Yansheng Liu, PhD

Assistant Professor of Pharmacology

Research Organizations

Yale Cancer Biology Institute

Yale Cancer Center: Signal Transduction

Yale Cancer Biology Institute

Research Summary

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

Particularly, the Liu laboratory works on the development of the state-of-the-art mass spectrometry method named DIA-MS (Liu Y. et al, Cell 2016) and the application into different systems.  These systems include, for example, the blood proteome of human monozygotic twins (Liu Y. et al, Molecular Systems Biology 2015), the phosphoproteomic signaling (Rosenberger R.*, Liu Y.* et al, Nature Biotechnology 2017), human genetic disorder (Liu Y. et al, Nature Communications 2017), RNA alternative splicing (Liu Y. et al, Cell Reports 2017).

The current major scientific focuses in Liu lab are cancer aneuploidy (Liu Y. et al, Nature Biotechnology 2019) and cancer signaling transduction.  Altogether, Dr. Liu's group is especially interested in revealing the impact of genetics at the translational and post-translational levels for cancer studies.

Extensive Research Description

Current research:

Copy number alterations (CNAs) of specific genes, whole chromosomes (Chr), or parts thereof are a major cause of various diseases and developmental abnormalities. Indeed, aneuploidy occurs in 88% of cancers. Gene expression analysis helps in understanding how CNA and aneuploidy cause detrimental phenotypes. In a simple “on-target” model, the increased CNAs may lead to higher gene expression, and vise versa. However, CNAs also affect the expression of genes located outside the duplication/deletion region itself via indirect mechanisms, namely “off-target” effect. Based on emerging literatures including ours, we argue that it is indispensable and imperative to measure the proteins, but not only the mRNAs, for understanding de facto CNA/aneuploidy consequence.

A major goal of our research is to study CNA/aneuploidy consequences by the usage of isogenic biological models and state-of-the-art quantitative mass spectrometry (MS).

Previous contribution:

Biologically, my previous research contributed to understanding of human “genotype-phenotype” association by characterizing proteome dosage. I performed the systematic quantification of genetic and post-transcriptional control of proteome through dosage and turnover, i.e., “proteotype” analysis. With very cautious design that uses isogenic samples for removing individual genomic variability, I accomplished several studies demonstrating the importance and mechanisms of protein-level control in humans. a) I quantified the different contribution from genetics, environment, and aging process in controlling 342 plasma proteins using 232 human healthy twins, which was widely discussed as a landmark study for blood proteomics, and mentioned at Science. b) We reported the strong quantitative contribution from alternative splicing on human proteome, which was highlighted in Nature Methods. c) We analyzed the primary fetal skin fibroblasts derived from a pair of monozygotic twins discordant for Trisomy 21 and genetically unrelated individuals, and reported proteostatsis mechanisms against aneuploidy through stoichiometry of protein complex and confinements of specific organelles. This paper was widely reported in media. d) Using HeLa cell lines from 13 labs, we quantified complex, non-linear response of the cells to genomic variability across the transcriptome and proteotype. This paper was published at Nature Biotechnology. The data indicates a striking degree of genomic variability and its complex translation into distinctive proteotypic and phenotypic patterns. Together, these papers provided new dimensions for understanding protein expression in diseases.

Technically, my previous research contributed to the development of DIA-MS (or, SWATH-MS in many papers) technique. These methods showed great potential and impact in proteomics field. With SWATH-MS emerged in proteomics, the last 6 years witnessed significant methodology paradigm shift from conventional shotgun proteomics to DIA. I was one of the core members of SWATH team and contributed significantly to its method (e.g., variable windows, cross-lab optimization), bioinformatics (e.g., TRIC for multi-sample alignment, IPF for large-scale PTM analysis, panHuman library for human protein identification), and quality control (e.g., in clinical tissue/ plasma materials). These publications are widely cited and referenced (highlighted in Nature Methods three times).

Long-term interest:

My long-term scientific interest is to investigate the translational and post-translational mechanisms underlying the development of human genetic disease and cancer phenotypes. The core component of my scientific goal focuses on the basic research investigating how the living cells organize its protein dosage, dynamics, turnover through protein complex stoichiometry control, protein-protein interaction, and protein sub-cellular localization to react against genetic variability and aberrations such as chromosomal abnormality.

Other general research interests in collaborative studies: 1) Protein localization and mis-locaization in diseases. 2) The interaction between genetics and proteomics and the correlation between mRNAs and proteins. 3) Development of cutting-edge, hybrid MS-based proteomic techniques and bioinformatic solutions. 4) Personalised medicine and biomarker discovery.


Selected Publications

Full List of PubMed Publications

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Contact Info

Yansheng Liu, PhD
Lab Location
Liu labWest Campus Advanced Biosciences Center
840 West Campus Drive, Fl 3rd Ste Cancer Biology Institute Rm 371A

West Haven, CT 06516
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Proteomics based on DIA MS

The next-generation proteomics, such as Data Independent Acquisition (DIA) based mass spectrometry, features the highly reproducible and precise quantification at MS2- level using signal traces presented as peak groups along liquid chromatography gradient. With such measurements, signal transduction can be profiled under various scenarios, such as steady state, long-term state changes, and short-term adaptation [1]. Questions like how protein abundance, phosphorylation, and turnover response longitudinally to different pharmacological interventions can be addressed. [1] Liu Y et al., (2016) Cell 165: 535-550