell. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics or spatial omics technologies.

systems biology↗