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Pyxis Unveiled: Advancing Single-Cell MALDI MSI Analysis for Deeper Molecular Insights

Pyxis Unveiled: Advancing Single-Cell MALDI MSI Analysis for Deeper Molecular Insights

72nd ASMS Conference on Mass Spectrometry. June 2024

Ismael Zamora1; Rachelle Balez2; Jayden C. McKinnon2; Reuben S.E. Young2; Liam Robinson2; Lezanne Ooi2; Giuseppe Arturi3; Giulia Sorbi3; Shane Ellis2; Sara Tortorella3
1Mass Analytica, Sant Cugat del Vallès, Spain; 2Molecular Horizons, University of Wollongong, Wollongong, Australia; 3Mass Analytica, Bettona, Italy

Abstract

Introduction

Single-cell metabolomics and lipidomics using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI MSI) is emerging as an exciting tool to study metabolic alterations across heterogeneous cell populations. Single-cell analysis using MALDI MSI does however come with technical challenges, including sample preparation workflows compatible with fluorescent microscopy and downstream MS analysis, as well as dedicated data processing workflow to coregister microscopy with MSI and extract profiles from defined cells for later analysis. Here, we introduce an efficient single-cell MSI data analysis pipeline utilising the cutting-edge Pyxis software platform. The performance and versatility of the pipeline are demonstrated by single-cell lipidomics and metabolomics analysis of human-derived astrocytes, unravelling intricate molecular insights at the single-cell level.

Methods

Human astrocytes were generated using induced pluripotent stem cell-derived neurons. Live astrocyte cultures were stained with CellBrite Green and Hoechst, fixed with PFA and washed with cold ammonium acetate prior to microscopy. Following microscopy cells were coated in 2,5-DHA matrix via sublimation for lipid imaging or NEDC matrix using a HTC TM-Sprayer for metabolite imaging for MALDI and MALDI-2 analysis, respectively, using an Orbitrap Elite coupled to a Spectroglyph MALDI/ESI ion source (Spectroglyph LLC, Kennewick, WA, USA). Coregistration with microscopy, segmentation at single-cell level, extraction of spectra generated from single cells, and dedicated statistics to process single-cell spectra were performed using Pyxis (Mass Analytica, Spain). Features of interest were annotated using HMDB and LIPID MAPS databases integrated in Pyxis.

Preliminary data

Rich lipid and metabolite data were recorded from single cells using both MALDI and MALDI-2 with pixel sizes as low as 10 microns. Given the relatively large size of the astrocytes multiple pixels were recorded across single cells, shedding light into sub-cellular metabolite distributions. For example the [M-H]- of adenine was detected predominantly in the soma of the cells.
Ad hoc algorithms were developed and integrated to cope with the data analysis challenges. Rigid and non-rigid strategies to coregister microscopy, fluorescence and MS images were evaluated. An hybrid approach was employed, where rigid registration was applied initially to achieve a coarse alignment, followed by non-rigid registration to refine the alignment at a finer spatial scale. Utilising both approaches in a complementary fashion enhanced the accuracy of spatial integration of multi-modal imaging data. Bisecting k-means and spatial denoising facilitated single-cell border identification. An algorithm was designed to automatically isolate cells, apply user-defined exclusion criteria, calculate single-cell profiles, which then underwent multivariate statistical analysis.
Demonstrated applications of this analytical and data analysis workflow include: (i) single-cell analysis of human-derived astrocyte populations  and (ii) investigating the single-cell metabolic and lipidomic responses to inflammatory cytokine stimulation and how these are correlated with cytockeletal remodelling upon inflammatory activation.
By implementing all the necessary data analysis steps into a single, user-friendly software platform, we enable the comprehensive exploration of single-cell MSI data. This integrated approach not only streamlines the intricate process of biochemical data interpretation but also ensures that users can fully exploit the richness of information within their single-cell datasets with ease.

 

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Revolutionizing Spatial Dermatology: Investigating Sunfilter Efficacy on Reconstructed Human Epidermis with AP-MALDI MSI Metabolomics and Dedicated Data Analysis Software

Revolutionizing Spatial Dermatology: Investigating Sunfilter Efficacy on Reconstructed Human Epidermis with AP-MALDI MSI Metabolomics and Dedicated Data Analysis Software

72nd ASMS Conference on Mass Spectrometry. June 2024

Sara Tortorella1; Maureen Feucherolles2; Giulia Sorbi1; Giuseppe Arturi1; Sue Kennerley3; Gilles Frache2; Ismael Zamora4

1Mass Analytica, Bettona, Italy; 2Luxembourg Institute of Science and Technology, Molecular and Thermal Analysis, Belvaux, Luxembourg; 3K R Analytical, Sandbach, United Kingdom; 4Mass Analytica, Sant Cugat del Vallès, Spain

Abstract

Introduction

Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization mass spectrometry imaging (AP-MALDI MSI) is a variant of the MALDI technique. The capacity of AP-MALDI MSI to work in an atmospheric environment eliminates the need for vacuum chambers, allowing for the preservation of native hydrated samples as well as the analysis of vacuum-incompatible compounds. This facilitates integration with other analytical techniques and increases sample preparation versatility. It has found applications in many fields such as biology, including spatial dermatology, where the analysis of complex biomolecules is essential. Here we introduce Pyxis, novel vendor neutral software for comprehensive AP-MALDI MSI data analysis, to investigate the spatial lipidome distribution and alteration within sunfilter-protected and -unprotected reconstructed human epidermis (RHE) sections, submitted to UV radiations.

Methods

RHE sections subjected to three test conditions: no UV stress and no sun filter (n=12), UV stress and no sun filter (n=12), and UV stress and sun filter (n=12), were washed, coated with HCCA matrix using the SunCollect MALDI Sprayer (SunChrom GmbH, Germany), and analysed by AP-MALDI MS in both positive and negative ion mode. Here, the compact AP-MALDI (ng) UHR system (MassTech Inc., Columbia, MD), was coupled to a high resolution Orbitrap Exploris 480 mass spectrometer (ThermoFisher, San Jose, CA). Imaging experiments were performed at spatial resolution of 5 µm per pixel, over a mass range of 205–2000 Da and at a mass resolution of 240,000@m/z 200. All data analysis and identification was performed using Pyxis (Mass Analytica, Spain).

Preliminary data

Applications of this unique analytical and data analysis workflow to RHE sections enabled lipids species accumulation within sections to be visualised and identified, providing insight into the metabolomics hallmarks of different sunfilters.
Leveraging the segmentation, based on clustering algorithms, and co-localization capabilities of the Pyxis software, we delineated regions of interest (ROIs) on the RHE sections to perform supervised statistical analyses, i.e. partial least squares discriminant analysis (PLS-DA). The comparison between RHE sections exposed and unexposed to UV light revealed distinctive changes in m/z values across specific cell layers. It allowed us to pinpoint inflammation biomarkers associated with UV exposure. Using these biomarkers, we directly visualised the efficacy of a test sunfilter, annotating the biomarkers, directly within Pyxis, with the integrated LIPID MAPS and HMDB databases. Annotations were ranked using a scoring system, enabling the precise identification of signal and spatial alterations in particular lipid classes, notably sterols. For instance, the oxysterol , 25-hydroxy-cholesterol 3-sulfate, recognized for its involvement in anti-inflammatory response, emerged as a key indicator. While this oxysterol is significantly expressed in UV-exposed and unprotected RHE sections, the opposite was true in the RHE sections not exposed to UV or protected by the sunscreen. Hence, the effectiveness of the sunfilter in mitigating this pathway was discerned by the observed modulation of this lipid. Our findings highlight the utility of the Pyxis platform, providing a user-friendly interface and a full exploitation of comprehensive AP-MALDI MSI data processing workflow, offering insights into metabolomic signatures relevant to dermatological research.

 

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An automated software-assisted approach for exploring metabolic susceptibility and degradation products in macromolecules using High-Resolution Mass Spectrometry

An automated software-assisted approach for exploring metabolic susceptibility and degradation products in macromolecules using High-Resolution Mass Spectrometry

72nd ASMS Conference on Mass Spectrometry. June 2024

Paula Cifuentes1,3; Ismael Zamora2; Fabien Fontaine2; Albert Garriga2; Luca Morettoni2; Tatiana Radchenko1

1Lead Molecular Design, Sant Cugat del Vallès, Spain; 2Mass Analytica, Sant Cugat del Vallès, Spain; 3Universitat Pompeu Fabra, Barcelona, Spain

Abstract

Introduction

An essential aspect of the drug development process is the comprehensive identification and characterization of the major metabolites of the candidate drug and the enzymes responsible for its metabolic transformation, commonly known as drug metabolism. Recently, there has been a strong emphasis on developing more efficient systems and tools aimed for these studies. However, to achieve this goal, different challenges must be faced, including computational aspects such as high data processing times, others related to peak detection using monoisotopic mass or the most abundant isotope for mass calculation, and complications in compound visualization. Even though automating data analysis has simplified many design stages, the analysis of metabolic study samples, particularly for macromolecules, remains time-consuming, emphasizing the necessity for customized solutions.

Methods

The work employed a software tool automating data analysis stages for LC-MS (High Resolution) data. This included selecting chromatographic peaks related to the compound, retrieving mass spectral information, assigning potential structures through theoretical fragmentation comparison with experimental m/z values, and scoring solutions based on fragment analysis. Results from different experimental conditions are clustered into a unified experiment entity and stored in a database. For each compound two distinct algorithms have been employed for peak selection, allowing for outcome comparison. After data consolidation, manual interpretation is performed according to predefined criteria. Data from different acquisition modes has been processed, and two structure visualization methods are presented: an expanded form depicting all atoms and bonds, and a non-expanded form linking monomer acronyms.

Preliminary data

The aim of this study is to describe new algorithms/approaches for automated LC-MS (High Resolution) data analysis that addresses the mentioned challenges encountered in the processing of macromolecules. These challenges encompass optimizing the input and visualization of chemical structures and degradation products. Additionally, it has successfully optimized the reduction of processing memory and time consumption (from 2 hours to 25 minutes) in the execution of algorithms for potential structure generation and fragmentation. Furthermore, the proposed methods aim to provide a workflow capable of interpreting results across various data acquisition formats and modes.

Analysis was conducted on six datasets spanning a molecular range of 700 to 15,000 Da. These datasets consist of both linear and cyclic peptides, incorporating natural and unnatural amino acids, as well as an oligonucleotide. Specifically, dataset-1 comprises nine commercially available peptides, dataset-2 includes one commercially available peptide and four synthetic analogues, dataset-3 involves a natural peptide hormone and seven synthetic analogues, dataset-4 features an antisense oligonucleotide, dataset-5 contains 28 commercially available peptides, and dataset-6 is composed of a peptide hormone.

Comparisons of the results obtained for certain compounds with those of prior studies have enabled a comprehensive evaluation across various parameters. This evaluation encompasses aspects such as the number and structure of identified metabolites, along with a consideration of the time consumed during the data processing step.

The results obtained indicate that, in larger molecules, the most abundant mass algorithm demonstrated higher scores and a greater number of matches, and therefore greater confidence in the accurate prediction of metabolite structures. Furthermore, this study shows three visualization options for representing macromolecules during data analysis. This visualization algorithm allows the combination of monomer and atom/bond notation, facilitating a clear depiction of metabolic changes in the molecular structure.

 

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Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows

Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows

72nd ASMS Conference on Mass Spectrometry. June 2024

Ramon Adàlia1,3; Fabien Fontaine2; Luca Morettoni2; Ismael Zamora2

1Lead Molecular Design, Sant Cugat del Vallès, Spain; 2Mass Analytica, Sant Cugat del Vallès, Spain; 3Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain

Abstract

Introduction

Metabolite identification is pivotal in both drug discovery and metabolomics, enabling the comprehensive analysis of small molecules within biological systems. However, the complexity inherent in mass spectrometry data often results in numerous false positive peak detections. Current methods for false positive detection rely on manual data inspection, a labor-intensive and time-consuming process that acts as a bottleneck in metabolite identification workflows.

In this study, we propose leveraging machine learning models to assist experts in identifying false positives. We demonstrate the viability of this approach by developing models that achieve high predictive performance across two distinct experimental protocols. Additionally, we utilize the SHapley Additive exPlanations (SHAP) method to analyze feature importances, offering insights into the primary factors driving predictions.

Methods

Metabolite identification data was gathered from public repositories and processed using specialized software to automatically identify corresponding metabolites. A field expert later examined the results, manually categorizing them as either true or false positives.

Extracting features from each metabolite peak involved utilizing chromatographic peak data, mass spectrometry data, and kinetic data where applicable. The dataset was then divided into training and test sets, with an 80/20 split. Within the training set, machine learning models were developed using gradient boosting decision trees (GBDTs). Hyperparameters underwent tuning through random search and 5-fold cross-validation. The best-performing model was evaluated using the test set. This entire process was repeated for each of the two distinct experimental protocols present in the data.

Preliminary data

Two distinct experimental protocols were present in the data. The primary difference between them lay in the number of incubation time points: one protocol involved 5 time points while the other featured just 1. Consequently, a separate model was developed for each protocol, given that the analysis of the data varied significantly, particularly with the former protocol requiring the incorporation of kinetic data.

For experiments following the 1-time-point protocol, a total of 2,570 metabolite peaks were examined, with 1,703 determined as false positives (66.26%). The primary objective of these experiments was the identification of gluthatione conjugates, formed by appending a gluthatione moiety to a parent molecule. Data for testing was constructed by splitting experiments, resulting in 431 metabolites, of which 287 were false positives (66.60%). The best-performing model achieved a recall of 93.73% and a precision of 89.67% on the test set.

Experiments adhering to the 5-time-point protocol involved a total of 1,543 metabolite peaks, with 1,068 identified as false positives (69.22%). The primary focus of these experiments was soft spot identification. Test data was generated by experiment splits, yielding 419 metabolites, of which 287 were false positives (68.50%). The top-performing model attained a recall of 97.56% and a precision of 93.33% on the test set.

Beyond evaluating the predictive performance of the models, the SHapley Additive exPlanations (SHAP) method was employed to analyze feature importances. The most significant features for each prediction were extracted and aggregated by category or source. This facilitated a clear understanding of the main reasons behind predictions, which were empirically verified to be accurate in most cases. Consequently, this method could serve as a guiding tool to aid experts in the manual inspection of metabolite identification results, as well as in reassessing and rectifying previous manual annotations.

 

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