Raman spectrum reveals Mesenchymal stem cells inhibiting HL60 cells growth

Su, Xin; Fang, Shaoyin; Zhang, Dao-Sen; Zhang, Qinnan; Lu, Xiaoxu; Tian, Jing; Fan, Jinping; LiyunZhong,

doi:10.1016/j.saa.2017.01.021

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Previous studies have done bulk Raman measurements in HL60 cell pellets, on single‐nuclei of HL60 cells, and on fixed HT29 cells . Single‐cell live label‐free Raman spectroscopy of these cell lines has been previously done comparing HCT116 cells with HT29 cells, studying apoptosis induction on HCT116 cells, studying proliferation effects caused by coculture of HL60 cells with mesenchymal stem cells and comparing HL60 cells with peripheral blood mononuclear cells, but the number of cells analysed in these studies were always below 30.…”

Section: Introductionmentioning

confidence: 99%

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Pablo

Armistead

Peyman

et al. 2018

J Raman Spectroscopy

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Label‐free live single‐cell Raman spectroscopy was used to obtain a chemical fingerprint of colorectal cancer cells including the classification of the SW480 and SW620 cell line model system, derived from primary and secondary tumour cells from the same patient. High‐quality Raman spectra were acquired from hundreds of live cells, showing high reproducibility between experiments. Principal component analysis with linear discriminant analysis yielded the best cell classification, with an accuracy of 98.7 ± 0.3% (standard error) when compared with discrimination trees or support vector machines. SW480 showed higher content of the disordered secondary protein structure Amide III band, whereas SW620 showed larger α‐helix and β‐sheet band content. The SW620 cell line also displayed higher nucleic acid, phosphates, saccharide, and CH 2 content. HL60, HT29, HCT116, SW620, and SW480 live single‐cell spectra were classified using principal component analysis or linear discriminant analysis with an accuracy of 92.4 ± 0.4% (standard error), showing differences mainly in the β‐sheet content, the cytochrome C bands, the CH‐stretching regions, the lactate contributions, and the DNA content. The lipids contributions above 2,900 cm −1 and the lactate contributions at 1,785 cm −1 appeared to be dependent on the colorectal adenocarcinoma stage, the advanced stage cell lines showing lower lipid, and higher lactate content. The results demonstrate that these cell lines can be distinguished with high confidence, suggesting that Raman spectroscopy on live cells can distinguish between different disease stages, and could play an important role clinically as a diagnostic tool for cell phenotyping.

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Section: Introductionmentioning

confidence: 99%

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Pablo

Armistead

Peyman

et al. 2018

J Raman Spectroscopy

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“…Compared with cells in the UT group, the peaks at 658 cm -1 (tyrosine and C-C twist) [29], 1001 cm -1 (phenylalanine) [27], and 1256 cm -1 (amide III, α-spiral, C=C str.) [27,37] increased in the 10T group and decreased in the 20T group. The 840 cm -1 (glucose), 941 cm -1 (C-C BK str.α-helix) [19], 1001 cm -1 (phenylalanine), 1256 cm -1 (amide III) [27,37], and 1320 cm -1 (C-H) peak positions were shifted to higher wavenumber; the 725 cm -1 (adenine) [38] peak position could also be identified.…”

Section: Ch3mentioning

confidence: 84%

“…[27,37] increased in the 10T group and decreased in the 20T group. The 840 cm -1 (glucose), 941 cm -1 (C-C BK str.α-helix) [19], 1001 cm -1 (phenylalanine), 1256 cm -1 (amide III) [27,37], and 1320 cm -1 (C-H) peak positions were shifted to higher wavenumber; the 725 cm -1 (adenine) [38] peak position could also be identified. We suspected that this may be due to the high concentration (20 µM) of drugs inducing stronger interactions with proteins and nucleic acids in the cells causing protein denaturation and the appearance of free adenine in the cells.…”

Section: Ch3mentioning

confidence: 84%

Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells

Li¹,

Wang²,

Qin³

et al. 2020

Int. J. Med. Sci.

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Confocal Raman microspectral imaging was adopted to elucidate the cellular drug responses of osteosarcoma cells (OC) to N-[N-(3, 5-difluorophenyl acetyl)-L-alanyl]-sphenylglycine butyl ester (DAPT), a γ-secretase inhibitor, by identifying the drug induced subcellular compositional and structural changes. Methods: Spectral information were acquired from cultured osteosarcoma cells treated with 0 (Untreated Group, UT), 10 (10 μM DAPT treated, 10T), 20 μM (20 μM DAPT treated, 20T) DAPT for 24 hours. A one-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple test were sequentially applied to address spectral features among three groups. Multivariate algorithms such as K-means clustering analysis (KCA) and Principal component analysis (PCA) were used to highlight the structural and compositional differences, while, univariate imaging was applied to illustrate the distribution pattern of certain cellular components after drug treatment. Results: Major biochemical changes in DAPT-induced apoptosis came from changes in the content and structure of proteins, lipids, and nucleic acids. By adopted multivariate algorithms, the drug induced cellular changes was identified by the morphology and spectral characteristics between untreated cells and treated cells, testified that DAPT mainly acted in the nuclear region. With the increase of the drug concentration, the content of main subcellular compositions, such nucleic acid, protein, and lipid decreased. In an addition, DAPT-induced nuclear fragmentation and apoptosis was depicted by the univariate Raman image of major cellular components (nucleic acids, proteins and lipids). Conclusions: The achieved Raman spectral and imaging results illustrated detailed DAPT-induced subcellular compositional and structural variations as a function of drug dose. Such observations can not only explain drug therapeutic mechanisms of OC DAPT treatment, and also provide new insights for accessing the medicine curative efficacy and predicting prognosis.

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“…LV2 loading exhibited positive values at 757 cm −1 (B‐DNA, tyrosine), 840 cm −1 (backbone O─P─O stretching), 1246 cm −1 (amide III, β ‐sheet), 1450 cm −1 (CH deformation), 1519 cm −1 ([C═C] stretching), and 1612 cm −1 (phenylalanine, tyrosine, tryptophan, C═C stretching) [36]. It was also observed that negative features were present at 883 cm −1 (CH 2 rocking), 1032 cm −1 (phenylalanine, CH in‐plane deformation, O─CH 3 stretching) [36, 50], 1096 cm −1 (PO 2 − symmetric stretching), 1172 cm −1 (cytosine, guanine, thymine, [CC] ring stretching, tyrosine, phenylalanine), 1314 cm −1 (guanine), 1335 cm −1 (guanine, [CC] ring stretching) and 1425 cm −1 (Z‐DNA, CH 2 in‐plane deformation) [36], all corresponding to nucleic acids and proteins. The positive loading of LV3 mostly showed the characteristics of proteins (1032, 1172, 1246, 1656 cm −1 ) and nucleic acids (840, 1096, 1172, 1335, 1425, 1580 cm −1 ).…”

Section: Resultsmentioning

confidence: 99%

Unveiling dose‐ and time‐dependent osteosarcoma cell responses to the γ‐secretase inhibitor, DAPT, by confocal Raman microscopy

Qin

Zeng³

et al. 2020

Journal of Biophotonics

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Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose-and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

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Raman spectrum reveals Mesenchymal stem cells inhibiting HL60 cells growth

Cited by 9 publications

References 18 publications

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Biochemical fingerprint of colorectal cancer cell lines using label‐free live single‐cell Raman spectroscopy

Confocal Raman Spectral Imaging Study of DAPT, a γ-secretase Inhibitor, Induced Physiological and Biochemical Reponses in Osteosarcoma Cells

Unveiling dose‐ and time‐dependent osteosarcoma cell responses to the γ‐secretase inhibitor, DAPT, by confocal Raman microscopy

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