1
|
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.
Collapse
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
| |
Collapse
|
2
|
Wichmann C, Dengler J, Hoffmann M, Rösch P, Popp J. Simulating a reference medium for determining bacterial growth in hospital wastewater for Raman spectroscopic investigation. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123425. [PMID: 37751647 DOI: 10.1016/j.saa.2023.123425] [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: 02/01/2023] [Revised: 08/23/2023] [Accepted: 09/16/2023] [Indexed: 09/28/2023]
Abstract
Wastewater is a very complex and diverse medium, which despite low nutrient density still harbors bacteria. Especially the wastewater from hospitals contains a high germ load. However, wastewater is also very variable and differs not only from day to day, but also from house to house. Since wastewater is always changing and medium has an impact on Raman spectra of bacteria, it is necessary to find a surrogate material in which bacteria can be cultured to mimic a real hospital wastewater sample. In this study, we investigate two different artificial wastewaters for their abilities as a good alternative to real wastewater from the Jena University Hospital and to serve as a reference material for bacterial cultivation with subsequent Raman measurement. Each of the artificial wastewater on its own was not suitable to be used as a reference medium. Only the combination of the two simulated wastewaters achieved satisfactory results in the Raman spectroscopic identification of bacteria from real wastewater. These results could be used later in new experiments as a reference dataset to identify bacteria from real hospital wastewater samples.
Collapse
Affiliation(s)
- Christina Wichmann
- 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; InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Jennifer Dengler
- Integrative Health and Security Management Center, Staff Section Environmental Protection and Sustainability, Jena University Hospital, Kastanienstraße 1, 07747 Jena, Germany
| | - Marc Hoffmann
- Integrative Health and Security Management Center, Staff Section Environmental Protection and Sustainability, Jena University Hospital, Kastanienstraße 1, 07747 Jena, Germany
| | - Petra Rösch
- 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; InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany.
| | - Jürgen Popp
- 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; InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany; 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
| |
Collapse
|
3
|
Macgregor-Fairlie M, De Gomes P, Weston D, Rickard JJS, Goldberg Oppenheimer P. Hybrid use of Raman spectroscopy and artificial neural networks to discriminate Mycobacterium bovis BCG and other Mycobacteriales. PLoS One 2023; 18:e0293093. [PMID: 38079400 PMCID: PMC10712843 DOI: 10.1371/journal.pone.0293093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 10/05/2023] [Indexed: 12/18/2023] Open
Abstract
Even in the face of the COVID-19 pandemic, Tuberculosis (TB) continues to be a major public health problem and the 2nd biggest infectious cause of death worldwide. There is, therefore, an urgent need to develop effective TB diagnostic methods, which are cheap, portable, sensitive and specific. Raman spectroscopy is a potential spectroscopic technique for this purpose, however, so far, research efforts have focused primarily on the characterisation of Mycobacterium tuberculosis and other Mycobacteria, neglecting bacteria within the microbiome and thus, failing to consider the bigger picture. It is paramount to characterise relevant Mycobacteriales and develop suitable analytical tools to discriminate them from each other. Herein, through the combined use of Raman spectroscopy and the self-optimising Kohonen index network and further multivariate tools, we have successfully undertaken the spectral analysis of Mycobacterium bovis BCG, Corynebacterium glutamicum and Rhodoccocus erythropolis. This has led to development of a useful tool set, which can readily discern spectral differences between these three closely related bacteria as well as generate a unique spectral barcode for each species. Further optimisation and refinement of the developed method will enable its application to other bacteria inhabiting the microbiome and ultimately lead to advanced diagnostic technologies, which can save many lives.
Collapse
Affiliation(s)
- Michael Macgregor-Fairlie
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Paulo De Gomes
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Daniel Weston
- School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
| | | | - Pola Goldberg Oppenheimer
- School of Chemical Engineering, Advanced Nanomaterials Structures and Applications Laboratories, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Healthcare Technologies Institute, Institute of Translational Medicine, University of Birmingham, Birmingham, United Kingdom
| |
Collapse
|
4
|
Wang T, Jiang J, Liu K, Wang S, Xu T, Niu P, Ma J, Yin J, Liu T. Simultaneous bond-selective deuterium-based isotopic labeling sensing with disposable ultra-miniature CARS fiber probe. OPTICS EXPRESS 2023; 31:40717-40729. [PMID: 38041364 DOI: 10.1364/oe.505939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/02/2023] [Indexed: 12/03/2023]
Abstract
Deuterium-based isotopic labeling is an important technique for tracking cellular metabolism with the Raman signals analysis of low-wavenumber (LW) C-D bonds and high-wavenumber (HW) C-H bonds. We propose and demonstrate a disposable ultra-miniature fiber probe to detect LW and HW coherent anti-Stokes Raman scattering (CARS) spectra for deuterated compounds simultaneously and bond-selectively sensing. The 10.78 µm diameter disposable fiber probe, comprised of focusing taper as fiber probe head and time-domain walk-off eliminating fiber section with designed length, realizes wide-frequency-interval dual Stokes pulse delivering and focusing. The fiber probe enables quantitative concentration determination with resolution down to 11 mM. The chemical vibration modes of LW region C-D bonds and HW region C-H bonds of the mixture samples of organic compounds and their deuterated counterparts in a simulated cell are simultaneously excited and characterized. The CARS disposable fiber probe introduces a promising handle for in vivo biochemical detection based on isotopic labeling sensing.
Collapse
|
5
|
Lu W, Wang L, Liang J, Lu Y, Wang J, Fu YV. Dynamically Quantifying Intracellular Elemental Sulfur and Predicting Pertinent Gene Transcription by Raman Spectroscopy in Living Cells. Anal Chem 2023. [PMID: 37330921 DOI: 10.1021/acs.analchem.3c00047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The ability to monitor changes in metabolites and corresponding gene transcription within living cells is highly desirable. However, most current assays for quantification of metabolites or for gene transcription are destructive, precluding tracking the real-time dynamics of living cells. Here, we used the intracellular elemental sulfur in a Thiophaeococcus mangrovi cell as a proof-of-concept to link the quantity of metabolites and relevant gene transcription in living cells by a nondestructive Raman approach. Raman spectroscopy was utilized to quantify intracellular elemental sulfur noninvasively, and a computational mRR (mRNA and Raman) model was developed to infer the transcription of genes relevant to elemental sulfur. The results showed a significant linear correlation between the exponentially transformed Raman spectral intensity of intracellular elemental sulfur and the mRNA levels of genes encoding sulfur globule proteins in T. mangrovi. The mRR model was verified independently in two genera of Thiocapsa and Thiorhodococcus, and the mRNA levels predicted by mRR showed high consistency with actual gene expression detected by real-time polymerase chain reaction (PCR). This approach could enable noninvasive assessment of the quantity of metabolites and link the pertinent gene expression profiles in living cells, providing useful baseline data to spectroscopically map various omics in real time.
Collapse
Affiliation(s)
- Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology Chinese Academy of Sciences, Beijing 100101, China
| | - Lu Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology Chinese Academy of Sciences, Beijing 100101, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jing Liang
- State Key Laboratory of Microbial Resources, Institute of Microbiology Chinese Academy of Sciences, Beijing 100101, China
| | - Yi Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology Chinese Academy of Sciences, Beijing 100101, China
| | - Jing Wang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology Chinese Academy of Sciences, Beijing 100101, China
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
6
|
Pahlow S, Richard-Lacroix M, Hornung F, Köse-Vogel N, Mayerhöfer TG, Hniopek J, Ryabchykov O, Bocklitz T, Weber K, Ehricht R, Löffler B, Deinhardt-Emmer S, Popp J. Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy. BIOSENSORS 2023; 13:594. [PMID: 37366959 DOI: 10.3390/bios13060594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/22/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023]
Abstract
We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.
Collapse
Affiliation(s)
- Susanne Pahlow
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Marie Richard-Lacroix
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Franziska Hornung
- Leibniz Centre for Photonics in Infection Research (LPI), Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Nilay Köse-Vogel
- Leibniz Centre for Photonics in Infection Research (LPI), Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Thomas G Mayerhöfer
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Julian Hniopek
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Oleg Ryabchykov
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Thomas Bocklitz
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
- Physics & Computer Science, Faculty of Mathematics, Institute of Computer Science, University Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany
| | - Karina Weber
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Ralf Ehricht
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Bettina Löffler
- Leibniz Centre for Photonics in Infection Research (LPI), Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Stefanie Deinhardt-Emmer
- Leibniz Centre for Photonics in Infection Research (LPI), Institute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| | - Jürgen Popp
- Abbe Center of Photonics, Institute of Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany
- Center for Applied Research, InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany
- Leibniz Centre for Photonics in Infection Research (LPI), Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, 07745 Jena, Germany
| |
Collapse
|
7
|
Puente C, Pineda Aguilar N, Gómez I, López I. Morphology Effect of Photoconverted Silver Nanoparticles on the Performance of Surface-Enhanced Raman Spectroscopy Substrates. ACS OMEGA 2023; 8:12630-12635. [PMID: 37065055 PMCID: PMC10099118 DOI: 10.1021/acsomega.2c05958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 03/15/2023] [Indexed: 06/19/2023]
Abstract
Nowadays, surface-enhanced Raman spectroscopy (SERS) substrates are of great interest for many researchers, aiming to fabricate substrates with high sensitivity and low fabrication costs. In this study, we photoconverted Ag nanoparticles by using a simple and affordable red-green-blue light-emitting diode photoreactor. The obtained dispersions were transformed into a paste of nanoparticles and used to fabricate SERS substrates by a simple drop-casting process under controlled humidity conditions. The performance of these substrates was tested using p-aminothiophenol as a Raman probe. The results indicate that the particle shape has an influence on the Raman intensities and substrate sensitivity, showing a significant enhancement as the number of faces and vertices in the particle increases.
Collapse
Affiliation(s)
- Carlos Puente
- Facultad
de Ciencias Químicas, Centro de Investigación en Biotecnología
y Nanotecnología, Laboratorio de Nanociencias y Nanotecnología, Universidad Autónoma de Nuevo León (UANL), Autopista al Aeropuerto Internacional
Mariano Escobedo Km. 10, Parque de Investigación e Innovación
Tecnológica, Apodaca 66629, Nuevo León, Mexico
| | - Nayely Pineda Aguilar
- Centro
de Investigación en Materiales Avanzados, S.C. (CIMAV), Unidad Monterrey, Alianza Norte
202, Apodaca 66628, Nuevo León, Mexico
| | - Idalia Gómez
- Facultad
de Ciencias Químicas, Laboratorio de Materiales I, Av. Universidad,
Cd. Universitaria, Universidad Autónoma
de Nuevo León, UANL, 66455 San Nicolás de los Garza, Nuevo León, Mexico
| | - Israel López
- Facultad
de Ciencias Químicas, Centro de Investigación en Biotecnología
y Nanotecnología, Laboratorio de Nanociencias y Nanotecnología, Universidad Autónoma de Nuevo León (UANL), Autopista al Aeropuerto Internacional
Mariano Escobedo Km. 10, Parque de Investigación e Innovación
Tecnológica, Apodaca 66629, Nuevo León, Mexico
| |
Collapse
|
8
|
Wang X, He Y, Zhou Y, Zhu B, Xu J, Pan K, Li Y. An attempt to simultaneously quantify the polysaccharide, total lipid, protein and pigment in single Cyclotella cryptica cell by Raman spectroscopy. BIOTECHNOLOGY FOR BIOFUELS AND BIOPRODUCTS 2023; 16:63. [PMID: 37031179 PMCID: PMC10082982 DOI: 10.1186/s13068-023-02314-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 03/28/2023] [Indexed: 04/10/2023]
Abstract
BACKGROUND At present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom. RESULTS The method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism. CONCLUSIONS First, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.
Collapse
Affiliation(s)
- Xiufen Wang
- The Key Laboratory of Mariculture (Ministry of Education), Ocean University of China, Qingdao, 266003, Shandong, China
| | - Yuehui He
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Yuanyuan Zhou
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Baohua Zhu
- The Key Laboratory of Mariculture (Ministry of Education), Ocean University of China, Qingdao, 266003, Shandong, China
| | - Jian Xu
- Single-Cell Center, CAS Key Laboratory of Biofuels, Shandong Key Laboratory of Energy Genetics, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
- Shandong Energy Institute, Qingdao, China
- Qingdao New Energy Shandong Laboratory, Qingdao, China
| | - Kehou Pan
- The Key Laboratory of Mariculture (Ministry of Education), Ocean University of China, Qingdao, 266003, Shandong, China.
- Function Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China.
| | - Yun Li
- The Key Laboratory of Mariculture (Ministry of Education), Ocean University of China, Qingdao, 266003, Shandong, China.
| |
Collapse
|
9
|
Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level. Talanta 2023; 254:124112. [PMID: 36463804 DOI: 10.1016/j.talanta.2022.124112] [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: 08/26/2022] [Revised: 11/07/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022]
Abstract
Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.
Collapse
|
10
|
Han JY, Yeh M, DeVoe DL. Nanogap traps for passive bacteria concentration and single-point confocal Raman spectroscopy. BIOMICROFLUIDICS 2023; 17:024101. [PMID: 36896354 PMCID: PMC9991444 DOI: 10.1063/5.0142118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
A microfluidic device enabling the isolation and concentration of bacteria for analysis by confocal Raman spectroscopy is presented. The glass-on-silicon device employs a tapered chamber surrounded by a 500 nm gap that serves to concentrate cells at the chamber apex during sample perfusion. The sub-micrometer gap retains bacteria by size exclusion while allowing smaller contaminants to pass unimpeded. Concentrating bacteria within the fixed volume enables the use of single-point confocal Raman detection for the rapid acquisition of spectral signatures for bacteria identification. The technology is evaluated for the analysis of E. cloacae, K. pneumoniae, and C. diphtheriae, with automated peak extraction yielding distinct spectral fingerprints for each pathogen at a concentration of 103 CFU/ml that compare favorably with spectra obtained from significantly higher concentration reference samples evaluated by conventional confocal Raman analysis. The nanogap technology offers a simple, robust, and passive approach to concentrating bacteria from dilute samples into well-defined optical detection volumes, enabling rapid and sensitive confocal Raman detection for label-free identification of focused cells.
Collapse
Affiliation(s)
| | - Michael Yeh
- Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, USA
| | - Don L. DeVoe
- Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742, USA
| |
Collapse
|
11
|
Pistiki A, Ryabchykov O, Bocklitz TW, Rösch P, Popp J. Use of polymers as wavenumber calibration standards in deep-UVRR. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 287:122062. [PMID: 36351311 DOI: 10.1016/j.saa.2022.122062] [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: 06/27/2022] [Revised: 10/10/2022] [Accepted: 10/29/2022] [Indexed: 06/16/2023]
Abstract
Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400-1900 cm-1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.
Collapse
Affiliation(s)
- Aikaterini Pistiki
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Str. 9, 07745 Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Lessingstraße 10, 07743 Jena, Germany
| | - Thomas W Bocklitz
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Str. 9, 07745 Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Lessingstraße 10, 07743 Jena, Germany
| | - Petra Rösch
- InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Lessingstraße 10, 07743 Jena, Germany.
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena, Albert-Einstein-Str. 9, 07745 Jena, Germany; InfectoGnostics Research Campus Jena, Philosophenweg 7, 07743 Jena, Germany; Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Lessingstraße 10, 07743 Jena, Germany; Jena Biophotonics and Imaging Laboratory, Albert-Einstein-Str. 9, 07745 Jena, Germany; Center for Sepsis Control and Care, Jena University Hospital, Am Klinikum 1, 07747 Jena, Germany
| |
Collapse
|
12
|
Lu W, Li H, Qiu H, Wang L, Feng J, Fu YV. Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning. Front Microbiol 2023; 13:1076965. [PMID: 36687641 PMCID: PMC9846160 DOI: 10.3389/fmicb.2022.1076965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/06/2022] [Indexed: 01/05/2023] Open
Abstract
Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.
Collapse
Affiliation(s)
- Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Haifei Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Haoning Qiu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Lu Wang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jie Feng
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China,Savaid Medical School, University of Chinese Academy of Sciences, Beijing, China,*Correspondence: Yu Vincent Fu,
| |
Collapse
|
13
|
Kanno N, Kato S, Ohkuma M, Matsui M, Iwasaki W, Shigeto S. Nondestructive microbial discrimination using single-cell Raman spectra and random forest machine learning algorithm. STAR Protoc 2022; 3:101812. [DOI: 10.1016/j.xpro.2022.101812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
|
14
|
Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates. Microbiol Spectr 2022; 10:e0076322. [PMID: 36005817 PMCID: PMC9603629 DOI: 10.1128/spectrum.00763-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.
Collapse
|
15
|
Singh S, Kumbhar D, Reghu D, Venugopal SJ, Rekha PT, Mohandas S, Rao S, Rangaiah A, Chunchanur SK, Saini DK, Umapathy S. Culture-Independent Raman Spectroscopic Identification of Bacterial Pathogens from Clinical Samples Using Deep Transfer Learning. Anal Chem 2022; 94:14745-14754. [PMID: 36214808 DOI: 10.1021/acs.analchem.2c03391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.
Collapse
Affiliation(s)
- Saumya Singh
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India
| | - Dipak Kumbhar
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India
| | - Dhanya Reghu
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India
| | - Shwetha J Venugopal
- Department of Microbiology, Bangalore Medical College and Research Institute, Bangalore 560002, India
| | - P T Rekha
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India
| | - Silpa Mohandas
- Department of Microbiology, Bangalore Medical College and Research Institute, Bangalore 560002, India
| | - Shruti Rao
- Department of Microbiology, Bangalore Medical College and Research Institute, Bangalore 560002, India
| | - Ambica Rangaiah
- Department of Microbiology, Bangalore Medical College and Research Institute, Bangalore 560002, India
| | - Sneha K Chunchanur
- Department of Microbiology, Bangalore Medical College and Research Institute, Bangalore 560002, India
| | - Deepak Kumar Saini
- Department of Molecular Reproduction and Genetics, Indian Institute of Science, Bangalore 560012, India.,Center for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India.,Center for Infectious Diseases Research, Indian Institute of Science, Bangalore 560012, India
| | - Siva Umapathy
- Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore 560012, India.,Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore 560012, India
| |
Collapse
|
16
|
Saikia D, Jadhav P, Hole AR, Krishna CM, Singh SP. Growth Kinetics Monitoring of Gram-Negative Pathogenic Microbes Using Raman Spectroscopy. APPLIED SPECTROSCOPY 2022; 76:1263-1271. [PMID: 35694822 DOI: 10.1177/00037028221109624] [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] [Indexed: 06/15/2023]
Abstract
Optical density based measurements are routinely performed to monitor the growth of microbes. These measurements solely depend upon the number of cells and do not provide any information about the changes in the biochemical milieu or biological status. An objective information about these parameters is essential for evaluation of novel therapies and for maximizing the metabolite production. In the present study, we have applied Raman spectroscopy to monitor growth kinetics of three different pathogenic Gram-negative microbes Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. Spectral measurements were performed under 532 nm excitation with 5 seconds of exposure time. Spectral features suggest temporal changes in the "peptide" and "nucleic acid" content of cells under different growth stages. Using principal component analysis (PCA), successful discrimination between growth phases was also achieved. Overall, the findings are supportive of the prospective adoption of Raman based approaches for monitoring microbial growth.
Collapse
Affiliation(s)
- Dimple Saikia
- Department of Biosciences and Bioengineering, 477529Indian Institute of Technology Dharwad, Dharwad, India
| | - Priyanka Jadhav
- Tata Memorial Centre, 29435Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai, India
- Training School Complex, Homi Bhabha National Institute, Anushakti Nagar, India
| | - Arti R Hole
- Tata Memorial Centre, 29435Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai, India
| | - Chilakapati Murali Krishna
- Tata Memorial Centre, 29435Advanced Centre for Treatment Research and Education in Cancer, Navi Mumbai, India
- Training School Complex, Homi Bhabha National Institute, Anushakti Nagar, India
| | - Surya P Singh
- Department of Biosciences and Bioengineering, 477529Indian Institute of Technology Dharwad, Dharwad, India
| |
Collapse
|
17
|
Thomsen BL, Christensen JB, Rodenko O, Usenov I, Grønnemose RB, Andersen TE, Lassen M. Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning. Sci Rep 2022; 12:16436. [PMID: 36180775 PMCID: PMC9524333 DOI: 10.1038/s41598-022-20850-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 09/20/2022] [Indexed: 11/09/2022] Open
Abstract
The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria. For this we have implemented a spectral transformer model for hyper-spectral Raman images of bacteria. We show that our model outperforms the standard convolutional neural network models on a multitude of classification problems, both in terms of accuracy and in terms of training time. We attain more than 96% classification accuracy on a dataset consisting of 15 different classes and 95.6% classification accuracy for six MR-MS bacteria species. More importantly, our results are obtained using only fast and easy-to-produce training and test data.
Collapse
Affiliation(s)
| | | | - Olga Rodenko
- Danish Fundamental Metrology, Kogle Allé 5, 2970, Hørsholm, Denmark
| | - Iskander Usenov
- Institute of Optics and Atomic Physics, Technische Universität Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany.,Art photonics GmbH, Rudower Ch 46, 12489, Berlin, Germany
| | - Rasmus Birkholm Grønnemose
- Research Unit of Clinical Microbiology, University of Southern Denmark and Odense University Hospital, J.B. Winsløws Vej 21.2, 5000, Odense, Denmark
| | - Thomas Emil Andersen
- Research Unit of Clinical Microbiology, University of Southern Denmark and Odense University Hospital, J.B. Winsløws Vej 21.2, 5000, Odense, Denmark
| | - Mikael Lassen
- Danish Fundamental Metrology, Kogle Allé 5, 2970, Hørsholm, Denmark.
| |
Collapse
|
18
|
Li G, Wu C, Wang D, Srinivasan V, Kaeli DR, Dy JG, Gu AZ. Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13473-13484. [PMID: 36048618 DOI: 10.1021/acs.est.1c08768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.
Collapse
Affiliation(s)
- Guangyu Li
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853-0001, United States
| | - Chieh Wu
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - Dongqi Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- Department of Municipal and Environmental Engineering, School of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, PRC
| | - Varun Srinivasan
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- Brown and Caldwell, One Tech Drive, Andover, Massachusetts 01810, United States
| | - David R Kaeli
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - Jennifer G Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - April Z Gu
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853-0001, United States
| |
Collapse
|
19
|
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
| |
Collapse
|
20
|
Nakar A, Pistiki A, Ryabchykov O, Bocklitz T, Rösch P, Popp J. Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy. JOURNAL OF BIOPHOTONICS 2022; 15:e202200005. [PMID: 35388631 DOI: 10.1002/jbio.202200005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/18/2022] [Accepted: 04/04/2022] [Indexed: 05/14/2023]
Abstract
Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.
Collapse
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, Jena, Germany
- Research Campus Infectognostics, Jena, Germany
| | - Aikaterini Pistiki
- 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, Jena, Germany
- Research Campus Infectognostics, Jena, Germany
| | - Oleg Ryabchykov
- 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, Jena, Germany
| | - Thomas Bocklitz
- 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, Jena, Germany
- Research Campus Infectognostics, Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, 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, Jena, Germany
- Research Campus Infectognostics, Jena, Germany
- Jena Biophotonics and Imaging Laboratory, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, Jena, Germany
| |
Collapse
|
21
|
Technique Evolutions for Microorganism Detection in Complex Samples: A Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125892] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Rapid detection of microorganisms is a major challenge in the medical and industrial sectors. In a pharmaceutical laboratory, contamination of medical products may lead to severe health risks for patients, such as sepsis. In the specific case of advanced therapy medicinal products, contamination must be detected as early as possible to avoid late production stop and unnecessary costs. Unfortunately, the conventional methods used to detect microorganisms are based on time-consuming and labor-intensive approaches. Therefore, it is important to find new tools to detect microorganisms in a shorter time frame. This review sums up the current methods and represents the evolution in techniques for microorganism detection. First, there is a focus on promising ligands, such as aptamers and antimicrobial peptides, cheaper to produce and with a broader spectrum of detection. Then, we describe methods achieving low limits of detection, thanks to Raman spectroscopy or precise handling of samples through microfluids devices. The last part is dedicated to techniques in real-time, such as surface plasmon resonance, preventing the risk of contamination. Detection of pathogens in complex biological fluids remains a scientific challenge, and this review points toward important areas for future research.
Collapse
|
22
|
Azemtsop Matanfack G, Taubert M, Reilly-Schott V, Küsel K, Rösch P, Popp J. Phenotypic Differentiation of Autotrophic and Heterotrophic Bacterial Cells Using Raman-D 2O Labeling. Anal Chem 2022; 94:7759-7766. [PMID: 35608509 DOI: 10.1021/acs.analchem.1c04097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Carbon cycling is one of the major biogeochemical processes driven by bacteria. Autotrophic bacteria convert carbon dioxide (CO2) into organic compounds that are used by heterotrophs. Mixotrophic bacteria can employ both autotrophy and heterotrophy for growth. The characterization of the lifestyle of individual cells is essential to understand the microbial activity and thus reveal the implication of bacteria in the carbon flux. In this study, we used groundwater bacteria to investigate the potential of Raman-D2O labeling in combination with chemometrics to identify the carbon assimilation strategies of bacteria. Classification models were built using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Autotrophs assimilated a significantly higher amount (mean C-D ratio between 16.63 and 21.69%) of deuterium than heterotrophs. The C-D signal only provides information about the activity since it appears in the Raman-silent region, where no interference with the taxonomic information is expected. The classification between autotrophs and heterotrophs achieved an overall accuracy of 96.3%. In the validation step with an independent dataset containing species not included in the model, the PCA-LDA model achieved 100% accuracy. This demonstrated that the C-D signal contributed to the identification of autotrophic and heterotrophic bacterial cells. This work reports a robust, rapid, and nondestructive approach for the identification of single cells based on their carbon acquisition strategies. The present study foresees the potential of Raman-D2O labeling as a promising method for automated discrimination of in situ functional activities of bacteria in environmental systems.
Collapse
Affiliation(s)
- Georgette Azemtsop Matanfack
- Institute of Physical Chemistry and Abbe Center of Photonics (IPC), Friedrich-Schiller-University Jena, Helmholtzweg 4, 07743 Jena, Germany.,Leibniz Institute of Photonic Technology (Leibniz-IPHT), Albert-Einstein-Straße 9, 07745 Jena, Germany.,Research Campus Infectognostics e.V., 07743 Jena, Germany
| | - Martin Taubert
- Aquatic Geomicrobiology, Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Str. 159, 07743 Jena, Germany
| | - Vincent Reilly-Schott
- Aquatic Geomicrobiology, Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Str. 159, 07743 Jena, Germany
| | - Kirsten Küsel
- Aquatic Geomicrobiology, Institute of Biodiversity, Friedrich Schiller University Jena, Dornburger Str. 159, 07743 Jena, Germany.,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstraße 4, 04103 Leipzig, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics (IPC), Friedrich-Schiller-University Jena, Helmholtzweg 4, 07743 Jena, Germany.,Research Campus Infectognostics e.V., 07743 Jena, Germany
| | - Jürgen Popp
- Institute of Physical Chemistry and Abbe Center of Photonics (IPC), Friedrich-Schiller-University Jena, Helmholtzweg 4, 07743 Jena, Germany.,Leibniz Institute of Photonic Technology (Leibniz-IPHT), Albert-Einstein-Straße 9, 07745 Jena, Germany.,Research Campus Infectognostics e.V., 07743 Jena, Germany
| |
Collapse
|
23
|
Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy. Foods 2022; 11:foods11101506. [PMID: 35627076 PMCID: PMC9141442 DOI: 10.3390/foods11101506] [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: 04/01/2022] [Revised: 05/16/2022] [Accepted: 05/19/2022] [Indexed: 01/27/2023] Open
Abstract
As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.
Collapse
|
24
|
Zhang P, Fu Y, Zhao H, Liu X, Wu X, Lin T, Wang H, Song L, Fang Y, Lu W, Liu M, Liu W, Zheng D. Dynamic insights into increasing antibiotic resistance in Staphylococcus aureus by label-free SERS using a portable Raman spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:121070. [PMID: 35231762 DOI: 10.1016/j.saa.2022.121070] [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: 11/20/2021] [Revised: 02/14/2022] [Accepted: 02/20/2022] [Indexed: 06/14/2023]
Abstract
Rapid and quantitative detection of bacterial antibiotic resistance is of great significance for the prevention and treatment of infections and understanding drug-resistant mechanism. In this study, label-free surface-enhanced Raman spectroscopy (SERS) technology was applied to dynamically explore oxacillin/cefazolin-derived resistance in Staphylococcus aureus using a portable Raman spectrometer. The results showed that S. aureus rapidly responded to oxacillin/cefazolin stimulation and gradually developed different degrees of drug resistance during the 21 days of exposure. The molecular changes that accumulated in the drug-resistant strains were sensitively recorded by SERS in a whole-cell manner. Principal components-linear discriminant analysis correctly distinguished various degrees of drug-resistant strains. The typical Raman peak intensities of I734/I867 showed a negative and non-linear correlation with the minimum inhibitory concentration (MIC). The correlation coefficient reached above 0.9. The target sites of oxacillin/cefazolin on S. aureus clearly reflected on SERS profiles. The results collected by SERS were further verified by other biological methods including the antibiotic susceptibility test, MIC determination, and PCR results. This study indicates that SERS technology provides a rapid and flexible alternative to current drug susceptibility testing, laying a foundation for qualitative and quantitative evaluation of drug resistance in clinical detection.
Collapse
Affiliation(s)
- Ping Zhang
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China.
| | - Yingying Fu
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Huimin Zhao
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Xiaoying Liu
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Xihao Wu
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Taifeng Lin
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Huiqin Wang
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Liang Song
- Chinarocket Co., Ltd., Beijing, 100070, PR China
| | - Yaping Fang
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Wenjing Lu
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Mengjia Liu
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Wenbo Liu
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China
| | - Dawei Zheng
- Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base of Antivirus Drug, Beijing University of Technology, Beijing, 100124, PR China.
| |
Collapse
|
25
|
Jiang N, Feng Y, Huang Q, Liu X, Guo Y, Yang Z, Peng C, Li S, Hao L. Effect of Environmental pH on Mineralization of Anaerobic Iron-Oxidizing Bacteria. Front Microbiol 2022; 13:885098. [PMID: 35633702 PMCID: PMC9134017 DOI: 10.3389/fmicb.2022.885098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
Abstract
Freshwater lakes are often polluted with various heavy metals in the Anthropocene. The iron-oxidizing microorganisms and their mineralized products can coprecipitate with many heavy metals, including Al, Zn, Cu, Cd, and Cr. As such, microbial iron oxidation can exert a profound impact on environmental remediation. The environmental pH is a key determinant regulating microbial growth and mineralization and then influences the structure of the final mineralized products of anaerobic iron-oxidizing bacteria. Freshwater lakes, in general, are neutral-pH environments. Understanding the effects of varying pH on the mineralization of iron-oxidizing bacteria under neutrophilic conditions could aid in finding out the optimal pH values that promote the coprecipitation of heavy metals. Here, two typical neutrophilic Fe(II)-oxidizing bacteria, the nitrate-reducing Acidovorax sp. strain BoFeN1 and the anoxygenic phototrophic Rhodobacter ferrooxidans strain SW2, were selected for studying how their growth and mineralization response to slight changes in circumneutral pH. By employing focused ion beam/scanning electron microscopy (FIB–SEM) and transmission electron microscopy (TEM), we examined the interplay between pH changes and anaerobic iron-oxidizing bacteria and observed that pH can significantly impact the microbial mineralization process and vice versa. Further, pH-dependent changes in the structure of mineralized products of bacterial iron oxidation were observed. Our study could provide mechanical insights into how to manipulate microbial iron oxidation for facilitating remediation of heavy metals in the environment.
Collapse
Affiliation(s)
- Na Jiang
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
- Institute of Geochemistry, University of Chinese Academy of Sciences, Beijing, China
- Minzu Normal University of Xingyi, Xingyi, China
| | - Yiqing Feng
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
- Institute of Geochemistry, University of Chinese Academy of Sciences, Beijing, China
| | - Qiang Huang
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
- Institute of Geochemistry, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoling Liu
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
| | - Yuan Guo
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
| | - Zhen Yang
- College of Urban and Environmental Science, Peking University, Beijing, China
| | - Chao Peng
- College of Life Sciences, China West Normal University, Nanchong, China
| | - Shun Li
- Ningbo Urban Environment Observation and Research Station, Chinese Academy of Sciences, Ningbo, China
| | - Likai Hao
- State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China
- Chinese Academy of Sciences (CAS) Center for Excellence in Quaternary Science and Global Change, Xi'an, China
- *Correspondence: Likai Hao
| |
Collapse
|
26
|
Rebrosova K, Samek O, Kizovsky M, Bernatova S, Hola V, Ruzicka F. Raman Spectroscopy—A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings. Front Cell Infect Microbiol 2022; 12:866463. [PMID: 35531343 PMCID: PMC9072635 DOI: 10.3389/fcimb.2022.866463] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/07/2022] [Indexed: 12/02/2022] Open
Abstract
Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful—quick, non-invasive, and non-destructive —tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.
Collapse
Affiliation(s)
- Katarina Rebrosova
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne’s University Hospital, Brno, Czechia
| | - Ota Samek
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Martin Kizovsky
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Silvie Bernatova
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czechia
| | - Veronika Hola
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne’s University Hospital, Brno, Czechia
- *Correspondence: Veronika Hola,
| | - Filip Ruzicka
- Department of Microbiology, Faculty of Medicine of Masaryk University and St. Anne’s University Hospital, Brno, Czechia
| |
Collapse
|
27
|
Pampoukis G, Lytou AE, Argyri AA, Panagou EZ, Nychas GJE. Recent Advances and Applications of Rapid Microbial Assessment from a Food Safety Perspective. SENSORS (BASEL, SWITZERLAND) 2022; 22:2800. [PMID: 35408414 PMCID: PMC9003504 DOI: 10.3390/s22072800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 06/14/2023]
Abstract
Unsafe food is estimated to cause 600 million cases of foodborne disease, annually. Thus, the development of methods that could assist in the prevention of foodborne diseases is of high interest. This review summarizes the recent progress toward rapid microbial assessment through (i) spectroscopic techniques, (ii) spectral imaging techniques, (iii) biosensors and (iv) sensors designed to mimic human senses. These methods often produce complex and high-dimensional data that cannot be analyzed with conventional statistical methods. Multivariate statistics and machine learning approaches seemed to be valuable for these methods so as to "translate" measurements to microbial estimations. However, a great proportion of the models reported in the literature misuse these approaches, which may lead to models with low predictive power under generic conditions. Overall, all the methods showed great potential for rapid microbial assessment. Biosensors are closer to wide-scale implementation followed by spectroscopic techniques and then by spectral imaging techniques and sensors designed to mimic human senses.
Collapse
Affiliation(s)
- George Pampoukis
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
- Food Microbiology, Department of Agrotechnology and Food Sciences, Wageningen University & Research, P.O. Box 17, 6700 AA Wageningen, The Netherlands
| | - Anastasia E. Lytou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
| | - Anthoula A. Argyri
- Institute of Technology of Agricultural Products, Hellenic Agricultural Organization DIMITRA, Sofokli Venizelou 1, 14123 Lycovrisi, Greece;
| | - Efstathios Z. Panagou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
| | - George-John E. Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece; (G.P.); (A.E.L.); (E.Z.P.)
| |
Collapse
|
28
|
Separation-free bacterial identification in arbitrary media via deep neural network-based SERS analysis. Biosens Bioelectron 2022; 202:113991. [DOI: 10.1016/j.bios.2022.113991] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 01/04/2022] [Accepted: 01/07/2022] [Indexed: 11/22/2022]
|
29
|
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]
|
30
|
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.
Collapse
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
| |
Collapse
|
31
|
Shen H, Rösch P, Popp J. Fiber Probe-Based Raman Spectroscopic Identification of Pathogenic Infection Microorganisms on Agar Plates. Anal Chem 2022; 94:4635-4642. [PMID: 35254815 DOI: 10.1021/acs.analchem.1c04507] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Rapid identification of microorganisms is clinically meaningful, and it helps to decelerate the spread of drug resistance and improve patient treatment. In this study, we present a rapid fiber probe-based Raman technique with an excitation wavelength of 785 nm, which is applied to classify and identify nine different species of microorganisms. The cost-effective fiber probe compresses the dimension of the system and provides a more reliable and stable database. All microorganisms were simply cultivated on Luria-Bertani (LB) agar, and Raman spectra were obtained directly from the microbial colonies with the fiber probe within 30 s. The classification model consists of principal component analysis (PCA) in combination with linear discriminant analysis (LDA) and was examined by applying leave-one-batch-out cross-validation (LOBOCV). This model achieved an accuracy of 98.9%. In addition, the validation and identification processes based on independent replicates achieved accuracies of 99.8% and 100%, respectively. The results demonstrated that fiber probe Raman spectroscopy in combination with chemometric analysis allowed a rapid classification and identification of microorganisms only with a normal culture. Therefore, it is promising especially for medical applications and could moreover be helpful to investigate and identify microorganisms rapidly in further studies.
Collapse
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
| | - 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
| |
Collapse
|
32
|
No JH, Nishu SD, Hong JK, Lyou ES, Kim MS, Wee GN, Lee TK. Raman-Deuterium Isotope Probing and Metagenomics Reveal the Drought Tolerance of the Soil Microbiome and Its Promotion of Plant Growth. mSystems 2022; 7:e0124921. [PMID: 35103487 PMCID: PMC8805637 DOI: 10.1128/msystems.01249-21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 01/11/2022] [Indexed: 01/07/2023] Open
Abstract
Drought has become a major agricultural threat leading crop yield loss. Although a few species of rhizobacteria have the ability to promote plant growth under drought, the drought tolerance of the soil microbiome and its relationship with the promotion of plant growth under drought are scarcely studied. This study aimed to develop a novel approach for assessing drought tolerance in agricultural land by quantitatively measuring microbial phenotypes using stable isotopes and Raman spectroscopy. Raman spectroscopy with deuterium isotope probing was used to identify the Raman signatures of drought effects from drought-tolerant bacteria. Counting drought-tolerant cells by applying these phenotypic properties to agricultural samples revealed that 0% to 52.2% of all measured single cells had drought-tolerant properties, depending on the soil sample. The proportions of drought-tolerant cells in each soil type showed similar tendencies to the numbers of revived pea plants cultivated under drought. The phenotype of the soil microbiome and plant behavior under drought conditions therefore appeared to be highly related. Studying metagenomics suggested that there was a reliable link between the phenotype and genotype of the soil microbiome that could explain mechanisms that promote plant growth in drought. In particular, the proportion of drought-tolerant cells was highly correlated with genes encoding phytohormone production, including tryptophan synthase and isopentenyl-diphosphate delta-isomerase; these enzymes are known to alleviate drought stress. Raman spectroscopy with deuterium isotope probing shows high potential as an alternative technology for quantitatively assessing drought tolerance through phenotypic analysis of the soil microbiome. IMPORTANCE Soil microbiome has played a critical role in the plant survival during drought. However, the drought tolerance of soil microbiome and its ability to promote plant growth under drought is still scarcely studied. In this study, we identified the Raman signature (i.e., phenotype) of drought effects from drought-tolerant bacteria in agricultural soil samples using Raman-deuterium isotope probing (Raman-DIP). Moreover, the number of drought-tolerant cells measured by Raman-DIP was highly related to the survival rate of plant cultivation under drought and the abundance of genes encoding phytohormone production alleviating drought stress in plant. These results suggest Raman-DIP is a promising technology for measuring drought tolerance of soil microbiome. This result give us important insight into further studies of a reliable link between phenotype and genotype of soil microbiome for future plant-bacteria interaction research.
Collapse
Affiliation(s)
- Jee Hyun No
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| | - Susmita Das Nishu
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| | - Jin-Kyung Hong
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| | - Eun Sun Lyou
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| | - Min Sung Kim
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| | - Gui Nam Wee
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| | - Tae Kwon Lee
- Department of Environmental Engineering, Yonsei University, Wonju, Republic of Korea
| |
Collapse
|
33
|
Tian T, Yi J, Liu Y, Li B, Liu Y, Qiao L, Zhang K, Liu B. Self-assembled plasmonic nanoarrays for enhanced bacterial identification and discrimination. Biosens Bioelectron 2022; 197:113778. [PMID: 34798500 DOI: 10.1016/j.bios.2021.113778] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 11/02/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022]
Abstract
The rapid and accurate bacterial testing is a critical step for the management of infectious diseases, but challenges remain largely due to a lack of advanced sensing tools. Here we report the development of highly plasmon-active, biofunctional nanoparticle arrays for simultaneous capture, identification, and differentiation of bacteria by surface-enhanced Raman scattering (SERS). The nanoarrays were facilely prepared through an electrostatic mechanism-controlled self-assembly of metallic nanoparticles at liquid-liquid interfaces, and exhibited high SERS sensitivity beyond femtomole, good reproducibility (relative standard deviation of 2.7%) and stability. Modification of the nanoarrays with concanavalin A allowed to effective capture of both Gram-positive and Gram-negative bacteria (bacterial-capture efficiency maintained beyond 50%) at bacterial concentrations ranging from 50 to 2000 CFU mL-1, as determined by the plate-counting method. Moreover, single-cell Raman fingerprinting and discrimination of eight different bacteria species with high signal-to-noise ratio, excellent spectral reproducibility, and a total assay time of 1.5 h was achieved under fairly mild conditions (24 μW, acquisition time: 1 s). Collectively, we believe that our biofunctionalized, SERS-based self-assembled nanoarrays have great potential to help in rapid and label-free bacterial diagnosis and phenotyping study.
Collapse
Affiliation(s)
- Tongtong Tian
- Department of Chemistry, Shanghai Stomatological Hospital, State Key Laboratory of Molecular Engineering of Polymers and Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China
| | - Jia Yi
- Department of Chemistry, Shanghai Stomatological Hospital, State Key Laboratory of Molecular Engineering of Polymers and Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China
| | - Yujie Liu
- Shanghai Institute for Pediatric Research, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Binxiao Li
- Department of Chemistry, Shanghai Stomatological Hospital, State Key Laboratory of Molecular Engineering of Polymers and Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China
| | - Yixin Liu
- Department of Chemistry, Shanghai Stomatological Hospital, State Key Laboratory of Molecular Engineering of Polymers and Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, State Key Laboratory of Molecular Engineering of Polymers and Institute of Biomedical Sciences, Fudan University, Shanghai, 200433, China
| | - Kun Zhang
- Shanghai Institute for Pediatric Research, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
| | - Baohong Liu
- Shanghai Institute for Pediatric Research, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
| |
Collapse
|
34
|
Nakar A, Pistiki A, Ryabchykov O, Bocklitz T, Rösch P, Popp J. Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy. Anal Bioanal Chem 2022; 414:1481-1492. [PMID: 34982178 PMCID: PMC8761712 DOI: 10.1007/s00216-021-03800-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/05/2021] [Accepted: 11/22/2021] [Indexed: 01/08/2023]
Abstract
In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.
Collapse
Affiliation(s)
- Amir Nakar
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
| | - Aikaterini Pistiki
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
| | - Oleg Ryabchykov
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany.
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany.
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena (a Member of Leibniz Health Technologies), Albert-Einstein-Straße 9, 07745, Jena, Germany
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743, Jena, Germany
- Research Campus Infectognostics Jena E.V, Philosophenweg 7, 07743, Jena, Germany
- Jena Biophotonics and Imaging Laboratory, Albert-Einstein-Straße 9, 07745, Jena, Germany
| |
Collapse
|
35
|
Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy. Int J Mol Sci 2022; 23:ijms23031356. [PMID: 35163280 PMCID: PMC8835768 DOI: 10.3390/ijms23031356] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/07/2022] [Accepted: 01/14/2022] [Indexed: 01/01/2023] Open
Abstract
The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTXr-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I990/I1348, and the R2 reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.
Collapse
|
36
|
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
| |
Collapse
|
37
|
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.
Collapse
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
| |
Collapse
|
38
|
|
39
|
Cialla-May D, Krafft C, Rösch P, Deckert-Gaudig T, Frosch T, Jahn IJ, Pahlow S, Stiebing C, Meyer-Zedler T, Bocklitz T, Schie I, Deckert V, Popp J. Raman Spectroscopy and Imaging in Bioanalytics. Anal Chem 2021; 94:86-119. [PMID: 34920669 DOI: 10.1021/acs.analchem.1c03235] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Dana Cialla-May
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,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
| | - Christoph Krafft
- 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
| | - Tanja Deckert-Gaudig
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Torsten Frosch
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Izabella J Jahn
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Susanne Pahlow
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,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
| | - Clara Stiebing
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Tobias Meyer-Zedler
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Iwan Schie
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Ernst-Abbe-Hochschule Jena, University of Applied Sciences, Department of Biomedical Engineering and Biotechnology, Carl-Zeiss-Promenade 2, 07745 Jena, Germany
| | - Volker Deckert
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,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
| |
Collapse
|
40
|
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.
Collapse
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.
| |
Collapse
|
41
|
Guo S, Popp J, Bocklitz T. Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling. Nat Protoc 2021; 16:5426-5459. [PMID: 34741152 DOI: 10.1038/s41596-021-00620-3] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 08/19/2021] [Indexed: 02/01/2023]
Abstract
Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.
Collapse
Affiliation(s)
- Shuxia Guo
- Institute for Brain and Intelligence, Southeast University, Nanjing, China.,Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, Jena, Germany.,Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich Schiller University of Jena, Jena, Germany
| | - Jürgen Popp
- Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, Jena, Germany.,Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich Schiller University of Jena, Jena, Germany
| | - Thomas Bocklitz
- Leibniz Institute of Photonic Technology Jena (IPHT Jena), Member of Leibniz Health Technologies, Jena, Germany. .,Institute of Physical Chemistry and Abbe Centre of Photonics, Friedrich Schiller University of Jena, Jena, Germany.
| |
Collapse
|
42
|
Using Stable Isotope Probing and Raman Microspectroscopy To Measure Growth Rates of Heterotrophic Bacteria. Appl Environ Microbiol 2021; 87:e0146021. [PMID: 34495689 DOI: 10.1128/aem.01460-21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The suitability of stable isotope probing (SIP) and Raman microspectroscopy to measure growth rates of heterotrophic bacteria at the single-cell level was evaluated. Label assimilation into Escherichia coli biomass during growth on a complex 13C-labeled carbon source was monitored in time course experiments. 13C incorporation into various biomolecules was measured by spectral "red shifts" of Raman-scattered emissions. The 13C- and 12C-isotopologues of the amino acid phenylalanine (Phe) proved to be quantitatively accurate reporter molecules of cellular isotopic fractional abundances (fcell). Values of fcell determined by Raman microspectroscopy and independently by isotope ratio mass spectrometry (IRMS) over a range of isotopic enrichments were statistically indistinguishable. Progressive labeling of Phe in E. coli cells among a range of 13C/12C organic substrate admixtures occurred predictably through time. The relative isotopologue abundances of Phe determined by Raman spectral analysis enabled the accurate calculation of bacterial growth rates as confirmed independently by optical density (OD) measurements. The results demonstrate that combining SIP and Raman microspectroscopy can be a powerful tool for studying bacterial growth at the single-cell level on defined or complex organic 13C carbon sources, even in mixed microbial assemblages. IMPORTANCE Population growth dynamics and individual cell growth rates are the ultimate expressions of a microorganism's fitness under its environmental conditions, whether natural or engineered. Natural habitats and many industrial settings harbor complex microbial assemblages. Their heterogeneity in growth responses to existing and changing conditions is often difficult to grasp by standard methodologies. In this proof-of-concept study, we tested whether Raman microspectroscopy can reliably quantify the assimilation of isotopically labeled nutrients into E. coli cells and enable the determination of individual growth rates among heterotrophic bacteria. Raman-derived growth rate estimates were statistically indistinguishable from those derived by standard optical density measurements of the same cultures. Raman microspectroscopy can also be combined with methods for phylogenetic identification. We report the development of Raman-based techniques that enable researchers to directly link genetic identity to functional traits and rate measurements of single cells within mixed microbial assemblages, currently a major technical challenge in microbiological research.
Collapse
|
43
|
Chung PJ, Singh GP, Huang CH, Koyyappurath S, Seo JS, Mao HZ, Diloknawarit P, Ram RJ, Sarojam R, Chua NH. Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy. FRONTIERS IN PLANT SCIENCE 2021; 12:746586. [PMID: 34745179 PMCID: PMC8566886 DOI: 10.3389/fpls.2021.746586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.
Collapse
Affiliation(s)
- Pil Joong Chung
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Gajendra P. Singh
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Chung-Hao Huang
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Sayuj Koyyappurath
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Jun Sung Seo
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Hui-Zhu Mao
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Piyarut Diloknawarit
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Rajeev J. Ram
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Rajani Sarojam
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Nam-Hai Chua
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| |
Collapse
|
44
|
Scholtz A, Ramoji A, Silge A, Jansson JR, de Moura IG, Popp J, Sram JP, Armani AM. COVID-19 Diagnostics: Past, Present, and Future. ACS PHOTONICS 2021; 8:2827-2838. [PMID: 37556281 PMCID: PMC8482784 DOI: 10.1021/acsphotonics.1c01052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 05/25/2023]
Abstract
In winter of 2020, SARS-CoV-2 emerged as a global threat, impacting not only health but also financial and political stability. To address the societal need for monitoring the spread of SARS-CoV-2, many existing diagnostic technologies were quickly adapted to detect SARS-CoV-2 RNA and antigens as well as the immune response, and new testing strategies were developed to accelerate time-to-decision. In parallel, the infusion of research support accelerated the development of new spectroscopic methods. While these methods have significantly reduced the impact of SARS-CoV-2 on society when coupled with behavioral changes, they also lay the groundwork for a new generation of platform technologies. With several epidemics on the horizon, such as the rise of antibiotic-resistant bacteria, the ability to quickly pivot the target pathogen of this diagnostic toolset will continue to have an impact.
Collapse
Affiliation(s)
- Alexis Scholtz
- Department of Biomedical Engineering,
University of Southern California, Los Angeles, California
90089, United States of America
| | - Anuradha Ramoji
- Institute of Physical Chemistry (IPC) and
Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena,
Germany
- Leibniz Institute of Photonic Technology
(IPHT) Jena, Member of the Leibniz Research Alliance - Leibniz Health
Technologies, Albert-Einstein-Straße 9, 07745 Jena, Germany
| | - Anja Silge
- Institute of Physical Chemistry (IPC) and
Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena,
Germany
- Leibniz Institute of Photonic Technology
(IPHT) Jena, Member of the Leibniz Research Alliance - Leibniz Health
Technologies, Albert-Einstein-Straße 9, 07745 Jena, Germany
- InfectoGnostics Research Campus
Jena, Centre of Applied Research, Philosophenweg 7, D-07743 Jena,
Germany
| | - Jakob R. Jansson
- Fulgent Genetics, Temple
City, California 91780, United States of America
| | - Ian G. de Moura
- Fulgent Genetics, Temple
City, California 91780, United States of America
| | - Jürgen Popp
- Institute of Physical Chemistry (IPC) and
Abbe Center of Photonics, Helmholtzweg 4, 07743 Jena,
Germany
- Leibniz Institute of Photonic Technology
(IPHT) Jena, Member of the Leibniz Research Alliance - Leibniz Health
Technologies, Albert-Einstein-Straße 9, 07745 Jena, Germany
- InfectoGnostics Research Campus
Jena, Centre of Applied Research, Philosophenweg 7, D-07743 Jena,
Germany
| | - Jakub P. Sram
- Fulgent Genetics, Temple
City, California 91780, United States of America
| | - Andrea M. Armani
- Department of Biomedical Engineering,
University of Southern California, Los Angeles, California
90089, United States of America
- Mork Family Department of Chemical Engineering,
University of Southern California, Los Angeles, California
90089, United States of America
| |
Collapse
|
45
|
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]
|
46
|
Gupta G, Ndiaye A, Filteau M. Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions. Front Microbiol 2021; 12:700752. [PMID: 34646243 PMCID: PMC8503676 DOI: 10.3389/fmicb.2021.700752] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/31/2021] [Indexed: 12/27/2022] Open
Abstract
Microorganisms are a fundamental part of virtually every ecosystem on earth. Understanding how collectively they interact, assemble, and function as communities has become a prevalent topic both in fundamental and applied research. Owing to multiple advances in technology, answering questions at the microbial system or network level is now within our grasp. To map and characterize microbial interaction networks, numerous computational approaches have been developed; however, experimentally validating microbial interactions is no trivial task. Microbial interactions are context-dependent, and their complex nature can result in an array of outcomes, not only in terms of fitness or growth, but also in other relevant functions and phenotypes. Thus, approaches to experimentally capture microbial interactions involve a combination of culture methods and phenotypic or functional characterization methods. Here, through our perspective of food microbiologists, we highlight the breadth of innovative and promising experimental strategies for their potential to capture the different dimensions of microbial interactions and their high-throughput application to answer the question; are microbial interaction patterns or network architecture similar along different contextual scales? We further discuss the experimental approaches used to build various types of networks and study their architecture in the context of cell biology and how they translate at the level of microbial ecosystem.
Collapse
Affiliation(s)
- Gunjan Gupta
- Département des Sciences des aliments, Université Laval, Québec, QC, Canada
- Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
| | - Amadou Ndiaye
- Département des Sciences des aliments, Université Laval, Québec, QC, Canada
- Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
| | - Marie Filteau
- Département des Sciences des aliments, Université Laval, Québec, QC, Canada
- Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Québec, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
| |
Collapse
|
47
|
Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes. iScience 2021; 24:102975. [PMID: 34485857 PMCID: PMC8397914 DOI: 10.1016/j.isci.2021.102975] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/27/2021] [Accepted: 08/09/2021] [Indexed: 12/13/2022] Open
Abstract
Accessing enormous uncultivated microorganisms (microbial dark matter) in various Earth environments requires accurate, nondestructive classification, and molecular understanding of the microorganisms in in situ and at the single-cell level. Here we demonstrate a combined approach of random forest (RF) machine learning and single-cell Raman microspectroscopy for accurate classification of phylogenetically diverse prokaryotes (three bacterial and three archaeal species from different phyla). Our RF classifier achieved a 98.8 ± 1.9% classification accuracy among the six species in pure populations and 98.4% for three species in an artificially mixed population. Feature importance scores against each wavenumber reveal that the presence of carotenoids and structure of membrane lipids play key roles in distinguishing the prokaryotic species. We also find unique Raman markers for an ammonia-oxidizing archaeon. Our approach with moderate data pretreatment and intuitive visualization of feature importance is easy to use for non-spectroscopists, and thus offers microbiologists a new single-cell tool for shedding light on microbial dark matter. Random forest models classify prokaryotic species with high accuracy of >98% Both bacteria and archaea are classified using minimally preprocessed Raman data Feature importance reveals what biomolecules contribute to species classification Raman marker bands for some archaeal species are discovered
Collapse
|
48
|
A polyyne toxin produced by an antagonistic bacterium blinds and lyses a Chlamydomonad alga. Proc Natl Acad Sci U S A 2021; 118:2107695118. [PMID: 34389682 PMCID: PMC8379975 DOI: 10.1073/pnas.2107695118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Algae live in association with microbes that interact by a variety of chemical mediators, resulting in mutualistic or antagonistic relationships. Although algae are key contributors to carbon fixation and are fundamental for food webs, we still know little about the underlying molecular mechanisms affecting their fitness. This study investigates the interaction between an antagonistic bacterium and a unicellular alga. It demonstrates multiple roles of a polyyne, protegencin, that is used by the bacteria to attack green algal cells. It is a highly effective toxin that alters a subcellular algal compartment used for vision, bleaches, and lyses the algal cells. These results expand our knowledge of the arsenal of chemical mediators in bacteria and their modes of action in algal communities. Algae are key contributors to global carbon fixation and form the basis of many food webs. In nature, their growth is often supported or suppressed by microorganisms. The bacterium Pseudomonas protegens Pf-5 arrests the growth of the green unicellular alga Chlamydomonas reinhardtii, deflagellates the alga by the cyclic lipopeptide orfamide A, and alters its morphology [P. Aiyar et al., Nat. Commun. 8, 1756 (2017)]. Using a combination of Raman microspectroscopy, genome mining, and mutational analysis, we discovered a polyyne toxin, protegencin, which is secreted by P. protegens, penetrates the algal cells, and causes destruction of the carotenoids of their primitive visual system, the eyespot. Together with secreted orfamide A, protegencin thus prevents the phototactic behavior of C. reinhardtii. A mutant of P. protegens deficient in protegencin production does not affect growth or eyespot carotenoids of C. reinhardtii. Protegencin acts in a direct and destructive way by lysing and killing the algal cells. The toxic effect of protegencin is also observed in an eyeless mutant and with the colony-forming Chlorophyte alga Gonium pectorale. These data reveal a two-pronged molecular strategy involving a cyclic lipopeptide and a conjugated tetrayne used by bacteria to attack select Chlamydomonad algae. In conjunction with the bloom-forming activity of several chlorophytes and the presence of the protegencin gene cluster in over 50 different Pseudomonas genomes [A. J. Mullins et al., bioRxiv [Preprint] (2021). https://www.biorxiv.org/content/10.1101/2021.03.05.433886v1 (Accessed 17 April 2021)], these data are highly relevant to ecological interactions between Chlorophyte algae and Pseudomonadales bacteria.
Collapse
|
49
|
Yu S, Li X, Lu W, Li H, Fu YV, Liu F. Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens. Anal Chem 2021; 93:11089-11098. [PMID: 34339167 DOI: 10.1021/acs.analchem.1c00431] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The need for efficient and accurate identification of pathogens in seafood and the environment has become increasingly urgent, given the current global pandemic. Traditional methods are not only time consuming but also lead to sample wastage. Here, we have proposed two new methods that involve Raman spectroscopy combined with a long short-term memory (LSTM) neural network and compared them with a method using a normal convolutional neural network (CNN). We used eight strains isolated from the marine organism Urechis unicinctus, including four kinds of pathogens. After the models were configured and trained, the LSTM methods that we proposed achieved average isolation-level accuracies exceeding 94%, not only meeting the requirement for identification but also indicating that the proposed methods were faster and more accurate than the normal CNN models. Finally, through a computational approach, we designed a loss function to explore the mechanism reflected by the Raman data, finding the Raman segments that most likely exhibited the characteristics of nucleic acids. These novel experimental results provide insights for developing additional deep learning methods to accurately analyze complex Raman data.
Collapse
Affiliation(s)
- Shixiang Yu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Xin Li
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Weilai Lu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Hanfei Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yu Vincent Fu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, P. R. China.,University of the Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Fanghua Liu
- Key Laboratory of Coastal Biology and Biological Resources Utilization, CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, P. R. China.,National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China, Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management, Institute of Eco-environmental and Soil Sciences, Guangdong Academy of Sciences, Guangzhou 510650, P. R. China
| |
Collapse
|
50
|
Wang L, Liu W, Tang JW, Wang JJ, Liu QH, Wen PB, Wang MM, Pan YC, Gu B, Zhang X. Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings. Front Microbiol 2021; 12:683580. [PMID: 34349740 PMCID: PMC8327204 DOI: 10.3389/fmicb.2021.683580] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/17/2021] [Indexed: 12/13/2022] Open
Abstract
Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.
Collapse
Affiliation(s)
- Liang Wang
- Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China
| | - Wei Liu
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Jia-Wei Tang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Jun-Jiao Wang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Qing-Hua Liu
- State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, China
| | - Peng-Bo Wen
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| | - Meng-Meng Wang
- Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, School of Pharmacy, Xuzhou Medical University, Xuzhou, China
| | - Ya-Cheng Pan
- School of Life Sciences, Xuzhou Medical University, Xuzhou, China
| | - Bing Gu
- Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Xiao Zhang
- Department of Bioinformatics, School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, China
| |
Collapse
|