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He YF, Cheng K, Zhong ZT, Hou XL, An CZ, Chen W, Liu B, Zhao YD. Simultaneous labeling and multicolor fluorescence imaging of multiple immune cells on liver frozen section by polychromatic quantum dots below freezing points. J Colloid Interface Sci 2023; 636:42-54. [PMID: 36621128 DOI: 10.1016/j.jcis.2022.12.165] [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: 10/18/2022] [Revised: 12/24/2022] [Accepted: 12/30/2022] [Indexed: 01/02/2023]
Abstract
A method for simultaneous labeling and multicolor fluorescence imaging of different hepatic immune cells below freezing point is established based on quantum dots. In the experiment, carbon quantum dots with emission wavelength of 435 nm, CdTe@CdS quantum dots at 542 nm and CdSe@ZnS quantum dots at 604 nm are synthesized respectively, it is found that when the mass fractions of KCl (as antifreeze) are 12 %, 14 %, and 12 %, respectively, the three quantum dot dispersion systems remain liquid state at -20 °C. After they are conjugated with the corresponding secondary antibodies, agarose gel electrophoresis, circular dichroism and capillary electrophoresis confirm the effectiveness of conjugation. By indirect immunofluorescence method, the above three quantum dot fluorescent probes are used to simultaneously and specifically target a variety of liver immune cells, and the multi-color simultaneous imaging of different liver immune cells is realized under the same excitation wavelength, it is found that hepatic macrophages are arranged radially in the liver, hepatic stellate cells present punctate distribution, and hepatic sinusoidal endothelial cells present circular distribution, which is consistent with the results of H&E staining and ultrathin section TEM. This study provides an important technical means for elucidating the structure and function of the liver.
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Affiliation(s)
- Yan-Fei He
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Kai Cheng
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Zi-Tao Zhong
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Xiao-Lin Hou
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Chang-Zhi An
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Wei Chen
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Bo Liu
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China
| | - Yuan-Di Zhao
- Britton Chance Center for Biomedical Photonics at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Ke Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China; Key Laboratory of Biomedical Photonics (HUST), Ministry of Education, Huazhong University of Science and Technology, Wuhan 430074, Hubei, PR China.
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van Gorkom T, Voet W, van Arkel GHJ, Heron M, Hoeve-Bakker BJA, Notermans DW, Thijsen SFT, Kremer K. Retrospective Evaluation of Various Serological Assays and Multiple Parameters for Optimal Diagnosis of Lyme Neuroborreliosis in a Routine Clinical Setting. Microbiol Spectr 2022; 10:e0006122. [PMID: 35404103 PMCID: PMC9241602 DOI: 10.1128/spectrum.00061-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/25/2022] [Indexed: 11/20/2022] Open
Abstract
Laboratory diagnosis of Lyme neuroborreliosis (LNB) is challenging, and validated diagnostic algorithms are lacking. Therefore, this retrospective cross-sectional study aimed to compare the diagnostic performance of seven commercial antibody assays for LNB diagnosis. Random forest (RF) modeling was conducted to investigate whether the diagnostic performance using the antibody assays could be improved by including several routine cerebrospinal fluid (CSF) parameters (i.e., leukocyte count, total protein, blood-CSF barrier functionality, and intrathecal total antibody synthesis), two-tier serology on serum, the CSF level of the B-cell chemokine (C-X-C motif) ligand 13 (CXCL13), and a Borrelia species PCR on CSF. In total, 156 patients were included who were classified as definite LNB (n = 10), possible LNB (n = 7), or non-LNB patient (n = 139) according to the criteria of the European Federation of Neurological Societies using a consensus strategy for intrathecal Borrelia-specific antibody synthesis. The seven antibody assays showed sensitivities ranging from 47.1% to 100% and specificities ranging from 95.7% to 100%. RF modeling demonstrated that the sensitivities of most antibody assays could be improved by including other parameters to the diagnostic repertoire for diagnosing LNB (range: 94.1% to 100%), although with slightly lower specificities (range: 92.8% to 96.4%). The most important parameters for LNB diagnosis are the detection of intrathecally produced Borrelia-specific antibodies, two-tier serology on serum, CSF-CXCL13, Reibergram classification, and pleocytosis. In conclusion, this study shows that LNB diagnosis is best supported using multiparameter analysis. Furthermore, a collaborative prospective study is proposed to investigate if a standardized diagnostic algorithm can be developed for improved LNB diagnosis. IMPORTANCE The diagnosis of LNB is established by clinical symptoms, pleocytosis, and proof of intrathecal synthesis of Borrelia-specific antibodies. Laboratory diagnosis of LNB is challenging, and validated diagnostic algorithms are lacking. Therefore, this retrospective cross-sectional study aimed to compare the diagnostic performance of seven commercial antibody assays for LNB diagnosis. Multiparameter analysis was conducted to investigate whether the diagnostic performance using the antibody assays could be improved by including several routine (CSF) parameters. The results of this study show that LNB diagnosis is best supported using the detection of intrathecally produced Borrelia-specific antibodies, two-tier serology on serum, CSF-CXCL13, Reibergram classification, and pleocytosis. Furthermore, we propose a collaborative prospective study to investigate the potential role of constructing a diagnostic algorithm using multiparameter analysis for improved LNB diagnosis.
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Affiliation(s)
- Tamara van Gorkom
- Department of Medical Microbiology and Immunology, Diakonessenhuis Hospital, Utrecht, The Netherlands
- Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Willem Voet
- Department of Neurology, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Gijs H. J. van Arkel
- Department of Medical Microbiology and Immunology, Diakonessenhuis Hospital, Utrecht, The Netherlands
- Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Michiel Heron
- Department of Medical Microbiology and Immunology, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - B. J. A. Hoeve-Bakker
- Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Daan W. Notermans
- Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Steven F. T. Thijsen
- Department of Medical Microbiology and Immunology, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Kristin Kremer
- Centre for Infectious Diseases Research, Diagnostics and Laboratory Surveillance, Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
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