1
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Zhu M, Chen X, Chi M, Wu Y, Zhang M, Gao S. Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells. Talanta 2024; 277:126297. [PMID: 38823327 DOI: 10.1016/j.talanta.2024.126297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 06/03/2024]
Abstract
The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.
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Affiliation(s)
- Mingyao Zhu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Applied Optics, Changchun, Jilin, 130033, China; Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, 130033, China
| | - Xing Chen
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Applied Optics, Changchun, Jilin, 130033, China; Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, 130033, China
| | - Mingbo Chi
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Applied Optics, Changchun, Jilin, 130033, China; Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, 130033, China.
| | - Yihui Wu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Applied Optics, Changchun, Jilin, 130033, China; Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun, Jilin, 130033, China.
| | - Ming Zhang
- Department of Hematology, The First Bethune Hospital, Jilin University, Changchun, Jilin, 130033, China
| | - Sujun Gao
- Department of Hematology, The First Bethune Hospital, Jilin University, Changchun, Jilin, 130033, China
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2
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Sim W, Song SW, Park S, Jang JI, Kim JH, Cho YM, Kim HM. Unveiling microplastics with hyperspectral Raman imaging: From macroscale observations to real-world applications. JOURNAL OF HAZARDOUS MATERIALS 2024; 463:132861. [PMID: 37939557 DOI: 10.1016/j.jhazmat.2023.132861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/10/2023]
Abstract
The widespread use of plastic materials, owing to their several advantageous properties, has resulted in a considerable increase in plastic consumption. Consequently, the production of primary and secondary microplastics has also increased. To identify, categorize, and quantify microplastics, several analytical methods, such as thermal analysis and spectroscopic methods, have been developed. They generally offer little insight into the size and shape of microplastics, require time-consuming sample preparation and classification, and are susceptible to background interference. Herein, we created a macroscale hyperspectral Raman method to quickly quantify and characterize large volumes of plastics. Using this approach, we successfully obtained Raman spectra of five different types of microplastics scattered over an area of 12.4 mm × 12.4 mm within just 550 s and perfectly classified these microplastics using a machine learning method. Additionally, we demonstrated that our system is effective for obtaining Raman spectra, even when the microplastics are suspended in aquatic environments or bound to metal-mesh nets. These results highlight the considerable potential of our proposed method for real-world applications.
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Affiliation(s)
- Wooseok Sim
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea
| | - Si Won Song
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea
| | - Subeen Park
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea; Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Jin Il Jang
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea
| | - Jae Hun Kim
- Sensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Yeo-Myoung Cho
- Department of Civil and Environmental Engineering, Stanford University, CA 94305, United States
| | - Hyung Min Kim
- Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea.
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3
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Wu Q, Xiao D, Wang N, Masia F, Langbein W, Li B. Rapid identification of micro and nanoplastics by line scan Raman micro-spectroscopy. Talanta 2024; 266:125067. [PMID: 37597340 DOI: 10.1016/j.talanta.2023.125067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/21/2023]
Abstract
Microplastic pollution has become an environmental problem that cannot be ignored in our society. Raman spectroscopy technology has been widely used in the field of microplastics detection due to its non-contact, non-destructive chemical specificity. Traditional point confocal Raman micro-spectroscopy technology uses single-point detection, resulting in long measurement times to scan the large areas of interest of typical samples. In this paper, we present a line scan confocal Raman micro-spectroscopy tool for fast detection and identification of microplastic particles. We show size and composition identification of particles and imaging over large areas. Compared with point confocal Raman imaging, the line scan confocal Raman technology increases the imaging speed by 1-2 orders of magnitude.
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Affiliation(s)
- Qingyi Wu
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Nan Wang
- Hooke Instruments, Changchun, 130033, China
| | - Francesco Masia
- School of Biosciences, Cardiff University, Cardiff, CF10 3AX, UK
| | - Wolfgang Langbein
- School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA, UK.
| | - Bei Li
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
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4
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Romei M, von Krusenstiern EV, Ridings ST, King RN, Fortier JC, McKeon CA, Nichols KM, Charkoudian LK, Londergan CH. Frequency Changes in Terminal Alkynes Provide Strong, Sensitive, and Solvatochromic Raman Probes of Biochemical Environments. J Phys Chem B 2023; 127:85-94. [PMID: 36538691 PMCID: PMC9841980 DOI: 10.1021/acs.jpcb.2c06176] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/19/2022] [Indexed: 12/24/2022]
Abstract
The C≡C stretching frequencies of terminal alkynes appear in the "clear" window of vibrational spectra, so they are attractive and increasingly popular as site-specific probes in complicated biological systems like proteins, cells, and tissues. In this work, we collected infrared (IR) absorption and Raman scattering spectra of model compounds, artificial amino acids, and model proteins that contain terminal alkyne groups, and we used our results to draw conclusions about the signal strength and sensitivity to the local environment of both aliphatic and aromatic terminal alkyne C≡C stretching bands. While the IR bands of alkynyl model compounds displayed surprisingly broad solvatochromism, their absorptions were weak enough that alkynes can be ruled out as effective IR probes. The same solvatochromism was observed in model compounds' Raman spectra, and comparisons to published empirical solvent scales (including a linear regression against four meta-aggregated solvent parameters) suggested that the alkyne C≡C stretching frequency mainly reports on local electronic interactions (i.e., short-range electron donor-acceptor interactions) with solvent molecules and neighboring functional groups. The strong solvatochromism observed here for alkyne stretching bands introduces an important consideration for Raman imaging studies based on these signals. Raman signals for alkynes (especially those that are π-conjugated) can be exceptionally strong and should permit alkynyl Raman signals to function as probes at very low concentrations, as compared to other widely used vibrational probe groups like azides and nitriles. We incorporated homopropargyl glycine into a transmembrane helical peptide via peptide synthesis, and we installed p-ethynylphenylalanine into the interior of the Escherichia coli fatty acid acyl carrier protein using a genetic code expansion technique. The Raman spectra from each of these test systems indicate that alkynyl C≡C bands can act as effective and unique probes of their local biomolecular environments. We provide guidance for the best possible future uses of alkynes as solvatochromic Raman probes, and while empirical explanations of the alkyne solvatochromism are offered, open questions about its physical basis are enunciated.
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Affiliation(s)
- Matthew
G. Romei
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Eliana V. von Krusenstiern
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Stephen T. Ridings
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Renee N. King
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Julia C. Fortier
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Caroline A. McKeon
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Krysta M. Nichols
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Louise K. Charkoudian
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
| | - Casey H. Londergan
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041-1392, United States
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5
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Parastar H, Tauler R. Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.201801134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hadi Parastar
- Department of Chemistry Sharif University of Technology Tehran Iran
| | - Roma Tauler
- Department of Environmental Chemistry IDAEA-CSIC 08034 Barcelona Spain
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6
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Li G, Wu C, Wang D, Srinivasan V, Kaeli DR, Dy JG, Gu AZ. Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13473-13484. [PMID: 36048618 DOI: 10.1021/acs.est.1c08768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.
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Affiliation(s)
- Guangyu Li
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853-0001, United States
| | - Chieh Wu
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - Dongqi Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- Department of Municipal and Environmental Engineering, School of Water Resources and Hydro-Electric Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, PRC
| | - Varun Srinivasan
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- Brown and Caldwell, One Tech Drive, Andover, Massachusetts 01810, United States
| | - David R Kaeli
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - Jennifer G Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115-5005, United States
| | - April Z Gu
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115-5026, United States
- School of Civil and Environmental Engineering, Cornell University, Ithaca, New York 14853-0001, United States
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7
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Organelle-targeted imaging based on fluorogen-activating RNA aptamers in living cells. Anal Chim Acta 2022; 1209:339816. [DOI: 10.1016/j.aca.2022.339816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 11/21/2022]
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8
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Bakthavatsalam S, Dodo K, Sodeoka M. A decade of alkyne-tag Raman imaging (ATRI): applications in biological systems. RSC Chem Biol 2021; 2:1415-1429. [PMID: 34704046 PMCID: PMC8496067 DOI: 10.1039/d1cb00116g] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/07/2021] [Indexed: 12/14/2022] Open
Abstract
Alkyne functional groups have Raman signatures in a region (1800 cm-1 to 2800 cm-1) that is free from interference from cell components, known as the "silent region", and alkyne signals in this region were first utilized a decade ago to visualize the nuclear localization of a thymidine analogue EdU. Since then, the strategy of Raman imaging of biological samples by using alkyne functional groups, called alkyne-tag Raman imaging (ATRI), has become widely used. This article reviews the applications of ATRI in biological samples ranging from organelles to whole animal models, and briefly discusses the prospects for this technique.
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Affiliation(s)
- Subha Bakthavatsalam
- Synthetic Organic Chemistry Laboratory, RIKEN Cluster for Pioneering Research Wako Saitama 351-0198 Japan
| | - Kosuke Dodo
- Synthetic Organic Chemistry Laboratory, RIKEN Cluster for Pioneering Research Wako Saitama 351-0198 Japan
- RIKEN Center for Sustainable Resource Science 2-1 Hirosawa Wako Saitama 351-0198 Japan
| | - Mikiko Sodeoka
- Synthetic Organic Chemistry Laboratory, RIKEN Cluster for Pioneering Research Wako Saitama 351-0198 Japan
- RIKEN Center for Sustainable Resource Science 2-1 Hirosawa Wako Saitama 351-0198 Japan
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9
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Subedi NR, Yaraghi S, Jung PS, Kukal G, McDonald AG, Christodoulides DN, Vasdekis AE. Airy light-sheet Raman imaging. OPTICS EXPRESS 2021; 29:31941-31951. [PMID: 34615275 DOI: 10.1364/oe.435293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/02/2021] [Indexed: 06/13/2023]
Abstract
Light-sheet fluorescence microscopy has greatly improved the speed and overall photostability of optically sectioning cellular and multi-cellular specimens. Similar gains have also been conferred by light-sheet Raman imaging; these schemes, however, rely on diffraction limited Gaussian beams that hinder the uniformity and size of the imaging field-of-view, and, as such, the resulting throughput rates. Here, we demonstrate that a digitally scanned Airy beam increases the Raman imaging throughput rates by more than an order of magnitude than conventional diffraction-limited beams. Overall, this, spectrometer-less, approach enabled 3D imaging of microparticles with high contrast and 1 µm axial resolution at 300 msec integration times per plane and orders of magnitude lower irradiation density than coherent Raman imaging schemes. We detail the apparatus and its performance, as well as its compatibility with fluorescence light-sheet and quantitative-phase imaging towards rapid and low phototoxicity multimodal imaging.
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10
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Ando J, Kawagoe H, Nakamura A, Iino R, Fujita K. Label-free monitoring of crystalline chitin hydrolysis by chitinase based on Raman spectroscopy. Analyst 2021; 146:4087-4094. [PMID: 34060547 DOI: 10.1039/d1an00581b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We demonstrate a method for label-free monitoring of hydrolytic activity of crystalline-chitin-degrading enzyme, chitinase, by means of Raman spectroscopy. We found that crystalline chitin exhibited a characteristic Raman peak at 2995 cm-1, which did not appear in the reaction product, N,N'-diacetylchitobiose. We used this Raman peak as a marker of crystalline chitin degradation to monitor the hydrolytic activity of chitinase. When the crystalline chitin suspension and chitinase were mixed together, the peak intensity of crystalline chitin at 2995 cm-1 was linearly decreased depending on incubation time. The decrease in peak intensity was inversely correlated with the increase in the amount of released N,N'-diacetylchitobiose, which was measured by conventional colorimetric assay with alkaline ferricyanide. Our result, presented here, provides a new method for simple, in situ, and label-free monitoring of enzymatic activity of chitinase.
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Affiliation(s)
- Jun Ando
- Department of Applied Physics, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan. and Institute for Molecular Science, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi 444-8787, Japan and Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan
| | - Hiroyuki Kawagoe
- Department of Applied Physics, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
| | - Akihiko Nakamura
- Institute for Molecular Science, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi 444-8787, Japan and Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan and Department of Applied Life Sciences, Shizuoka University, Shizuoka, Shizuoka 422-8529, Japan
| | - Ryota Iino
- Institute for Molecular Science, National Institutes of Natural Sciences, 5-1 Higashiyama, Myodaiji, Okazaki, Aichi 444-8787, Japan and Department of Functional Molecular Science, School of Physical Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa 240-0193, Japan
| | - Katsumasa Fujita
- Department of Applied Physics, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan. and Advanced Photonics and Biosensing Open Innovation Laboratory, AIST-Osaka University, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan and Institute for Open and Transdisciplinary Research Initiatives, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
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11
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Lee H, Kang K, Mochizuki K, Lee C, Toh KA, Lee SA, Fujita K, Kim D. Surface Plasmon Localization-Based Super-resolved Raman Microscopy. NANO LETTERS 2020; 20:8951-8958. [PMID: 33186047 DOI: 10.1021/acs.nanolett.0c04219] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We investigate label-free measurement of molecular distribution by super-resolved Raman microscopy using surface plasmon (SP) localization. Localized SP was formed with plasmonic nanopost arrays (PNAs) for measurement of the molecular distribution in HeLa cells. Compared with conventional Raman microscopy on gold thin films, PNAs induce a localized near-field, which allows for the enhancement of the peak signal-to-noise ratio by as much as 4.5 dB in the Raman shifts. Super-resolved distributions of aromatic amino acids and lipids (C-C stretching and C-H2 twist mode) as targets in HeLa cells were obtained after image reconstruction. Results show almost 4-fold improvement on average in the lateral precision over conventional diffraction-limited Raman microscopy images. Combined with axial imaging in an evanescent field, the results suggest an improvement in optical resolution due to superlocalized light volume by more than an order of magnitude over that of conventional diffraction-limited Raman microscopy.
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Affiliation(s)
- Hongki Lee
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea
| | - Kyungnam Kang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea
| | - Kentaro Mochizuki
- Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
| | - Changhun Lee
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea
| | - Kar-Ann Toh
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea
| | - Seung Ah Lee
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea
| | - Katsumasa Fujita
- Department of Applied Physics, Osaka University, Osaka 565-0871, Japan
- Advanced Photonics and Biosensing Open Innovation Laboratory, AIST-Osaka University, Osaka 565-0871, Japan
- Transdimensional Life Imaging Division, Institute of Open and Transdisciplinary Research Initiatives, Osaka University, Osaka 565-0871, Japan
| | - Donghyun Kim
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea
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12
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Hatzenpichler R, Krukenberg V, Spietz RL, Jay ZJ. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol 2020; 18:241-256. [PMID: 32055027 DOI: 10.1038/s41579-020-0323-1] [Citation(s) in RCA: 146] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2020] [Indexed: 12/14/2022]
Abstract
The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.
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Affiliation(s)
- Roland Hatzenpichler
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA.
| | - Viola Krukenberg
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA
| | - Rachel L Spietz
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA
| | - Zackary J Jay
- Department of Chemistry and Biochemistry, Center for Biofilm Engineering, and Thermal Biology Institute, Montana State University, Bozeman, MT, USA
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13
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Line Scan Raman Microspectroscopy for Label-Free Diagnosis of Human Pituitary Biopsies. Molecules 2019; 24:molecules24193577. [PMID: 31590270 PMCID: PMC6804209 DOI: 10.3390/molecules24193577] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 09/30/2019] [Accepted: 10/03/2019] [Indexed: 12/14/2022] Open
Abstract
Pituitary adenomas are neoplasia of the anterior pituitary gland and can be subdivided into hormone-producing tumors (lactotroph, corticotroph, gonadotroph, somatotroph, thyreotroph or plurihormonal) and hormone-inactive tumors (silent or null cell adenomas) based on their hormonal status. We therefore developed a line scan Raman microspectroscopy (LSRM) system to detect, discriminate and hyperspectrally visualize pituitary gland from pituitary adenomas based on molecular differences. By applying principal component analysis followed by a k-nearest neighbor algorithm, specific hormone states were identified and a clear discrimination between pituitary gland and various adenoma subtypes was achieved. The classifier yielded an accuracy of 95% for gland tissue and 84–99% for adenoma subtypes. With an overall accuracy of 92%, our LSRM system has proven its potential to differentiate pituitary gland from pituitary adenomas. LSRM images based on the presence of specific Raman bands were created, and such images provided additional insight into the spatial distribution of particular molecular compounds. Pathological states could be molecularly differentiated and characterized with texture analysis evaluating Grey Level Cooccurrence Matrices for each LSRM image, as well as correlation coefficients between LSRM images.
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14
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Yang Y, Zhu M, Wang Y, Yang H, Wu Y, Li B. Super-Resolution Reconstruction of Cell Pseudo-Color Image Based on Raman Technology. SENSORS 2019; 19:s19194076. [PMID: 31547194 PMCID: PMC6806596 DOI: 10.3390/s19194076] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 09/12/2019] [Accepted: 09/13/2019] [Indexed: 12/22/2022]
Abstract
Raman spectroscopy visualization is a challenging task due to the interference of complex background noise and the number of selected measurement points. In this paper, a super-resolution image reconstruction algorithm for Raman spectroscopy is studied to convert raw Raman data into pseudo-color super-resolution imaging. Firstly, the Raman spectrum data of a single measurement point is measured multiple times to calculate the mean value to remove the random background noise, and innovatively introduce the Retinex algorithm and the median filtering algorithm which improve the signal-to-noise ratio. The novel method of using deep neural network performs a super-resolution reconstruction operation on the gray image. An adaptive guided filter that automatically adjusts the filter radius and penalty factor is proposed to highlight the contour of the cell, and the super-resolution reconstruction of the pseudo-color image of the Raman spectrum is realized. The average signal-to-noise ratio of the reconstructed pseudo-color image sub-band reaches 14.29 db, and the average value of information entropy reaches 4.30 db. The results show that the Raman-based cell pseudo-color image super-resolution reconstruction algorithm is an effective tool to effectively remove noise and high-resolution visualization. The contrast experiments show that the pseudo-color image Kullback–Leiber (KL) entropy of the color image obtained by the method is small, the boundary is obvious, and the noise is small, which provide technical support for the development of sophisticated single-cell imaging Raman spectroscopy instruments.
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Affiliation(s)
- Yifan Yang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Ming Zhu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
| | - Yuqing Wang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
| | - Hang Yang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
| | - Yanfeng Wu
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Bei Li
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
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15
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He H, Xu M, Zong C, Zheng P, Luo L, Wang L, Ren B. Speeding Up the Line-Scan Raman Imaging of Living Cells by Deep Convolutional Neural Network. Anal Chem 2019; 91:7070-7077. [PMID: 31063356 DOI: 10.1021/acs.analchem.8b05962] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Raman imaging is a promising technique that allows the spatial distribution of different components in the sample to be obtained using the molecular fingerprint information on individual species. However, the imaging speed is the bottleneck for the current Raman imaging methods to monitor the dynamic process of living cells. In this paper, we developed an artificial intelligence assisted fast Raman imaging method over the already fast line scan Raman imaging method. The reduced imaging time is realized by widening the slit and laser beam, and scanning the sample with a large scan step. The imaging quality is improved by a data-driven approach to train a deep convolutional neural network, which statistically learns to transform low-resolution images acquired at a high speed into high-resolution ones that previously were only possible with a low imaging speed. Accompanied with the improvement of the image resolution, the deteriorated spectral resolution as a consequence of a wide slit is also restored, thereby the fidelity of the spectral information is retained. The imaging time can be reduced to within 1 min, which is about five times faster than the state-of-the-art line scan Raman imaging techniques without sacrificing spectral and spatial resolution. We then demonstrated the reliability of the current method using fixed cells. We finally used the method to monitor the dynamic evolution process of living cells. Such an imaging speed opens a door to the label-free observation of cellular events with conventional Raman microscopy.
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Affiliation(s)
- Hao He
- School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China
| | - Mengxi Xu
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. China
| | - Cheng Zong
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. China
| | - Peng Zheng
- School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China
| | - Lilan Luo
- School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China
| | - Lei Wang
- School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China
| | - Bin Ren
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering , Xiamen University , Xiamen 361005 , P. R. China
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16
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Kumamoto Y, Mochizuki K, Hashimoto K, Harada Y, Tanaka H, Fujita K. High-Throughput Cell Imaging and Classification by Narrowband and Low-Spectral-Resolution Raman Microscopy. J Phys Chem B 2019; 123:2654-2661. [PMID: 30830787 DOI: 10.1021/acs.jpcb.8b11295] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We investigated the use of narrowband Raman spectra for rapid label-free molecular imaging aimed at cell classification using principal component regression and linear discriminant analysis. In the classification of breast nontumorigenic epithelial and cancer cell lines, the classification accuracies using a spectral range of 100 cm-1 were equivalent to or better than that with using the fingerprint and high-wavenumber regions. Narrowing the Raman spectral range for analysis allows reduction of the charge-coupled device (CCD) pixels required for spectrum detection, resulting in the improvement of image acquisition speed with adequate classification accuracy. Our measurements revealed that the wavenumber region at 1397-1501 cm-1 can provide molecular information sufficient for cell classification without causing notable errors in the baseline-correction. A spectral resolution of ∼9 cm-1 was found to be sufficient to provide high accuracy in cell classification, which allowed us to apply pixel binning at the CCD readout for further acceleration of the imaging speed. As a result, the acquisition time for a 1200 × 1500 pixels Raman hyperspectral image at 1397-1501 cm-1 was reduced to 21 min. Under this condition, different cell lines were classified at accuracies higher than 90%. The presented approach will improve throughput of cell and tissue analysis and classification using Raman spectroscopy and extend practical uses of Raman imaging in biology and medicine.
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Affiliation(s)
- Yasuaki Kumamoto
- Department of Pathology and Cell Regulation, Graduate School of Medical Sciences , Kyoto Prefectural University of Medicine , 465 Kajiicho, Kawaramachi-Hirokoji , Kamigyo, Kyoto , Kyoto 6028566 , Japan
| | | | - Kosuke Hashimoto
- Department of Pathology and Cell Regulation, Graduate School of Medical Sciences , Kyoto Prefectural University of Medicine , 465 Kajiicho, Kawaramachi-Hirokoji , Kamigyo, Kyoto , Kyoto 6028566 , Japan
| | - Yoshinori Harada
- Department of Pathology and Cell Regulation, Graduate School of Medical Sciences , Kyoto Prefectural University of Medicine , 465 Kajiicho, Kawaramachi-Hirokoji , Kamigyo, Kyoto , Kyoto 6028566 , Japan
| | - Hideo Tanaka
- Department of Pathology and Cell Regulation, Graduate School of Medical Sciences , Kyoto Prefectural University of Medicine , 465 Kajiicho, Kawaramachi-Hirokoji , Kamigyo, Kyoto , Kyoto 6028566 , Japan
| | - Katsumasa Fujita
- Department of Pathology and Cell Regulation, Graduate School of Medical Sciences , Kyoto Prefectural University of Medicine , 465 Kajiicho, Kawaramachi-Hirokoji , Kamigyo, Kyoto , Kyoto 6028566 , Japan
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17
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Schulze HG, Rangan S, Piret JM, Blades MW, Turner RFB. Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples. APPLIED SPECTROSCOPY 2018; 72:1322-1340. [PMID: 29855196 DOI: 10.1177/0003702818778031] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.
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Affiliation(s)
- H Georg Schulze
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
| | - Shreyas Rangan
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
| | - James M Piret
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 2 Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Michael W Blades
- 3 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - Robin F B Turner
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 3 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
- 4 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
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18
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Raghunathan K, Kenworthy AK. Dynamic pattern generation in cell membranes: Current insights into membrane organization. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2018; 1860:2018-2031. [PMID: 29752898 DOI: 10.1016/j.bbamem.2018.05.002] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Revised: 04/30/2018] [Accepted: 05/03/2018] [Indexed: 12/18/2022]
Abstract
It has been two decades since the lipid raft hypothesis was first presented. Even today, whether these nanoscale cholesterol-rich domains are present in cell membranes is not completely resolved. However, especially in the last few years, a rich body of literature has demonstrated both the presence and the importance of non-random distribution of biomolecules on the membrane, which is the focus of this review. These new developments have pushed the experimental limits of detection and have brought us closer to observing lipid domains in the plasma membrane of live cells. Characterization of biomolecules associated with lipid rafts has revealed a deep connection between biological regulation and function and membrane compositional heterogeneities. Finally, tantalizing new developments in the field have demonstrated that lipid domains might not just be associated with the plasma membrane of eukaryotes but could potentially be a ubiquitous membrane-organizing principle in several other biological systems. This article is part of a Special Issue entitled: Emergence of Complex Behavior in Biomembranes edited by Marjorie Longo.
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Affiliation(s)
- Krishnan Raghunathan
- Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, PA 15224, USA.
| | - Anne K Kenworthy
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN 37232, USA.
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19
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Tauler R, Parastar H. Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists. Angew Chem Int Ed Engl 2018; 61:e201801134. [DOI: 10.1002/anie.201801134] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Indexed: 11/08/2022]
Affiliation(s)
- Roma Tauler
- IDAEA-CSIC Environmental Chemistry Jordi Girona 18-26 08034 Barcelona SPAIN
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20
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Georg Schulze H, Konorov SO, Piret JM, Blades MW, Turner RFB. Empirical Factors Affecting the Quality of Non-Negative Matrix Factorization of Mammalian Cell Raman Spectra. APPLIED SPECTROSCOPY 2017; 71:2681-2691. [PMID: 28937262 DOI: 10.1177/0003702817732117] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Mammalian cells contain various macromolecules that can be investigated non-invasively with Raman spectroscopy. The particular mixture of major macromolecules present in a cell being probed are reflected in the measured Raman spectra. Determining macromolecular identities and estimating their concentrations from these mixture Raman spectra can distinguish cell types and otherwise enable biological research. However, the application of canonical multivariate methods, such as principal component analysis (PCA), to perform spectral unmixing yields mathematical solutions that can be difficult to interpret. Non-negative matrix factorization (NNMF) improves the interpretability of unmixed macromolecular components, but can be difficult to apply because ambiguities produced by overlapping Raman bands permit multiple solutions. Furthermore, theoretically sound methods can be difficult to implement in practice. Here we examined the effects of a number of empirical approaches on the quality of NNMF results. These approaches were evaluated on simulated mammalian cell Raman hyperspectra and the results were used to develop an enhanced procedure for implementing NNMF. We demonstrated the utility of this procedure using a Raman hyperspectral data set measured from human islet cells to recover the spectra of insulin and glucagon. This was compared to the relatively inferior PCA of these data.
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Affiliation(s)
- H Georg Schulze
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
| | - Stanislav O Konorov
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 2 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - James M Piret
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 3 Department of Chemical and Biological Engineering, The University of British Columbia, Vancouver, BC, Canada
| | - Michael W Blades
- 2 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
| | - Robin F B Turner
- 1 Michael Smith Laboratories, The University of British Columbia, Vancouver, BC, Canada
- 2 Department of Chemistry, The University of British Columbia, Vancouver, BC, Canada
- 4 Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada
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21
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Syed A, Smith EA. Raman Imaging in Cell Membranes, Lipid-Rich Organelles, and Lipid Bilayers. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2017; 10:271-291. [PMID: 28301746 DOI: 10.1146/annurev-anchem-061516-045317] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Raman-based optical imaging is a promising analytical tool for noninvasive, label-free chemical imaging of lipid bilayers and cellular membranes. Imaging using spontaneous Raman scattering suffers from a low intensity that hinders its use in some cellular applications. However, developments in coherent Raman imaging, surface-enhanced Raman imaging, and tip-enhanced Raman imaging have enabled video-rate imaging, excellent detection limits, and nanometer spatial resolution, respectively. After a brief introduction to these commonly used Raman imaging techniques for cell membrane studies, this review discusses selected applications of these modalities for chemical imaging of membrane proteins and lipids. Finally, recent developments in chemical tags for Raman imaging and their applications in the analysis of selected cell membrane components are summarized. Ongoing developments toward improving the temporal and spatial resolution of Raman imaging and small-molecule tags with strong Raman scattering cross sections continue to expand the utility of Raman imaging for diverse cell membrane studies.
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Affiliation(s)
- Aleem Syed
- Department of Chemistry, Iowa State University, Ames, Iowa 50011; ,
- Ames Laboratory, US Department of Energy, Ames, Iowa 50011
| | - Emily A Smith
- Department of Chemistry, Iowa State University, Ames, Iowa 50011; ,
- Ames Laboratory, US Department of Energy, Ames, Iowa 50011
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22
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Zhou W, Li Q, Liu H, Yang J, Liu D. Building Electromagnetic Hot Spots in Living Cells via Target-Triggered Nanoparticle Dimerization. ACS NANO 2017; 11:3532-3541. [PMID: 28264152 DOI: 10.1021/acsnano.7b00531] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Electromagnetic hot spots of surface-enhanced Raman scattering have been extensively employed for bioanalysis in solution or on a substrate, but building hot spots in living systems for probing targets of interest has not been achieved yet because of the complex and dynamic physiological environment. Herein, we show that a target-programmed nanoparticle dimerization can be combined with the background-free Raman reporters (alkyne, C≡C; nitrile, C≡N) for multiplexed imaging of microRNAs (miRNAs) in living cells. The in situ formation of plasmonic dimers results in an intense hot spot, thus dramatically enhancing the Raman signals of the reporters residing in the hot spot. More significantly, the reporters exhibit single nonoverlapping peaks in the cellular Raman-silent region (1800-2800 cm-1), thus eliminating spectral unmixing and background interference. A 3D Raman mapping technique was harnessed to monitor the spatial distribution of the dimers and thus the multiple miRNAs in cells. This approach could be extended to probe other biomarkers of interest for monitoring specific pathophysiological events at the live-cell level.
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Affiliation(s)
- Wen Zhou
- College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, and Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University , Tianjin 300071, China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) , Tianjin 300071, China
| | - Qiang Li
- College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, and Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University , Tianjin 300071, China
| | - Huiqiao Liu
- College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, and Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University , Tianjin 300071, China
| | - Jie Yang
- College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, and Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University , Tianjin 300071, China
| | - Dingbin Liu
- College of Chemistry, Research Center for Analytical Sciences, State Key Laboratory of Medicinal Chemical Biology, and Tianjin Key Laboratory of Molecular Recognition and Biosensing, Nankai University , Tianjin 300071, China
- Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) , Tianjin 300071, China
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23
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den Broeder MJ, Moester MJB, Kamstra JH, Cenijn PH, Davidoiu V, Kamminga LM, Ariese F, de Boer JF, Legler J. Altered Adipogenesis in Zebrafish Larvae Following High Fat Diet and Chemical Exposure Is Visualised by Stimulated Raman Scattering Microscopy. Int J Mol Sci 2017; 18:E894. [PMID: 28441764 PMCID: PMC5412473 DOI: 10.3390/ijms18040894] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/08/2017] [Accepted: 04/18/2017] [Indexed: 02/08/2023] Open
Abstract
Early life stage exposure to environmental chemicals may play a role in obesity by altering adipogenesis; however, robust in vivo methods to quantify these effects are lacking. The goal of this study was to analyze the effects of developmental exposure to chemicals on adipogenesis in the zebrafish (Danio rerio). We used label-free Stimulated Raman Scattering (SRS) microscopy for the first time to image zebrafish adipogenesis at 15 days post fertilization (dpf) and compared standard feed conditions (StF) to a high fat diet (HFD) or high glucose diet (HGD). We also exposed zebrafish embryos to a non-toxic concentration of tributyltin (TBT, 1 nM) or Tris(1,3-dichloroisopropyl)phosphate (TDCiPP, 0.5 µM) from 0-6 dpf and reared larvae to 15 dpf under StF. Potential molecular mechanisms of altered adipogenesis were examined by qPCR. Diet-dependent modulation of adipogenesis was observed, with HFD resulting in a threefold increase in larvae with adipocytes, compared to StF and HGD. Developmental exposure to TBT but not TDCiPP significantly increased adipocyte differentiation. The expression of adipogenic genes such as pparda, lxr and lepa was altered in response to HFD or chemicals. This study shows that SRS microscopy can be successfully applied to zebrafish to visualize and quantify adipogenesis, and is a powerful approach for identifying obesogenic chemicals in vivo.
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Affiliation(s)
- Marjo J den Broeder
- Institute of Environmental, Health and Societies, Brunel University, UB8 3PH London, UK.
- Institute for Environmental Studies, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
| | - Miriam J B Moester
- Institute for Lasers, Life and Biophotonics, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
| | - Jorke H Kamstra
- Department of Basic Sciences and Aquatic Medicine, Faculty of Veterinary Medicine, Norwegian University of Life Sciences, P.O. BOX 8146, Dep 0033 Oslo, Norway.
| | - Peter H Cenijn
- Institute for Environmental Studies, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
| | - Valentina Davidoiu
- Institute for Lasers, Life and Biophotonics, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
- Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, 3011 BH Rotterdam, The Netherlands.
| | - Leonie M Kamminga
- Radboud University Nijmegen, Faculty of Science, Department of Molecular Biology, Radboud Institute for Molecular Life Sciences, 6525 GA Nijmegen, The Netherlands.
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, 6525 GA Nijmegen, The Netherlands.
| | - Freek Ariese
- Institute for Lasers, Life and Biophotonics, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
| | - Johannes F de Boer
- Institute for Lasers, Life and Biophotonics, Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
| | - Juliette Legler
- Institute of Environmental, Health and Societies, Brunel University, UB8 3PH London, UK.
- Institute for Environmental Studies, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
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24
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Harrison JP, Berry D. Vibrational Spectroscopy for Imaging Single Microbial Cells in Complex Biological Samples. Front Microbiol 2017; 8:675. [PMID: 28450860 PMCID: PMC5390015 DOI: 10.3389/fmicb.2017.00675] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Accepted: 03/31/2017] [Indexed: 01/08/2023] Open
Abstract
Vibrational spectroscopy is increasingly used for the rapid and non-destructive imaging of environmental and medical samples. Both Raman and Fourier-transform infrared (FT-IR) imaging have been applied to obtain detailed information on the chemical composition of biological materials, ranging from single microbial cells to tissues. Due to its compatibility with methods such as stable isotope labeling for the monitoring of cellular activities, vibrational spectroscopy also holds considerable power as a tool in microbial ecology. Chemical imaging of undisturbed biological systems (such as live cells in their native habitats) presents unique challenges due to the physical and chemical complexity of the samples, potential for spectral interference, and frequent need for real-time measurements. This Mini Review provides a critical synthesis of recent applications of Raman and FT-IR spectroscopy for characterizing complex biological samples, with a focus on developments in single-cell imaging. We also discuss how new spectroscopic methods could be used to overcome current limitations of single-cell analyses. Given the inherent complementarity of Raman and FT-IR spectroscopic methods, we discuss how combining these approaches could enable us to obtain new insights into biological activities either in situ or under conditions that simulate selected properties of the natural environment.
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Affiliation(s)
- Jesse P Harrison
- Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network "Chemistry Meets Microbiology", University of ViennaVienna, Austria
| | - David Berry
- Division of Microbial Ecology, Department of Microbiology and Ecosystem Science, Research Network "Chemistry Meets Microbiology", University of ViennaVienna, Austria
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