Noninvasive and label-free determination of virus infected cells by Raman spectroscopy

Moor, Kamila; Ohtani, Kiyoshi; Myrzakozha, Diyas; Zhanserkenova, Orik; Andriana, Bibin Bintang; Sato, Hidetoshi

doi:10.1117/1.jbo.19.6.067003

Cited by 33 publications

(35 citation statements)

References 19 publications

(16 reference statements)

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“…Hamden et al . also showed that KSHV-infected cells demonstrate unique Raman spectrum signature compared to uninfected cells (Hamden et al 2005; Moor et al 2014). …”

Section: Introductionmentioning

confidence: 84%

Electrical response of a B lymphoma cell line latently infected with Kaposi’s sarcoma herpesvirus

Safavieh

Khetani

Juillard

et al. 2016

Biosensors and Bioelectronics

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Certain viruses, such as herpesviruses, are capable of persistent and latent infection of host cells. Distinguishing and separating live, latently infected cells from uninfected cells is not easily attainable using current approaches. The ability to perform such separation would greatly enhance the ability to study primary, infected cells and potentially enable elimination of latently infected cells from the host. Here, the dielectrophoretic response of B cells infected with Kaposi’s sarcoma-associated herpesvirus (KSHV) were investigated and compared to uninfected B cells. We evaluated the effect of applied voltage, signal frequency, and flow rate of the sample on the cell capture efficiency. We achieved 37.1%%±8.5% difference in capture efficiencies between latently KSHV-infected and uninfected BJAB cells at the chip operational conditions of 1V, 50 KHz and 0.02 μl/min sample flow rate. Our results show that latently infected B cell lines demonstrated significantly different electrical response compared to uninfected B cells and DEP-based microchips can be potentially used for sorting latently infected cells based on their electrical properties.

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“…Hamden et al . also showed that KSHV-infected cells demonstrate unique Raman spectrum signature compared to uninfected cells (Hamden et al 2005; Moor et al 2014). …”

Section: Introductionmentioning

confidence: 84%

Electrical response of a B lymphoma cell line latently infected with Kaposi’s sarcoma herpesvirus

Safavieh

Khetani

Juillard

et al. 2016

Biosensors and Bioelectronics

View full text Add to dashboard Cite

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“…When submitted to PCA, the Raman spectra of the three-class samples showed results suggesting that the infection induces rapid (24 h) production of a specific protein in the cells, which can differentiate infected samples from uninfected ones [53]. In 2014, Moor et al [14] continued their work on the power of Raman spectroscopy as a tool for detecting virus-infected cells, and developed a study where they successfully detected embryonic kidney cells of 293 adenovirusinfected humans at 12, 24 and 48 h after infection onset. The PCA score plot was effective in discriminating the spectra and segregating the uninfected cell classes of the infected cells after 12 h (Fig.…”

Section: Molecular Fluorescencementioning

confidence: 99%

“…8b) and 48 h (Fig. 8c) [14]. [54] performed a study of the diagnosis of malaria-infected cells based on synchrotron Fourier transform infrared (FTIR) imaging.…”

Section: Molecular Fluorescencementioning

confidence: 99%

“…The potential of spectroscopic techniques in the detection and identification of virus-infected cells has been studied using statistical methods as a sensitive, rapid and reliable methodology. The ability to discriminate between contaminated and noncontaminated samples in a short time with good sensitivity and specificity is pragmatic, suggesting that biospectroscopy is a field that should be more studied in virology [7,8,14].…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that SERS combined with these multivariate methods can be used as a rapid methodology for identification and viral classification. In the study by Moor et al [14] on non-invasive and label-free determination of virus-infected cells by Raman spectroscopy, it was also observed that Raman spectroscopy is a powerful tool for detecting virus-infected cells. In this study, 293 human embryonic kidney cells were infected by an adenovirus and were successfully detected at 12, 24 and 48 h after infection started.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectroscopy with computational analysis in virological studies: A decade (2006–2016)

Santos

Morais

Nascimento

et al. 2017

TrAC Trends in Analytical Chemistry

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a b s t r a c tThis review presents a retrospective of the studies carried out in the last 10 years (2006e2016) using spectroscopic methods as a research tool in the field of virology. Spectroscopic analyses are sensitive to variations in the biochemical composition of the sample, are non-destructive, fast and require the least sample preparation, making spectroscopic techniques tools of great interest in biological studies. Herein important chemometric algorithms that have been used in virological studies are also evidenced as a good alternative for analyzing the spectra, discrimination and classification of samples. Techniques that have not yet been used in the field of virology are also suggested. This methodology emerges as a new and promising field of research, and may be used in the near future as diagnosis tools for detecting diseases caused by viruses.

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Label‐free laser spectroscopy for respiratory virus detection: A review

Kistenev

Das

Mazumder

et al. 2022

Journal of Biophotonics

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Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real‐time, low cost and label‐free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

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Noninvasive and label-free determination of virus infected cells by Raman spectroscopy

Cited by 33 publications

References 19 publications

Electrical response of a B lymphoma cell line latently infected with Kaposi’s sarcoma herpesvirus

Electrical response of a B lymphoma cell line latently infected with Kaposi’s sarcoma herpesvirus

Spectroscopy with computational analysis in virological studies: A decade (2006–2016)

Label‐free laser spectroscopy for respiratory virus detection: A review

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