Distinguishing Different Cancerous Human Cells by Raman Spectroscopy Based on Discriminant Analysis Methods

Tang, Mingjie; Xia, Liangping; Wei, Dongshan; Yan, Shihan; Du, Chunlei; Cui, Hong‐Liang

doi:10.3390/app7090900

Cited by 17 publications

(12 citation statements)

References 36 publications

(43 reference statements)

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“…However, instead of identifying the component axes maximizing the variance of data as made by PCA method, LDA additionally finds the axes that maximize the separation between multiple classes, eventually previously identified by PCA, see Statistical methods in Methods section. Thanks to such peculiar features, PCA and LDA are widely used in Raman spectroscopy investigations for pathological classification 33 .…”

Section: Discussionmentioning

confidence: 99%

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

D’Acunto

Gaeta

Capanna

et al. 2020

Sci Rep

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In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm 2 to approximately 200×200 μm 2 , respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis. Cancer diagnosis remains one of the biggest challenges in medicine. The development of new noninvasive strategies or the improvements of existing ones makes Raman Spectroscopy (RS) fundamental for diagnosing the chemical compositions of cells and tissues. RS is able to probe fundamentals vibrational states of biomolecules, and exploits a label-free and non-destructive optical approach. RS is thus being used more and more frequently to analyses biological tissues 1-6. In fact, for various types of cancers, in vivo biopsy imaging and histopathological analyses are carried out using RS 7-11. RS is also exploited to evaluate the biochemical attributes of bones, and has revealed pathological changes in the components of the bone matrices. These changes include alterations in phosphate, carbonate and collagen degradation, as well as spectral changes in terms of bone metastasis primed by prostate and breast cancer 11-13. With these abilities, the application of RS to the early diagnosis of bone tumors is more than ever necessary. In the present pilot study, we apply the RS spectral imaging technique to improve non-destructive diagnosis and grading of chondrogenic tumors. Cartilaginous tumors are the most frequent primary bone tumors. While the true incidence of enchondromas (ECs) is difficult to determine because they are often asymptomatic, central chondrosarcoma (CS) accounts for approximately 20% of malignant bone tumors 14 , with an incidence, for example, of 8.78 per million inhabitants between 2005 and 2013 in the Netherlands 15 , whereas the overall rate inci...

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Section: Discussionmentioning

confidence: 99%

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

D’Acunto

Gaeta

Capanna

et al. 2020

Sci Rep

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“…A variant of Fisher’s linear discriminant analysis, quadratic discriminant analysis (QDA), was applied to determine the ability of each designated spectral region to classify the various microorganisms. The use of QDA has been implemented in applications such as classifying Raman spectra of human cancer cell lines 54 and Raman imaging of naive versus activated T-cells. 55 First, a quadratic classifier was created on the basis of designated classes (each of the S. aureus mutants and SCVs) using the PCA scores from each spectral region as input parameters.…”

Section: Methodsmentioning

confidence: 99%

Drug-Resistant Staphylococcus aureus Strains Reveal Distinct Biochemical Features with Raman Microspectroscopy

et al. 2018

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Staphylococcus aureus ( S. aureus) is a leading cause of hospital-acquired infections, such as bacteremia, pneumonia, and endocarditis. Treatment of these infections can be challenging since strains of S. aureus, such as methicillin-resistant S. aureus (MRSA), have evolved resistance to antimicrobials. Current methods to identify infectious agents in hospital environments often rely on time-consuming, multistep culturing techniques to distinguish problematic strains (i.e., antimicrobial resistant variants) of a particular bacterial species. Therefore, a need exists for a rapid, label-free technique to identify drug-resistant bacterial strains to guide proper antibiotic treatment. Here, our findings demonstrate the ability to characterize and identify microbes at the subspecies level using Raman microspectroscopy, which probes the vibrational modes of molecules to provide a biochemical "fingerprint". This technique can distinguish between different isolates of species such as Streptococcus agalactiae and S. aureus. To determine the ability of this analytical approach to detect drug-resistant bacteria, isogenic variants of S. aureus including the comparison of strains lacking or expressing antibiotic resistance determinants were evaluated. Spectral variations observed may be associated with biochemical components such as amino acids, carotenoids, and lipids. Mutants lacking carotenoid production were distinguished from wild-type S. aureus and other strain variants. Furthermore, spectral biomarkers of S. aureus isogenic bacterial strains were identified. These results demonstrate the feasibility of Raman microspectroscopy for distinguishing between various genetically distinct forms of a single bacterial species in situ. This is important for detecting antibiotic-resistant strains of bacteria and indicates the potential for future identification of other multidrug resistant pathogens with this technique.

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“…The comprehensive contribution rate of three PCs was 55.25%, which represented the main variances. Due to the more PC numbers retains more original Raman spectrum information (Tang et al, 2017), to improve the accuracy of subsequent predictions, we increased the number to the first 20 PCs, which described 85% of variables. Subsequently, the LDA model was constructed with these PCs.…”

Section: Resultsmentioning

confidence: 99%

Detection and Classification of Multi-Type Cells by Using Confocal Raman Spectroscopy

et al. 2021

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Tumor cells circulating in the peripheral blood are the prime cause of cancer metastasis and death, thus the identification and discrimination of these rare cells are crucial in the diagnostic of cancer. As a label-free detection method without invasion, Raman spectroscopy has already been indicated as a promising method for cell identification. This study uses a confocal Raman spectrometer with 532 nm laser excitation to obtain the Raman spectrum of living cells from the kidney, liver, lung, skin, and breast. Multivariate statistical methods are applied to classify the Raman spectra of these cells. The results validate that these cells can be distinguished from each other. Among the models built to predict unknown cell types, the quadratic discriminant analysis model had the highest accuracy. The demonstrated analysis model, based on the Raman spectrum of cells, is propitious and has great potential in the field of biomedical for classifying circulating tumor cells in the future.

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Distinguishing Different Cancerous Human Cells by Raman Spectroscopy Based on Discriminant Analysis Methods

Cited by 17 publications

References 36 publications

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

Drug-Resistant Staphylococcus aureus Strains Reveal Distinct Biochemical Features with Raman Microspectroscopy

Detection and Classification of Multi-Type Cells by Using Confocal Raman Spectroscopy

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