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Nordmann, T. M.

Publications and source records attributed to Nordmann, T. M..

4 recordsLinked to original sources

Cell-type-resolved spatial proteogenomics from matched genome and proteome of the same cells

The genome and proteome of the same cells are rarely measured together, which is especially consequential in cancer, where somatic mutations vary across clones and drive disease. We show that a single standard proteomics extraction tip can retain peptides on-tip after digestion while genomic DNA passes into the normally discarded flowthrough. Combined with Deep Visual Proteomics, flowthrough co-isolation enables cell-type-resolved spatial proteogenomics from archival FFPE tissue, demonstrated in melanoma.

biochemistry↗

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↗

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↗

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↗