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Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth.

Martijn R Molenaar¹, Mohammed Shahraz¹, Jeany Delafiori^1,2

¹Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.

Frontiers in Molecular Biosciences

|December 12, 2022

Summary

This study introduces a new method to evaluate pixel-cell deconvolution in single-cell metabolomics using fluorescein diacetate (FDA) as a ground truth. The weighted average method outperformed linear inverse modeling, and a novel ion suppression compensation improved cell separation.

Keywords:

SpaceM fluorescein diacetate (FDA)imaging mass spectrometry (imaging MS)ion suppression pixel-cell deconvolution spatial single-cell metabolomics

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Area of Science:

Single-cell analysis
Metabolomics
Mass spectrometry imaging

Background:

Imaging mass spectrometry (MS) is crucial for single-cell metabolomics.
Pixel-cell deconvolution is a challenging step in assigning MS intensities to individual cells.
Existing methods lack robust evaluation strategies.

Purpose of the Study:

To develop a novel approach for evaluating pixel-cell deconvolution methods.
To assess the performance of different deconvolution algorithms.
To address challenges like ion suppression and data drop-outs in single-cell metabolomics.

Main Methods:

Utilized fluorescein diacetate (FDA) as a dual-detection (fluorescence microscopy and MALDI-MS) ground truth.
Evaluated 'weighted average' and 'linear inverse modelling' deconvolution methods.
Developed a data-driven approach to compensate for ion suppression effects.

Main Results:

The 'weighted average' deconvolution method demonstrated superior performance compared to 'linear inverse modelling'.
A significant ion suppression effect was quantified, negatively impacting deconvolution accuracy.
The proposed ion suppression compensation method enhanced cell type separation in co-cultured cells.

Conclusions:

The FDA-based ground truth approach provides a reliable evaluation for pixel-cell deconvolution.
Effective ion suppression compensation is critical for accurate single-cell metabolomics.
Optimized data processing improves the quality of single-cell metabolomics data.