1
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Kim E, Yang SM, Ham JH, Lee W, Jung DH, Kim HY. Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction. Food Chem 2024; 462:140931. [PMID: 39217752 DOI: 10.1016/j.foodchem.2024.140931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 07/26/2024] [Accepted: 08/19/2024] [Indexed: 09/04/2024]
Abstract
This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.
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Affiliation(s)
- Eiseul Kim
- Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Seung-Min Yang
- Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Jun-Hyeok Ham
- Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Woojung Lee
- Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Dae-Hyun Jung
- Department of Smart Farm Science, Kyung Hee University, Yongin 17104, Republic of Korea
| | - Hae-Yeong Kim
- Institute of Life Sciences & Resources and Department of Food Science and Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
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2
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Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
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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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Lu XY, Wu HP, Ma H, Li H, Li J, Liu YT, Pan ZY, Xie Y, Wang L, Ren B, Liu GK. Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives. Anal Chem 2024; 96:7959-7975. [PMID: 38662943 DOI: 10.1021/acs.analchem.4c01639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hao-Ping Wu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
| | - Hao Ma
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Hui Li
- Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen 361005, P. R. China
| | - Jia Li
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, P. R. China
| | - Yan-Ti Liu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Zheng-Yan Pan
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
| | - Yi Xie
- School of Informatics, Xiamen University, Xiamen 361005, P. R. China
| | - Lei Wang
- Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361005, P. R. China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, P. R. China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, P. R. China
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5
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Wevers D, Ramautar R, Clark C, Hankemeier T, Ali A. Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics. Electrophoresis 2023; 44:2000-2024. [PMID: 37667867 DOI: 10.1002/elps.202300105] [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: 05/11/2023] [Revised: 08/08/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023]
Abstract
Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.
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Affiliation(s)
- Dirk Wevers
- Wageningen University and Research, Wageningen, The Netherlands
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Rawi Ramautar
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Charlie Clark
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Ahmed Ali
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
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6
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Kobeissy F, Goli M, Yadikar H, Shakkour Z, Kurup M, Haidar MA, Alroumi S, Mondello S, Wang KK, Mechref Y. Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects. Front Neurol 2023; 14:1288740. [PMID: 38073638 PMCID: PMC10703396 DOI: 10.3389/fneur.2023.1288740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 11/01/2023] [Indexed: 02/12/2024] Open
Abstract
Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.
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Affiliation(s)
- Firas Kobeissy
- Department of Neurobiology, School of Medicine, Neuroscience Institute, Atlanta, GA, United States
| | - Mona Goli
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Hamad Yadikar
- Department of Biological Sciences Faculty of Science, Kuwait University, Safat, Kuwait
| | - Zaynab Shakkour
- Department of Pathology and Anatomical Sciences, University of Missouri School of Medicine, Columbia, MO, United States
| | - Milin Kurup
- Alabama College of Osteopathic Medicine, Dothan, AL, United States
| | | | - Shahad Alroumi
- Department of Biological Sciences Faculty of Science, Kuwait University, Safat, Kuwait
| | - Stefania Mondello
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy
| | - Kevin K. Wang
- Department of Neurobiology, School of Medicine, Neuroscience Institute, Atlanta, GA, United States
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
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7
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Seddiki K, Precioso F, Sanabria M, Salzet M, Fournier I, Droit A. Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification. Anal Chem 2023; 95:13431-13437. [PMID: 37624777 PMCID: PMC10501374 DOI: 10.1021/acs.analchem.3c00613] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023]
Abstract
Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology that addresses all of the above challenges at once, while preserving the high potential of LC-MS data. Our DL model is designed to early discriminate between tumoral and normal tissues. It is a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) Network. The CNN network allows for significantly reducing the high dimensionality of the data while learning spatially relevant features. The LSTM network enables our model to capture temporal patterns. We show that our model outperforms not only benchmark models but also state-of-the-art models developed on the same data. Our framework is a promising strategy for improving early cancer detection during a diagnostic process.
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Affiliation(s)
- Khawla Seddiki
- Centre
de Recherche du CHU de Québec-Université Laval, Québec City, Québec G1V 4G2, Canada
- Univ.
Lille, Inserm, CHU Lille,
U1192-Protéomique Réponse Inflammatoire Spectrométrie
de Masse-PRISM, Lille F-59000, France
| | - Fŕed́eric Precioso
- Université
Ĉote d’Azur, CNRS, INRIA, I3S, Sophia Antipolis 06900, France
| | - Melissa Sanabria
- Université
Ĉote d’Azur, CNRS, INRIA, I3S, Sophia Antipolis 06900, France
| | - Michel Salzet
- Univ.
Lille, Inserm, CHU Lille,
U1192-Protéomique Réponse Inflammatoire Spectrométrie
de Masse-PRISM, Lille F-59000, France
| | - Isabelle Fournier
- Univ.
Lille, Inserm, CHU Lille,
U1192-Protéomique Réponse Inflammatoire Spectrométrie
de Masse-PRISM, Lille F-59000, France
| | - Arnaud Droit
- Centre
de Recherche du CHU de Québec-Université Laval, Québec City, Québec G1V 4G2, Canada
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8
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Cheng YY, Chen Z, Cao X, Ross TD, Falbel TG, Burton BM, Venturelli OS. Programming bacteria for multiplexed DNA detection. Nat Commun 2023; 14:2001. [PMID: 37037805 PMCID: PMC10086068 DOI: 10.1038/s41467-023-37582-x] [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: 02/13/2023] [Accepted: 03/23/2023] [Indexed: 04/12/2023] Open
Abstract
DNA is a universal and programmable signal of living organisms. Here we develop cell-based DNA sensors by engineering the naturally competent bacterium Bacillus subtilis (B. subtilis) to detect specific DNA sequences in the environment. The DNA sensor strains can identify diverse bacterial species including major human pathogens with high specificity. Multiplexed detection of genomic DNA from different species in complex samples can be achieved by coupling the sensing mechanism to orthogonal fluorescent reporters. We also demonstrate that the DNA sensors can detect the presence of species in the complex samples without requiring DNA extraction. The modularity of the living cell-based DNA-sensing mechanism and simple detection procedure could enable programmable DNA sensing for a wide range of applications.
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Affiliation(s)
- Yu-Yu Cheng
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Zhengyi Chen
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Xinyun Cao
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Tyler D Ross
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Tanya G Falbel
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Briana M Burton
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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9
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Advances in Mass Spectrometry-Based Single Cell Analysis. BIOLOGY 2023; 12:biology12030395. [PMID: 36979087 PMCID: PMC10045136 DOI: 10.3390/biology12030395] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023]
Abstract
Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.
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10
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Rossel S, Kaiser P, Bode-Dalby M, Renz J, Laakmann S, Auel H, Hagen W, Arbizu PM, Peters J. Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda, Crustacea) from the Arctic Ocean. Mol Ecol Resour 2023; 23:382-395. [PMID: 36114815 DOI: 10.1111/1755-0998.13714] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/29/2022] [Accepted: 09/09/2022] [Indexed: 01/04/2023]
Abstract
Species identification is pivotal in biodiversity assessments and proteomic fingerprinting by MALDI-TOF mass spectrometry has already been shown to reliably identify calanoid copepods to species level. However, MALDI-TOF data may contain more information beyond mere species identification. In this study, we investigated different ontogenetic stages (copepodids C1-C6 females) of three co-occurring Calanus species from the Arctic Fram Strait, which cannot be identified to species level based on morphological characters alone. Differentiation of the three species based on mass spectrometry data was without any error. In addition, a clear stage-specific signal was detected in all species, supported by clustering approaches as well as machine learning using Random Forest. More complex mass spectra in later ontogenetic stages as well as relative intensities of certain mass peaks were found as the main drivers of stage distinction in these species. Through a dilution series, we were able to show that this did not result from the higher amount of biomass that was used in tissue processing of the larger stages. Finally, the data were tested in a simulation for application in a real biodiversity assessment by using Random Forest for stage classification of specimens absent from the training data. This resulted in a successful stage-identification rate of almost 90%, making proteomic fingerprinting a promising tool to investigate polewards shifts of Atlantic Calanus species and, in general, to assess stage compositions in biodiversity assessments of Calanoida, which can be notoriously difficult using conventional identification methods.
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Affiliation(s)
- Sven Rossel
- German Centre for Marine Biodiversity Research (DZMB), Senckenberg Research Institute, Wilhelmshaven, Germany
| | - Patricia Kaiser
- Universität Bremen, BreMarE - Bremen Marine Ecology, Marine Zoology, Bremen, Germany
| | - Maya Bode-Dalby
- Universität Bremen, BreMarE - Bremen Marine Ecology, Marine Zoology, Bremen, Germany
| | - Jasmin Renz
- German Centre for Marine Biodiversity Research (DZMB), Senckenberg Research Institute, Hamburg, Germany
| | - Silke Laakmann
- Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB), Oldenburg, Germany.,Alfred Wegener Institute, Helmholtz-Centre for Polar and Marine Research (AWI), Bremerhaven, Germany
| | - Holger Auel
- Universität Bremen, BreMarE - Bremen Marine Ecology, Marine Zoology, Bremen, Germany
| | - Wilhelm Hagen
- Universität Bremen, BreMarE - Bremen Marine Ecology, Marine Zoology, Bremen, Germany
| | - Pedro Martínez Arbizu
- German Centre for Marine Biodiversity Research (DZMB), Senckenberg Research Institute, Wilhelmshaven, Germany
| | - Janna Peters
- German Centre for Marine Biodiversity Research (DZMB), Senckenberg Research Institute, Hamburg, Germany
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11
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Joshi PB. Navigating with chemometrics and machine learning in chemistry. Artif Intell Rev 2023; 56:1-26. [PMID: 36714038 PMCID: PMC9870782 DOI: 10.1007/s10462-023-10391-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/09/2023] [Indexed: 01/25/2023]
Abstract
Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.
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Affiliation(s)
- Payal B. Joshi
- Operations and Method Development, Shefali Research Laboratories, Ambernath (East), Thane, Maharashtra 421501 India
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12
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Hu R, Li Y, Yang Y, Liu M. Mass spectrometry-based strategies for single-cell metabolomics. MASS SPECTROMETRY REVIEWS 2023; 42:67-94. [PMID: 34028064 DOI: 10.1002/mas.21704] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 05/05/2021] [Accepted: 05/11/2021] [Indexed: 06/12/2023]
Abstract
Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.
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Affiliation(s)
- Rui Hu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ying Li
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yunhuang Yang
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Maili Liu
- Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
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13
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Daniel F, Kesterson D, Lei K, Hord C, Patel A, Kaffenes A, Congivaram H, Prakash S. Application of Microfluidics for Bacterial Identification. Pharmaceuticals (Basel) 2022; 15:ph15121531. [PMID: 36558982 PMCID: PMC9781190 DOI: 10.3390/ph15121531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/29/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Bacterial infections continue to pose serious public health challenges. Though anti-bacterial therapeutics are effective remedies for treating these infections, the emergence of antibiotic resistance has imposed new challenges to treatment. Often, there is a delay in prescribing antibiotics at initial symptom presentation as it can be challenging to clinically differentiate bacterial infections from other organisms (e.g., viruses) causing infection. Moreover, bacterial infections can arise from food, water, or other sources. These challenges have demonstrated the need for rapid identification of bacteria in liquids, food, clinical spaces, and other environments. Conventional methods of bacterial identification rely on culture-based approaches which require long processing times and higher pathogen concentration thresholds. In the past few years, microfluidic devices paired with various bacterial identification methods have garnered attention for addressing the limitations of conventional methods and demonstrating feasibility for rapid bacterial identification with lower biomass thresholds. However, such culture-free methods often require integration of multiple steps from sample preparation to measurement. Research interest in using microfluidic methods for bacterial identification is growing; therefore, this review article is a summary of current advancements in this field with a focus on comparing the efficacy of polymerase chain reaction (PCR), loop-mediated isothermal amplification (LAMP), and emerging spectroscopic methods.
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Affiliation(s)
- Fraser Daniel
- Department of Mechanical and Aerospace Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Delaney Kesterson
- Center for Life Sciences Education, The Ohio State University, Columbus, OH 43210, USA
| | - Kevin Lei
- Department of Chemical and Biomolecular Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Catherine Hord
- Center for Life Sciences Education, The Ohio State University, Columbus, OH 43210, USA
| | - Aarti Patel
- Department of Biomedical Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
| | - Anastasia Kaffenes
- Department of Neuroscience, College of Arts and Sciences and College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Harrshavasan Congivaram
- School of Health and Rehabilitation Sciences, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Shaurya Prakash
- Department of Mechanical and Aerospace Engineering, College of Engineering, The Ohio State University, Columbus, OH 43210, USA
- Correspondence:
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14
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Boiko DA, Kozlov KS, Burykina JV, Ilyushenkova VV, Ananikov VP. Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning. J Am Chem Soc 2022; 144:14590-14606. [PMID: 35939718 DOI: 10.1021/jacs.2c03631] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers. We present the framework for efficient MS data interpretation, describing a novel approach for detailed analysis based on deisotoping performed by gradient-boosted decision trees and a neural network that generates molecular formulas from the fine isotopic structure, approaching the long-standing inverse spectral problem. The methods were successfully tested on three examples: fragment ion analysis in protein sequencing for proteomics, analysis of the natural samples for life sciences, and study of the cross-coupling catalytic system for chemistry.
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Affiliation(s)
- Daniil A Boiko
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Konstantin S Kozlov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Julia V Burykina
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Valentina V Ilyushenkova
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
| | - Valentine P Ananikov
- Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow 119991, Russia
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15
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Lanekoff I, Sharma VV, Marques C. Single-cell metabolomics: where are we and where are we going? Curr Opin Biotechnol 2022; 75:102693. [DOI: 10.1016/j.copbio.2022.102693] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 01/12/2022] [Accepted: 01/20/2022] [Indexed: 12/11/2022]
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16
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Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis. Microbiol Spectr 2022; 10:e0176921. [PMID: 35234514 PMCID: PMC8941854 DOI: 10.1128/spectrum.01769-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection.
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17
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Zheng H, Zhao J, Wang X, Yan S, Chu H, Gao M, Zhang X. Integrated Pipeline of Rapid Isolation and Analysis of Human Plasma Exosomes for Cancer Discrimination Based on Deep Learning of MALDI-TOF MS Fingerprints. Anal Chem 2022; 94:1831-1839. [PMID: 35025210 DOI: 10.1021/acs.analchem.1c04762] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Plasma exosomes have shown great potential for liquid biopsy in clinical cancer diagnosis. Herein, we present an integrated strategy for isolating and analyzing exosomes from human plasma rapidly and then discriminating different cancers excellently based on deep learning fingerprints of plasma exosomes. Sequential size-exclusion chromatography (SSEC) was developed efficiently for separating exosomes from human plasma. SSEC isolated plasma exosomes, taking as less as 2 h for a single sample with high purity such that the discard rates of high-density lipoproteins and low/very low-density lipoproteins were 93 and 85%, respectively. Benefitting from the rapid and high-purity isolation, the contents encapsulated in exosomes, covered by plasma proteins, were well profiled by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS). We further analyzed 220 clinical samples, including 79 breast cancer patients, 57 pancreatic cancer patients, and 84 healthy controls. After MS data pre-processing and feature selection, the extracted MS feature peaks were utilized as inputs for constructing a multi-classifier artificial neural network (denoted as Exo-ANN) model. The optimized model avoided overfitting and performed well in both training cohorts and test cohorts. For the samples in the independent test cohort, it realized a diagnosed accuracy of 80.0% with an area under the curve of 0.91 for the whole group. These results suggest that our integrated pipeline may become a generic tool for liquid biopsy based on the analysis of plasma exosomes in clinics.
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Affiliation(s)
- Haoyang Zheng
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Jiandong Zhao
- Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xuantang Wang
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Shaohan Yan
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Huimin Chu
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Mingxia Gao
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
| | - Xiangmin Zhang
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China
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18
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Zhou B, Tong YK, Zhang R, Ye A. RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra. RSC Adv 2022; 12:26463-26469. [PMID: 36275115 PMCID: PMC9478993 DOI: 10.1039/d2ra03722j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022] Open
Abstract
Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra. We propose a novel CNN model named RamanNet for rapid and accurate identification of bacteria at the species-level based on Raman spectra. Compared to previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset.![]()
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Affiliation(s)
- Bo Zhou
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Yu-Kai Tong
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Ru Zhang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Anpei Ye
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
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19
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Mortier T, Wieme AD, Vandamme P, Waegeman W. Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: A large-scale benchmarking study. Comput Struct Biotechnol J 2021; 19:6157-6168. [PMID: 34938408 PMCID: PMC8649224 DOI: 10.1016/j.csbj.2021.11.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/03/2021] [Accepted: 11/03/2021] [Indexed: 11/17/2022] Open
Abstract
Today machine learning methods are commonly deployed for bacterial species identification using MALDI-TOF mass spectrometry data. However, most of the studies reported in literature only consider very traditional machine learning methods on small datasets that contain a limited number of species. In this paper we present benchmarking results on an unprecedented scale for a wide range of machine learning methods, using datasets that contain almost 100,000 spectra and more than 1000 different species. The size and the diversity of the data allow to compare three important identification scenarios that are often not distinguished in literature, i.e., identification for novel biological replicates, novel strains and novel species that are not present in the training data. The results demonstrate that in all three scenarios acceptable identification rates are obtained, but the numbers are typically lower than those reported in studies with a more limited analysis. Using hierarchical classification methods, we also demonstrate that taxonomic information is in general not well preserved in MALDI-TOF mass spectrometry data. For the novel species scenario, we apply for the first time neural networks with Monte Carlo dropout, which have shown to be successful in other domains, such as computer vision, for the detection of novel species.
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Affiliation(s)
- Thomas Mortier
- KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
| | - Anneleen D. Wieme
- BCCM/LMG Bacteria Collection, Laboratory of Microbiology, Faculty of Sciences, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium
| | - Peter Vandamme
- BCCM/LMG Bacteria Collection, Laboratory of Microbiology, Faculty of Sciences, Ghent University, K.L. Ledeganckstraat 35, B-9000 Ghent, Belgium
| | - Willem Waegeman
- KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
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20
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Fu Q, Zhang Y, Wang P, Pi J, Qiu X, Guo Z, Huang Y, Zhao Y, Li S, Xu J. Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis. Anal Bioanal Chem 2021; 413:7401-7410. [PMID: 34673992 DOI: 10.1007/s00216-021-03691-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/23/2021] [Accepted: 09/23/2021] [Indexed: 11/24/2022]
Abstract
The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.
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Affiliation(s)
- Qiuyue Fu
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Yanjiao Zhang
- School of Basic Medicine, Guangdong Medical University, Dongguan, 523808, China
| | - Peng Wang
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Jiang Pi
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Xun Qiu
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Zhusheng Guo
- Donghua Hospital Laboratory Department, Dongguan, 523808, Guangdong, China
| | - Ya Huang
- Donghua Hospital Laboratory Department, Dongguan, 523808, Guangdong, China
| | - Yi Zhao
- Guangdong Provincial Key Laboratory of Molecular Diagnosis, Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Shaoxin Li
- Biomedical Photonics Laboratory, School of Biomedical Engineering, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
| | - Junfa Xu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
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21
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Han SS, Jeong YS, Choi SK. Current Scenario and Challenges in the Direct Identification of Microorganisms Using MALDI TOF MS. Microorganisms 2021; 9:microorganisms9091917. [PMID: 34576812 PMCID: PMC8466008 DOI: 10.3390/microorganisms9091917] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/01/2021] [Accepted: 09/07/2021] [Indexed: 01/12/2023] Open
Abstract
MALDI TOF MS-based microbial identification significantly lowers the operational costs because of minimal requirements of substrates and reagents for extraction. Therefore, it has been widely used in varied applications such as clinical, food, military, and ecological research. However, the MALDI TOF MS method is laced with many challenges including its limitation of the reference spectrum. This review briefly introduces the background of MALDI TOF MS technology, including sample preparation and workflow. We have primarily discussed the application of MALDI TOF MS in the identification of microorganisms. Furthermore, we have discussed the current trends for bioaerosol detection using MALDI TOF MS and the limitations and challenges involved, and finally the approaches to overcome these challenges.
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Affiliation(s)
- Sang-Soo Han
- Advanced Defense Science & Technology Research Institute, Agency for Defense Development, Daejeon 34186, Korea;
| | - Young-Su Jeong
- Chem-Bio Technology Center, Agency for Defense Development, Daejeon 34186, Korea;
- Correspondence: ; Tel.: +82-42-821-4843; Fax: +82-42-823-3400
| | - Sun-Kyung Choi
- Chem-Bio Technology Center, Agency for Defense Development, Daejeon 34186, Korea;
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22
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Xu S, Yang C, Yan X, Liu H. Towards high throughput and high information coverage: advanced single-cell mass spectrometric techniques. Anal Bioanal Chem 2021; 414:219-233. [PMID: 34435209 DOI: 10.1007/s00216-021-03624-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/13/2021] [Accepted: 08/17/2021] [Indexed: 12/23/2022]
Abstract
Mass spectrometry (MS) is attractive for single-cell analysis because of its high sensitivity, rich information, and large dynamic ranges, especially for the single-cell metabolome and proteome analysis. Efforts have been made to deal with the throughput and information coverage problems in typical manual single-cell MS techniques. In this review, advanced techniques to improve the automation and throughput for single-cell sampling and single-cell metabolome and proteome MS detection have been discussed. Furthermore, representative MS-based strategies that can increase the in-depth cellular information coverage and achieve the more comprehensive single-cell multiomics information during high throughput detection have been highlighted, providing an ongoing perspective of the MS performance for the single-cell research.
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Affiliation(s)
- Shuting Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Cheng Yang
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Xiuping Yan
- State Key Laboratory of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China. .,Institute of Analytical Food Safety, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China.
| | - Huwei Liu
- Institute of Analytical Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
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23
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Okai CA, Wölter M, Russ M, Koy C, Petre BA, Rath W, Pecks U, Glocker MO. Profiling of intact blood proteins by matrix-assisted laser desorption/ionization mass spectrometry without the need for freezing - Dried serum spots as future clinical tools for patient screening. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2021; 35:e9121. [PMID: 33955049 DOI: 10.1002/rcm.9121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/30/2021] [Accepted: 05/02/2021] [Indexed: 06/12/2023]
Abstract
RATIONALE To open up new ways for matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS)-based patient screening, blood serum is the most preferred specimen because of its richness in patho-physiological information and due to ease of collection. To overcome deleterious freeze/thaw cycles and to reduce high costs for shipping and storage, we sought to develop a procedure which enables MALDI-MS protein profiling of blood serum proteins without the need for serum freezing. METHODS Blood sera from patients/donors were divided into portions which after pre-incubation were fast frozen. Thawed aliquots were deposited on filter paper discs and air-dried at room temperature. Intact serum proteins were eluted with acid-labile detergent-containing solutions and were desalted by employing a reversed-phase bead system. Purified protein solutions were screened by MALDI-MS using standardized instrument settings. RESULTS MALDI mass spectra from protein solutions which were eluted from filter paper discs and desalted showed on average 25 strong ion signals (mass range m/z 6000 to 10,000) from intact serum proteins (apolipoproteins, complement proteins, transthyretin and hemoglobin) and from proteolytic processing products. Semi-quantitative analysis of three ion pairs: m/z 6433 and 6631, m/z 8205 and 8916, as well as m/z 9275 and 9422, indicated that the mass spectra from either pre-incubated fast-frozen serum or pre-incubated dried serum spot eluted serum contained the same information on protein composition. CONCLUSIONS A workflow that avoids the conventional cold-chain and yet enables the investigation of intact serum proteins and/or serum proteolysis products by MALDI-MS profiling was developed. The presented protocol tremendously broadens the clinical application of MALDI-MS and simultaneously allows a reduction in the costs for storage and shipping of serum samples. This will pave the way for clinical screening of patients also in areas with limited access to health care systems, and/or specialized laboratories.
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Affiliation(s)
- Charles A Okai
- Proteome Center Rostock, Medical Faculty and Natural Science Faculty, University of Rostock, Schillingallee 69, Rostock, 18057, Germany
| | - Manja Wölter
- Proteome Center Rostock, Medical Faculty and Natural Science Faculty, University of Rostock, Schillingallee 69, Rostock, 18057, Germany
- Department of Obstetrics and Gynecology, Medical Faculty, University of Rostock, Clinic Südstadt, Rostock, 18059, Germany
| | - Manuela Russ
- Proteome Center Rostock, Medical Faculty and Natural Science Faculty, University of Rostock, Schillingallee 69, Rostock, 18057, Germany
| | - Cornelia Koy
- Proteome Center Rostock, Medical Faculty and Natural Science Faculty, University of Rostock, Schillingallee 69, Rostock, 18057, Germany
| | - Brindusa A Petre
- Department of Chemistry, Alexandru Ioan Cuza University, Bd. Carol I, No.11, Iasi, 700506, Romania
- Transcend Research Center, Regional Institute of Oncology, General Henri Mathias, No.2-4, Iasi, 700483, Romania
| | - Werner Rath
- Department of Obstetrics and Gynecology, Medical Faculty, RWTH Aachen University, Aachen, 52062, Germany
| | - Ulrich Pecks
- Department of Obstetrics and Gynecology, Medical Faculty, University of Schleswig-Holstein, Campus Kiel, Arnold-Heller-Straße 3, Kiel, 24105, Germany
| | - Michael O Glocker
- Proteome Center Rostock, Medical Faculty and Natural Science Faculty, University of Rostock, Schillingallee 69, Rostock, 18057, Germany
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24
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Xue Y, Shi H, Feng B, Qiao L, Ding C, Yu S. Rapid identification of bacteria directly from blood cultures by Co-magnetic bead enrichment and MALDI-TOF MS profiling. Talanta 2021; 233:122472. [PMID: 34215106 DOI: 10.1016/j.talanta.2021.122472] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Revised: 04/19/2021] [Accepted: 04/23/2021] [Indexed: 12/12/2022]
Abstract
Direct identification of bacteria in blood cultures using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is interfered with by a variety of non-bacterial proteins derived from blood cells and culture media. Thus, appropriate pre-treatments are needed for successful identification. Here, the bacteria in blood culture bottles were enriched using co-magnetic beads and processed for MALDI-TOF MS profiling. In this strategy, the Fc-containing mannose-binding lectin-coated Fe3O4 (Fc-MBL@Fe3O4) is incorporated with human IgG-coated Fe3O4 (IgG@Fe3O4) to form co-magnetic beads, which can recognize both Gram-positive and Gram-negative bacteria. Compared to single magnetic beads Fc-MBL@Fe3O4 or IgG@Fe3O4, co-magnetic beads resulted in better bacterial capture efficiency and, therefore, could decrease the false-negative results. Our proposed strategy is much more suitable for enrichment of clinically unknown bacteria from blood culture bottles for MALDI-TOF MS database identification.
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Affiliation(s)
- Yuyan Xue
- Department of Chemistry, Fudan University, Shanghai, 200438, China; Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, 315211, China
| | - Haimei Shi
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, 315211, China
| | - Bin Feng
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, 315211, China
| | - Liang Qiao
- Department of Chemistry, Fudan University, Shanghai, 200438, China
| | - Chuanfan Ding
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, 315211, China.
| | - Shaoning Yu
- Department of Chemistry, Fudan University, Shanghai, 200438, China; Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang, 315211, China.
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Ying J, Gao W, Huang D, Ding C, Ling L, Pan T, Yu S. Application of MALDI-TOF MS Profiling Coupled With Functionalized Magnetic Enrichment for Rapid Identification of Pathogens in a Patient With Open Fracture. Front Chem 2021; 9:672744. [PMID: 33996766 PMCID: PMC8120279 DOI: 10.3389/fchem.2021.672744] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 04/19/2021] [Indexed: 01/17/2023] Open
Abstract
Posttraumatic infections can occur in orthopedic trauma patients, especially in open fractures. Rapid and accurate identification of pathogens in orthopedic trauma is important for clinical diagnosis and antimicrobial treatment. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been successfully used for first-line identification of pathogens grown on culture plates. However, for direct analysis of liquid clinical specimens, pre-purification of the sample is necessary. Herein, we investigated the feasibility of coupling Fc-MBL@Fe3O4 enrichment with MALDI-TOF MS profiling in the identification of pathogens in liquid-cultured samples. This method is successfully used for the identification of pathogens in a patient with an open-leg fracture obtained at sea. Pathogens were enriched by Fc-MBL@Fe3O4 from briefly pre-cultured liquid media and identified by MALDI-TOF MS. We identified an opportunistic pathogen, Vibrio alginolyticus, which is uncommon in clinical orthopedic trauma infection but exists widely in the sea. Therefore, combining Fc-MBL@Fe3O4 enrichment and MALDI-TOF MS profiling has great potential for direct identification of microbes in clinical samples.
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Affiliation(s)
| | - Wenjing Gao
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | | | - Chuanfan Ding
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Ling Ling
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Tao Pan
- Department of Breast Surgery and Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaoning Yu
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
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Taylor M, Lukowski JK, Anderton CR. Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:872-894. [PMID: 33656885 PMCID: PMC8033567 DOI: 10.1021/jasms.0c00439] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/20/2021] [Accepted: 01/25/2021] [Indexed: 05/02/2023]
Abstract
Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.
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Affiliation(s)
- Michael
J. Taylor
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jessica K. Lukowski
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Christopher R. Anderton
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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Desaire H, Patabandige MW, Hua D. The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data. Anal Bioanal Chem 2021; 413:1583-1593. [PMID: 33580828 PMCID: PMC8516084 DOI: 10.1007/s00216-020-03117-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/17/2020] [Accepted: 12/08/2020] [Indexed: 11/25/2022]
Abstract
One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist’s ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30 year old statistical approach to address the problem. The solution, “local-balanced model,” relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.
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Affiliation(s)
- Heather Desaire
- Department of Chemistry, University of Kansas, Lawrence, KS, 66045, USA.
| | | | - David Hua
- Department of Chemistry, University of Kansas, Lawrence, KS, 66045, USA
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