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

Imaging mass spectrometry (MS) is becoming increasingly applied for single-cell analyses. Multiple methods for imaging MS-based single-cell metabolomics were proposed, including our recent method SpaceM. An important step in imaging MS-based single-cell metabolomics is the assignment of MS intensiti...

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Autores principales: Molenaar, Martijn R., Shahraz, Mohammed, Delafiori, Jeany, Eisenbarth, Andreas, Ekelöf, Måns, Rappez, Luca, Alexandrov, Theodore
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730270/
https://www.ncbi.nlm.nih.gov/pubmed/36504713
http://dx.doi.org/10.3389/fmolb.2022.1021889
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author Molenaar, Martijn R.
Shahraz, Mohammed
Delafiori, Jeany
Eisenbarth, Andreas
Ekelöf, Måns
Rappez, Luca
Alexandrov, Theodore
author_facet Molenaar, Martijn R.
Shahraz, Mohammed
Delafiori, Jeany
Eisenbarth, Andreas
Ekelöf, Måns
Rappez, Luca
Alexandrov, Theodore
author_sort Molenaar, Martijn R.
collection PubMed
description Imaging mass spectrometry (MS) is becoming increasingly applied for single-cell analyses. Multiple methods for imaging MS-based single-cell metabolomics were proposed, including our recent method SpaceM. An important step in imaging MS-based single-cell metabolomics is the assignment of MS intensities from individual pixels to single cells. In this process, referred to as pixel-cell deconvolution, the MS intensities of regions sampled by the imaging MS laser are assigned to the segmented single cells. The complexity of the contributions from multiple cells and the background, as well as lack of full understanding of how input from molecularly-heterogeneous areas translates into mass spectrometry intensities make the cell-pixel deconvolution a challenging problem. Here, we propose a novel approach to evaluate pixel-cell deconvolution methods by using a molecule detectable both by mass spectrometry and fluorescent microscopy, namely fluorescein diacetate (FDA). FDA is a cell-permeable small molecule that becomes fluorescent after internalisation in the cell and subsequent cleavage of the acetate groups. Intracellular fluorescein can be easily imaged using fluorescence microscopy. Additionally, it is detectable by matrix-assisted laser desorption/ionisation (MALDI) imaging MS. The key idea of our approach is to use the fluorescent levels of fluorescein as the ground truth to evaluate the impact of using various pixel-cell deconvolution methods onto single-cell fluorescein intensities obtained by the SpaceM method. Following this approach, we evaluated multiple pixel-cell deconvolution methods, the ‘weighted average’ method originally proposed in the SpaceM method as well as the novel ‘linear inverse modelling’ method. Despite the potential of the latter method in resolving contributions from individual cells, this method was outperformed by the weighted average approach. Using the ground truth approach, we demonstrate the extent of the ion suppression effect which considerably worsens the pixel-cell deconvolution quality. For compensating the ion suppression individually for each analyte, we propose a novel data-driven approach. We show that compensating the ion suppression effect in a single-cell metabolomics dataset of co-cultured HeLa and NIH3T3 cells considerably improved the separation between both cell types. Finally, using the same ground truth, we evaluate the impact of drop-outs in the measurements and discuss the optimal filtering parameters of SpaceM processing steps before pixel-cell deconvolution.
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spelling pubmed-97302702022-12-09 Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth Molenaar, Martijn R. Shahraz, Mohammed Delafiori, Jeany Eisenbarth, Andreas Ekelöf, Måns Rappez, Luca Alexandrov, Theodore Front Mol Biosci Molecular Biosciences Imaging mass spectrometry (MS) is becoming increasingly applied for single-cell analyses. Multiple methods for imaging MS-based single-cell metabolomics were proposed, including our recent method SpaceM. An important step in imaging MS-based single-cell metabolomics is the assignment of MS intensities from individual pixels to single cells. In this process, referred to as pixel-cell deconvolution, the MS intensities of regions sampled by the imaging MS laser are assigned to the segmented single cells. The complexity of the contributions from multiple cells and the background, as well as lack of full understanding of how input from molecularly-heterogeneous areas translates into mass spectrometry intensities make the cell-pixel deconvolution a challenging problem. Here, we propose a novel approach to evaluate pixel-cell deconvolution methods by using a molecule detectable both by mass spectrometry and fluorescent microscopy, namely fluorescein diacetate (FDA). FDA is a cell-permeable small molecule that becomes fluorescent after internalisation in the cell and subsequent cleavage of the acetate groups. Intracellular fluorescein can be easily imaged using fluorescence microscopy. Additionally, it is detectable by matrix-assisted laser desorption/ionisation (MALDI) imaging MS. The key idea of our approach is to use the fluorescent levels of fluorescein as the ground truth to evaluate the impact of using various pixel-cell deconvolution methods onto single-cell fluorescein intensities obtained by the SpaceM method. Following this approach, we evaluated multiple pixel-cell deconvolution methods, the ‘weighted average’ method originally proposed in the SpaceM method as well as the novel ‘linear inverse modelling’ method. Despite the potential of the latter method in resolving contributions from individual cells, this method was outperformed by the weighted average approach. Using the ground truth approach, we demonstrate the extent of the ion suppression effect which considerably worsens the pixel-cell deconvolution quality. For compensating the ion suppression individually for each analyte, we propose a novel data-driven approach. We show that compensating the ion suppression effect in a single-cell metabolomics dataset of co-cultured HeLa and NIH3T3 cells considerably improved the separation between both cell types. Finally, using the same ground truth, we evaluate the impact of drop-outs in the measurements and discuss the optimal filtering parameters of SpaceM processing steps before pixel-cell deconvolution. Frontiers Media S.A. 2022-11-24 /pmc/articles/PMC9730270/ /pubmed/36504713 http://dx.doi.org/10.3389/fmolb.2022.1021889 Text en Copyright © 2022 Molenaar, Shahraz, Delafiori, Eisenbarth, Ekelöf, Rappez and Alexandrov. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Molenaar, Martijn R.
Shahraz, Mohammed
Delafiori, Jeany
Eisenbarth, Andreas
Ekelöf, Måns
Rappez, Luca
Alexandrov, Theodore
Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
title Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
title_full Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
title_fullStr Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
title_full_unstemmed Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
title_short Increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
title_sort increasing quantitation in spatial single-cell metabolomics by using fluorescence as ground truth
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9730270/
https://www.ncbi.nlm.nih.gov/pubmed/36504713
http://dx.doi.org/10.3389/fmolb.2022.1021889
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