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Zhang Y, Zhao C, Picchetti P, Zheng K, Zhang X, Wu Y, Shen Y, De Cola L, Shi J, Guo Z, Zou X. Quantitative SERS sensor for mycotoxins with extraction and identification function. Food Chem 2024; 456:140040. [PMID: 38878539 DOI: 10.1016/j.foodchem.2024.140040] [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: 03/06/2024] [Revised: 05/15/2024] [Accepted: 06/07/2024] [Indexed: 07/24/2024]
Abstract
The development of new sensors for on-site food toxin monitoring that combine extraction, analytes distinction and detection is important in resource-limited environments. Surface-enhanced Raman scattering (SERS)-based signal readout features fast response and high sensitivity, making it a powerful method for detecting mycotoxins. In this work, a SERS-based assay for the detection of multiple mycotoxins is presented that combines extraction and subsequent detection, achieving an analytically relevant detection limit (∼ 1 ng/mL), which is also tested in corn samples. This sensor consists of a magnetic-core and mycotoxin-absorbing polydopamine-shell, with SERS-active Au nanoparticles on the outer surface. The assay can concentrate multiple mycotoxins, which are identified through multiclass partite least squares analysis based on their SERS spectra. We developed a strategy for the analysis of multiple mycotoxins with minimal sample pretreatment, enabling in situ analytical extraction and subsequent detection, displaying the potential to rapidly identify lethal mycotoxin contamination on site.
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Affiliation(s)
- Yang Zhang
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Chuping Zhao
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Pierre Picchetti
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany
| | - Kaiyi Zheng
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xinai Zhang
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Yanling Wu
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Ye Shen
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Luisa De Cola
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstrasse 12, 76131 Karlsruhe, Germany; Department DISFARM, University of Milano, via Camillo Golgi 19, 20133 Milano, Italy; Department of Molecular Biochemistry and Pharmacology, Istituto di Ricerche Farmacologiche Mario Negri IRRCCS, 20156 Milano, Italy
| | - Jiyong Shi
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhiming Guo
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xiaobo Zou
- International Joint Research Laboratory of Intelligent Agriculture and Agriproducts Processing, China Light Industry Key Laboratory of Food Intelligent Detection & Processing, School of Food and Biological Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
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Liu Y, Lang C, Zhang K, Feng L, Li J, Wang T, Sun S, Sun G. Injectable chitosan-polyvinylpyrrolidone composite thermosensitive hydrogels with sustained submucosal lifting for endoscopic submucosal dissection. Int J Biol Macromol 2024; 276:133165. [PMID: 38901518 DOI: 10.1016/j.ijbiomac.2024.133165] [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: 03/01/2024] [Revised: 05/30/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024]
Abstract
To develop a submucosal injection material with sustained submucosal lifting for endoscopic submucosal dissection (ESD), this study designed and prepared a novel composite thermosensitive hydrogel system with high pH chitosan-polyvinylpyrrolidone-β-glycerophosphate (HpHCS-PVP-GP). HpHCS improved the injectability of the hydrogels and retained the rapid gelation ability at low concentrations. The modification of PVP significantly improved the stability of low-temperature hydrogel precursor solutions and the integrity of hydrogels formed at 37 °C through hydrogen bonds between PVP and HpHCS. A mathematical model was established using response surface methodology (RSM) to evaluate the synergistic effect of HpHCS, GP, and PVP concentrations on gelation time. This RSM model and submucosal lifting evaluation using in vitro pig esophageal models were used to determine the optimal formula of HpHCS-PVP-GP hydrogels. Although the higher PVP concentration (5 % (w/v)) prolonged gelation time, it improved hydrogel mechanical strength, resulting in better submucosal lifting performance. The experiments of Bama mini pigs showed that the heights of the cushions elevated by the HpHCS-5%PVP-GP hydrogel remained about 80 % 1 h after injection. Repeated injections were avoided, and the hydrogel had no cytotoxicity after electric cutting. Therefore, the HpHCS-PVP-GP thermosensitive hydrogel might be a promising submucosal injection material for ESD.
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Affiliation(s)
- Yang Liu
- Innovative Engineering Technology Research Center for Cell Therapy, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China; Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China
| | - Chuang Lang
- Innovative Engineering Technology Research Center for Cell Therapy, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China
| | - Kai Zhang
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China
| | - Linlin Feng
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China
| | - Junying Li
- Innovative Engineering Technology Research Center for Cell Therapy, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China
| | - Tingting Wang
- Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China
| | - Siyu Sun
- Innovative Engineering Technology Research Center for Cell Therapy, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China; Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China.
| | - Guangwei Sun
- Innovative Engineering Technology Research Center for Cell Therapy, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China; Department of Gastroenterology, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110022, People's Republic of China.
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3
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Kong L, Wang Y, Cui D, He W, Zhang C, Zheng C. Application of single-cell Raman-deuterium isotope probing to reveal the resistance of marine ammonia-oxidizing archaea SCM1 against common antibiotics. CHEMOSPHERE 2024; 362:142500. [PMID: 38852635 DOI: 10.1016/j.chemosphere.2024.142500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 06/11/2024]
Abstract
Antimicrobial resistance (AMR) in oceans poses a significant threat to human health through the seafood supply chain. Ammonia-oxidizing archaea (AOA) are important marine microorganisms and play a key role in the biogeochemical nitrogen cycle around the world. However, the AMR of marine AOA to aquicultural antibiotics is poorly explored. Here, Raman-deuterium isotope probing (Raman-DIP), a single-cell tool, was developed to reveal the AMR of a typical marine species of AOA, Nitrosopumilus maritimus (designated SCM1), against six antibiotics, including erythromycin, tetracycline, novobiocin, neomycin, bacitracin, and vancomycin. The D2O concentration (30% v/v) and culture period (9 days) were optimized for the precise detection of metabolic activity in SCM1 cells through Raman-DIP. The relative metabolic activity of SCM1 upon exposure to antibiotics was semi-quantitatively calculated based on single-cell Raman spectra. SCM1 exhibited high resistance to erythromycin, tetracycline, novobiocin, neomycin, and vancomycin, with minimum inhibitory concentration (MIC) values between 100 and 400 mg/L, while SCM1 is very sensitive to bacitracin (MIC: 0.8 mg/L). Notably, SCM1 cells were completely inactive under the metabolic activity minimum inhibitory concentration conditions (MA-MIC: 1.6-800 mg/L) for the six antibiotics. Further genomic analysis revealed the antibiotic resistance genes (ARGs) of SCM1, including 14 types categorized into 33 subtypes. This work increases our knowledge of the AMR of marine AOA by linking the resistant phenome to the genome, contributing to the risk assessment of AMR in the underexplored ocean environment. As antibiotic resistance in marine microorganisms is significantly affected by the concentration of antibiotics in coastal environments, we encourage more studies concentrating on both the phenotypic and genotypic antibiotic resistance of marine archaea. This may facilitate a comprehensive evaluation of the capacity of marine microorganisms to spread AMR and the implementation of suitable control measures to protect environmental safety and human health.
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Affiliation(s)
- Lingchao Kong
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China; Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, 315200, China
| | - Yi Wang
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, 315200, China.
| | - Dongyu Cui
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wei He
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Chuanlun Zhang
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Chunmiao Zheng
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China; Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, 315200, China
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Razi S, Tarcea N, Henkel T, Ravikumar R, Pistiki A, Wagenhaus A, Girnus S, Taubert M, Küsel K, Rösch P, Popp J. Raman-Activated, Interactive Sorting of Isotope-Labeled Bacteria. SENSORS (BASEL, SWITZERLAND) 2024; 24:4503. [PMID: 39065901 PMCID: PMC11281290 DOI: 10.3390/s24144503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Revised: 07/03/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024]
Abstract
Due to its high spatial resolution, Raman microspectroscopy allows for the analysis of single microbial cells. Since Raman spectroscopy analyzes the whole cell content, this method is phenotypic and can therefore be used to evaluate cellular changes. In particular, labeling with stable isotopes (SIPs) enables the versatile use and observation of different metabolic states in microbes. Nevertheless, static measurements can only analyze the present situation and do not allow for further downstream evaluations. Therefore, a combination of Raman analysis and cell sorting is necessary to provide the possibility for further research on selected bacteria in a sample. Here, a new microfluidic approach for Raman-activated continuous-flow sorting of bacteria using an optical setup for image-based particle sorting with synchronous acquisition and analysis of Raman spectra for making the sorting decision is demonstrated, showing that active cells can be successfully sorted by means of this microfluidic chip.
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Affiliation(s)
- Sepehr Razi
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance—Leibniz Health Technologies, 07745 Jena, Germany; (S.R.); (N.T.); (T.H.); (A.P.)
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany; (M.T.); (K.K.)
| | - Nicolae Tarcea
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance—Leibniz Health Technologies, 07745 Jena, Germany; (S.R.); (N.T.); (T.H.); (A.P.)
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, 07743 Jena, Germany; (R.R.); (P.R.)
| | - Thomas Henkel
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance—Leibniz Health Technologies, 07745 Jena, Germany; (S.R.); (N.T.); (T.H.); (A.P.)
| | - Ramya Ravikumar
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, 07743 Jena, Germany; (R.R.); (P.R.)
| | - Aikaterini Pistiki
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance—Leibniz Health Technologies, 07745 Jena, Germany; (S.R.); (N.T.); (T.H.); (A.P.)
| | - Annette Wagenhaus
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, 07743 Jena, Germany; (R.R.); (P.R.)
| | - Sophie Girnus
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, 07743 Jena, Germany; (R.R.); (P.R.)
| | - Martin Taubert
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany; (M.T.); (K.K.)
- Aquatic Geomicrobiology, Institute of Biodiversity, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Kirsten Küsel
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany; (M.T.); (K.K.)
- Aquatic Geomicrobiology, Institute of Biodiversity, Friedrich Schiller University Jena, 07743 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, 07743 Jena, Germany; (R.R.); (P.R.)
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance—Leibniz Health Technologies, 07745 Jena, Germany; (S.R.); (N.T.); (T.H.); (A.P.)
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany; (M.T.); (K.K.)
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, 07743 Jena, Germany; (R.R.); (P.R.)
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Yan S, Guo X, Zong Z, Li Y, Li G, Xu J, Jin C, Liu Q. Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens. Foods 2024; 13:1886. [PMID: 38928827 PMCID: PMC11203195 DOI: 10.3390/foods13121886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 06/09/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024] Open
Abstract
Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.
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Affiliation(s)
- Shuaishuai Yan
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xinru Guo
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
| | - Zheng Zong
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
| | - Yang Li
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
| | - Guoliang Li
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
- School of Food and Biological Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
| | - Jianguo Xu
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
| | - Chengni Jin
- College of Food Science, Shanxi Normal University, Taiyuan 030031, China; (S.Y.); (X.G.); (Z.Z.); (Y.L.); (G.L.); (J.X.)
| | - Qing Liu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Zhou Y, Yan A, Yang J, He W, Guo S, Li Y, Wu J, Dai Y, Pan X, Cui D, Pereira O, Teng W, Bi R, Chen S, Fan L, Wang P, Liao Y, Qin W, Sui SF, Zhu Y, Zhang C, Liu Z. Ultrastructural insights into cellular organization, energy storage and ribosomal dynamics of an ammonia-oxidizing archaeon from oligotrophic oceans. Front Microbiol 2024; 15:1367658. [PMID: 38737410 PMCID: PMC11082331 DOI: 10.3389/fmicb.2024.1367658] [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: 01/09/2024] [Accepted: 04/16/2024] [Indexed: 05/14/2024] Open
Abstract
Introduction Nitrososphaeria, formerly known as Thaumarchaeota, constitute a diverse and widespread group of ammonia-oxidizing archaea (AOA) inhabiting ubiquitously in marine and terrestrial environments, playing a pivotal role in global nitrogen cycling. Despite their importance in Earth's ecosystems, the cellular organization of AOA remains largely unexplored, leading to a significant unanswered question of how the machinery of these organisms underpins metabolic functions. Methods In this study, we combined spherical-chromatic-aberration-corrected cryo-electron tomography (cryo-ET), scanning transmission electron microscopy (STEM), and energy dispersive X-ray spectroscopy (EDS) to unveil the cellular organization and elemental composition of Nitrosopumilus maritimus SCM1, a representative member of marine Nitrososphaeria. Results and Discussion Our tomograms show the native ultrastructural morphology of SCM1 and one to several dense storage granules in the cytoplasm. STEM-EDS analysis identifies two types of storage granules: one type is possibly composed of polyphosphate and the other polyhydroxyalkanoate. With precise measurements using cryo-ET, we observed low quantity and density of ribosomes in SCM1 cells, which are in alignment with the documented slow growth of AOA in laboratory cultures. Collectively, these findings provide visual evidence supporting the resilience of AOA in the vast oligotrophic marine environment.
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Affiliation(s)
- Yangkai Zhou
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - An Yan
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Jiawen Yang
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Wei He
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Shuai Guo
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yifan Li
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Jing Wu
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yanchao Dai
- Shanghai NanoPort, Thermo Fisher Scientific Inc., Shanghai, China
| | - Xijiang Pan
- Shanghai NanoPort, Thermo Fisher Scientific Inc., Shanghai, China
| | - Dongyu Cui
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Olivier Pereira
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Institut AMU-WUT, Aix-Marseille Université and Wuhan University of Technology, Wuhan, Hubei, China
| | - Wenkai Teng
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Ran Bi
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Songze Chen
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Lu Fan
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Peiyi Wang
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yan Liao
- Australian Institute for Microbiology & Infection, University of Technology Sydney, Ultimo, NSW, Australia
| | - Wei Qin
- School of Biological Sciences and Institute for Environmental Genomics, University of Oklahoma, Norman, OK, United States
| | - Sen-Fang Sui
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
- State Key Laboratory of Membrane Biology, Beijing Frontier Research Center for Biological Structures, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Yuanqing Zhu
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai, China
| | - Chuanlun Zhang
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai, China
- Advanced Institute for Ocean Research, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Zheng Liu
- Cryo-Electron Microscopy Center, Southern University of Science and Technology, Shenzhen, Guangdong, China
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Xu J, Morten KJ. Raman micro-spectroscopy as a tool to study immunometabolism. Biochem Soc Trans 2024; 52:733-745. [PMID: 38477393 PMCID: PMC11088913 DOI: 10.1042/bst20230794] [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: 12/05/2023] [Revised: 02/27/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.
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Affiliation(s)
- Jiabao Xu
- Division of Biomedical Engineering, James Watt School of Engineering, University of Glasgow, Glasgow G12 8LT, U.K
| | - Karl J Morten
- Nuffield Department of Women's and Reproductive Health, University of Oxford, The Women Centre, John Radcliffe Hospital, Headley Way, Headington, Oxford OX3 9DU, U.K
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8
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Lu S, Yang J, Gu Y, He D, Wu H, Sun W, Xu D, Li C, Guo C. Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors. ACS Sens 2024; 9:1134-1148. [PMID: 38363978 DOI: 10.1021/acssensors.3c02670] [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] [Indexed: 02/18/2024]
Abstract
Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.
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Affiliation(s)
- Shasha Lu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Jianyu Yang
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Yu Gu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Dongyuan He
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Haocheng Wu
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Wei Sun
- College of Chemistry and Chemical Engineering, Hainan Normal University, Haikou 571158, China
| | - Dong Xu
- Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou 310022, China
| | - Changming Li
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
| | - Chunxian Guo
- School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215011, China
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Xie M, Zhu Y, Li Z, Yan Y, Liu Y, Wu W, Zhang T, Li Z, Wang H. Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis. Talanta 2024; 268:125281. [PMID: 37832450 DOI: 10.1016/j.talanta.2023.125281] [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: 07/18/2023] [Revised: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023]
Abstract
Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.
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Affiliation(s)
- Maomei Xie
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yiting Zhu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zhiyao Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yueling Yan
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Yidan Liu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Wenbo Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Tong Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
| | - Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine (TCM), Tianjin University of TCM, Tianjin, 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin, 301617, China; State Key Laboratory of Component-based Chinese Medicine, Tianjin University of TCM, Tianjin, 301617, China.
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10
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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11
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Liu Z, Xue Y, Yang C, Li B, Zhang Y. Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy. Front Microbiol 2023; 14:1065173. [PMID: 36778844 PMCID: PMC9909742 DOI: 10.3389/fmicb.2023.1065173] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 01/03/2023] [Indexed: 01/27/2023] Open
Abstract
Respiratory infections rank fourth in the global economic burden of disease. Lower respiratory tract infections are the leading cause of death in low-income countries. The rapid identification of pathogens causing lower respiratory tract infections to help guide the use of antibiotics can reduce the mortality of patients with lower respiratory tract infections. Single-cell Raman spectroscopy is a "whole biological fingerprint" technique that can be used to identify microbial samples. It has the advantages of no marking and fast and non-destructive testing. In this study, single-cell Raman spectroscopy was used to collect spectral data of six respiratory tract pathogen isolates. The T-distributed stochastic neighbor embedding (t-SNE) isolation analysis algorithm was used to compare the differences between the six respiratory tract pathogens. The eXtreme Gradient Boosting (XGBoost) algorithm was used to establish a Raman phenotype database model. The classification accuracy of the isolated samples was 93-100%, and the classification accuracy of the clinical samples was more than 80%. Combined with heavy water labeling technology, the drug resistance of respiratory tract pathogens was determined. The study showed that single-cell Raman spectroscopy-D2O (SCRS-D2O) labeling could rapidly identify the drug resistance of respiratory tract pathogens within 2 h.
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Affiliation(s)
- Ziyu Liu
- Department of Pediatric Respiratory, The First Hospital of Jilin University, Changchun, China,School of Life Science, Jilin University, Changchun, China
| | - Ying Xue
- HOOKE Instruments Ltd., Changchun, China
| | - Chun Yang
- Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, Jilin, China
| | - Bei Li
- HOOKE Instruments Ltd., Changchun, China,The State Key Lab of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CAS), Changchun, China
| | - Ying Zhang
- Department of Pediatric Respiratory, The First Hospital of Jilin University, Changchun, China,*Correspondence: Ying Zhang ✉
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Xu J, Luo Y, Wang J, Tu W, Yi X, Xu X, Song Y, Tang Y, Hua X, Yu Y, Yin H, Yang Q, Huang WE. Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy. Front Microbiol 2023; 14:1125676. [PMID: 37032865 PMCID: PMC10073597 DOI: 10.3389/fmicb.2023.1125676] [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: 12/16/2022] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.
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Affiliation(s)
- Jiabao Xu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Yanjun Luo
- Shanghai Hesen Biotech Co., Shanghai, China
| | - Jingkai Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Weiming Tu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Xiaofei Yi
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaogang Xu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yizhi Song
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Yuguo Tang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xiaoting Hua
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yunsong Yu
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Huabing Yin
- James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | - Qiwen Yang
- Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Qiwen Yang,
| | - Wei E. Huang
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
- Wei E. Huang,
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13
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Li G, Wu C, Wang D, Srinivasan V, Kaeli DR, Dy JG, Gu AZ. Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13473-13484. [PMID: 36048618 DOI: 10.1021/acs.est.1c08768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.
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Affiliation(s)
- Guangyu Li
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853-0001, United States
| | - Chieh Wu
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - Dongqi Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- Department of Municipal and Environmental Engineering, School of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, PRC
| | - Varun Srinivasan
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- Brown and Caldwell, One Tech Drive, Andover, Massachusetts 01810, United States
| | - David R Kaeli
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - Jennifer G Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - April Z Gu
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853-0001, United States
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14
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Cui D, Kong L, Wang Y, Zhu Y, Zhang C. In situ identification of environmental microorganisms with Raman spectroscopy. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2022; 11:100187. [PMID: 36158754 PMCID: PMC9488013 DOI: 10.1016/j.ese.2022.100187] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/13/2022] [Accepted: 05/15/2022] [Indexed: 05/28/2023]
Abstract
Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.
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Affiliation(s)
- Dongyu Cui
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Lingchao Kong
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science & Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yi Wang
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yuanqing Zhu
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai Earthquake Agency, Shanghai, 200062, China
| | - Chuanlun Zhang
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
- Shenzhen Key Laboratory of Marine Archaea Geo-Omics, University of Southern University of Science and Technology, Shenzhen, 518055, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai Earthquake Agency, Shanghai, 200062, China
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