Single Cell Profiling Using Ionic Liquid Matrix-Enhanced Secondary Ion Mass Spectrometry for Neuronal Cell Type
Thanh D Do1, Troy J Comi1, Sage J B Dunham1
1Department of Chemistry and the Beckman Institute, University of Illinois at Urbana-Champaign , Urbana, Illinois 61801, United States.
Analytical Chemistry
|February 15, 2017
Summary
A new matrix-enhanced secondary ion mass spectrometry (ME-SIMS) method enables high-throughput lipid profiling of individual neuronal cells. This technique accurately classifies cell types and subpopulations based on their unique lipid signatures.
Area of Science:
- Mass Spectrometry
- Cell Biology
- Neuroscience
Background:
- Investigating cellular lipid profiles is crucial for understanding neuronal function and disease.
- Existing single-cell analysis methods often lack the throughput or specificity required for comprehensive lipid profiling.
Purpose of the Study:
- To develop and validate a high-throughput single-cell profiling method using matrix-enhanced secondary ion mass spectrometry (ME-SIMS) for neuronal lipid analysis.
- To demonstrate the capability of ME-SIMS for distinguishing cell types and subpopulations based on lipid composition.
Main Methods:
- Developed an optically guided ME-SIMS technique for high-throughput analysis of dispersed neuronal cells.
- Utilized ionic liquids as a matrix to detect phosphatidylcholines (PCs) and their fragments.
- Employed tandem mass spectrometry for verification and t-distributed stochastic neighbor embedding for data analysis.
Main Results:
- Successfully profiled up to 2,000 cells per experiment at a rate of 6 seconds per cell.
- Detected and verified multiple saturated and unsaturated PCs from individual cells.
- Distinguished rat central nervous system cell types and subpopulations based on the relative abundance of four dominant lipids (PC(32:0), PC(34:1), PC(36:1), PC(38:5)).
Conclusions:
- Optically guided ME-SIMS is an effective high-throughput method for single-cell lipid profiling across various neuronal cell sizes.
- The method allows for the classification of distinct cell types and subpopulations based on endogenous lipid ratios.
- This approach offers broad applicability for high-throughput single-cell chemical analyses in diverse biological contexts.
