Biology subjects

Kabatnik, S.

Publications and source records attributed to Kabatnik, S..

3 recordsLinked to original sources

bioRxiv2024

Deep Visual Proteomics reveals DNA replication stress as a hallmark of Signet Ring Cell Carcinoma

Signet Ring Cell Carcinoma (SRCC) is a rare and highly malignant form of adenocarcinoma with increasing incidence and poor prognosis due to late diagnosis and limited treatment options. We employed Deep Visual Proteomics (DVP), which combines AI directed cell segmentation and classification with laser microdissection and ultra-high sensitivity mass spectrometry, for cell-type specific proteomic analysis of SRCC across the bladder, prostate, liver, and lymph nodes of a single patient. DVP identified significant alterations in DNA damage response (DDR) proteins, particularly within the ATR and mismatch repair (MMR) pathways, indicating replication stress as a crucial factor in SRCC mutagenicity. Additionally, we observed substantial enrichment of immune-related proteins, reflecting high levels of cytotoxic T lymphocyte infiltration and elevated PD-1 expression. These findings suggest that pembrolizumab immunotherapy may be more effective than conventional chemotherapy for this patient. Our results provide novel insights into the proteomic landscape of SRCC, identifying potential targets and open up for personalized therapeutic strategies in managing SRCC.

cancer biology↗

bioRxiv2024

Deep Visual Proteomics advances human colon organoid models by revealing a switch to an in vivo-like phenotype upon xenotransplantation

AbstractIntestinal epithelial damage predisposes to chronic disorders like inflammatory bowel disease. The organoid model allows cultivation, expansion and analysis of primary intestinal epithelial cells and has been instrumental in studying epithelial behavior in homeostasis and disease. Recent advances in organoid transplantation allow studying human epithelial cell behavior within the intestinal tissue context. However, it remained unclear how organoid transplantation into the colon affects epithelial phenotypes, which is key to assessing the models suitability to study human epithelial cells. We employed Deep Visual Proteomics, integrating AI-guided cell classification, laser microdissection, and an improved proteomics pipeline to study the human colon. This created an in-depth cell type-resolved proteomics resource of human intestinal epithelial cells within human tissue, in vitro organoids, and the murine colon post-xenotransplantation. Our findings reveal that in vitro conditions induce a proliferative organoid phenotype, which was reversible upon transplantation and adjustment of organoid culturing conditions.

cell biology↗

bioRxiv2020

A Systematic Protein Turnover Map for Decoding Protein Degradation

Protein degradation is mediated by an expansive and complex network of protein modification and degradation enzymes. Matching degradation enzymes with their targets and determining globally which proteins are degraded by the proteasome or lysosome/vacuole has been a major challenge. Further, an integrated view of protein degradation for cellular pathways has been lacking. Here we present a novel analytical platform that combines systematic gene deletions with quantitative measures of protein turnover to deconvolve protein degradation pathways for S. cerevisiae. The resulting turnover map (T-MAP) reveals target candidates of nearly all E2 and E3 ubiquitin ligases and identifies the primary degradation routes for most proteins. We further mined this T-MAP to identify new substrates of ER-associated Degradation (ERAD) involved in sterol biosynthesis and to uncover novel regulatory nodes for sphingolipid biosynthesis. The T-MAP approach should be broadly applicable to the study of other cellular processes and systems, including mammalian systems. One Sentence SummaryA systematic, global map of protein turnover for a large set of yeast mutants reveals scope and specificity of degradation pathways and identifies novel regulatory nodes for lipid metabolism.

systems biology↗