Revealing cellular behavior with single-cell multi-omics

Introduction

Chemical reactions govern cellular behavior and are revealed by analysis of small molecules involved in intracellular metabolism. Individual cells in biological systems continuously adapt to improve survival and biological function, making them chemically and behaviorally heterogeneous. Unraveling this heterogeneity is essential to realize the correlation to disease state and health, but it is masked in bulk analyses of millions of cells.

Proposed Analytical Approach

I propose to develop a groundbreaking analytical approach for multi-omics of living individual cells to reveal variability in cellular behavior. This will be achieved by:

1. Coupling a microfluidic device that enables controlled chemical exposure of a cell.
2. Utilizing an efficient ionization probe for on-line time-resolved mass spectrometric measurements.

By measuring the dynamics of each cell’s metabolome, lipidome, and secretome, novel insights into heterogeneity in intracellular activities will be gained. In addition, the level of heterogeneity will be uncovered through correlation with the cell’s transcriptome.

Focus on β-Cells

A special emphasis will be given to characterize individual β-cells that are key regulators of blood glucose by insulin secretion and whose dysfunction leads to type 2 diabetes. The behavior of individual β-cells is heterogeneous and ranges from complete failure to secrete insulin to compensating with increased secretion.

Research Hypothesis

I will use the single-cell multi-omics approach to test the hypothesis that intracellular metabolism is the key to β-cell dysfunction. The analysis will focus on:

i) Their metabolic heterogeneity and differences,
ii) Variations and temporal dynamics in their metabolic behavior, and
iii) Metabolic roadblocks that correlate with β-cell dysfunction.

Conclusion

The single-cell multi-omics approach will open new horizons for understanding cellular heterogeneity, realizing cellular behavior that promotes health, and identifying treatment targets.