Andrew Leduc

The paper uses single-cell proteomics and transcriptomics to reveal cell-type-specific functional coordination in PBMCs, showing protein covariation that reflects protein-protein interactions and pathway rewiring.

The paper identifies abundant alternate translation products in human proteins, showing their association with protein stability and disease.

The paper quantifies protein synthesis and clearance across 4,200 cells, revealing that translation and clearance are linearly dependent on growth rate and identifying cell-type specific regulatory mechanisms.

JMod improves sequence identification and quantification in multiplexed DIA proteomics by jointly modeling overlapping mass spectra, enabling higher throughput with maintained accuracy.

The paper identifies a signature for protein leakage in single-cell proteomics and develops a classifier to detect damaged cells across species and cell types.

The paper quantifies how growth-dependent dilution and protein degradation affect protein abundance across human B-cells, tissues, and proteoforms, showing dilution explains 40% of abundance changes.

The paper quantifies proteomes and RNA levels in human testis cells to infer post-transcriptional regulation using a Bayesian model called BayesPG.

The paper introduces nPOP, a method for preparing single-cell samples for multiplexed proteomics, achieving accurate quantification of thousands of proteins per cell.

The paper identifies regulators of nucleocytoplasmic protein transport in macrophages using single-nucleus proteomics and validates findings through knockdown experiments.

DO-MS v2.0 optimizes DIA mass spectrometry parameters and quality control for single-cell proteomics, supporting label-free and plexDIA methods with interactive analysis and reporting.

The paper introduces nPOP, a method for single-cell protein covariation analysis, revealing cell cycle dynamics and functional differences between melanoma subpopulations.

Multiplexing nonisobaric isotopologous mass tags (mTRAQ) with data-independent acquisition and DIA-NN increases sensitive proteomics throughput 3-fold while cutting missing data below 3% in bulk and single cells.