Natalie de Souza

This paper develops a method to quantitatively analyze the structural states of alpha-synuclein in vitro and in situ, revealing conformational changes in yeast and human neurons.

The paper identifies and quantifies in-source fragmentation (ISF) in proteomics data, showing its prevalence and impact, and proposes an open-source method to detect and remove ISF peptides.

This study identifies structural and functional differences in alpha-synuclein fibrils from Parkinson's disease, Dementia with Lewy Bodies, and Multiple System Atrophy, linking these differences to disease-specific cellular responses and potential therapeutic targets.

The paper introduces in-cell LiP-MS to study protein structural changes and biomolecular condensates in live human cells, revealing structural alterations under stress and validating them with microscopy.

This study systematically maps metal-binding sites in E. coli using Metal-LiP, identifies 424 candidate metal-binding proteins, and suggests functional roles for metal binding.

This paper introduces a method for high-plex protein imaging using MALDI-IHC with PCMT-labeled antibodies and presents an open-source analysis pipeline called gutenTAG.

The paper uses imaging mass cytometry to analyze human pancreatic samples and identifies early β-cell dysfunction and immune changes in type 1 diabetes progression.

This study systematically identifies structure-specific protein-protein interactions using LiP-MS, demonstrating its application to alpha-synuclein in Parkinson's disease.

This paper introduces a structural proteomic pipeline to study anti-amyloidogenic compounds' mechanisms in vitro and in situ, revealing off-target effects and structural interactions with α-Synuclein.

The paper uses single-cell proteomics and perturbation experiments to improve drug sensitivity predictions in breast cancer and identify genomic features associated with treatment response.

The paper quantifies how environment, neighborhood, and cell state influence phenotypic variability in 3D spheroids using mass cytometric imaging and a linear model.

LiP-Quant identifies drug targets in complex proteomes using limited proteolysis and mass spectrometry, providing EC50 estimates and distinguishing between homologous proteins.