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Xie YR, Chari VK, Castro DC, Grant R, Rubakhin SS, Sweedler JV. Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry. J Proteome Res 2023; 22:491-500. [PMID: 36695570 PMCID: PMC9901547 DOI: 10.1021/acs.jproteome.2c00714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Varsha K. Chari
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Romans Grant
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Stanislav S. Rubakhin
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States
| | - Jonathan V. Sweedler
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, United States,Mailing Address: Department of Chemistry, University of Illinois, 71 RAL, Box 63-5, 600 South Mathews Avenue, Urbana, Illinois 61801, United States; Phone: (217) 244-7359;
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Hu H, Laskin J. Emerging Computational Methods in Mass Spectrometry Imaging. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203339. [PMID: 36253139 PMCID: PMC9731724 DOI: 10.1002/advs.202203339] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/17/2022] [Indexed: 05/10/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.
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Affiliation(s)
- Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN47907USA
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Joint selection of essential pixels and essential variables across hyperspectral images. Anal Chim Acta 2021; 1141:36-46. [DOI: 10.1016/j.aca.2020.10.040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/13/2020] [Accepted: 10/19/2020] [Indexed: 12/18/2022]
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Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization. Nat Methods 2021; 18:1233-1238. [PMID: 34594032 PMCID: PMC8490150 DOI: 10.1038/s41592-021-01277-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/18/2021] [Indexed: 02/08/2023]
Abstract
Peptidergic dense-core vesicles are involved in packaging and releasing neuropeptides and peptide hormones-critical processes underlying brain, endocrine and exocrine function. Yet, the heterogeneity within these organelles, even for morphologically defined vesicle types, is not well characterized because of their small volumes. We present image-guided, high-throughput mass spectrometry-based protocols to chemically profile large populations of both dense-core vesicles and lucent vesicles for their lipid and peptide contents, allowing observation of the chemical heterogeneity within and between these two vesicle populations. The proteolytic processing products of four prohormones are observed within the dense-core vesicles, and the mass spectral features corresponding to the specific peptide products suggest three distinct dense-core vesicle populations. Notable differences in the lipid mass range are observed between the dense-core and lucent vesicles. These single-organelle mass spectrometry approaches are adaptable to characterize a range of subcellular structures.
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Verbeeck N, Caprioli RM, Van de Plas R. Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry. MASS SPECTROMETRY REVIEWS 2020; 39:245-291. [PMID: 31602691 PMCID: PMC7187435 DOI: 10.1002/mas.21602] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/27/2018] [Indexed: 05/20/2023]
Abstract
Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.
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Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Aspect Analytics NVGenkBelgium
- STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Department of Electrical Engineering (ESAT)KU LeuvenLeuvenBelgium
| | - Richard M. Caprioli
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
- Department of ChemistryVanderbilt UniversityNashvilleTN
- Department of PharmacologyVanderbilt UniversityNashvilleTN
- Department of MedicineVanderbilt UniversityNashvilleTN
| | - Raf Van de Plas
- Delft Center for Systems and ControlDelft University of Technology ‐ TU DelftDelftThe Netherlands
- Mass Spectrometry Research CenterVanderbilt UniversityNashvilleTN
- Department of BiochemistryVanderbilt UniversityNashvilleTN
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Ghaffari M, Omidikia N, Ruckebusch C. Essential Spectral Pixels for Multivariate Curve Resolution of Chemical Images. Anal Chem 2019; 91:10943-10948. [DOI: 10.1021/acs.analchem.9b02890] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Mahdiyeh Ghaffari
- Université Lille, CNRS, UMR 8516 Laboratoire de Spectrochimie Infrarouge et Raman, F-59000 Lille, France
| | - Nematollah Omidikia
- University of Sistan and Baluchestan, Department of Chemistry, Faculty of Science, P.O. Box 98135-674, Zahedan, Iran
| | - Cyril Ruckebusch
- Université Lille, CNRS, UMR 8516 Laboratoire de Spectrochimie Infrarouge et Raman, F-59000 Lille, France
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Rubel O, Bowen BP. BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2018; 24:1025-1035. [PMID: 28866551 DOI: 10.1109/tvcg.2017.2744479] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. We demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.
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Verbeeck N, Spraggins JM, Murphy MJM, Wang HD, Deutch AY, Caprioli RM, Van de Plas R. Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017; 1865:967-977. [PMID: 28254588 DOI: 10.1016/j.bbapap.2017.02.016] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2016] [Accepted: 02/13/2017] [Indexed: 12/16/2022]
Abstract
Imaging mass spectrometry (IMS) is a molecular imaging technology that can measure thousands of biomolecules concurrently without prior tagging, making it particularly suitable for exploratory research. However, the data size and dimensionality often makes thorough extraction of relevant information impractical. To help guide and accelerate IMS data analysis, we recently developed a framework that integrates IMS measurements with anatomical atlases, opening up opportunities for anatomy-driven exploration of IMS data. One example is the automated anatomical interpretation of ion images, where empirically measured ion distributions are automatically decomposed into their underlying anatomical structures. While offering significant potential, IMS-atlas integration has thus far been restricted to the Allen Mouse Brain Atlas (AMBA) and mouse brain samples. Here, we expand the applicability of this framework by extending towards new animal species and a new set of anatomical atlases retrieved from the Scalable Brain Atlas (SBA). Furthermore, as many SBA atlases are based on magnetic resonance imaging (MRI) data, a new registration pipeline was developed that enables direct non-rigid IMS-to-MRI registration. These developments are demonstrated on protein-focused FTICR IMS measurements from coronal brain sections of a Parkinson's disease (PD) rat model. The measurements are integrated with an MRI-based rat brain atlas from the SBA. The new rat-focused IMS-atlas integration is used to perform automated anatomical interpretation and to find differential ions between healthy and diseased tissue. IMS-atlas integration can serve as an important accelerator in IMS data exploration, and with these new developments it can now be applied to a wider variety of animal species and modalities. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Nico Verbeeck
- Delft Center for Systems and Control (DCSC), Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands.
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center (MSRC), Vanderbilt University, 465 21st Ave. South, 9160 Medical Research Building III, Nashville, TN 37240, USA; Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA; Department of Chemistry, Vanderbilt University, 7330 Stevenson Center Station B 351822, Nashville, TN 37235, USA.
| | - Monika J M Murphy
- Program in Neuroscience, Vanderbilt University, U-1205 Medical Research Building III, 465 21st Ave. South, Nashville, TN 37232, USA.
| | - Hui-Dong Wang
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1211 Medical Center Dr. Nashville, TN 37232, USA.
| | - Ariel Y Deutch
- Program in Neuroscience, Vanderbilt University, U-1205 Medical Research Building III, 465 21st Ave. South, Nashville, TN 37232, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, 1211 Medical Center Dr. Nashville, TN 37232, USA; Department of Pharmacology, Vanderbilt University, Nashville, 460B Preston Research Building, Nashville, TN 37232, USA.
| | - Richard M Caprioli
- Mass Spectrometry Research Center (MSRC), Vanderbilt University, 465 21st Ave. South, 9160 Medical Research Building III, Nashville, TN 37240, USA; Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA; Department of Chemistry, Vanderbilt University, 7330 Stevenson Center Station B 351822, Nashville, TN 37235, USA; Department of Medicine, Vanderbilt University Medical Center, 1161 21st Ave. South, D-3100 Medical Center North, Nashville, TN 37232, USA.
| | - Raf Van de Plas
- Delft Center for Systems and Control (DCSC), Delft University of Technology, Mekelweg 2, 2628 CD Delft, the Netherlands; Mass Spectrometry Research Center (MSRC), Vanderbilt University, 465 21st Ave. South, 9160 Medical Research Building III, Nashville, TN 37240, USA; Department of Biochemistry, Vanderbilt University, 607 Light Hall, Nashville, TN 37205, USA.
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9
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Fischer CR, Ruebel O, Bowen BP. An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses. Arch Biochem Biophys 2015; 589:18-26. [PMID: 26365033 DOI: 10.1016/j.abb.2015.08.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 08/21/2015] [Accepted: 08/28/2015] [Indexed: 10/23/2022]
Abstract
Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.
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Affiliation(s)
- Curt R Fischer
- Life Sciences Division, Lawrence Berkeley National Lab, One Cyclotron Road, Berkeley CA 94720, USA
| | - Oliver Ruebel
- Computational Research Division, Lawrence Berkeley National Lab, USA
| | - Benjamin P Bowen
- Life Sciences Division, Lawrence Berkeley National Lab, One Cyclotron Road, Berkeley CA 94720, USA.
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