1
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Vanna R, Masella A, Bazzarelli M, Ronchi P, Lenferink A, Tresoldi C, Morasso C, Bedoni M, Cerullo G, Polli D, Ciceri F, De Poli G, Bregonzio M, Otto C. High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach. Anal Chem 2024; 96:9468-9477. [PMID: 38821490 DOI: 10.1021/acs.analchem.4c00787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.
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Affiliation(s)
- Renzo Vanna
- Istituto di Fotonica e Nanotecnologie - Consiglio Nazionale delle Ricerche (IFN-CNR), c/o Politecnico di Milano, Milan 20133, Italy
| | | | | | - Paola Ronchi
- IRCCS Ospedale San Raffaele, University Vita-Salute San Raffaele, Milan 20132, Italy
| | - Aufried Lenferink
- Medical Cell BioPhysics, Department of Science and Technology, TechMed Center, University of Twente, Enschede, NL 7500 AE, The Netherlands
| | - Cristina Tresoldi
- IRCCS Ospedale San Raffaele, University Vita-Salute San Raffaele, Milan 20132, Italy
| | - Carlo Morasso
- Istituti Clinici Scientifici Maugeri IRCCS, Via Maugeri 4, Pavia 27100, Italy
| | - Marzia Bedoni
- IRCCS, Fondazione Don Carlo Gnocchi, Milan 20148, Italy
| | - Giulio Cerullo
- Istituto di Fotonica e Nanotecnologie - Consiglio Nazionale delle Ricerche (IFN-CNR), c/o Politecnico di Milano, Milan 20133, Italy
- Dipartimento di Fisica, Politecnico di Milano, Milan 20133, Italy
| | - Dario Polli
- Istituto di Fotonica e Nanotecnologie - Consiglio Nazionale delle Ricerche (IFN-CNR), c/o Politecnico di Milano, Milan 20133, Italy
- Dipartimento di Fisica, Politecnico di Milano, Milan 20133, Italy
| | - Fabio Ciceri
- IRCCS Ospedale San Raffaele, University Vita-Salute San Raffaele, Milan 20132, Italy
| | | | | | - Cees Otto
- Medical Cell BioPhysics, Department of Science and Technology, TechMed Center, University of Twente, Enschede, NL 7500 AE, The Netherlands
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2
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Song K, Xue W, Li X, Chang Y, Liu M. Self-Assembly of Single-Virus SERS Hotspots for Highly Sensitive In Situ Detection of SARS-CoV-2 on Solid Surfaces. Anal Chem 2024; 96:8830-8836. [PMID: 38693713 DOI: 10.1021/acs.analchem.4c01607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
Microbial surface transmission has aroused great attention since the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Developing a simple in situ detection method for viruses on solid surfaces is of great significance for timely public health surveillance. Taking advantage of the natural structure of SARS-CoV-2, we reported the assembly of Au@AgNPs on the surface of a single virus by the specific aptamer-spike protein interaction. Multiple hotspots can be created between the neighboring Au@AgNPs for the highly sensitive surface-enhanced Raman scattering (SERS) detection of SARS-CoV-2. Using two different aptamers labeled with Cy3 and Au@AgNPs, in situ SERS detection of pseudotyped SARS-CoV-2 (PSV) on packaging surfaces was achieved within 20 min, with a detection limit of 5.26 TCID50/mL. For the blind testing of 20 PSV-contaminated packaging samples, this SERS aptasensor had a sensitivity of 100% and an accuracy of 100%. This assay has been successfully applied to in situ detection of PSV on the surfaces of different packaging materials, suggesting its potential applicability.
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Affiliation(s)
- Kaiyun Song
- School of Environmental Science and Technology, Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian POCT laboratory, Dalian University of Technology, Dalian 116024, China
| | - Wei Xue
- School of Environmental Science and Technology, Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian POCT laboratory, Dalian University of Technology, Dalian 116024, China
| | - Xiaona Li
- School of Environmental Science and Technology, Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian POCT laboratory, Dalian University of Technology, Dalian 116024, China
| | - Yangyang Chang
- School of Environmental Science and Technology, Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian POCT laboratory, Dalian University of Technology, Dalian 116024, China
| | - Meng Liu
- School of Environmental Science and Technology, Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian POCT laboratory, Dalian University of Technology, Dalian 116024, China
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3
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Liu Y, Zhou X, Wang T, Luo A, Jia Z, Pan X, Cai W, Sun M, Wang X, Wen Z, Zhou G. Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor. Biotechnol Bioeng 2024; 121:1583-1595. [PMID: 38247359 DOI: 10.1002/bit.28661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/23/2024]
Abstract
As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.
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Affiliation(s)
- Yuan Liu
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Xiaotian Zhou
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Teng Wang
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - An Luo
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Zhaojun Jia
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Xingquan Pan
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Weiqi Cai
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Mengge Sun
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Xuezhong Wang
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Zhenguo Wen
- Department of Pharmaceutical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
| | - Guangzheng Zhou
- Beijing Key Laboratory of Enze Biomass and Fine Chemicals, Beijing Institute of Petrochemical Technology, Beijing, China
- Beijing Institute of Petrochemical Technology, College of New Materials and Chemical Engineering, Beijing, China
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4
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Lopez E, Etxebarria-Elezgarai J, García-Sebastián M, Altuna M, Ecay-Torres M, Estanga A, Tainta M, López C, Martínez-Lage P, Amigo JM, Seifert A. Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics. Int J Mol Sci 2024; 25:4737. [PMID: 38731955 PMCID: PMC11084676 DOI: 10.3390/ijms25094737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 04/23/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.
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Affiliation(s)
- Eneko Lopez
- CIC nanoGUNE BRTA, 20018 San Sebasián, Spain; (E.L.); (J.E.-E.)
- Department of Physics, University of the Basque Country (UPV/EHU), 20018 San Sebastián, Spain
| | | | - Maite García-Sebastián
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Miren Altuna
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Mirian Ecay-Torres
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Ainara Estanga
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Mikel Tainta
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Carolina López
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Pablo Martínez-Lage
- Center for Research and Advanced Therapies, CITA-Alzhéimer Foundation, 20009 San Sebastián, Spain; (M.G.-S.); (M.A.); (M.E.-T.); (A.E.); (M.T.); (C.L.); (P.M.-L.)
| | - Jose Manuel Amigo
- IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
- Department of Analytical Chemistry, University of the Basque Country, 48940 Leioa, Spain
| | - Andreas Seifert
- CIC nanoGUNE BRTA, 20018 San Sebasián, Spain; (E.L.); (J.E.-E.)
- IKERBASQUE, Basque Foundation for Science, 48009 Bilbao, Spain
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5
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Hano H, Lawrie CH, Suarez B, Paredes Lario A, Elejoste Echeverría I, Gómez Mediavilla J, Crespo Cruz MI, Lopez E, Seifert A. Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer. ACS OMEGA 2024; 9:14084-14091. [PMID: 38559992 PMCID: PMC10975667 DOI: 10.1021/acsomega.3c09537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the urgent need for reliable and efficient diagnostic methods. Conventional approaches often involve invasive procedures and can be time-consuming and costly, thereby delaying the effective treatment. The current study explores the potential of Raman spectroscopy, as a promising noninvasive technique, by analyzing human blood plasma samples from lung cancer patients and healthy controls. In a benchmark study, 16 machine learning models were evaluated by employing four strategies: the combination of dimensionality reduction with classifiers; application of feature selection prior to classification; stand-alone classifiers; and a unified predictive model. The models showed different performances due to the inherent complexity of the data, achieving accuracies from 0.77 to 0.85 and areas under the curve for receiver operating characteristics from 0.85 to 0.94. Hybrid methods incorporating dimensionality reduction and feature selection algorithms present the highest figures of merit. Nevertheless, all machine learning models deliver creditable scores and demonstrate that Raman spectroscopy represents a powerful method for future in vitro diagnostics of lung cancer.
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Affiliation(s)
- Harun Hano
- CIC
nanoGUNE BRTA, 20018 San Sebastián, Spain
- Department
of Physics, University of the Basque Country
(UPV/EHU), 20018 San Sebastián, Spain
| | - Charles H. Lawrie
- IKERBASQUE—Basque
Foundation for Science, 48009 Bilbao, Spain
- Biogipuzkoa
Health Research Institute, 20014 San Sebastián, Spain
- Sino-Swiss
Institute of Advanced Technology (SSIAT), University of Shanghai, 201800 Shanghai, China
- Radcliffe
Department of Medicine, University of Oxford, OX3 9DU Oxford, U.K.
| | - Beatriz Suarez
- Faculty
of Nursing and Medicine, University of the
Basque Country (UPV/EHU), 20014 San Sebastián, Spain
- Biogipuzkoa
Health Research Institute, 20014 San Sebastián, Spain
| | - Alfredo Paredes Lario
- Servicio
de Oncología Médica, Hospital
Universitario Donostia, 20014 San Sebastián, Spain
| | | | | | | | - Eneko Lopez
- CIC
nanoGUNE BRTA, 20018 San Sebastián, Spain
- Department
of Physics, University of the Basque Country
(UPV/EHU), 20018 San Sebastián, Spain
| | - Andreas Seifert
- CIC
nanoGUNE BRTA, 20018 San Sebastián, Spain
- IKERBASQUE—Basque
Foundation for Science, 48009 Bilbao, Spain
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6
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Coca-Lopez N. An intuitive approach for spike removal in Raman spectra based on peaks' prominence and width. Anal Chim Acta 2024; 1295:342312. [PMID: 38355231 DOI: 10.1016/j.aca.2024.342312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/22/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024]
Abstract
BACKGROUND Raman spectroscopists are familiar with the challenge of dealing with spikes caused by cosmic rays. These artifacts may lead to errors in subsequent data processing steps, such as for example calibration, normalization or spectral search. Spike removal is therefore a fundamental step in Raman spectral data pre-treatment, but access to publicly accessible code for spike removal tools is limited, and their performance for spectra correction often unknown. Therefore, there is a need for development and testing open-source and easy-to-implement algorithms that improve the Raman data processing workflow. RESULTS In this work, we present and validate two approaches for spike detection and correction in Raman spectral data from graphene: i) An algorithm based on the peaks' widths and prominences and ii) an algorithm based on the ratio of these two peak features. The first algorithm provides an efficient and reliable approach for spike detection in real and synthetic Raman spectra by imposing thresholds on the peaks' width and prominence. The second approach leverages the prominence/width ratio for outlier detection. It relies on the calculation of a limit of detection based on either one or several spectra, enabling the automatic identification of cosmic ray and low-intensity noise-originated spikes alike. Both algorithms detect low-intensity spikes down to at least ≈10% of the highest Raman peak of spectra with different noise levels. To address their limitations and prove their versatility, the algorithms were further tested in Raman spectra from calcite and polystyrene. SIGNIFICANCE Our intuitive, open-source algorithms have been validated and allow automatic correction for a given set of samples. They do not require any pre-processing steps such as calibration or baseline subtraction, and their implementation with Python libraries is computationally efficient, allowing for immediate utilization within existing open-source packages for Raman spectra processing.
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Affiliation(s)
- Nicolas Coca-Lopez
- Instituto de Catálisis y Petroleoquímica (ICP), CSIC, Marie Curie, 2, Madrid, 28049, Spain.
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7
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Liao F, Yang W, Long L, Yu R, Qu H, Peng Y, Lu J, Ren C, Wang Y, Fu C. Elucidating Iron Metabolism through Molecular Imaging. Curr Issues Mol Biol 2024; 46:2798-2818. [PMID: 38666905 PMCID: PMC11049567 DOI: 10.3390/cimb46040175] [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: 02/07/2024] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024] Open
Abstract
Iron is essential for many physiological processes, and the dysregulation of its metabolism is implicated in the pathogenesis of various diseases. Recent advances in iron metabolism research have revealed multiple complex pathways critical for maintaining iron homeostasis. Molecular imaging, an interdisciplinary imaging technique, has shown considerable promise in advancing research on iron metabolism. Here, we comprehensively review the multifaceted roles of iron at the cellular and systemic levels (along with the complex regulatory mechanisms of iron metabolism), elucidate appropriate imaging methods, and summarize their utility and fundamental principles in diagnosing and treating diseases related to iron metabolism. Utilizing molecular imaging technology to deeply understand the complexities of iron metabolism and its critical role in physiological and pathological processes offers new possibilities for early disease diagnosis, treatment monitoring, and the development of novel therapies. Despite technological limitations and the need to ensure the biological relevance and clinical applicability of imaging results, molecular imaging technology's potential to reveal the iron metabolic process is unparalleled, providing new insights into the link between iron metabolism abnormalities and various diseases.
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Affiliation(s)
- Feifei Liao
- Beijing University of Traditional Chinese Medicine Graduate School, Beijing University of Chinese Medicine, Beijing 100105, China; (F.L.); (R.Y.); (Y.P.); (J.L.); (C.R.)
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Wenwen Yang
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Linzi Long
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Ruotong Yu
- Beijing University of Traditional Chinese Medicine Graduate School, Beijing University of Chinese Medicine, Beijing 100105, China; (F.L.); (R.Y.); (Y.P.); (J.L.); (C.R.)
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Hua Qu
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Yuxuan Peng
- Beijing University of Traditional Chinese Medicine Graduate School, Beijing University of Chinese Medicine, Beijing 100105, China; (F.L.); (R.Y.); (Y.P.); (J.L.); (C.R.)
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Jieming Lu
- Beijing University of Traditional Chinese Medicine Graduate School, Beijing University of Chinese Medicine, Beijing 100105, China; (F.L.); (R.Y.); (Y.P.); (J.L.); (C.R.)
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Chenghuan Ren
- Beijing University of Traditional Chinese Medicine Graduate School, Beijing University of Chinese Medicine, Beijing 100105, China; (F.L.); (R.Y.); (Y.P.); (J.L.); (C.R.)
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
| | - Yueqi Wang
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Changgeng Fu
- Graduate School, China Academy of Chinese Medical Sciences, Beijing 100091, China; (W.Y.); (L.L.); (H.Q.)
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8
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Wu M, Flores-Fernandez JM, Wang Y, Ahmed H, Wille H, Stepanova M. SERS probing of fungal HET-s fibrils formed at neutral and acidic pH conditions. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 309:123817. [PMID: 38211445 DOI: 10.1016/j.saa.2023.123817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/06/2023] [Accepted: 12/23/2023] [Indexed: 01/13/2024]
Abstract
Advances in precision medical diagnostics require accurate and sensitive characterization of pathogens. In particular, health conditions associated with protein misfolding require an identification of proteinaceous amyloid fibrils or their precursors. These pathogenic entities express specific molecular structures, which require ultra-sensitive, molecular-level detection methods. A potentially transformative technique termed nanoplasmonics employs electro-optical phenomena in the vicinity of specially engineered metal nanostructures. A signature application of nanoplasmonics exploits enhancement of inelastic scattering of light in specific locations near metallic nanostructures, known as surface-enhanced Raman scattering (SERS). We applied SERS complemented with confocal microscopy imaging for ultra-sensitive, non-invasive, and label-free characterization of the fungal prion HET-s (218-289) as a model for β-sheet rich amyloid structures. This characterization employed Au-coated dielectric supports as plasmonic substrates. After confirming the formation of HET-s fibrils at both pH 7.5 and 2.8 using negative staining transmission electron microscopy, we subjected the fibril-containing solutions to multimodal analysis using confocal microscopy and SERS. The SERS spectral fingerprints from all HET-s samples expressed vibrational markers for β-structure, unstructured backbone, and aromatic side-chains. However, relative intensities of major SERS bands were pronouncedly different for the two pH levels. We have analyzed potential origins of the most pronounced SERS bands and proposed hypothetical mechanistic models that could explain the observed SERS fingerprints from HET-s fibrils grown at pH 7.5 and 2.8.
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Affiliation(s)
- Min Wu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 1H9, AB, Canada
| | - Jose Miguel Flores-Fernandez
- Department of Biochemistry & Centre for Prions and Protein Folding Diseases, University of Alberta, Edmonton T6G 2M8, AB, Canada; Department of Research and Innovation, Universidad Tecnológica de Oriental, Oriental 75020, Mexico
| | - YongLiang Wang
- Department of Biochemistry & Centre for Prions and Protein Folding Diseases, University of Alberta, Edmonton T6G 2M8, AB, Canada
| | - Haseeb Ahmed
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 1H9, AB, Canada
| | - Holger Wille
- Department of Biochemistry & Centre for Prions and Protein Folding Diseases, University of Alberta, Edmonton T6G 2M8, AB, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton T6G 2E1, AB, Canada
| | - Maria Stepanova
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 1H9, AB, Canada.
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9
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Saurabh A, Brown PT, Bryan JS, Fox ZR, Kruithoff R, Thompson C, Kural C, Shepherd DP, Pressé S. A Structured Illumination Microscopy Framework with Spatial-Domain Noise Propagation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570701. [PMID: 38106139 PMCID: PMC10723446 DOI: 10.1101/2023.12.07.570701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Biological images captured by a microscope are characterized by heterogeneous signal-to-noise ratios (SNRs) across the field of view due to spatially varying photon emission and camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled manner remains a challenging task. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating all noise sources in the spatial domain. To accelerate the reconstruction process for computational feasibility, we devise a parallelized Monte Carlo sampling strategy for inference. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.
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Affiliation(s)
- Ayush Saurabh
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Peter T. Brown
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - J. Shepard Bryan
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Zachary R. Fox
- Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Rory Kruithoff
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | | | - Comert Kural
- Department of Physics, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| | - Douglas P. Shepherd
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
| | - Steve Pressé
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
- Department of Physics, Arizona State University, Tempe, AZ, USA
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
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10
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Mayorova OA, Saveleva MS, Bratashov DN, Prikhozhdenko ES. Combination of Machine Learning and Raman Spectroscopy for Determination of the Complex of Whey Protein Isolate with Hyaluronic Acid. Polymers (Basel) 2024; 16:666. [PMID: 38475349 DOI: 10.3390/polym16050666] [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: 12/29/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
Macromolecules and their complexes remain interesting topics in various fields, such as targeted drug delivery and tissue regeneration. The complex chemical structure of such substances can be studied with a combination of Raman spectroscopy and machine learning. The complex of whey protein isolate (WPI) and hyaluronic acid (HA) is beneficial in terms of drug delivery. It provides HA properties with the stability obtained from WPI. However, differences between WPI-HA and WPI solutions can be difficult to detect by Raman spectroscopy. Especially when the low HA (0.1, 0.25, 0.5% w/v) and the constant WPI (5% w/v) concentrations are used. Before applying the machine learning techniques, all the collected data were divided into training and test sets in a ratio of 3:1. The performances of two ensemble methods, random forest (RF) and gradient boosting (GB), were evaluated on the Raman data, depending on the type of problem (regression or classification). The impact of noise reduction using principal component analysis (PCA) on the performance of the two machine learning methods was assessed. This procedure allowed us to reduce the number of features while retaining 95% of the explained variance in the data. Another application of these machine learning methods was to identify the WPI Raman bands that changed the most with the addition of HA. Both the RF and GB could provide feature importance data that could be plotted in conjunction with the actual Raman spectra of the samples. The results show that the addition of HA to WPI led to changes mainly around 1003 cm-1 (correspond to ring breath of phenylalanine) and 1400 cm-1, as demonstrated by the regression and classification models. For selected Raman bands, where the feature importance was greater than 1%, a direct evaluation of the effect of the amount of HA on the Raman intensities was performed but was found not to be informative. Thus, applying the RF or GB estimators to the Raman data with feature importance evaluation could detect and highlight small differences in the spectra of substances that arose from changes in the chemical structure; using PCA to filter out noise in the Raman data could improve the performance of both the RF and GB. The demonstrated results will make it possible to analyze changes in chemical bonds during various processes, for example, conjugation, to study complex mixtures of substances, even with small additions of the components of interest.
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Affiliation(s)
- Oksana A Mayorova
- Science Medical Center, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia
| | - Mariia S Saveleva
- Science Medical Center, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia
| | - Daniil N Bratashov
- Science Medical Center, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia
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11
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Frempong SB, Salbreiter M, Mostafapour S, Pistiki A, Bocklitz TW, Rösch P, Popp J. Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms. Molecules 2024; 29:1077. [PMID: 38474589 DOI: 10.3390/molecules29051077] [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: 12/19/2023] [Revised: 02/13/2024] [Accepted: 02/18/2024] [Indexed: 03/14/2024] Open
Abstract
Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.
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Affiliation(s)
- Sandra Baaba Frempong
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Markus Salbreiter
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Sara Mostafapour
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Aikaterini Pistiki
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance-Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Thomas W Bocklitz
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance-Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Jürgen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
- InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance-Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
- Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743 Jena, Germany
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12
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Merdalimova A, Barmin R, Vorobev V, Aleksandrov A, Terentyeva D, Estifeeva T, Chernyshev V, German S, Maslov O, Skibina Y, Rudakovskaya P, Gorin D. Two-in-one sensor of refractive index and Raman scattering using hollow-core microstructured optical waveguides for colloid characterization. Colloids Surf B Biointerfaces 2024; 234:113705. [PMID: 38194837 DOI: 10.1016/j.colsurfb.2023.113705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/01/2023] [Accepted: 12/07/2023] [Indexed: 01/11/2024]
Abstract
Hollow-core microstructured optical waveguides (HC-MOW) have recently emerged in sensing technologies, including the gas and liquid detection for industrial as well as clinical applications. Antiresonant HC-MOW provide capabilities for applications in refractive index (RI) sensing, while the long optical path for analyte-light interaction in HC-MOW leads to increased sensitivity of sensor based on Raman scattering signal measurements. In this study, we developed a two-in-one sensor device using HC-MOW for RI and Raman scattering detection. The performance of the sensor was evaluated by characterizing protein-copolymer multicomponent colloids, specifically, bovine serum albumin (BSA) and poly(N - vinyl-2 -pyrrolidone-co-acrylic acid) P(VP-AA) nano-sized complexes and microbubbles of the corresponding shell. Monocomponent solutions showed linear dependencies of RI and characteristic Raman peak intensities on mass concentration. Multicomponent Raman sensing of BSA@P(VP-AA) complexes and microbubbles revealed that changes in P(VP-AA) characteristic peak intensities can describe interactions between components needed to produce colloid systems. RI sensing of multicomponent colloids demonstrated linear dependence on total mass concentrations for BSA@P(VP-AA) complexes, while corresponding BSA@P(VP-AA) microbubbles can be detected with concentrations as high as 4.0 × 108 MB/mL. Therefore, the developed two-in-one sensor of RI and Raman scattering can be used the robust characterization of albumin-based colloids designed for therapeutic and diagnostic needs.
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Affiliation(s)
- Anastasiia Merdalimova
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia; Laboratory of Photonic Gas Sensors, University of Science and Technology MISIS, Moscow 119049, Russia.
| | - Roman Barmin
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
| | - Viktor Vorobev
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Artem Aleksandrov
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia; Faculty of Materials Science, Lomonosov Moscow State University, Moscow 119991, Russia; National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov, Moscow 117997, Russia
| | - Daria Terentyeva
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Tatiana Estifeeva
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Vasiliy Chernyshev
- National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov, Moscow 117997, Russia
| | - Sergey German
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Oleg Maslov
- Department of Nanomaterials and Nanotechnology, Dmitry Mendeleev University of Chemical Technology of Russia, Moscow 125047, Russia
| | - Yulia Skibina
- SPE LLC Nanostructured Glass Technology, Saratov 410033, Russia
| | - Polina Rudakovskaya
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Dmitry Gorin
- Center for Photonic Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia.
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13
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Liu C, Jiménez-Avalos G, Zhang WS, Sheen P, Zimic M, Popp J, Cialla-May D. Prussian blue (PB) modified gold nanoparticles as a SERS-based sensing platform for capturing and detection of pyrazinoic acid (POA). Talanta 2024; 266:125038. [PMID: 37574604 DOI: 10.1016/j.talanta.2023.125038] [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: 04/18/2023] [Revised: 07/28/2023] [Accepted: 08/03/2023] [Indexed: 08/15/2023]
Abstract
Pyrazinoic acid (POA) is a metabolite of the anti-tuberculosis drug pyrazinamide (PZA), and its detection can be used to assess the resistance of Mycobacterium tuberculosis in cultures, as only sensitive strains of the bacteria can metabolize PZA into POA. Prussian blue is a well-known metal-organic framework compound widely used in various sensing platforms such as electrochemical, photochemical, and magnetic sensors. In this study, we present a novel sensing platform based on Prussian blue-modified gold nanoparticles (AuNPs) designed to enhance the affinity of POA towards the sensing surface and to capture POA molecules from aqueous solutions. This SERS-based method allows for the selective enrichment of POA, which can be detected in both pure aqueous solution and in the presence of its pro-drug PZA. The limit of detection (LOD) for POA was estimated to be 1.08 μM in pure aqueous solution and 0.18 mM in the presence of PZA. Furthermore, the precision of the SERS method was verified by the relative standard deviation (RSD) of 3.34-12.02% for three parallel samples using different matrices, i.e. aqueous solution, spiked river water and spiked simulated saliva. The recoveries of the samples ranged from 92.65 to 118.51%. These all demonstrate the potential application of the proposed detection scheme in medical research.
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Affiliation(s)
- Chen Liu
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745, Jena, Germany; Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany
| | - Gabriel Jiménez-Avalos
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Wen-Shu Zhang
- China Fire and Rescue Institute, Beijing, 102202, China
| | - Patricia Sheen
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Mirko Zimic
- Laboratorio de Bioinformática, Biología Molecular y Desarrollos Tecnológicos, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745, Jena, Germany; Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany
| | - Dana Cialla-May
- Leibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745, Jena, Germany; Institute of Physical Chemistry (IPC) and Abbe Center of Photonics (ACP), Friedrich Schiller University Jena, Member of the Leibniz Centre for Photonics in Infection Research (LPI), Helmholtzweg 4, 07743, Jena, Germany.
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14
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Jiménez-Arroyo C, Tamargo A, Molinero N, Reinosa JJ, Alcolea-Rodriguez V, Portela R, Bañares MA, Fernández JF, Moreno-Arribas MV. Simulated gastrointestinal digestion of polylactic acid (PLA) biodegradable microplastics and their interaction with the gut microbiota. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166003. [PMID: 37549707 DOI: 10.1016/j.scitotenv.2023.166003] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023]
Abstract
The accumulation of microplastics (MPs) in the environment as well as their presence in foods and humans highlight the urgent need for studies on the effects of these particles on humans. Polylactic acid (PLA) is the most widely used bioplastic in the food industry and medical field. Despite its biodegradability, biocompatibility, and "Generally Recognized As Safe" (GRAS) status, recent animal model studies have shown that PLA MPs can alter the intestinal microbiota; however, to date, no studies have been reported on the possible gut and health consequences of its intake by humans. This work simulates the ingestion of a realistic daily amount of PLA MPs and their pass through the gastrointestinal tract by combining the INFOGEST method and the gastrointestinal simgi® model to evaluate possible effects on the human colonic microbiota composition (16S rRNA gene sequencing analysis) and metabolic functionality (lactic acid and short-chain fatty acids (SCFA) production). Although PLA MPs did not clearly alter the microbial community homeostasis, increased Bifidobacterium levels tended to increase in presence of millimetric PLA particles. Furthermore, shifts detected at the functional level suggest an alteration of microbial metabolism, and a possible biotransformation of PLA by the human microbial colonic community. Raman spectroscopy and field emission scanning electron microscopy (FESEM) characterization revealed morphological changes on the PLA MPs after the gastric phase of the digestion, and the adhesion of organic matter as well as a microbial biofilm, with surface biodegradation, after the intestinal and colonic phases. With this evidence and the emerging use of bioplastics, understanding their impact on humans and potential biodegradation through gastrointestinal digestion and the human microbiota merits critical investigation.
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Affiliation(s)
- C Jiménez-Arroyo
- Institute of Food Science Research, CIAL, CSIC-UAM, C/ Nicolás Cabrera 9, 28049 Madrid, Spain
| | - A Tamargo
- Institute of Food Science Research, CIAL, CSIC-UAM, C/ Nicolás Cabrera 9, 28049 Madrid, Spain
| | - N Molinero
- Institute of Food Science Research, CIAL, CSIC-UAM, C/ Nicolás Cabrera 9, 28049 Madrid, Spain
| | - J J Reinosa
- Instituto de Cerámica y Vidrio, CSIC, c/ Kelsen, 28049 Madrid, Spain; Encapsulae S.L., c/Lituania 10, 12006 Castellón de la Plana, Spain
| | - V Alcolea-Rodriguez
- Instituto de Catálisis y Petroleoquímica, CSIC, c/ Marie Curie, 2, 28049 Madrid, Spain
| | - R Portela
- Instituto de Catálisis y Petroleoquímica, CSIC, c/ Marie Curie, 2, 28049 Madrid, Spain
| | - M A Bañares
- Instituto de Catálisis y Petroleoquímica, CSIC, c/ Marie Curie, 2, 28049 Madrid, Spain
| | - J F Fernández
- Encapsulae S.L., c/Lituania 10, 12006 Castellón de la Plana, Spain
| | - M V Moreno-Arribas
- Institute of Food Science Research, CIAL, CSIC-UAM, C/ Nicolás Cabrera 9, 28049 Madrid, Spain.
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15
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Rajapitamahuni S, Lyou ES, Kang BR, Lee TK. Microbial interaction-induced siderophore dynamics lead to phenotypic differentiation of Staphylococcus aureus. Front Cell Infect Microbiol 2023; 13:1277176. [PMID: 38045757 PMCID: PMC10690949 DOI: 10.3389/fcimb.2023.1277176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/27/2023] [Indexed: 12/05/2023] Open
Abstract
This study investigated the impact of microbial interactions on siderophore dynamics and phenotypic differentiation of Staphylococcus aureus under iron-deficient conditions. Optimization of media demonstrated that the glycerol alanine salts medium was best suited for analyzing the dynamics of siderophore production because of its stable production of diverse siderophore types. The effects of pH and iron concentration on siderophore yield revealed a maximum yield at neutral pH and low iron concentration (10 µg). Microbial interaction studies have highlighted variations in siderophore production when different strains (Staphylococcus epidermidis, Pseudomonas aeruginosa, and Escherichia coli) are co-cultured with S. aureus. Co-culture of S. aureus with P. aeruginosa eliminated siderophore production in S. aureus, while co-culture of S. aureus with E. coli and S. epidermidis produced one or two siderophores, respectively. Raman spectroscopy revealed that microbial interactions and siderophore dynamics play a crucial role in directing the phenotypic differentiation of S. aureus, especially under iron-deficient conditions. Our results suggest that microbial interactions profoundly influence siderophore dynamics and phenotypic differentiation and that the study of these interactions could provide valuable insights for understanding microbial survival strategies in iron-limited environments.
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Affiliation(s)
| | | | | | - Tae Kwon Lee
- Department of Environmental and Energy Engineering, Yonsei University, Wonju, Republic of Korea
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16
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Jiang L, Chen HY, He CH, Xu HB, Zhou ZR, Wu MS, Fodjo EK, He Y, Hafez ME, Qian RC, Li DW. Dual-Modal Apoptosis Assay Enabling Dynamic Visualization of ATP and Reactive Oxygen Species in Living Cells. Anal Chem 2023; 95:3507-3515. [PMID: 36724388 DOI: 10.1021/acs.analchem.2c05671] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
ATP and reactive oxygen species (ROS) are considered significant indicators of cell apoptosis. However, visualizing the interplay between apoptosis-related ATP and ROS is challenging. Herein, we developed a metal-organic framework (MOF)-based nanoprobe for an apoptosis assay using duplex imaging of cellular ATP and ROS. The nanoprobe was fabricated through controlled encapsulation of gold nanorods with a thin zirconium-based MOF layer, followed by modification of the ROS-responsive molecules 2-mercaptohydroquinone and 6-carboxyfluorescein-labeled ATP aptamer. The nanoprobe enables ATP and ROS visualization via fluorescence and surface-enhanced Raman spectroscopy, respectively, avoiding the mutual interference that often occurs in single-mode methods. Moreover, the dual-modal assay effectively showed dynamic imaging of ATP and ROS in cancer cells treated with various drugs, revealing their apoptosis-related pathways and interactions that differ from those under normal conditions. This study provides a method for studying the relationship between energy metabolism and redox homeostasis in cell apoptosis processes.
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Affiliation(s)
- Lei Jiang
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China.,College of Material, Chemistry and Chemical Engineering, Key Laboratory of Organosilicon Chemistry and Material Technology, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, Zhejiang, P. R. China
| | - Hua-Ying Chen
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Cai-Hong He
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Han-Bin Xu
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Ze-Rui Zhou
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Man-Sha Wu
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Essy Kouadio Fodjo
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China.,Laboratory of Physical Chemistry, Felix Houphouet Boigny University, Abidjan 225, Cote d'Ivoire
| | - Yue He
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Mahmoud Elsayed Hafez
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China.,Department of Chemistry, Faculty of Science Beni-Suef University, Beni-Suef 62511, Egypt
| | - Ruo-Can Qian
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Da-Wei Li
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology & Dynamic Chemistry, School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
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17
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Giraldo JP, Kruss S. Nanosensors for monitoring plant health. NATURE NANOTECHNOLOGY 2023; 18:107-108. [PMID: 36609485 DOI: 10.1038/s41565-022-01307-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
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18
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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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19
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Liu B, Tang H, Liu Q, Wang W, Li H, Zheng S, Sun F, Zhao X. Core-shell SERS nanotags-based western blot. Talanta 2023; 253:123888. [PMID: 36087412 DOI: 10.1016/j.talanta.2022.123888] [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: 07/24/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/13/2022]
Abstract
Western blot (WB) is the most commonly used scheme for protein identification in life science, but it still faces great challenges in the accurate quantitative detection of low-abundance proteins. Here, we proposed a novel surface-enhanced Raman scattering-based Western blot (SERS-WB) to solve this challenge. SERS nanotags were used as quantitative labels of proteins, which were composed of gold-silver core-shell nanoparticles, and Nile blue A (NBA) molecules were anchored on the interface of the core and shell. The results show that the SERS-WB possessed excellent sensitivity with detection limit of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) protein of 0.15 pg, as well as wide linear dynamic range (LDR) of 382 fg to 382 ng. In addition, the target protein on nitrocellulose (NC) membrane could be directly identified by colorimetric signal due to the aggregation effect of nanoparticles, which greatly simplifies the procedure. This as-proposed strategy will bring new thoughts to technological innovation of WB.
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Affiliation(s)
- Bing Liu
- Medical School, Institute of Reproductive Medicine, Nantong University, Nantong, 226001, China.
| | - Hanyu Tang
- Medical School, Institute of Reproductive Medicine, Nantong University, Nantong, 226001, China
| | - Qian Liu
- Medical School, Institute of Reproductive Medicine, Nantong University, Nantong, 226001, China
| | - Wenwen Wang
- Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China; Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Haitao Li
- Medical School, Institute of Reproductive Medicine, Nantong University, Nantong, 226001, China
| | - Shiya Zheng
- Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009, China
| | - Fei Sun
- Medical School, Institute of Reproductive Medicine, Nantong University, Nantong, 226001, China.
| | - Xiangwei Zhao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China; Southeast University Shenzhen Research Institute, Shenzhen, 518000, China.
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20
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Yu B, Mao Y, Li J, Wang J, Zhou B, Li P, Ma Y, Han Z. Hydrophobic expanded graphite-covered support to construct flexible and stable SERS substrate for sensitive determination by paste-sampling from irregular surfaces. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 282:121708. [PMID: 35933774 DOI: 10.1016/j.saa.2022.121708] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 07/18/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
Surface enhanced Raman spectroscopy (SERS) is a promising technique for trace determination. More and more attention is focused on hybrid SERS substrates, which coupled with noble metal nanoparticles and carbon-based materials. Herein, expanded graphite (EG) is used to prepare EG-covered support by ultrasonic washing and filtration. Such support is flexible and can be cut into any shape. And the contact angle (θe) for Au nanorods (Au NRs) sol on the EG-covered support was 108.2° and the hydrophobic surface is helpful for Au NRs to construct 'hot spots' during evaporation. The limits of detection (LOD) for crystal violet (CV), thiram, malachite green (MG) and methylene blue (MB) were as low as 1 ppb, 50 ppb, 1 ppb and 1 ppb, respectively. Moreover, a fast and convenient 'paste-sampling' method could be employed for trace contaminants on real samples, because EG-based Au NRs substrate is of flexibility and porosity. Thus, CV residue on shrimp could be determined lower than 1 ppb and thiram residue on grapes could be identified lower than 50 ppb. In addition to high sensitivity, the stability of EG-based Au NRs substrate is also very good. Even after acid/alkali pretreatment (pH = 4∼10) or 30 min of thermal treatment (T = 20∼100 °C), the enhancement of the substrate remained stable. What's more, the substrate could be stored as long as 30 days. The highly stable, sensitive, cost-effective and easy-to-produce EG-based Au NRs substrates exhibit a great potential to promote application of SERS for routine analysis.
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Affiliation(s)
- Borong Yu
- Hebei Key Laboratory of Organic Functional Molecules, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, Hebei, China.
| | - Yue Mao
- Hebei Key Laboratory of Organic Functional Molecules, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, Hebei, China
| | - Jiangli Li
- Hebei Key Laboratory of Organic Functional Molecules, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, Hebei, China
| | - Jiaosuo Wang
- Hebei Key Laboratory of Organic Functional Molecules, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, Hebei, China
| | - Binbin Zhou
- Shenzhen Institute of Advanced Electronic Materials, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Pan Li
- Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
| | - Yuanyuan Ma
- Hebei Key Laboratory of Organic Functional Molecules, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, Hebei, China
| | - Zhangang Han
- Hebei Key Laboratory of Organic Functional Molecules, College of Chemistry and Materials Science, Hebei Normal University, Shijiazhuang 050024, Hebei, China
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21
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Kalendar R, Ghamkhar K, Franceschi P, Egea-Cortines M. Editorial: Spectroscopy for crop and product phenotyping. FRONTIERS IN PLANT SCIENCE 2022; 13:1058333. [PMID: 36420036 PMCID: PMC9677824 DOI: 10.3389/fpls.2022.1058333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Affiliation(s)
- Ruslan Kalendar
- Helsinki Institute of Life Science HiLIFE, University of Helsinki, Biocenter 3, Helsinki, Finland
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
| | - Kioumars Ghamkhar
- Margot Forde Germplasm Centre, Grasslands Research Centre, AgResearch, Palmerston North, New Zealand
| | - Pietro Franceschi
- Unit of Computational Biology, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy
| | - Marcos Egea-Cortines
- Instituto de Biotecnología Vegegal, Universidad Politécnica de Cartagena, Cartagena, Spain
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22
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Balytskyi Y, Bendesky J, Paul T, Hagen GM, McNear K. Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach. Anal Chem 2022; 94:15297-15306. [DOI: 10.1021/acs.analchem.2c02666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Yaroslav Balytskyi
- Department of Physics and Energy Science, University of Colorado, Colorado Springs, Colorado 80918, United States
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
| | - Justin Bendesky
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Tristan Paul
- Department of Physics and Energy Science, University of Colorado, Colorado Springs, Colorado 80918, United States
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
| | - Guy M. Hagen
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
| | - Kelly McNear
- UCCS BioFrontiers Center, University of Colorado, Colorado Springs, Colorado 80918, United States
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23
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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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24
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Yu Y, Tang Y, Chu K, Gao T, Smith ZJ. High-Resolution Low-Power Hyperspectral Line-Scan Imaging of Fast Cellular Dynamics Using Azo-Enhanced Raman Scattering Probes. J Am Chem Soc 2022; 144:15314-15323. [PMID: 35969674 DOI: 10.1021/jacs.2c06275] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Small-molecule Raman probes for cellular imaging have attracted great attention owing to their sharp peaks that are sensitive to environmental changes. The small cross section of molecular Raman scattering limits dynamic cellular Raman imaging to expensive and complex coherent approaches that acquire single-channel images and lose hyperspectral Raman information. We introduce a new method, dynamic azo-enhanced Raman imaging (DAERI), to couple the new class of azo-enhanced Raman probes with a high-speed line-scan Raman imaging system. DAERI achieved high-resolution low-power imaging of fast cellular dynamics resolved at ∼270 nm along the confocal direction, 75 μW/μm2 and 3.5 s/frame. Based on the azo-enhanced Raman probes with characteristic signals 102-104 stronger than classic Raman labels, DAERI was not restricted to the cellular Raman-silent region as in prior work and enabled multiplex visualization of organelle motions and interactions. We anticipate DAERI to be a powerful tool for future studies in biophysics and cell biology.
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Affiliation(s)
- Yajun Yu
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yuchen Tang
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Kaiqin Chu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Tingjuan Gao
- Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, College of Chemistry, Central China Normal University, Wuhan 430079, China
| | - Zachary J Smith
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
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25
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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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26
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Shen H, Rösch P, Pletz MW, Popp J. In Vitro Fiber-Probe-Based Identification of Pathogens in Biofilms by Raman Spectroscopy. Anal Chem 2022; 94:5375-5381. [PMID: 35319199 DOI: 10.1021/acs.analchem.2c00029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Biofilms are the preferred habitat of microorganisms on living and artificial surfaces. Biofilm-related infections, such as infections of medical implants, are difficult to treat, and due to a reduced cultivability of the included bacteria, difficult to diagnose. Therefore, it is highly important to rapidly identify and investigate biofilms on implant surfaces, e.g., during surgery. In this study, we present fiber-probe-based Raman spectroscopy with an excitation wavelength of 785 nm, which was applied to investigate six different pathogen species involved in biofilm-related infections. Biofilms were cultivated in a drip flow reactor, which can model a biofilm growth environment. The signals collected from a fiber probe allowed us to collect Raman spectra not only from the embedded bacterial and yeast cells but also the surrounding extracellular polymeric substance matrix. This information was used in a classification model. The model consists of a principal component analysis in combination with linear discriminant analysis and was examined by applying a leave-one-batch-out cross-validation. This model achieved a classification accuracy of 93.8%. In addition, the identification accuracy increased up to 97.5% when clinical strains were used for identification. A fiber-probe-based Raman spectroscopy method combined with a chemometric analysis might therefore serve as a fast, accurate, and portable strategy for the species identification of biofilm-related infections, e.g., during surgical procedures.
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Affiliation(s)
- Haodong Shen
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Philosophenweg 7, D-07743 Jena, Germany.,Leibniz Institute of Photonic Technology Jena - Member of the Research Alliance "Leibniz Health Technologies", Albert-Einstein-Str. 9, D-07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Philosophenweg 7, D-07743 Jena, Germany
| | - Mathias W Pletz
- Institute for Infectious Diseases and Infection Control, Jena University Hospital, Am Klinikum 1, D-07747 Jena, Germany
| | - Jürgen Popp
- Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, D-07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Philosophenweg 7, D-07743 Jena, Germany.,Leibniz Institute of Photonic Technology Jena - Member of the Research Alliance "Leibniz Health Technologies", Albert-Einstein-Str. 9, D-07745 Jena, Germany
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27
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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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28
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Nakar A, Wagenhaus A, Rösch P, Popp J. Raman spectroscopy for the differentiation of Enterobacteriaceae: a comparison of two methods. Analyst 2022; 147:3938-3946. [DOI: 10.1039/d2an00822j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A comprehensive dataset of bacteria of the family Enterobacteriaceae was collected and measured with Raman spectroscopy. Fiber-probe based Raman spectroscopy enabled classification with 100% accuracy and remained robust with a validation dataset.
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Affiliation(s)
- Amir Nakar
- Leibniz Institute of Photonic Technology Jena – Member of the research alliance “Leibniz Health Technologies”, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany
- Research Campus Infectognostics, Jena, Germany
| | - Annette Wagenhaus
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany
- Research Campus Infectognostics, Jena, Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena – Member of the research alliance “Leibniz Health Technologies”, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany
- Research Campus Infectognostics, Jena, Germany
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