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Embracing lipidomics at single-cell resolution: Promises and pitfalls. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Yang Z, Li B, Stuart DD, Cheng Q. Three‐dimensional printed microfluidic mixer/extractor for cell lysis and lipidomic profiling by matrix‐assisted laser desorption/ionization mass spectrometry. VIEW 2022. [DOI: 10.1002/viw.20220041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Zhengdong Yang
- Department of Chemistry University of California Riverside California USA
| | - Bochao Li
- Environmental Toxicology University of California Riverside California USA
| | - Daniel D. Stuart
- Department of Chemistry University of California Riverside California USA
| | - Quan Cheng
- Department of Chemistry University of California Riverside California USA
- Environmental Toxicology University of California Riverside California USA
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Du B, Lu H, Zhang Z, Wang Y, Hu X, Chen Q, Song M, Liu M. Self-powered aptasensor for picomole level pollutants based on a novel enzyme-free photofuel cell. Biosens Bioelectron 2022; 216:114661. [DOI: 10.1016/j.bios.2022.114661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/17/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022]
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Bien T, Koerfer K, Schwenzfeier J, Dreisewerd K, Soltwisch J. Mass spectrometry imaging to explore molecular heterogeneity in cell culture. Proc Natl Acad Sci U S A 2022; 119:e2114365119. [PMID: 35858333 PMCID: PMC9303856 DOI: 10.1073/pnas.2114365119] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 05/13/2022] [Indexed: 01/13/2023] Open
Abstract
Molecular analysis on the single-cell level represents a rapidly growing field in the life sciences. While bulk analysis from a pool of cells provides a general molecular profile, it is blind to heterogeneities between individual cells. This heterogeneity, however, is an inherent property of every cell population. Its analysis is fundamental to understanding the development, function, and role of specific cells of the same genotype that display different phenotypical properties. Single-cell mass spectrometry (MS) aims to provide broad molecular information for a significantly large number of cells to help decipher cellular heterogeneity using statistical analysis. Here, we present a sensitive approach to single-cell MS based on high-resolution MALDI-2-MS imaging in combination with MALDI-compatible staining and use of optical microscopy. Our approach allowed analyzing large amounts of unperturbed cells directly from the growth chamber. Confident coregistration of both modalities enabled a reliable compilation of single-cell mass spectra and a straightforward inclusion of optical as well as mass spectrometric features in the interpretation of data. The resulting multimodal datasets permit the use of various statistical methods like machine learning-driven classification and multivariate analysis based on molecular profile and establish a direct connection of MS data with microscopy information of individual cells. Displaying data in the form of histograms for individual signal intensities helps to investigate heterogeneous expression of specific lipids within the cell culture and to identify subpopulations intuitively. Ultimately, t-MALDI-2-MSI measurements at 2-µm pixel sizes deliver a glimpse of intracellular lipid distributions and reveal molecular profiles for subcellular domains.
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Affiliation(s)
- Tanja Bien
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
- Interdisciplinary Center for Clinical Research (IZKF), University of Münster, 48149 Münster, Germany
| | - Krischan Koerfer
- Institute for Psychology, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioural Neuroscience, University of Münster, 48149 Münster, Germany
| | - Jan Schwenzfeier
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
| | - Klaus Dreisewerd
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
- Interdisciplinary Center for Clinical Research (IZKF), University of Münster, 48149 Münster, Germany
| | - Jens Soltwisch
- Institute of Hygiene, University of Münster, 48149 Münster, Germany
- Interdisciplinary Center for Clinical Research (IZKF), University of Münster, 48149 Münster, Germany
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Li B, Stuart DD, Shanta PV, Pike CD, Cheng Q. Probing Herbicide Toxicity to Algae ( Selenastrum capricornutum) by Lipid Profiling with Machine Learning and Microchip/MALDI-TOF Mass Spectrometry. Chem Res Toxicol 2022; 35:606-615. [PMID: 35289601 DOI: 10.1021/acs.chemrestox.1c00397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS)-based lipid profiling is a powerful method to study the cytotoxicity of chemical exposure to microorganisms at the single cell level. We report here a combined approach of machine learning (ML) and microchip-based MALDI-time of flight (TOF) mass spectrometry to investigate the cytotoxic effect of herbicides on algae through single cell lipid profiling. Algal species Selenastrum capricornutum was chosen as the target system, and its exposure to different doses of common chemical herbicides and the resulting cytotoxic behaviors under various stress conditions were characterized. A lipid library for S. capricornutum has been established with 63 identified lipids that include glycosyldiacylglycerols and triacylglycerols. We demonstrated that major alternations occurred for lipids with functional groups of digalactosyldiacylglycerol (DGDG), triacylglycerol (TAG), and monogalactosyldiacylglycerol (MGDG). DGDG was shown to decrease upon exposure to herbicides of norflurazon and atrazine, while some MGDG and TAG lipids would increase for norflurazon. Compared to other algae, S. capricornutum was more strongly impacted by norflurazon than atrazine while the latter was observed to have a greater effect on C. reinhardtii. Machine learning algorithms have been applied to improve the classification of herbicide impact and help identify lipid species affected by the chemical exposure. A total of 69 machine learning models were trained and tested for the identification of ideal algorithms in the classification process, in which flexible discriminant analysis and support vector machine model were found to be the most accurate and consistent. The ML algorithms accurately differentiated herbicide impact and have identified cytotoxic differences that were previously hidden. The results suggest that herbicides express toxicity among different algae likely on the basis of metabolic differences. The ML-assisted method proves to be highly effective and can provide an advanced technological platform for probing cytotoxicity for bacterial species and in metabolic pathway analysis.
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Watson ER, Taherian Fard A, Mar JC. Computational Methods for Single-Cell Imaging and Omics Data Integration. Front Mol Biosci 2022; 8:768106. [PMID: 35111809 PMCID: PMC8801747 DOI: 10.3389/fmolb.2021.768106] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/29/2021] [Indexed: 12/12/2022] Open
Abstract
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
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Affiliation(s)
| | - Atefeh Taherian Fard
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
| | - Jessica Cara Mar
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, QLD, Australia
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Zhu G, Shao Y, Liu Y, Pei T, Li L, Zhang D, Guo G, Wang X. Single-cell metabolite analysis by electrospray ionization mass spectrometry. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116351] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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Kulkarni AS, Huang L, Qian K. Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications. J Mater Chem B 2021; 9:3622-3639. [PMID: 33871513 DOI: 10.1039/d1tb00289a] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.
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Affiliation(s)
- Anuja Shreeram Kulkarni
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China and School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.
| | - Lin Huang
- Stem Cell Research Center, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China.
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related Genes, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, P. R. China and School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China.
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Shanta PV, Li B, Stuart DD, Cheng Q. Lipidomic Profiling of Algae with Microarray MALDI-MS toward Ecotoxicological Monitoring of Herbicide Exposure. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:10558-10568. [PMID: 34286960 DOI: 10.1021/acs.est.1c01138] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Misuse of agrochemicals has a long-lasting negative impact on aquatic systems. Mismanagement of herbicides in agri-food sectors is often linked to a simultaneous decline in the health of downstream waterways. However, monitoring the herbicide levels in these areas is a laborious task, and modern analytical approaches, such as solid-phase extraction-liquid chromatography-mass spectrometry (SPE-LC-MS) and enzyme-linked immunosorbent assay, are low-throughput and require significant sample preparation. We report here the use of microchip technology in combination with matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) for the assessment of the ecotoxicological effect of agrochemicals on aquatic species at the single-cell level. This approach quantifies the fluctuations in lipid content in sentinel organisms and targets the microalga, Chlamydomonas reinhardtii (C. reinhardtii), as the model system. Specifically, we investigated the cytotoxicity of three herbicides (atrazine, clomazone, and norflurazon) on C. reinhardtii by analyzing the lipid component variation upon assorted herbicide exposure. Lipidomic profiling reveals a significantly altered lipid content at >EC50 in atrazine-exposed cells. The response for norflurazon showed similar trends but diminished in magnitude, while the result for clomazone was near muted. At lower herbicide concentrations, digalactosyldiacylglycerols showed a rapid decrease in abundance, while several other lipids displayed a moderate increase. The microchip-based MALDI technique demonstrates the ability to achieve lipidomic profiling of aquatic species exposed to different stressors, proving effective for high-throughput screening and single-cell analysis in ecotoxicity studies.
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Affiliation(s)
- Peter V Shanta
- Environmental Toxicology, University of California, Riverside, California 92521, United States
| | - Bochao Li
- Environmental Toxicology, University of California, Riverside, California 92521, United States
| | - Daniel D Stuart
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Quan Cheng
- Environmental Toxicology, University of California, Riverside, California 92521, United States
- Department of Chemistry, University of California, Riverside, California 92521, United States
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Abstract
The ultimate goal of single-cell analyses is to obtain the biomolecular content for each cell in unicellular and multicellular organisms at different points of their life cycle under variable environmental conditions. These require an assessment of: a) the total number of cells, b) the total number of cell types, and c) the complete and quantitative single molecular detection and identification for all classes of biopolymers, and organic and inorganic compounds, in each individual cell. For proteins, glycans, lipids, and metabolites, whose sequences cannot be amplified by copying as in the case of nucleic acids, the detection limit by mass spectrometry is about 105 molecules. Therefore, proteomic, glycomic, lipidomic, and metabolomic analyses do not yet permit the assembly of the complete single-cell omes. The construction of novel nanoelectrophoretic arrays and nano in microarrays on a single 1-cm-diameter chip has shown proof of concept for a high throughput platform for parallel processing of thousands of individual cells. Combined with dynamic secondary ion mass spectrometry, with 3D scanning capability and lateral resolution of 50 nm, the sensitivity of single molecular quantification and identification for all classes of biomolecules could be reached. Further development and routine application of such technological and instrumentation solution would allow assembly of complete omes with a quantitative assessment of structural and functional cellular diversity at the molecular level.
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Malinick AS, Lambert AS, Stuart DD, Li B, Puente E, Cheng Q. Detection of Multiple Sclerosis Biomarkers in Serum by Ganglioside Microarrays and Surface Plasmon Resonance Imaging. ACS Sens 2020; 5:3617-3626. [PMID: 33115236 DOI: 10.1021/acssensors.0c01935] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheaths of nerve cells in the central nervous system. An individual suffering from MS produces increased levels of antibodies that target cell membrane components, such as phospholipids, gangliosides, and membrane proteins. Among them, anti-ganglioside antibodies are considered as important biomarkers to differentiate MS from other diseases that exhibit similar symptoms. We report here a label-free method for detecting a series of antibodies against gangliosides in serum by surface plasmon resonance imaging (SPRi) in combination with a carbohydrate microarray. The ganglioside array was fabricated with a plasmonically tuned, background-free biochip, and coated with a perfluorodecyltrichlorosilane (PFDTS) layer for antigen attachment as a self-assembled pseudo-myelin sheath. The chip was characterized with AFM and matrix-assisted laser desorption ionization mass spectrometry, demonstrating effective functionalization of the surface. SPRi measurements of patients' mimicking blood samples were conducted. A multiplexed detection of antibodies for anti-GT1b, anti-GM1, and anti-GA1 in serum was demonstrated, with a working range of 1 to 100 ng/mL, suggesting that it is well suited for clinical assessment of antibody abnormality in MS patients. Statistical analyses, including PLS-DA and PCA show the array allows comprehensive characterization of cross reactivity patterns between the MS specific antibodies and can generate a wide range of information compared to traditional end point assays. This work uses PFDTS surface functionalization and enables direct MS biomarker detection in serum, offering a powerful alternative for MS assessment and potentially improved patient care.
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Affiliation(s)
- Alexander S. Malinick
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Alexander S. Lambert
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Daniel D. Stuart
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Bochao Li
- Environmental Toxicology, University of California, Riverside, California 92521, United States
| | - Ellie Puente
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Quan Cheng
- Department of Chemistry, University of California, Riverside, California 92521, United States
- Environmental Toxicology, University of California, Riverside, California 92521, United States
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Recent Advances in Single Cell Analysis Methods Based on Mass Spectrometry. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2020. [DOI: 10.1016/s1872-2040(20)60038-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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