Edward Lau

This study uses MXene nanosheets to capture Alzheimer's disease-associated plasma protein signatures, revealing disease-specific proteomic changes and potential biomarkers.

This study uses SPLAT-TR to analyze proteome changes in ER stress, revealing distinct temporal phases of protein translocation, clearance, and degradation in different organelles.

This study uses middle-down phosphoproteomics to analyze how protein partitioning into biomolecular condensates changes under proteostatic stress, revealing phosphorylation-dependent and independent mechanisms.

The paper develops an mPBA enrichment workflow to analyze N-glycoproteome remodeling under ER stress, revealing dynamic changes in glycosylation sites and glycoforms.

The paper investigates the differential stability of cardiac protein isoforms using proteogenomics and heavy water-based turnover analysis in mouse hearts, revealing widespread stability differences.

The paper examines how maternal BMI affects nanoparticle protein corona composition in late pregnancy, identifying BMI-specific proteomic signatures.

SALVE predicts interorgan communication by analyzing RNA-seq data to link secretome-encoding transcripts with gene modules in distal organs, identifying new candidate organokines and their signaling mechanisms.

The paper identifies widespread isoform usage shifts in fetal and postnatal mouse hearts using proteome-wide ratio quantification, revealing novel isoform switches and evolutionary patterns.

This study uses deuterium labeling to analyze protein turnover kinetics in hiPSCs and identifies fast-turnover proteins involved in cell division and differentiation.

This paper introduces a method to improve protein turnover rate calculation using heavy water labeling and mass spectrometry, increasing coverage of well-fitted peptides by 25-82% in mouse organ proteomes.

The paper shows that protein abundance is often poorly predicted from mRNA levels and that interacting proteins' transcripts better predict protein abundance.

The paper identifies translated noncanonical isoforms in the human proteome using a splice junction-centric approach and mass spectrometry data.

The paper identifies high-priority proteins across the human diseasome using semantic similarity and provides a systematic collection of popular proteins across diseases, phenotypes, and pathways.