Lisa Bramer

This study evaluates plasma extracellular vesicle proteomics for identifying predictive biomarkers of type 1 diabetes development, finding 448 differentially abundant proteins and a 0.81 AUC in predicting islet autoimmunity.

This study maps age-related cellular senescence in human lymph nodes, identifying senescence markers and spatial shifts in germinal center B cells.

This paper benchmarks feature screening methods for omics data classification, finding BcorSIS to be the most effective and efficient across various datasets.

This study identifies sex-specific islet responses to inflammation and shows that estrogen reduces inflammatory signaling and promotes immune tolerance in beta cells, contributing to sex differences in type 1 diabetes susceptibility.

This paper develops a spatial proteomics workflow to identify and map proteoforms in human kidney tissue, revealing spatially distinct proteomic profiles between glomeruli and tubules.

The paper examines lipidomic changes in maternal blood and placental tissue in obesity and gestational diabetes, revealing sexually dimorphic effects in placental fatty acid composition.

The paper introduces nanoSPLITS, a platform for simultaneous transcriptome and proteome profiling of single cells, and demonstrates its application in cell cycle regulation and cell type identification.

This study investigates changes in human milk composition after SARS-CoV-2 infection or vaccination using multi-omics approaches, finding greater compositional changes post-infection than post-vaccination.

This paper improves axon segmentation in light-sheet microscopy images by fine-tuning the TrailMap model with expert-labeled data, achieving better recall and adjusted F1-score.

The paper presents best practices for analyzing and integrating data from wearable silicone wristbands to enable meta-analyses and broader community research.

The paper systematically analyzes the sphingomyelin synthase family in C. elegans, identifying three SM synthases and one CPE synthase with distinct subcellular distributions.

This study evaluates and refines assumptions in retention index scores for GC-MS metabolite identification to improve accuracy and reduce manual verification.

This paper improves false discovery rate estimation in GC-MS metabolomics by extending Gaussian mixture models to incorporate similarity scores and experimental data.