1
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HUANG D, LIU X, XU G. [Research progress of deep learning applications in mass spectrometry imaging data analysis]. Se Pu 2024; 42:669-680. [PMID: 38966975 PMCID: PMC11224939 DOI: 10.3724/sp.j.1123.2023.10035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Indexed: 07/06/2024] Open
Abstract
Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.
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2
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Gardner W, Winkler DA, Bamford SE, Muir BW, Pigram PJ. Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning. SMALL METHODS 2024; 8:e2301230. [PMID: 38204217 DOI: 10.1002/smtd.202301230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/03/2023] [Indexed: 01/12/2024]
Abstract
Supervised and unsupervised machine learning algorithms are routinely applied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts.
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Affiliation(s)
- Wil Gardner
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia
| | - David A Winkler
- Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Sciences, La Trobe University, Melbourne, Victoria, 3086, Australia
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria, 3052, Australia
- Advanced Materials and Healthcare Technologies, School of Pharmacy, University of Nottingham, Nottingham, NG7 2RD, UK
| | - Sarah E Bamford
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia
| | | | - Paul J Pigram
- Centre for Materials and Surface Science and Department of Mathematical and Physical Sciences, La Trobe University, Bundoora, Victoria, 3086, Australia
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3
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Beck A, Muhoberac M, Randolph CE, Beveridge CH, Wijewardhane PR, Kenttämaa HI, Chopra G. Recent Developments in Machine Learning for Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:233-246. [PMID: 38910862 PMCID: PMC11191731 DOI: 10.1021/acsmeasuresciau.3c00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 06/25/2024]
Abstract
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
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Affiliation(s)
- Armen
G. Beck
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Matthew Muhoberac
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Caitlin E. Randolph
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Connor H. Beveridge
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Prageeth R. Wijewardhane
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Hilkka I. Kenttämaa
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Gaurav Chopra
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
- Department
of Computer Science (by courtesy), Purdue University, West Lafayette, Indiana 47907, United States
- Purdue
Institute for Drug Discovery, Purdue Institute for Cancer Research,
Regenstrief Center for Healthcare Engineering, Purdue Institute for
Inflammation, Immunology and Infectious Disease, Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907 United States
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4
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Piga I, Magni F, Smith A. The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations. FEBS Lett 2024; 598:621-634. [PMID: 38140823 DOI: 10.1002/1873-3468.14795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/06/2023] [Accepted: 12/09/2023] [Indexed: 12/24/2023]
Abstract
Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.
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Affiliation(s)
- Isabella Piga
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Fulvio Magni
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
| | - Andrew Smith
- Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro, Italy
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5
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Bi S, Wang M, Pu Q, Yang J, Jiang N, Zhao X, Qiu S, Liu R, Xu R, Li X, Hu C, Yang L, Gu J, Du D. Multi-MSIProcessor: Data Visualizing and Analysis Software for Spatial Metabolomics Research. Anal Chem 2024; 96:339-346. [PMID: 38102989 DOI: 10.1021/acs.analchem.3c04192] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Mass spectrometry imaging (MSI) has emerged as a revolutionary analytical strategy in biomedical research for molecular visualization. By linking the characterization of functional metabolites with tissue architecture, it is now possible to reveal unknown biological functions of tissues. However, due to the complexity and high dimensionality of MSI data, mining bioinformatics-related peaks from batch MSI data sets and achieving complete spatially resolved metabolomics analysis remain a great challenge. Here, we propose novel MSI data processing software, Multi-MSIProcessor (MMP), which integrates the data read-in, MSI visualization, processed data preservation, and biomarker discovery functions. The MMP focuses on the AFADESI-MSI data platform but also supports mzXML and imzmL data input formats for compatibility with data generated by other MSI platforms such as MALDI/SIMS-MSI. MMP enables deep mining of batch MSI data and has flexible adaptability with the source code opened that welcomes new functions and personalized analysis strategies. Using multiple clinical biosamples with complex heterogeneity, we demonstrated that MMP can rapidly establish complete MSI analysis workflows, assess batch sample data quality, screen and annotate differential MS peaks, and obtain abnormal metabolic pathways. MMP provides a novel platform for spatial metabolomics analysis of multiple samples that could meet the diverse analysis requirements of scholars.
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Affiliation(s)
- Siwei Bi
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Manjiangcuo Wang
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Qianlun Pu
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Jinxi Yang
- West China Centre of Excellence for Pancreatitis, Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital/West China Medical School, Sichuan University, Chengdu 610041, China
| | - Na Jiang
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Xueshan Zhao
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Siyuan Qiu
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu,Sichuan 610041, China
| | - Ruiqi Liu
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Renjie Xu
- Department of Respiratory Health West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xia Li
- West China School of Nursing, Sichuan University, Chengdu, Sichuan 610041, China
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chenggong Hu
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Lie Yang
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu,Sichuan 610041, China
| | - Jun Gu
- Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Dan Du
- Advanced Mass Spectrometry Center, Research Core Facility, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
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6
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Bemis KA, Föll MC, Guo D, Lakkimsetty SS, Vitek O. Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis. Nat Methods 2023; 20:1883-1886. [PMID: 37996752 DOI: 10.1038/s41592-023-02070-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 10/06/2023] [Indexed: 11/25/2023]
Abstract
Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v.3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass recalibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multitissue experiments.
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Affiliation(s)
- Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Institute of Surgical Pathology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | | | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA.
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7
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Bemis KA, Föll MC, Guo D, Lakkimsetty SS, Vitek O. Cardinal v3 - a versatile open source software for mass spectrometry imaging analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.20.529280. [PMID: 36865170 PMCID: PMC9980127 DOI: 10.1101/2023.02.20.529280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Cardinal v3 is an open source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass re-calibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multi-tissue experiments.
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Affiliation(s)
- Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
- Institute of Surgical Pathology, Medical Center – University of Freiburg, Faculty of Medicine, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | | | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
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8
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Guo D, Föll MC, Bemis KA, Vitek O. A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images. Bioinformatics 2023; 39:btad067. [PMID: 36744928 PMCID: PMC9942547 DOI: 10.1093/bioinformatics/btad067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 11/23/2022] [Accepted: 02/06/2023] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in the form of ion images. Unsupervised clustering of ion images facilitates the interpretation in the spectral domain, by identifying groups of ions with similar spatial distributions. Unfortunately, many current methods for clustering ion images ignore the spatial features of the images, and are therefore unable to learn these features for clustering purposes. Alternative methods extract spatial features using deep neural networks pre-trained on natural image tasks; however, this is often inadequate since ion images are substantially noisier than natural images. RESULTS We contribute a deep clustering approach for ion images that accounts for both spatial contextual features and noise. In evaluations on a simulated dataset and on four experimental datasets of different tissue types, the proposed method grouped ions from the same source into a same cluster more frequently than existing methods. We further demonstrated that using ion image clustering as a pre-processing step facilitated the interpretation of a subsequent spatial segmentation as compared to using either all the ions or one ion at a time. As a result, the proposed approach facilitated the interpretability of MSI data in both the spectral domain and the spatial domain. AVAILABILITYAND IMPLEMENTATION The data and code are available at https://github.com/DanGuo1223/mzClustering. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dan Guo
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Melanie Christine Föll
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
- Institute for Surgical Pathology, Medical Center – University of Freiburg, Freiburg 79106, Germany
- Faculty of Medicine, University of Freiburg, Freiburg 79110, Germany
| | - Kylie Ariel Bemis
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA
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9
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Guo L, Dong J, Xu X, Wu Z, Zhang Y, Wang Y, Li P, Tang Z, Zhao C, Cai Z. Divide and Conquer: A Flexible Deep Learning Strategy for Exploring Metabolic Heterogeneity from Mass Spectrometry Imaging Data. Anal Chem 2023; 95:1924-1932. [PMID: 36633187 PMCID: PMC9878502 DOI: 10.1021/acs.analchem.2c04045] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 12/29/2022] [Indexed: 01/13/2023]
Abstract
Research on metabolic heterogeneity provides an important basis for the study of the molecular mechanism of a disease and personalized treatment. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration on disease progress aberrant phenotypes, even carcinogenesis and metastasis. The mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of an organism based on in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous regions of interest (ROIs) or spatially sporadic ROIs by designing two specific modes, spat-contig and spat-spor. Comparison results on fetus mouse data demonstrate that the dc-DeepMSI outperforms state-of-the-art MSI segmentation methods. We demonstrate that the novel learning strategy successfully obtained sub-regions that are statistically linked to the invasion status and molecular phenotypes of breast cancer as well as organizing principles during developmental phase.
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Affiliation(s)
- Lei Guo
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Jiyang Dong
- Department
of Electronic Science, National Institute
for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361005, China
| | - Xiangnan Xu
- School
of Mathematics and Statistics, The University
of Sydney, Sydney, NSW 2006, Australia
| | - Zhichao Wu
- School
of Artificial Intelligence, Beijing Normal
University, Beijing 100875, China
| | - Yinbin Zhang
- Department
of Oncology, The Second Affiliated Hospital
of Medical College, Xi’an Jiaotong University, Xi’an, Shaanxi 710004, China
| | - Yongwei Wang
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Pengfei Li
- Bruker
Scientific Technology Co., Ltd., Beijing 100086, China
| | - Zhi Tang
- School
of Public Health, Dongguan Key Laboratory of Environmental Medicine, Institute of Environmental Health, Guangdong Medical
University, Dongguan, Guangdong 523808, China
| | - Chao Zhao
- Bionic
Sensing and Intelligence Center, Institute of Biomedical and Health
Engineering, Shenzhen Institute of Advanced
Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
| | - Zongwei Cai
- State
Key Laboratory of Environmental and Biological Analysis, Department
of Chemistry, Hong Kong Baptist University, Hong Kong SAR 999077, China
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10
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Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat. Foods 2022; 12:foods12010141. [PMID: 36613357 PMCID: PMC9818861 DOI: 10.3390/foods12010141] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/13/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.
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11
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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: 20] [Impact Index Per Article: 10.0] [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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12
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Guo L, Liu X, Zhao C, Hu Z, Xu X, Cheng KK, Zhou P, Xiao Y, Shah M, Xu J, Dong J, Cai Z. iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data. Anal Chem 2022; 94:14522-14529. [PMID: 36223650 DOI: 10.1021/acs.analchem.2c01456] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
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Affiliation(s)
- Lei Guo
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Xingxing Liu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Chao Zhao
- Bionic Sensing and Intelligence Center, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055, China
| | - Zhenxing Hu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Xiangnan Xu
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW2006, Australia
| | - Kian-Kai Cheng
- Innovation Centre in Agritechnology, Universiti Teknologi Malaysia, Muar, Johor84600, Malaysia
| | - Peng Zhou
- Department of Thyroid and Breast Surgery, Shenzhen Second People's Hospital, Shenzhen518025, China
| | - Yu Xiao
- Department of Thyroid and Breast Surgery, Shenzhen Second People's Hospital, Shenzhen518025, China
| | - Mudassir Shah
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Jingjing Xu
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Jiyang Dong
- Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen361005, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong KongSAR999077, China
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13
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Yamada H, Xu L, Eto F, Takeichi R, Islam A, Mamun MA, Zhang C, Yao I, Sakamoto T, Aramaki S, Kikushima K, Sato T, Takahashi Y, Machida M, Kahyo T, Setou M. Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1607-1614. [PMID: 35881989 DOI: 10.1021/jasms.2c00080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents' brain sections. CNN produced classification models with high accuracy and low loss rate in any test data sets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS and of mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. On the basis of the extracted mass spectra pattern features, the histologically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging generated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no significant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment.
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Affiliation(s)
- Hidemoto Yamada
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Lili Xu
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Fumihiro Eto
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Rei Takeichi
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Ariful Islam
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Md Ai Mamun
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Chi Zhang
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Ikuko Yao
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- Department of Biomedical Sciences, School of Biological and Environmental Sciences, Kwansei Gakuin University, 2-1 Gakuen, Sanda, Hyogo 669-1337, Japan
| | - Takumi Sakamoto
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Shuhei Aramaki
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- Department of Radiation Oncology, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Kenji Kikushima
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Tomohito Sato
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Yutaka Takahashi
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Manabu Machida
- Department of Systems Molecular Anatomy, Institute for Medical Photonics Research, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Tomoaki Kahyo
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
| | - Mitsutoshi Setou
- Department of Cellular and Molecular Anatomy, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- International Mass Imaging Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
- Department of Systems Molecular Anatomy, Institute for Medical Photonics Research, Preeminent Medical Photonics Education & Research Center, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka 431-3192, Japan
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14
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Plyusnin I, Truong Nguyen PT, Sironen T, Vapalahti O, Smura T, Kant R. ClusTRace, a bioinformatic pipeline for analyzing clusters in virus phylogenies. BMC Bioinformatics 2022; 23:196. [PMID: 35643449 PMCID: PMC9143711 DOI: 10.1186/s12859-022-04709-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/04/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND SARS-CoV-2 is the highly transmissible etiologic agent of coronavirus disease 2019 (COVID-19) and has become a global scientific and public health challenge since December 2019. Several new variants of SARS-CoV-2 have emerged globally raising concern about prevention and treatment of COVID-19. Early detection and in-depth analysis of the emerging variants allowing pre-emptive alert and mitigation efforts are thus of paramount importance. RESULTS Here we present ClusTRace, a novel bioinformatic pipeline for a fast and scalable analysis of sequence clusters or clades in large viral phylogenies. ClusTRace offers several high-level functionalities including lineage assignment, outlier filtering, aligning, phylogenetic tree reconstruction, cluster extraction, variant calling, visualization and reporting. ClusTRace was developed as an aid for COVID-19 transmission chain tracing in Finland with the main emphasis on fast screening of phylogenies for markers of super-spreading events and other features of concern, such as high rates of cluster growth and/or accumulation of novel mutations. CONCLUSIONS ClusTRace provides an effective interface that can significantly cut down learning and operating costs related to complex bioinformatic analysis of large viral sequence sets and phylogenies. All code is freely available from https://bitbucket.org/plyusnin/clustrace/.
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Affiliation(s)
- Ilya Plyusnin
- Department of Veterinary Bioscience, University of Helsinki, 00014, Helsinki, Finland.
- Department of Virology, University of Helsinki, 00014, Helsinki, Finland.
| | | | - Tarja Sironen
- Department of Veterinary Bioscience, University of Helsinki, 00014, Helsinki, Finland
- Department of Virology, University of Helsinki, 00014, Helsinki, Finland
| | - Olli Vapalahti
- Department of Veterinary Bioscience, University of Helsinki, 00014, Helsinki, Finland
- Department of Virology, University of Helsinki, 00014, Helsinki, Finland
- Department of Virology and Immunology, Helsinki University Hospital, Diagnostic Center, 00029, Helsinki, Finland
| | - Teemu Smura
- Department of Virology, University of Helsinki, 00014, Helsinki, Finland
- Department of Virology and Immunology, Helsinki University Hospital, Diagnostic Center, 00029, Helsinki, Finland
| | - Ravi Kant
- Department of Veterinary Bioscience, University of Helsinki, 00014, Helsinki, Finland
- Department of Virology, University of Helsinki, 00014, Helsinki, Finland
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15
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Missinou AA, Ferreira de Carvalho J, Marnet N, Delhaye T, Hamzaoui O, Abdel Sayed D, Guitton Y, Lebreton L, Langrume C, Laperche A, Delourme R, Manzanares-Dauleux MJ, Bouchereau A, Gravot A. Identification and Quantification of Glucosinolates and Phenolics in a Large Panel of Brassica napus Highlight Valuable Genetic Resources for Chemical Ecology and Breeding. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:5245-5261. [PMID: 35420430 DOI: 10.1021/acs.jafc.1c08118] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Glucosinolate (GLS) and phenolic contents in Brassicaceae contribute to biotic and abiotic stress responses. Breeding crop accessions harboring agroecologically relevant metabolic profiles require a characterization of the chemical diversity in Brassica germplasm. This work investigates the diversity of specialized metabolites in 281 accessions of B. napus. First, an LC-HRMS2-based approach allowed the annotation of 32 phenolics and 36 GLSs, revealing 13 branched and linear alkyl-GLSs and 4 isomers of hydroxyphenylalkyl-GLSs, many of which have been rarely reported in Brassica. Then, quantitative UPLC-UV-MS-based profiling was performed in leaves and roots for the whole panel. This revealed striking variations in the content of 1-methylpropyl-GLS (glucocochlearin) and a large variation of tetra- and penta-glucosyl kaempferol derivatives among accessions. It also highlighted two main chemotypes related to sinapoyl-O-hexoside and kaempferol-O-trihexoside contents. By offering an unprecedented overview of the phytochemical diversity in B. napus, this work provides a useful resource for chemical ecology and breeding.
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Affiliation(s)
| | | | | | | | | | | | | | - Lionel Lebreton
- Univ Rennes, Institut Agro, INRAE, IGEPP, 35653 Le Rheu, France
| | | | - Anne Laperche
- Univ Rennes, Institut Agro, INRAE, IGEPP, 35653 Le Rheu, France
| | - Régine Delourme
- Univ Rennes, Institut Agro, INRAE, IGEPP, 35653 Le Rheu, France
| | | | | | - Antoine Gravot
- Univ Rennes, Institut Agro, INRAE, IGEPP, 35653 Le Rheu, France
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16
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Abdelmoula WM, Stopka SA, Randall EC, Regan M, Agar JN, Sarkaria JN, Wells WM, Kapur T, Agar NYR. massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation. Bioinformatics 2022; 38:2015-2021. [PMID: 35040929 PMCID: PMC8963284 DOI: 10.1093/bioinformatics/btac032] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 01/04/2022] [Accepted: 01/13/2022] [Indexed: 01/21/2023] Open
Abstract
MOTIVATION Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation. RESULTS We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine. AVAILABILITY AND IMPLEMENTATION https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Walid M Abdelmoula
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Invicro LLC, Boston, MA 02210, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elizabeth C Randall
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Michael Regan
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey N Agar
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02111, USA
| | - Jann N Sarkaria
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55902, USA
| | - William M Wells
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA,Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA,To whom correspondence should be addressed.
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17
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Pino L, Schilling B. Proximity labeling and other novel mass spectrometric approaches for spatiotemporal protein dynamics. Expert Rev Proteomics 2021; 18:757-765. [PMID: 34496693 PMCID: PMC8650568 DOI: 10.1080/14789450.2021.1976149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/31/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Proteins are highly dynamic and their biological function is controlled by not only temporal abundance changes but also via regulated protein-protein interaction networks, which respond to internal and external perturbations. A wealth of novel analytical reagents and workflows allow studying spatiotemporal protein environments with great granularity while maintaining high throughput and ease of analysis. AREAS COVERED We review technology advances for measuring protein-protein proximity interactions with an emphasis on proximity labeling, and briefly summarize other spatiotemporal approaches including protein localization, and their dynamic changes over time, specifically in human cells and mammalian tissues. We focus especially on novel technologies and workflows emerging within the past 5 years. This includes enrichment-based techniques (proximity labeling and crosslinking), separation-based techniques (organelle fractionation and size exclusion chromatography), and finally sorting-based techniques (laser capture microdissection and mass spectrometry imaging). EXPERT OPINION Spatiotemporal proteomics is a key step in assessing biological complexity, understanding refined regulatory mechanisms, and forming protein complexes and networks. Studying protein dynamics across space and time holds promise for gaining deep insights into how protein networks may be perturbed during disease and aging processes, and offer potential avenues for therapeutic interventions, drug discovery, and biomarker development.
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Affiliation(s)
- Lindsay Pino
- University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Birgit Schilling
- Buck Institute for Research on Aging, Novato, California, CA 94945, USA
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18
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Tuck M, Blanc L, Touti R, Patterson NH, Van Nuffel S, Villette S, Taveau JC, Römpp A, Brunelle A, Lecomte S, Desbenoit N. Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review. Anal Chem 2020; 93:445-477. [PMID: 33253546 DOI: 10.1021/acs.analchem.0c04595] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael Tuck
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Landry Blanc
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Rita Touti
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575, United States
| | - Sebastiaan Van Nuffel
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sandrine Villette
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Jean-Christophe Taveau
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Andreas Römpp
- Bioanalytical Sciences and Food Analysis, University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Alain Brunelle
- Laboratoire d'Archéologie Moléculaire et Structurale, LAMS UMR 8220, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, France
| | - Sophie Lecomte
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nicolas Desbenoit
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
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