Minimizing Visceral Fat Delocalization on Tissue Sections with Porous Aluminum Oxide Slides for Imaging Mass Spectrometry

Fournelle, Frédéric; Yang, Eun Jung; Dufresne, Martin; Chaurand, Pierre

doi:10.1021/acs.analchem.9b05665

Cited by 16 publications

(25 citation statements)

References 47 publications

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“…Note the slightly noisy region to the top-left of the medulla in Figure 10a. The difficulties encountered by the model in this region, which is outlined by a black circle in Figure 9, are probably due to a sample preparation artefact known as visceral fat delocalization [75]. Figure 10b shows the top ten molecular markers out of the global ranking of 1,428 features (i.e.…”

Section: Resultsmentioning

confidence: 99%

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

Tideman

Migas

Patterson

et al. 2020

Preprint

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The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species’ biomarker potential, our workflow delivers spatially localized explanations of the classification model’s decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species’ potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.HighlightsOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class.A model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class.By providing spatially localized explanations for a classification model’s decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative.

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Section: Resultsmentioning

confidence: 99%

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

Tideman

Migas

Patterson

et al. 2020

Preprint

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show abstract

“…Reprinted with permission from Lai et al (2016). © 2016 American Chemical Society [Color figure can be viewed at wileyonlinelibrary.com] care must be taken to preserve the integrity and the spatial distribution of the analytes in the sample, which is critical in imaging analyses (Fournelle et al, 2020).…”

Section: Analytical Strategies For Saldi-ms Imagingmentioning

confidence: 99%

“…Indeed, the heterogeneity in the analyte‐matrix co‐crystallization is responsible for the formation of hot spots leading to a lack of reproducibility (both shot‐to‐shot and sample‐to‐sample reproducibility) (Goodwin, 2012 ; Kaletas et al, 2009 ). Also, the integrity of the molecular spatial distributions may be affected by an inappropriate matrix application, which may in turn cause significant migration or delocalization of the molecules of interest (Chaurand, 2012 ; Fournelle et al, 2020 ; Römpp & Spengler, 2013 ), affecting the spatial resolution and/or leading to misinterpretation of the MSI results. Furthermore, the formation of matrix crystals larger than the laser spot size may also affect the spatial resolution (Kaletas et al, 2009 ; Phan et al, 2016 ).…”

Section: Introduction: a Brief History Of Mass Spectrometry Imagingmentioning

confidence: 99%

Surface‐assisted laser desorption/ionization mass spectrometry imaging: A review

Müller

Verdin

Pauw

et al. 2020

Mass Spectrometry Reviews

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In the last decades, surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has attracted increasing interest due to its unique capabilities, achievable through the nanostructured substrates used to promote the analyte desorption/ionization. While the most widely recognized asset of SALDI-MS is the untargeted analysis of small molecules, this technique also offers the possibility of targeted approaches. In particular, the implementation of SALDI-MS imaging (SALDI-MSI), which is the focus of this review, opens up new opportunities. After a brief discussion of the nomenclature and the fundamental mechanisms associated with this technique, which are still highly controversial, the

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“…[ 7 , 8 ] The properties of the substrates, including their chemistry, conductivity, and micropatterning impact sample ionization efficiency and, thus, measurement sensitivity. [ 8 , 9 , 10 , 11 ] For example, micrometer‐scale wells are useful for segregating samples of distinct compositions so they can be separately analyzed. [ 12 , 13 , 14 ] Well arrays are also compatible with active [ 15 , 16 ] or passive loading techniques, [ 12 , 17 ] to simplify the preparation of samples for analysis.…”

Section: Introductionmentioning

confidence: 99%

Microbowls with Controlled Concavity for Accurate Microscale Mass Spectrometry

et al. 2022

Advanced Materials

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The properties of the substrates, including their chemistry, conductivity, and micropatterning impact sample ionization efficiency and, thus, measurement sensitivity. [8][9][10][11] For example, micrometer-scale wells are useful for segregating samples of distinct compositions so they can be separately analyzed. [12][13][14] Well arrays are also compatible with active [15,16] or passive loading techniques, [12,17] to simplify the preparation of samples for analysis. However, MALDI-MS requires samples to be dried prior to analysis. When droplets are dried on flat surfaces, they tend to distribute their analytes about the perimeter due to the coffee ring effect. [18,19] Similar processes occur in cylindrical wells, leading to precipitation along the periphery [20,21] where the signal is inhibited due to laser occlusion by the walls. The result in both cases is lowered sensitivity and increased measurement variability due to inhomogeneity of the sample spots. [18,22] Bowl-shaped wells with curved bases are advantageous because, upon drying, precipitated analytes concentrate at the center in a more uniform fashion, [23] where they are efficiently ionized. [24] These wells, however, are difficult to fabricate, requiring micromachining, subtractive etching methods, embossing of plastics or specialized deposition methods. [23,[25][26][27][28] Additive fabrication approaches are superior because they use photolithographic techniques that are simple, inexpensive, and ubiquitous. However, photolithographic fabrication of wells with controlled curvature is challenging, requiring complex mask and lens systems to modulate light intensity with micrometer resolution across the array. [29,30] Consequently, simpler but inferior methods with stamping or backfilling of sharp features with polymer are more common, even though they are tedious and provide limited control. [6,[31][32][33] A superior approach would use simple photolithographic techniques without sacrificing control over well shape and curvature.In this paper, we describe a simple method to photolithographically fabricate wells with controlled curvature in SU8. This photoresist has several properties useful for mass spectrometry, including chemical robustness, precision control of surface features, and scalable fabrication. Using the approach, we fabricate curved-bottom wells at a density of 100 000 per square centimeter on a glass slide. To demonstrate the utility of these wells, we show they yield enhanced sensitivity in microscale mass spectrometry compared to cylindrical wells. Several approaches, such as the AnchorChip [34] and the μFocus plate, [35] use surface rather than physical modification to control drying and enhance MALDI MS, but do not Patterned surfaces can enhance the sensitivity of laser desorption ionization mass spectrometry by segregating and concentrating analytes, but their fabrication can be challenging. Here, a simple method to fabricate substrates patterned with micrometer-scale wells that yield more accurate and sensitive mass spectrometry mea...

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Minimizing Visceral Fat Delocalization on Tissue Sections with Porous Aluminum Oxide Slides for Imaging Mass Spectrometry

Cited by 16 publications

References 47 publications

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

Automated Biomarker Candidate Discovery in Imaging Mass Spectrometry Data Through Spatially Localized Shapley Additive Explanations

Surface‐assisted laser desorption/ionization mass spectrometry imaging: A review

Microbowls with Controlled Concavity for Accurate Microscale Mass Spectrometry

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