SCSP Lab Single-Cell & Spatial Proteomics

Research

Complex biological processes are driven by the coordinated interactions of individual cells, yet cellular heterogeneity and the molecular mechanisms underlying cell specific functions remain incompletely understood.

The Single Cell and Spatial Proteomics Laboratory develops next-generation mass spectrometry-based technologies for sensitive, comprehensive, and quantitative proteome profiling at single cell resolution and across spatially resolved biological contexts. Our research focuses on overcoming key analytical challenges in single cell proteomics.

Key challenges in single cell proteomics

Sensitivity & reproducibility

Limited robustness and proteome coverage across individual cells

Throughput & scalability

Low throughput and limited scalability compared with scRNA-seq

Biological relevance

Predominant focus on method development using cell lines

Molecular depth

Limited technologies for studying PTMs and non-canonical events

Research plan

Building a world-leading program in single cell and spatial proteomics for biomedical research

Aim 1

Single cell proteomics

Ultra-sensitive and high-throughput proteomics for single cell analysis

  • Advanced sample preparation
    • Miniaturized liquid handling platform
  • Advanced LC system
    • Multi-analytical columns
    • Online PTM enrichment system
  • Advanced mass spectrometry
    • Targeted proteome profiling
    • Targeted analysis of PTMs
    • Multiplexing to enhance throughput

ApplicationsCell lines · Primary cells (immune cells)

Technologies developed in Aim 1 are transferred to Aim 2.

Aim 2

Spatial proteomics

Mapping proteins in space and at subcellular resolution

  • LCM-based proteomics
  • Single organelle isolation
    • Single nuclei
    • Single mitochondria
  • Subcellular proteome profiling
    • Tissue expansion
    • Enrichment — immunoprecipitation
    • Proximity labeling

ApplicationsTissues and FFPE tissues · Cancers (tumor microenvironment) · Neurodegenerative diseases

Aim 3

Computational integration

Enabling advanced single cell proteomics data analysis, trained on data generated in Aims 1 and 2

  • Spectral libraries for PTMs and non-canonical events
  • ML-based approach for identification and quantitation
  • Proteogenomics at the single cell level

Expected outcomes

  • Deeper insights into cellular heterogeneity
  • Spatial and subcellular proteome profiles
  • Integrative analysis of transcriptome and proteome
  • Applications in cancer, immunology, metabolic diseases, and neuroscience
Our goal

By advancing single cell and spatial proteomics, we aim to uncover novel cancer biology, deepen our understanding of tumor heterogeneity and cancer progression, and improve cancer diagnosis and treatment.