Biology subjects

Rosenberger, F. A.

Publications and source records attributed to Rosenberger, F. A..

3 recordsLinked to original sources

bioRxiv2023

Inhibition of mammalian mtDNA transcription paradoxically activates liver fatty acid oxidation to reverse diet-induced hepatosteatosis and obesity

The oxidative phosphorylation (OXPHOS) system in mammalian mitochondria plays a key role in harvesting energy from ingested nutrients1, 2. Mitochondrial metabolism is very dynamic and can be reprogrammed to support both catabolic and anabolic reactions, depending on physiological demands or disease states3, 4. Rewiring of mitochondrial metabolism is intricately linked to metabolic diseases5, 6 and is also necessary to promote tumour growth7-11. Here, we demonstrate that per oral treatment with an inhibitor of mitochondrial transcription (IMT)11 shifts whole animal metabolism towards fatty acid oxidation, which, in turn, leads to rapid normalization of body weight, reversal of hepatosteatosis and restoration of glucose tolerance in mice on high-fat diet. Paradoxically, the IMT treatment causes a severe reduction of OXPHOS capacity concomitant with a marked upregulation of fatty acid oxidation in the liver, as determined by proteomics and non-targeted metabolomics analyses. The IMT treatment leads to a marked reduction of complex I, the main dehydrogenase that feeds electrons into the ubiquinone (Q) pool, whereas the levels of electron transfer flavoprotein dehydrogenase (ETF-DH) and other dehydrogenases connected to the Q pool are increased. This rewiring of metabolism caused by reduced mtDNA expression in the liver provides a novel principle for drug treatment of obesity and obesity-related pathology.

physiology↗

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↗