Yansheng Liu, PhD

My research goal is to discover quantitative proteomic rules determining cell signaling and phenotypes in diseases such as cancer. Please see GS citations.

Specific Research areas:

1. Impact of post-translational modifications (PTMs) on protein lifetime

Protein turnover is a key parameter in signaling rewiring, but its control by PTMs has not been studied on a large scale. We systematically quantified effects of 6,000-8,000 protein phosphorylation sites on protein turnover using a pioneering method called DeltaSILAC (Developmental Cell, 2021). We found that phosphorylation often reduces protein turnover, which is underappreciated in earlier studies. We continue to develop refined data analysis strategies (Proteomics 2022) for applying this technique in dynamic systems such as the cell-fate decision process.

2. Understanding biodiversity and its origins

Impact of aneuploidy on the proteome in cancer and genetic diseases.

Genotype impacts proteotype in a non-linear fashion. Following my postdoctoral work on human trisomy 21 (Nature Communications 2017), we led a multi-lab investigation that revealed surprising heterogeneity in HeLa cell aneuploidy worldwide (Nature Biotechnology 2019).

We are now studying how cancer aneuploidy leads proteins to acquire new “off-target” cellular activities through altered protein homeostasis and protein-protein interactions.

Quantifying and understanding biodiversity at variable scale.

While our previous studies have characterized proteome variability across humans, we recently extended our analysis to 11 mammalian species (Science Advances 2022). We discovered that RNA metabolism processes in particular show higher inter-species than inter-individual variations, and identified a phosphorylation co-evolution network.

We are deeply interested in summarizing universal quantitative rules governing proteome variabilities across individuals and species.

3. The development of DIA-MS and MALIDI-imaging MS techniques and bioinformatic tools for PTM and turnover analysis

My Yale lab continues to develop cutting-edge quantitative MS techniques and bioinformatic tools. To increase DIA-MS selectivity while keeping analytical throughput, we developed two new DIA-MS methods RTwinDIA (JASMS 2019) and BoxCarmax-DIA (Analytical Chemistry, 2021). My lab also led the development of bioinformatic tools such as NAguideR, which performs/prioritizes 23 missing-value imputation algorithms for proteomics (Nucleic Acids Research, 2020), and developed a workflow for DIA-based protein turnover analysis (Mol. Systems Biology, 2020).

Collaborations at Yale

The proteomics platform developed in my lab has contributed to more than 30 Yale research laboratories through respective collaborations.