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Liu D, Hennelly BM. Wavenumber Calibration Protocol for Raman Spectrometers Using Physical Modelling and a Fast Search Algorithm. APPLIED SPECTROSCOPY 2024; 78:790-805. [PMID: 38825581 PMCID: PMC11340246 DOI: 10.1177/00037028241254847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/15/2024] [Indexed: 06/04/2024]
Abstract
A wavenumber calibration protocol is proposed that replaces polynomial fitting to relate the detector axis and the wavenumber shift. The physical model of the Raman spectrometer is used to derive a mathematical expression relating the detector plane to the wavenumber shift, in terms of the system parameters including the spectrograph focal length, the grating angle, and the laser wavelength; the model is general to both reflection and transmission gratings. A fast search algorithm detects the set of parameters that best explains the position of spectral lines recorded on the detector for a known reference standard. Using three different reference standards, four different systems, and hundreds of spectra recorded with a rotating grating, we demonstrate the superior accuracy of the technique, especially in bands outside of the outermost reference peaks when compared with polynomial fitting. We also provide a thorough review of wavenumber calibration for Raman spectroscopy and we introduce several new evaluation metrics to this field borrowed from chemometrics, including leave-one-out and leave-half-out cross-validation.
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Affiliation(s)
- Dongyue Liu
- Department of Electronic Engineering, Maynooth University, Kildare, Ireland
| | - Bryan M. Hennelly
- Department of Electronic Engineering, Maynooth University, Kildare, Ireland
- Department of Computer Science, Maynooth University, Kildare, Ireland
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2
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Xu L, Liang Y, Huang WE, Shang L, Chai L, Zhang X, Shi J, Li B, Wang Y, Xu Z, Lu Z. Rapid detection of six Oceanobacillus species in Daqu starter using single-cell Raman spectroscopy combined with machine learning. Microb Biotechnol 2024; 17:e14416. [PMID: 38381051 PMCID: PMC10880574 DOI: 10.1111/1751-7915.14416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/09/2024] [Accepted: 01/19/2024] [Indexed: 02/22/2024] Open
Abstract
Many traditional fermented foods and beverages industries around the world request the addition of multi-species starter cultures. However, the microbial community in starter cultures is subject to fluctuations due to their exposure to an open environment during fermentation. A rapid detection approach to identify the microbial composition of starter culture is essential to ensure the quality of the final products. Here, we applied single-cell Raman spectroscopy (SCRS) combined with machine learning to monitor Oceanobacillus species in Daqu starter, which plays crucial roles in the process of Chinese baijiu. First, a total of six Oceanobacillus species (O. caeni, O. kimchii, O. iheyensis, O. sojae, O. oncorhynchi subsp. Oncorhynchi and O. profundus) were detected in 44 Daqu samples by amplicon sequencing and isolated by pure culture. Then, we created a reference database of these Oceanobacillus strains which correlated their taxonomic data and single-cell Raman spectra (SCRS). Based on the SCRS dataset, five machine-learning algorithms were used to classify Oceanobacillus strains, among which support vector machine (SVM) showed the highest rate of accuracy. For validation of SVM-based model, we employed a synthetic microbial community composed of varying proportions of Oceanobacillus species and demonstrated a remarkable accuracy, with a mean error was less than 1% between the predicted result and the expected value. The relative abundance of six different Oceanobacillus species during Daqu fermentation was predicted within 60 min using this method, and the reliability of the method was proved by correlating the Raman spectrum with the amplicon sequencing profiles by partial least squares regression. Our study provides a rapid, non-destructive and label-free approach for rapid identification of Oceanobacillus species in Daqu starter culture, contributing to real-time monitoring of fermentation process and ensuring high-quality products.
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Affiliation(s)
- Lei Xu
- Key Laboratory of Industrial Biotechnology of Ministry of Education, School of BiotechnologyJiangnan UniversityWuxiChina
- National Engineering Research Center of Cereal Fermentation and Food BiomanufacturingJiangnan UniversityWuxiChina
| | - Yuan Liang
- Key Laboratory of Industrial Biotechnology of Ministry of Education, School of BiotechnologyJiangnan UniversityWuxiChina
| | - Wei E Huang
- Oxford Suzhou Centre for Advanced ResearchSuzhouChina
- Department of Engineering ScienceUniversity of OxfordOxfordUK
| | - Lin‐Dong Shang
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesChangchunChina
| | - Li‐Juan Chai
- National Engineering Research Center of Cereal Fermentation and Food BiomanufacturingJiangnan UniversityWuxiChina
| | - Xiao‐Juan Zhang
- National Engineering Research Center of Cereal Fermentation and Food BiomanufacturingJiangnan UniversityWuxiChina
| | - Jin‐Song Shi
- School of Life Sciences and Health EngineeringJiangnan UniversityWuxiChina
| | - Bei Li
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesChangchunChina
| | - Yun Wang
- Oxford Suzhou Centre for Advanced ResearchSuzhouChina
| | - Zheng‐Hong Xu
- Key Laboratory of Industrial Biotechnology of Ministry of Education, School of BiotechnologyJiangnan UniversityWuxiChina
- National Engineering Research Center of Cereal Fermentation and Food BiomanufacturingJiangnan UniversityWuxiChina
- National Engineering Research Center of Solid‐State BrewingLuzhouChina
| | - Zhen‐Ming Lu
- Key Laboratory of Industrial Biotechnology of Ministry of Education, School of BiotechnologyJiangnan UniversityWuxiChina
- National Engineering Research Center of Cereal Fermentation and Food BiomanufacturingJiangnan UniversityWuxiChina
- National Engineering Research Center of Solid‐State BrewingLuzhouChina
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3
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Lelliott PM, Hobro AJ, Pavillon N, Nishide M, Okita Y, Mizuno Y, Obata S, Nameki S, Yoshimura H, Kumanogoh A, Smith NI. Single-cell Raman microscopy with machine learning highlights distinct biochemical features of neutrophil extracellular traps and necrosis. Sci Rep 2023; 13:10093. [PMID: 37344494 DOI: 10.1038/s41598-023-36667-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/07/2023] [Indexed: 06/23/2023] Open
Abstract
The defining biology that distinguishes neutrophil extracellular traps (NETs) from other forms of cell death is unresolved, and techniques which unambiguously identify NETs remain elusive. Raman scattering measurement provides a holistic overview of cell molecular composition based on characteristic bond vibrations in components such as lipids and proteins. We collected Raman spectra from NETs and freeze/thaw necrotic cells using a custom built high-throughput platform which is able to rapidly measure spectra from single cells. Principal component analysis of Raman spectra from NETs clearly distinguished them from necrotic cells despite their similar morphology, demonstrating their fundamental molecular differences. In contrast, classical techniques used for NET analysis, immunofluorescence microscopy, extracellular DNA, and ELISA, could not differentiate these cells. Additionally, machine learning analysis of Raman spectra indicated subtle differences in lipopolysaccharide (LPS)-induced as opposed to phorbol myristate acetate (PMA)-induced NETs, demonstrating the molecular composition of NETs varies depending on the stimulant used. This study demonstrates the benefits of Raman microscopy in discriminating NETs from other types of cell death and by their pathway of induction.
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Affiliation(s)
- Patrick Michael Lelliott
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan.
| | - Alison Jane Hobro
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan
| | - Nicolas Pavillon
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan
| | - Masayuki Nishide
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yasutaka Okita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yumiko Mizuno
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Sho Obata
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Shinichiro Nameki
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hanako Yoshimura
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory of Immunopathology, Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Open and Transdisciplinary Research Institute (OTRI), Osaka University, Osaka, Japan
| | - Nicholas Isaac Smith
- Laboratory of Biophotonics, Immunology Frontier Research Center, Osaka University, Yamadaoka 3-1, Suita, Osaka, 565-0871, Japan.
- Open and Transdisciplinary Research Institute (OTRI), Osaka University, Osaka, Japan.
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4
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Olayah F, Senan EM, Ahmed IA, Awaji B. Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features. Diagnostics (Basel) 2023; 13:diagnostics13111899. [PMID: 37296753 DOI: 10.3390/diagnostics13111899] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
White blood cells (WBCs) are one of the main components of blood produced by the bone marrow. WBCs are part of the immune system that protects the body from infectious diseases and an increase or decrease in the amount of any type that causes a particular disease. Thus, recognizing the WBC types is essential for diagnosing the patient's health and identifying the disease. Analyzing blood samples to determine the amount and WBC types requires experienced doctors. Artificial intelligence techniques were applied to analyze blood samples and classify their types to help doctors distinguish between types of infectious diseases due to increased or decreased WBC amounts. This study developed strategies for analyzing blood slide images to classify WBC types. The first strategy is to classify WBC types by the SVM-CNN technique. The second strategy for classifying WBC types is by SVM based on hybrid CNN features, which are called VGG19-ResNet101-SVM, ResNet101-MobileNet-SVM, and VGG19-ResNet101-MobileNet-SVM techniques. The third strategy for classifying WBC types by FFNN is based on a hybrid model of CNN and handcrafted features. With MobileNet and handcrafted features, FFNN achieved an AUC of 99.43%, accuracy of 99.80%, precision of 99.75%, specificity of 99.75%, and sensitivity of 99.68%.
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Affiliation(s)
- Fekry Olayah
- Department of Information System, Faculty Computer Science and information System, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | | | - Bakri Awaji
- Department of Computer Science, Faculty of Computer Science and Information System, Najran University, Najran 66462, Saudi Arabia
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5
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Wang J, Meng S, Lin K, Yi X, Sun Y, Xu X, He N, Zhang Z, Hu H, Qie X, Zhang D, Tang Y, Huang WE, He J, Song Y. Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections. Anal Chim Acta 2023; 1239:340658. [PMID: 36628751 DOI: 10.1016/j.aca.2022.340658] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/21/2022] [Accepted: 11/21/2022] [Indexed: 11/26/2022]
Abstract
Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.
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Affiliation(s)
- Jingkai Wang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, China
| | - Siyu Meng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Kaicheng Lin
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xiaofei Yi
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 20040, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yixiang Sun
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Xiaogang Xu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 20040, China; National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Na He
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhiqiang Zhang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Huijie Hu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, China
| | - Xingwang Qie
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Dayi Zhang
- College of New Energy and Environment, Jilin University, Changchun, 130021, PR China
| | - Yuguo Tang
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Wei E Huang
- Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
| | - Jian He
- State Key Laboratory of Oncogenes and Related Genes, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Yizhi Song
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, China.
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6
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Naumann M, Arend N, Guliev RR, Kretzer C, Rubio I, Werz O, Neugebauer U. Label-Free Characterization of Macrophage Polarization Using Raman Spectroscopy. Int J Mol Sci 2023; 24:824. [PMID: 36614272 PMCID: PMC9821063 DOI: 10.3390/ijms24010824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/23/2022] [Accepted: 12/28/2022] [Indexed: 01/05/2023] Open
Abstract
Macrophages are important cells of the innate immune system that play many different roles in host defense, a fact that is reflected by their polarization into many distinct subtypes. Depending on their function and phenotype, macrophages can be grossly classified into classically activated macrophages (pro-inflammatory M1 cells), alternatively activated macrophages (anti-inflammatory M2 cells), and non-activated cells (resting M0 cells). A fast, label-free and non-destructive characterization of macrophage phenotypes could be of importance for studying the contribution of the various subtypes to numerous pathologies. In this work, single cell Raman spectroscopic imaging was applied to visualize the characteristic phenotype as well as to discriminate between different human macrophage phenotypes without any label and in a non-destructive manner. Macrophages were derived by differentiation of peripheral blood monocytes of human healthy donors and differently treated to yield M0, M1 and M2 phenotypes, as confirmed by marker analysis using flow cytometry and fluorescence imaging. Raman images of chemically fixed cells of those three macrophage phenotypes were processed using chemometric methods of unmixing (N-FINDR) and discrimination (PCA-LDA). The discrimination models were validated using leave-one donor-out cross-validation. The results show that Raman imaging is able to discriminate between pro- and anti-inflammatory macrophage phenotypes with high accuracy in a non-invasive, non-destructive and label-free manner. The spectral differences observed can be explained by the biochemical characteristics of the different phenotypes.
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Affiliation(s)
- Max Naumann
- Leibniz Institute of Photonic Technology Jena, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research, LPI, Albert-Einstein-Str. 9, 07745 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Natalie Arend
- Leibniz Institute of Photonic Technology Jena, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research, LPI, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Rustam R. Guliev
- Leibniz Institute of Photonic Technology Jena, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research, LPI, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Christian Kretzer
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, Philosophenweg 14, 07743 Jena, Germany
| | - Ignacio Rubio
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Department of Anaesthesiology and Intensive Care Medicine, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Oliver Werz
- Department of Pharmaceutical/Medicinal Chemistry, Institute of Pharmacy, Friedrich Schiller University Jena, Philosophenweg 14, 07743 Jena, Germany
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
| | - Ute Neugebauer
- Leibniz Institute of Photonic Technology Jena, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research, LPI, Albert-Einstein-Str. 9, 07745 Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
- Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Philosophenweg 7, 07743 Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
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7
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Pistiki A, Hornung F, Silge A, Ramoji A, Ryabchykov O, Bocklitz TW, Weber K, Löffler B, Popp J, Deinhardt‐Emmer S. Raman spectroscopic cellomics for the detection of SARS-CoV-2-associated neutrophil activation after TNF-α stimulation. Clin Transl Med 2022; 12:e1139. [PMID: 36536489 PMCID: PMC9763540 DOI: 10.1002/ctm2.1139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/18/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022] Open
Affiliation(s)
- Aikaterini Pistiki
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of PhotonicsJenaGermany,InfectoGnostics Research Campus JenaCenter for Applied ResearchJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
| | - Franziska Hornung
- Institute of Medical Microbiology, Jena University HospitalJenaGermany
| | - Anja Silge
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of PhotonicsJenaGermany,InfectoGnostics Research Campus JenaCenter for Applied ResearchJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
| | - Anuradha Ramoji
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of PhotonicsJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
| | - Oleg Ryabchykov
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of PhotonicsJenaGermany,InfectoGnostics Research Campus JenaCenter for Applied ResearchJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
| | - Thomas W. Bocklitz
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of PhotonicsJenaGermany,InfectoGnostics Research Campus JenaCenter for Applied ResearchJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
| | - Karina Weber
- InfectoGnostics Research Campus JenaCenter for Applied ResearchJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
| | - Bettina Löffler
- Institute of Medical Microbiology, Jena University HospitalJenaGermany
| | - Jürgen Popp
- Friedrich Schiller University Jena, Institute of Physical Chemistry and Abbe Center of PhotonicsJenaGermany,InfectoGnostics Research Campus JenaCenter for Applied ResearchJenaGermany,Leibniz Institute of Photonic TechnologyJenaGermany
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8
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Pistiki A, Salbreiter M, Sultan S, Rösch P, Popp J. Application of Raman spectroscopy in the hospital environment. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202200011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Aikaterini Pistiki
- Leibniz‐Institute of Photonic Technology Member of the Leibniz Research Alliance–Leibniz Health Technologies Jena Germany
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
| | - Markus Salbreiter
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
| | - Salwa Sultan
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
| | - Petra Rösch
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
| | - Jürgen Popp
- Leibniz‐Institute of Photonic Technology Member of the Leibniz Research Alliance–Leibniz Health Technologies Jena Germany
- InfectoGnostics Research Campus Jena Center of Applied Research Jena Germany
- Institute of Physical Chemistry and Abbe Center of Photonics Friedrich Schiller University Jena Germany
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9
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Liu Y, Wang Z, Zhou Z, Xiong T. Analysis and comparison of machine learning methods for blood identification using single-cell laser tweezer Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 277:121274. [PMID: 35500354 DOI: 10.1016/j.saa.2022.121274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 06/14/2023]
Abstract
Raman spectroscopy, a "fingerprint" spectrum of substances, can be used to characterize various biological and chemical samples. To allow for blood classification using single-cell Raman spectroscopy, several machine learning algorithms were implemented and compared. A single-cell laser optical tweezer Raman spectroscopy system was established to obtain the Raman spectra of red blood cells. The Boruta algorithm extracted the spectral feature frequency shift, reduced the spectral dimension, and determined the essential features that affect classification. Next, seven machine learning classification models are analyzed and compared based on the classification accuracy, precision, and recall indicators. The results show that support vector machines and artificial neural networks are the two most appropriate machine learning algorithms for single-cell Raman spectrum blood classification, and this finding provides essential guidance for future research studies.
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Affiliation(s)
- Yiming Liu
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instruments, Beijing Information Science and Technology University, Beijing, China
| | - Ziqi Wang
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instruments, Beijing Information Science and Technology University, Beijing, China
| | - Zhehai Zhou
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instruments, Beijing Information Science and Technology University, Beijing, China.
| | - Tao Xiong
- Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instruments, Beijing Information Science and Technology University, Beijing, China
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10
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Yan L, Li Y, Wu C, Shi Y, Kong C. Clinical Value of sTREM-1, PCT, and 1,3- β-D Glucan in Diagnosis of Immune-Associated Pulmonary Interstitial Disease with Fungal Infection. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6095441. [PMID: 35937405 PMCID: PMC9348935 DOI: 10.1155/2022/6095441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/08/2022] [Accepted: 06/28/2022] [Indexed: 11/25/2022]
Abstract
Background Fungal infection in the lungs can cause fungal infectious diseases. This disease develops rapidly and involves a wide range. Pathogenic fungi are also more serious types of pathogenic bacteria. If it invades deep organs and tissues, it will endanger life, so it needs timely diagnosis. Aim To investigate the diagnostic value of serum soluble myeloid cell triggering receptor-1 (sTREM-1), procalcitonin (PCT), and 1,3-β-D glucan detection in immune related lung disease complicated with fungal infection. Methods In this study, a case-control study was conducted. 50 patients with immune-related pulmonary disease complicated with fungal infection (infection group) diagnosed by sputum culture in our hospital from January 2017 to December 2021 were selected as the control group, and 50 patients with immune-related pulmonary disease without fungal infection were selected as the control group. The levels of sTREM-1, PCT, and 1,3-β-D glucan were compared in the two groups. The receiver operating characteristic (ROC) was used to analyze the value of the three indicators in the diagnosis of immune-related pulmonary disease complicated with fungal infection, and the changes of the three indicators before and after treatment were compared. Results The levels of sTREM-1, PCT, and 1,3-β-D glucan in the infection group were higher than those in the control group (P < 0.05). The levels of sTREM-1, PCT, and 1,3-β-D glucan in the infection group after treatment were significantly lower than those before treatment (P < 0.05). The AUC value of sTREM-1 in the diagnosis of immune-related pulmonary diseases complicated with fungal infection was 0.980, the sensitivity was 97.11%, and the specificity was 83.06%. The AUC value of PCT in the diagnosis of immune-related pulmonary diseases complicated with fungal infection was 0.860, the sensitivity was 80.00%, and the specificity was 72.41%. The AUC value of 1,3-β-D glucan in the diagnosis of immune-related pulmonary diseases complicated with fungal infection was 0.993, the sensitivity was 98.74%, and the specificity was 99.16%. The levels of sTREM-1, PCT, and 1,3-β-D glucan in the infection group after treatment were considerably lower than those before treatment, and the difference was statistically significant (P < 0.05). Conclusion The detection of sTREM-1, PCT, and 1,3-β-D glucan levels has high clinical value for the diagnosis of immune-related pulmonary diseases complicated with fungal infection.
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Affiliation(s)
- Lei Yan
- Department of Rheumatology and Immunology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Yuan Li
- Department of Rheumatology and Immunology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Chunye Wu
- Department of Rheumatology and Immunology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Yuquan Shi
- Department of Rheumatology and Immunology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Chunyu Kong
- Department of Rheumatology and Immunology, Tianjin First Central Hospital, Tianjin 300192, China
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11
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Szittner Z, Péter B, Kurunczi S, Székács I, Horváth R. Functional blood cell analysis by label-free biosensors and single-cell technologies. Adv Colloid Interface Sci 2022; 308:102727. [DOI: 10.1016/j.cis.2022.102727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/01/2022]
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12
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Belyaev I, Marolda A, Praetorius JP, Sarkar A, Medyukhina A, Hünniger K, Kurzai O, Thilo Figge M. Automated Characterisation of Neutrophil Activation Phenotypes in Ex Vivo Human Candida Blood Infections. Comput Struct Biotechnol J 2022; 20:2297-2308. [PMID: 35615019 PMCID: PMC9120255 DOI: 10.1016/j.csbj.2022.05.007] [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: 01/21/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022] Open
Abstract
Candida bloodstream infections are difficult to diagnose and treat in humans. Infection processes give rise to activation of host immune cells. Immune cell activation is reflected by characteristic cell morphology. Neutrophils exhibit distinct morphodynamics for different Candida species.
Rapid identification of pathogens is required for early diagnosis and treatment of life-threatening bloodstream infections in humans. This requirement is driving the current developments of molecular diagnostic tools identifying pathogens from human whole blood after successful isolation and cultivation. An alternative approach is to determine pathogen-specific signatures from human host immune cells that have been exposed to pathogens. We hypothesise that activated immune cells, such as neutrophils, may exhibit a characteristic behaviour — for instance in terms of their speed, dynamic cell morphology — that allows (i) identifying the type of pathogen indirectly and (ii) providing information on therapeutic efficacy. In this feasibility study, we propose a method for the quantitative assessment of static and morphodynamic features of neutrophils based on label-free time-lapse imaging data. We investigate neutrophil activation phenotypes after confrontation with fungal pathogens and isolation from a human whole-blood assay. In particular, we applied a machine learning supported approach to time-lapse microscopy data from different infection scenarios and were able to distinguish between Candida albicans and C. glabrata infection scenarios with test accuracies well above 75%, and to identify pathogen-free samples with accuracy reaching 100%. These results significantly exceed the test accuracies achieved using state-of-the-art deep neural networks to classify neutrophils by their morphodynamics.
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13
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He Q, Yang W, Luo W, Wilhelm S, Weng B. Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging. BIOSENSORS 2022; 12:250. [PMID: 35448310 PMCID: PMC9031282 DOI: 10.3390/bios12040250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/10/2022] [Accepted: 04/12/2022] [Indexed: 11/17/2022]
Abstract
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quick Raman spectroscopic imaging, a hyperspectral data processing approach based on machine learning methods proved capable of presenting the cell structure and distinguishing cancer cells from non-cancer muscle cells without compromising full-spectrum information. This study discovered that biomolecular information-nucleic acids, proteins, and lipids-from cells could be retrieved efficiently from low-quality hyperspectral Raman datasets and then employed for cell line differentiation.
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Affiliation(s)
- Qing He
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73072, USA
| | - Wen Yang
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA; (W.Y.); (S.W.)
| | - Weiquan Luo
- Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50010, USA;
| | - Stefan Wilhelm
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73072, USA; (W.Y.); (S.W.)
| | - Binbin Weng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73072, USA
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14
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Trends in pharmaceutical analysis and quality control by modern Raman spectroscopic techniques. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116623] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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15
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Allakhverdiev ES, Khabatova VV, Kossalbayev BD, Zadneprovskaya EV, Rodnenkov OV, Martynyuk TV, Maksimov GV, Alwasel S, Tomo T, Allakhverdiev SI. Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research. Cells 2022; 11:cells11030386. [PMID: 35159196 PMCID: PMC8834270 DOI: 10.3390/cells11030386] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/17/2022] [Accepted: 01/22/2022] [Indexed: 02/06/2023] Open
Abstract
Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.
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Affiliation(s)
- Elvin S. Allakhverdiev
- Russian National Medical Research Center of Cardiology, 3rd Cherepkovskaya St., 15A, 121552 Moscow, Russia; (E.S.A.); (O.V.R.); (T.V.M.)
- Biology Faculty, Lomonosov Moscow State University, Leninskie Gory 1/12, 119991 Moscow, Russia;
| | - Venera V. Khabatova
- K.A. Timiryazev Institute of Plant Physiology, RAS, Botanicheskaya str., 35, 127276 Moscow, Russia; (V.V.K.); (E.V.Z.)
| | - Bekzhan D. Kossalbayev
- Geology and Oil-gas Business Institute Named after K. Turyssov, Satbayev University, Satpaeva, 22, Almaty 050043, Kazakhstan;
- Department of Biotechnology, Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Al-Farabi Avenue 71, Almaty 050038, Kazakhstan
| | - Elena V. Zadneprovskaya
- K.A. Timiryazev Institute of Plant Physiology, RAS, Botanicheskaya str., 35, 127276 Moscow, Russia; (V.V.K.); (E.V.Z.)
| | - Oleg V. Rodnenkov
- Russian National Medical Research Center of Cardiology, 3rd Cherepkovskaya St., 15A, 121552 Moscow, Russia; (E.S.A.); (O.V.R.); (T.V.M.)
| | - Tamila V. Martynyuk
- Russian National Medical Research Center of Cardiology, 3rd Cherepkovskaya St., 15A, 121552 Moscow, Russia; (E.S.A.); (O.V.R.); (T.V.M.)
| | - Georgy V. Maksimov
- Biology Faculty, Lomonosov Moscow State University, Leninskie Gory 1/12, 119991 Moscow, Russia;
- Department of Physical Materials Science, Technological University “MISiS”, Leninskiy Prospekt 4, Office 626, 119049 Moscow, Russia
| | - Saleh Alwasel
- Zoology Department, College of Science, King Saud University, Riyadh 12372, Saudi Arabia;
| | - Tatsuya Tomo
- Department of Biology, Faculty of Science, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan;
| | - Suleyman I. Allakhverdiev
- K.A. Timiryazev Institute of Plant Physiology, RAS, Botanicheskaya str., 35, 127276 Moscow, Russia; (V.V.K.); (E.V.Z.)
- Zoology Department, College of Science, King Saud University, Riyadh 12372, Saudi Arabia;
- Institute of Basic Biological Problems, RAS, Pushchino, 142290 Moscow, Russia
- Correspondence:
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16
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Ramoji A, Pahlow S, Pistiki A, Rueger J, Shaik TA, Shen H, Wichmann C, Krafft C, Popp J. Understanding Viruses and Viral Infections by Biophotonic Methods. TRANSLATIONAL BIOPHOTONICS 2022. [DOI: 10.1002/tbio.202100008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Anuradha Ramoji
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- Center for Sepsis Control and Care Jena University Hospital, Am Klinikum 1, 07747 Jena Germany
| | - Susanne Pahlow
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
| | - Aikaterini Pistiki
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Jan Rueger
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Tanveer Ahmed Shaik
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Haodong Shen
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
| | - Christina Wichmann
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
| | - Christoph Krafft
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
| | - Juergen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4 Jena Germany
- Leibniz Institute of Photonic Technology Jena (a member of Leibniz Health Technologies) , Albert‐Einstein Str. 9 Jena Germany
- Center for Sepsis Control and Care Jena University Hospital, Am Klinikum 1, 07747 Jena Germany
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena Germany
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17
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Silva ALM, Silva ECO, Botelho RM, Tenorio LPG, Marques ALX, Rodrigues IBAC, Almeida LIM, Sousa AKA, Pires KSN, Tanabe ISB, Allard MJ, Sébire G, Souza ST, Fonseca EJS, Borbely KSC, Borbely AU. Uvaol Prevents Group B Streptococcus-Induced Trophoblast Cells Inflammation and Possible Endothelial Dysfunction. Front Physiol 2021; 12:766382. [PMID: 34925062 PMCID: PMC8678414 DOI: 10.3389/fphys.2021.766382] [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: 08/29/2021] [Accepted: 11/02/2021] [Indexed: 12/25/2022] Open
Abstract
Group B Streptococcus (GBS) infection during pregnancy is involved in maternal sepsis, chorioamnionitis, prematurity, fetal infection, neonatal sepsis, and neurodevelopmental alterations. The GBS-induced chorioamnionitis leads to a plethora of immune and trophoblast cells alterations that could influence endothelial cells to respond differently to angiogenic mediators and alter placental vascular structure and function in pregnant women. In this context, preventive measures are needed to reduce such dysfunctions. As such, we evaluated the effects of a non-lethal exposure to inactivated GBS on trophoblast cells and chorionic villi explants, and if the treatment with uvaol would mitigate these effects. The concentration of 106 CFU of GBS was chosen since it was unable to reduce the HTR-8/SVneo cell line nor term chorionic villi explant viability. Raman spectroscopy of trophoblast cells showed significant alterations in their biochemical signature, mostly reverted by uvaol. GBS exposure increased HTR-8/SVneo cells IL-1β and IFN-γ production, phagocytosis, oxidative stress, and decreased trophoblast cell migration. The Ea.hy926 endothelial cell line produced angiopoietin-2, CXCL-8, EGF, FGF-b, IL-6, PlGF, sPECAM-1, and VEGF in culture. When co-cultured in invasion assay with HTR-8/SVneo trophoblast cells, the co-culture had increased production of angiopoietin-2, CXCL-8, FGF-b, and VEGF, while reduced sPECAM-1 and IL-6. GBS exposure led to increased CXCL-8 and IL-6 production, both prevented by uvaol. Chorionic villi explants followed the same patterns of production when exposed to GBS and response to uvaol treatment as well. These findings demonstrate that, even a non-lethal concentration of GBS causes placental inflammation and oxidative stress, reduces trophoblast invasion of endothelial cells, and increases CXCL-8 and IL-6, key factors that participate in vascular dysregulation observed in several diseases. Furthermore, uvaol treatment prevented most of the GBS-provoked changes. Hence, uvaol could prevent the harmful effects of GBS infection for both the mother and the fetus.
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Affiliation(s)
- Ana Lucia Mendes Silva
- Cell Biology Laboratory, Institute of Health and Biological Sciences, Federal University of Alagoas, Maceio, Brazil
| | | | - Rayane Martins Botelho
- Cell Biology Laboratory, Institute of Health and Biological Sciences, Federal University of Alagoas, Maceio, Brazil
| | | | - Aldilane Lays Xavier Marques
- Cell Biology Laboratory, Institute of Health and Biological Sciences, Federal University of Alagoas, Maceio, Brazil
| | | | | | | | - Keyla Silva Nobre Pires
- Cell Biology Laboratory, Institute of Health and Biological Sciences, Federal University of Alagoas, Maceio, Brazil
| | | | | | - Guillaume Sébire
- Department of Pediatrics, McGill University, Montreal, QC, Canada.,Department of Pediatrics, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Samuel Teixeira Souza
- Optics and Nanoscopy Group, Physics Institute, Federal University of Alagoas, Maceio, Brazil
| | | | - Karen Steponavicius Cruz Borbely
- Cell Biology Laboratory, Institute of Health and Biological Sciences, Federal University of Alagoas, Maceio, Brazil.,Faculty of Nutrition, Federal University of Alagoas, Maceio, Brazil
| | - Alexandre Urban Borbely
- Cell Biology Laboratory, Institute of Health and Biological Sciences, Federal University of Alagoas, Maceio, Brazil
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18
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Takamura A, Ozawa T. Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis. Analyst 2021; 146:7431-7449. [PMID: 34813634 DOI: 10.1039/d1an01637g] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Biological materials found at a crime scene are crucially important evidence for forensic investigation because they provide contextual information about a crime and can be linked to the donor-individuals through combination with DNA analysis. Applications of vibrational spectroscopy to forensic biological analysis have been emerging because of its advantageous characteristics such as the non-destructivity, rapid measurement, and quantitative evaluation, compared to most current methods based on histological observation or biochemical techniques. This review presents an overview of recent developments in vibrational spectroscopy for forensic biological analysis. We also emphasize chemometric techniques, which can elicit reliable and advanced analytical outputs from highly complex spectral data from forensic biological materials. The analytical subjects addressed herein include body fluids, hair, soft tissue, bones, and bioagents. Promising applications for various analytical purposes in forensic biology are presented. Simultaneously, future avenues of study requiring further investigation are discussed.
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Affiliation(s)
- Ayari Takamura
- Department of Chemistry, Graduate School of Science, The University of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan. .,RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
| | - Takeaki Ozawa
- Department of Chemistry, Graduate School of Science, The University of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.
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19
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Dai Y, Li W, Wang L, Luo C, Huang Q, Pang L. Correlation and Difference Between Raman Spectral Characteristic and Feature Evaluation for Leukocytes and Tumor Cells. APPLIED SPECTROSCOPY 2021; 75:1516-1525. [PMID: 34643137 DOI: 10.1177/00037028211050663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Tumor detection supported by Raman spectroscopy is becoming increasingly popular, yet the relevance of spectral variation and feature selection retains unclear. Here we determined the correlation and difference between spectral characteristic and feature evaluation for leukocytes and tumor cells. Some peaks were found to show noticeable spectral differences, and their intensity distributions were investigated, finding using log-normal distribution to describe Raman intensity pattern may be more appropriate. Further the importance of all Raman features was calculated, where some other peak features occupied the top status. By surveying the intensity variation and feature evaluation for those peaks, we concluded the peak with the highest importance does not correspond to the peak location with the most noticeable intensity difference in spectra. Moreover, the peak intensity ratio of I1517/I719 associated with protein to nucleic acid level presented the maximum separation, thus, it can be recognized as a special indicator to develop an alternative cancer detection. It is inspiring to introduce advanced statistical models into bio-spectroscopic fields but those intrinsic spectral variations rather than classification performance should be valued. Our explorations can provide possibilities to reveal the essences within tumor carcinogenesis based on Raman spectroscopy, further overwhelming the obstacles during the translation into clinical applications.
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Affiliation(s)
- Yixin Dai
- College of Physics, Sichuan University, Chengdu, China
| | - Wenxue Li
- College of Physics, Sichuan University, Chengdu, China
| | - Liu Wang
- Department of Laboratory Medicine, Army Medical University Daping Hospital, Chongqing, China
| | - Chuan Luo
- Department of Laboratory Medicine, Army Medical University Southwest Hospital, Chongqing, China
| | - Qing Huang
- Department of Laboratory Medicine, Army Medical University Daping Hospital, Chongqing, China
| | - Lin Pang
- College of Physics, Sichuan University, Chengdu, China
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20
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Cui L, Li HZ, Yang K, Zhu LJ, Xu F, Zhu YG. Raman biosensor and molecular tools for integrated monitoring of pathogens and antimicrobial resistance in wastewater. Trends Analyt Chem 2021. [DOI: 10.1016/j.trac.2021.116415] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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21
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Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy. Int J Mol Sci 2021; 22:ijms221910481. [PMID: 34638822 PMCID: PMC8508974 DOI: 10.3390/ijms221910481] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/14/2021] [Accepted: 09/23/2021] [Indexed: 11/25/2022] Open
Abstract
Biochemical information from activated leukocytes provide valuable diagnostic information. In this study, Raman spectroscopy was applied as a label-free analytical technique to characterize the activation pattern of leukocyte subpopulations in an in vitro infection model. Neutrophils, monocytes, and lymphocytes were isolated from healthy volunteers and stimulated with heat-inactivated clinical isolates of Candida albicans, Staphylococcus aureus, and Klebsiella pneumoniae. Binary classification models could identify the presence of infection for monocytes and lymphocytes, classify the type of infection as bacterial or fungal for neutrophils, monocytes, and lymphocytes and distinguish the cause of infection as Gram-negative or Gram-positive bacteria in the monocyte subpopulation. Changes in single-cell Raman spectra, upon leukocyte stimulation, can be explained with biochemical changes due to the leukocyte’s specific reaction to each type of pathogen. Raman spectra of leukocytes from the in vitro infection model were compared with spectra from leukocytes of patients with infection (DRKS-ID: DRKS00006265) with the same pathogen groups, and a good agreement was revealed. Our study elucidates the potential of Raman spectroscopy-based single-cell analysis for the differentiation of circulating leukocyte subtypes and identification of the infection by probing the molecular phenotype of those cells.
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22
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Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification. Cancers (Basel) 2021; 13:cancers13194767. [PMID: 34638253 PMCID: PMC8507544 DOI: 10.3390/cancers13194767] [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: 08/16/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 11/25/2022] Open
Abstract
Simple Summary We demonstrate an automated Raman cytology system designed for high-throughput and reproducibility. The system uses a Raman spectroscopy system integrated into a conventional microscope, all controlled electronically via and open source software, Micro-Manager. The system can automatically identify and probe epithelial cell nuclei for Raman spectroscopy. 6426 HT1197 (high-grade bladder cancer) cell spectra, and 7499 RT112 (low-grade bladdercancer) cell spectra were recorded. The data was subsequently culled and processed for denoising and artifact removal. We demonstrate, using multivariate statistical analysis, that the cells can be distinguished, using a variety of approaches with accuracy, sensitivity and specificity in excess of 95%. Abstract Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.
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Ramoji A, Thomas-Rüddel D, Ryabchykov O, Bauer M, Arend N, Giamarellos-Bourboulis EJ, Eugen-Olsen J, Kiehntopf M, Bocklitz T, Popp J, Bloos F, Neugebauer U. Leukocyte Activation Profile Assessed by Raman Spectroscopy Helps Diagnosing Infection and Sepsis. Crit Care Explor 2021; 3:e0394. [PMID: 34079942 PMCID: PMC8162546 DOI: 10.1097/cce.0000000000000394] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES Leukocytes are first responders to infection. Their activation state can reveal information about specific host immune response and identify dysregulation in sepsis. This study aims to use the Raman spectroscopic fingerprints of blood-derived leukocytes to differentiate inflammation, infection, and sepsis in hospitalized patients. Diagnostic sensitivity and specificity shall demonstrate the added value of the direct characterization of leukocyte's phenotype. DESIGN Prospective nonrandomized, single-center, observational phase-II study (DRKS00006265). SETTING Jena University Hospital, Germany. PATIENTS Sixty-one hospitalized patients (19 with sterile inflammation, 23 with infection without organ dysfunction, 18 with sepsis according to Sepsis-3 definition). INTERVENTIONS None (blood withdrawal). MEASUREMENTS AND MAIN RESULTS Individual peripheral blood leukocytes were characterized by Raman spectroscopy. Reference diagnostics included established clinical scores, blood count, and biomarkers (C-reactive protein, procalcitonin and interleukin-6). Binary classification models using Raman data were able to distinguish patients with infection from patients without infection, as well as sepsis patients from patients without sepsis, with accuracies achieved with established biomarkers. Compared with biomarker information alone, an increase of 10% (to 93%) accuracy for the detection of infection and an increase of 18% (to 92%) for detection of sepsis were reached by adding the Raman information. Leukocytes from sepsis patients showed different Raman spectral features in comparison to the patients with infection that point to the special immune phenotype of sepsis patients. CONCLUSIONS Raman spectroscopy can extract information on leukocyte's activation state in a nondestructive, label-free manner to differentiate sterile inflammation, infection, and sepsis.
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Affiliation(s)
- Anuradha Ramoji
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
| | - Daniel Thomas-Rüddel
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
- Department of Anaesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology Jena, (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
- Institute for Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany
| | - Michael Bauer
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
- Department of Anaesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
| | - Natalie Arend
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
- Leibniz Institute of Photonic Technology Jena, (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
| | | | - Jesper Eugen-Olsen
- Clinical Research Centre, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark
| | - Michael Kiehntopf
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
- Institute for Clinical Chemistry and Laboratory Diagnostics and Integrated Biobank Jena, Jena University Hospital, Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology Jena, (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
- Institute for Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany
| | - Jürgen Popp
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
- Leibniz Institute of Photonic Technology Jena, (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany
- Institute for Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany
| | - Frank Bloos
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
- Department of Anaesthesiology and Intensive Care Medicine, Jena University Hospital, Jena, Germany
| | - Ute Neugebauer
- Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany
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Li M, Xu Y, Men J, Yan C, Tang H, Zhang T, Li H. Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 251:119430. [PMID: 33485240 DOI: 10.1016/j.saa.2021.119430] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/23/2020] [Accepted: 01/03/2021] [Indexed: 06/12/2023]
Abstract
With the trend of portable and miniaturization, Raman spectrometer requires more advanced analytical methods providing more rapid and accurate analysis performance for in-situ analysis. In this work, a hybrid variable selection method based on V-WSP and variable importance measurement (VIM) coupled with random forest (RF) was used to improve the quantitative analysis performance of portable laser Raman instruments for quantitative analysis of methanol content in methanol gasoline. First, five preprocessing methods were applied to reduce the infection information in the raw spectra, respectively. Based on the spectra data processed by multivariate scattering correction (MSC), V-WSP was employed to filter the infection or redundant information in Raman spectroscopy, and 579 variables were obtained when the correlation threshold is 0.9600. Then, the variables were further eliminated by VIM. Finally, 43 variables were obtained by the V-WSP-VIM method. In data processing, out of bag (OOB) error estimation and 10-flod cross validation (CV) were applied to optimize the parameters of preprocessing methods, V-WSP, VIM and RF model. The results fully demonstrated that compared with the RF model based on raw spectra, the RF model based on V-WSP-VIM method can achieve a better prediction performance for the quantitative analysis of methanol content in methanol-gasoline, with the coefficients of determination of cross-validation (R2CV) improving from 0.9100 to 0.9662, the root mean square error of cross-validation (RMSECV) reducing from 0.0572 to 0.0365%, the coefficients of determination of prediction set (R2P) improving from 0.9214 to 0.9407, the root mean square error of prediction set (RMSEP) reducing from 0.0420 to 0.0382%, the variables reducing from 1044 to 43 and the modeling time reducing from 72.94 to 6.41 s. The results indicates that V-WSP-VIM coupled with RF is an effective method to improve the performance of portable laser Raman spectrometer for quantitative analysis of methanol content in methanol gasoline.
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Affiliation(s)
- Maogang Li
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, 710127, China
| | - Yanyan Xu
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, 710127, China
| | - Jing Men
- Xi'an WanLong Pharmaceutical Co., Ltd., Xi'an, 710119, China
| | - Chunhua Yan
- College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an, 710065, China
| | - Hongsheng Tang
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, 710127, China.
| | - Tianlong Zhang
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, 710127, China
| | - Hua Li
- Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry & Materials Science, Northwest University, Xi'an, 710127, China; College of Chemistry and Chemical Engineering, Xi'an Shiyou University, Xi'an, 710065, China.
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