Fabian Coscia

The paper introduces MEMBRYO, a method for continuous SILAC proteomics in live embryos, revealing post-transcriptional regulation during neurogenesis.

The paper identifies PCDH10 as a regulator of neuronal clustering during cortical neuron migration, showing its interaction with FLRT proteins and its role in cortical folding.

The paper uses spatial single-cell proteomics to analyze tangle-bearing neurons in Alzheimer's disease, revealing a continuum of proteomic changes associated with tau pathology.

The paper develops an ultrasensitive spatial tissue proteomics workflow capable of processing over 100 proteomes per day, demonstrating its utility in analyzing rare cancers.

This study uses a ubiquitin replacement strategy to show K29-linked ubiquitylation regulates SUV39H1 stability and H3K9me3 levels in human cells.

This paper introduces PhenoSCoP, a framework that uses single-cell proteomics and microscopy to identify drug-resistant proteotypes in cancer cells and their progression in patient tissues.

This study uses ultrasensitive proteomics to uncover nociceptor diversity and identify novel pain targets by analyzing protein expression in sensory neurons.

The paper introduces openDVP, a framework for deep visual proteomics, demonstrating its application in three tissue studies to integrate spatial single-cell phenotypes with proteomic data.

This study shows maternal smoking disrupts placental function by altering syncytiotrophoblast and macrophage biology, leading to fetal growth restriction.

The paper uses protein mass spectrometry to analyze drug responses in ovarian cancer cells, identifies resistance mechanisms, and proposes drug combinations and single agents to overcome resistance.

This study uses spatial proteomics to analyze ovarian cancer precursor lesions, identifying early molecular changes and therapeutic targets.

This study maps acute murine myocardial infarction with subcellular resolution, identifying a novel endocardial route of immune cell infiltration and vWF as a key mediator.

The paper introduces an automated sample preparation workflow for laser microdissection guided proteomics, validated on human tonsil tissue with high cell type specificity.

This paper introduces a workflow for ultra-low input spatial tissue proteomics, demonstrating high protein quantification from single cells and microregions in murine and human tissues.

This paper presents a 3D spatial atlas of a human lung carcinoma, revealing tumor microenvironment niches and their associated immune interactions and survival outcomes.

The paper identifies a disrupted trophoblast transition in preeclampsia, linking placental pathology to maternal syndrome through multi-omics profiling and in vitro analyses.

The paper develops a high-sensitivity mass spectrometry workflow to quantify single-cell proteome changes upon perturbation, revealing a stable core proteome despite transcriptomic variability.

Pairing AI-driven image analysis with automated laser microdissection and sensitive mass spectrometry yields proteomes of single cells or nuclei chosen by their morphology, with tissue position retained.

The Clinical Knowledge Graph (CKG) links over 16 million nodes and 220 million relationships with statistical and machine learning workflows to accelerate proteomics data analysis and clinical biomarker interpretation.

This paper presents a streamlined mass spectrometry-based proteomics workflow for analyzing FFPE tissue samples, demonstrating efficient and scalable proteome profiling of clinical specimens.