"Proteome translates the code of life into diversity. We hope to understand the fundamental rules of proteomics. "

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Specific Research Areas
 

1. Impact of Post-Translational Modifications (PTMs) on Protein Stability and Lifeti

Protein turnover is a key parameter in signaling rewiring, yet its regulation by PTMs has not been systematically explored at scale. We quantified the effects of thousands of phosphorylation sites on protein turnover using a pioneering method we developed, DeltaSILAC (Cell 2025 & Developmental Cell 2021). Our findings reveal that phosphorylation often reduces protein turnover—a phenomenon underappreciated in earlier studies. We continue to develop advanced data analysis strategies (Nature Communications 2025 & Proteomics 2022) to apply this technique to dynamic systems, such as those involved in cell fate decisions. These findings provide important and timely translational insights for both Alzheimer’s disease and cancer.
 

2. Understanding Biodiversity and Its Origins

Impact of aneuploidy on the proteome in cancer and genetic diseases

Genotype affects the proteotype in a non-linear fashion. Building on my postdoctoral work on human trisomy 21 (Nature Communications 2017), we led a multi-lab investigation that uncovered striking heterogeneity in HeLa cell aneuploidy across the globe (Nature Biotechnology 2019). We are now investigating how cancer-associated aneuploidy rewires protein homeostasis and interaction networks, allowing proteins to acquire new, context-specific cellular functions.

Quantifying and understanding biodiversity at multiple scales

While our earlier studies examined proteome variability across human populations, we have extended this work to 11 mammalian species (Science Advances 2022). We found that RNA metabolism processes, in particular, exhibit greater inter-species than inter-individual variation and identified a phosphorylation co-evolution network.

We also demonstrated how a single kinase (AKT1) can guide distinct cellular signaling outcomes through different temporal activation patterns (Nature Communications 2023), and how temporal signaling dynamics influence cancer drug responses (Nature Communications 2024). Our lab remains deeply interested in identifying universal quantitative rules that govern proteome variability across individuals and species.
 

3. Development of DIA-MS and MALDI Imaging MS Techniques and Bioinformatic Tools for PTM and Turnover Analysis

Our lab continues to advance quantitative mass spectrometry and computational pipelines for studying proteome dynamics. To improve DIA-MS selectivity while maintaining throughput, we developed two novel methods: RTwinDIA (JASMS 2019) and BoxCarmax-DIA (Analytical Chemistry 2021). We also developed NAguideR, a tool that evaluates and prioritizes 23 algorithms for missing-value imputation in proteomics datasets (Nucleic Acids Research 2020), and a DIA-based workflow for protein turnover analysis (Molecular Systems Biology 2020).

Recently, we also established state-of-the-art MALDI mass spectrometry imaging (MALDI-MSI), enabling spatial omics studies (lipidomics, metabolomics, and proteomics) at the tissue and single-cell levels. We integrate these techniques with cancer models and patient-derived clinical samples to uncover how proteins are synthesized, modified, interact, and ultimately degraded—particularly in disease contexts.
 

Collaborations at Yale

Our proteomics platform and methods have contributed to research in more than 35 Yale laboratories through active collaborations.

In summary, our central research goal is to uncover quantitative proteomic and proteostasis principles that govern cell signaling and phenotypic outcomes in diseases such as cancer.