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Belashov AV, Zhikhoreva AA, Salova AV, Belyaeva TN, Litvinov IK, Kornilova ES, Semenova IV. SLIM-assisted automatic cartography of cell death types and rates resulting from localized photodynamic treatment. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2024; 41:C72-C81. [PMID: 39889066 DOI: 10.1364/josaa.534241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Accepted: 09/20/2024] [Indexed: 02/02/2025]
Abstract
We report a spatial light interference microscopy (SLIM)-based methodology aimed at automatic monitoring and analysis of changes in cellular morphology within extended fields of view in cytological samples. The experimental validation was performed on HeLa cells in vitro subjected to localized photodynamic treatment. The performed long-term noninvasive monitoring using the SLIM technique allowed us to estimate quantitative parameters characterizing the dynamics of average phase shift in individual cells and to reveal changes in their morphology specific for different mechanisms of cell death. The results obtained evidenced that the proposed SLIM-based methodology provides an opportunity for identification of cell death type and quantification of cell death rate in an automatic mode. The major sources of potential errors that can affect the results obtained are discussed. The developed methodology is promising for automatic monitoring of large ensembles of individual cells and for quantitative characterization of their response to various treatment modalities.
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Durdevic L, Relaño Ginés A, Roueff A, Blivet G, Baffou G. Biomass measurements of single neurites in vitro using optical wavefront microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:6550-6560. [PMID: 36589583 PMCID: PMC9774852 DOI: 10.1364/boe.471284] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/05/2022] [Accepted: 10/25/2022] [Indexed: 06/17/2023]
Abstract
Quantitative phase microscopies (QPMs) enable label-free, non-invasive observation of living cells in culture, for arbitrarily long periods of time. One of the main benefits of QPMs compared with fluorescence microscopy is the possibility to measure the dry mass of individual cells or organelles. While QPM dry mass measurements on neural cells have been reported this last decade, dry mass measurements on their neurites has been very little addressed. Because neurites are tenuous objects, they are difficult to precisely characterize and segment using most QPMs. In this article, we use cross-grating wavefront microscopy (CGM), a high-resolution wavefront imaging technique, to measure the dry mass of individual neurites of primary neurons in vitro. CGM is based on the simple association of a cross-grating positioned in front of a camera, and can detect wavefront distortions smaller than a hydrogen atom (∼0.1 nm). In this article, an algorithm for dry-mass measurement of neurites from CGM images is detailed and provided. With objects as small as neurites, we highlight the importance of dealing with the diffraction rings for proper image segmentation and accurate biomass measurements. The high precision of the measurements we obtain using CGM and this semi-manual algorithm enabled us to detect periodic oscillations of neurites never observed before, demonstrating the sufficient degree of accuracy of CGM to capture the cell dynamics at the single neurite level, with a typical precision of 2%, i.e., 0.08 pg in most cases, down to a few fg for the smallest objects.
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Affiliation(s)
- Ljiljana Durdevic
- Institut Fresnel, CNRS, Aix Marseille Univ, Centrale Marseille, Marseille, France
- REGEnLIFE, Montpellier, France
| | | | - Antoine Roueff
- Institut Fresnel, CNRS, Aix Marseille Univ, Centrale Marseille, Marseille, France
| | | | - Guillaume Baffou
- Institut Fresnel, CNRS, Aix Marseille Univ, Centrale Marseille, Marseille, France
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Jin X, Fu R, Du W, Shan X, Mao Z, Deng A, Lin X, Su Y, Yang H, Lv W, Zhong H, Huang G. Rapid, Highly Sensitive, and Label-Free Pathogen Assay System Using a Solid-Phase Self-Interference Recombinase Polymerase Amplification Chip and Hyperspectral Interferometry. Anal Chem 2022; 94:2926-2933. [PMID: 35107980 DOI: 10.1021/acs.analchem.1c04858] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Recombinase polymerase amplification (RPA) is a useful pathogen identification method. Several label-free detection methods for RPA amplicons have been developed in recent years. However, these methods still lack sensitivity, specificity, efficiency, or simplicity. In this study, we propose a rapid, highly sensitive, and label-free pathogen assay system based on a solid-phase self-interference RPA chip (SiSA-chip) and hyperspectral interferometry. The SiSA-chips amplify and capture RPA amplicons on the chips, rather than irrelevant amplicons such as primer dimers, and the SiSA-chips are then analysed by hyperspectral interferometry. Optical length increases of SiSA-chips are used to demonstrate RPA detection results, with a limit of detection of 1.90 nm. This assay system can detect as few as six copies of the target 18S rRNA gene of Plasmodium falciparum within 20 min, with a good linear relationship between the detection results and the concentration of target genes (R2 = 0.9903). Single nucleotide polymorphism (SNP) genotyping of the dhfr gene of Plasmodium falciparum is also possible using the SiSA-chip, with as little as 1% of mutant gene distinguished from wild-type loci (m/wt). This system offers a high-efficiency (20 min), high-sensitivity (6 copies/reaction), high-specificity (1% m/wt), and low-cost (∼1/50 of fluorescence assays for RPA) diagnosis method for pathogen DNA identification. Therefore, this system is promising for fast identification of pathogens to help diagnose infectious diseases, including SNP genotyping.
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Affiliation(s)
- Xiangyu Jin
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Rongxin Fu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Wenli Du
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiaohui Shan
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zeyin Mao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Anni Deng
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xue Lin
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ya Su
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Han Yang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Wenqi Lv
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Hao Zhong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Guoliang Huang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.,National Engineering Research Center for Beijing Biochip Technology, Beijing 102206, China
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