scholarly journals Fingerprinting of skin cells by live cell Raman spectroscopy reveals melanoma cell heterogeneity and cell-type specific responses to UVR

2021 ◽  
Author(s):  
Richard Mort ◽  
Emma Wilkinson ◽  
Sarah Allinson ◽  
Jemma G Kerns ◽  
Lorna Ashton
Keyword(s):  
Raman Spectroscopy ◽  
Principal Component ◽  
Cell Types ◽  
Melanoma Cells ◽  
Biosynthesis Pathway ◽  
Label Free ◽  
Cell Type ◽  
Melanin Biosynthesis ◽  
Differentiation Status ◽  
Β Carotene

Raman spectroscopy is an emerging dermatological technique with the potential to discriminate biochemically between cell types in a label free and non-invasive manner. Here we use live single cell Raman spectroscopy and principal component analysis (PCA) to fingerprint mouse melanoblasts, melanocytes, keratinocytes and melanoma cells. We show the differences in their spectra are attributable to biomarkers in the melanin biosynthesis pathway and that melanoma cells are a heterogeneous population that sit on a trajectory between undifferentiated melanoblasts and differentiated melanocytes. We demonstrate the utility of Raman spectroscopy as a highly sensitive tool to probe the melanin biosynthesis pathway and its immediate response to UV irradiation revealing previously undescribed opposing responses to UVA and UVB irradiation in melanocytes. Finally, we identify melanocyte specific accumulation of β-carotene correlated with a stabilisation of the UVR response in lipids and proteins consistent with a β-carotene mediated photoprotective mechanism. In summary our data show that Raman spectroscopy can be used to determine the differentiation status of cells of the melanocyte lineage and describe the immediate and temporal biochemical changes associated with UV exposure which differ depending on cell type, differentiation status and competence to synthesise melanin.

scholarly journals Diagnosing COVID-19 in human serum using Raman spectroscopy: a preliminary study

2021 ◽  
Author(s):  
Ana Cristina Castro Goulart ◽  
Landulfo Silveira ◽  
Henrique Cunha Carvalho ◽  
Cristiane Bissoli Dorta ◽  
Marcos Tadeu Tavares Pacheco ◽  
...  
Keyword(s):  
Amino Acids ◽  
Raman Spectroscopy ◽  
Discriminant Analysis ◽  
Blood Serum ◽  
Principal Component ◽  
Label Free ◽  
Rt Pcr ◽  
Difference Spectra ◽  
Biochemical Alterations ◽  
Preliminary Study

This preliminary study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC 2, PC 4, PC 5 and PC 6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free and costless technique for diagnosing COVID-19 infection.

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Jing Wen ◽  
Tianchen Tang ◽  
Saima Kanwal ◽  
Yongzheng Lu ◽  
Chunxian Tao ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectrum ◽  
Tumor Cells ◽  
Cancer Metastasis ◽  
Cell Types ◽  
Label Free ◽  
Analysis Model ◽  
Multivariate Statistical ◽  
Label Free Detection ◽  
Confocal Raman

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.

scholarly journals Identification of an alternatively spliced site in human plasma fibronectin that mediates cell type-specific adhesion.

1986 ◽  
Vol 103 (6) ◽  
pp. 2637-2647 ◽  
Author(s):  
M J Humphries ◽  
S K Akiyama ◽  
A Komoriya ◽  
K Olden ◽  
K M Yamada
Keyword(s):  
Cell Adhesion ◽  
Melanoma Cell ◽  
Synthetic Peptides ◽  
Cell Types ◽  
Melanoma Cells ◽  
Cell Type ◽  
Fibroblastic Cell ◽  
Type Iii ◽  
Alternative Mrna Splicing ◽  
Alternatively Spliced

We have compared the molecular specificities of the adhesive interactions of melanoma and fibroblastic cells with fibronectin. Several striking differences were found in the sensitivity of the two cell types to inhibition by a series of synthetic peptides modeled on the Arg-Gly-Asp-Ser (RGDS) tetrapeptide adhesion signal. Further evidence for differences between the melanoma and fibroblastic cell adhesion systems was obtained by examining adhesion to proteolytic fragments of fibronectin. Fibroblastic BHK cells spread readily on fl3, a 75-kD fragment representing the RGDS-containing, "cell-binding" domain of fibronectin, but B16-F10 melanoma cells could not. The melanoma cells were able to spread instead on f9, a 113-kD fragment derived from the large subunit of fibronectin that contains at least part of the type III connecting segment difference region (or "V" region); f7, a fragment from the small fibronectin subunit that lacks this alternatively spliced polypeptide was inactive. Monoclonal antibody and fl3 inhibition experiments confirmed the inability of the melanoma cells to use the RGDS sequence; neither molecule affected melanoma cell spreading, but both completely abrogated fibroblast adhesion. By systematic analysis of a series of six overlapping synthetic peptides spanning the entire type III connecting segment, a novel attachment site was identified in a peptide near the COOH-terminus of this region. The tetrapeptide sequence Arg-Glu-Asp-Val (REDV), which is somewhat related to RGDS, was present in this peptide in a highly hydrophilic region of the type III connecting segment. REDV appeared to be functionally important, since this synthetic tetrapeptide was inhibitory for melanoma cell adhesion to fibronectin but was inactive for fibroblastic cell adhesion. REDV therefore represents a novel adhesive recognition signal in fibronectin that possesses cell type specificity. These results suggest that, for some cell types, regulation of the adhesion-promoting activity of fibronectin may occur by alternative mRNA splicing.

scholarly journals Biochemical Profiles of In Vivo Oral Mucosa by Using a Portable Raman Spectroscopy System

Optics ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 134-147
Author(s):  
Marcelo Saito Nogueira ◽  
Victoria Ribeiro ◽  
Marianna Pires ◽  
Felipe Peralta ◽  
Luis Felipe das Chagas e Silva de Carvalho
Keyword(s):  
Raman Spectroscopy ◽  
Biochemical Characterization ◽  
Principal Component ◽  
Histopathological Analysis ◽  
Label Free ◽  
Healthy Individuals ◽  
Normal Tissues ◽  
The Difference

Most oral injuries are diagnosed by histopathological analysis of invasive and time-consuming biopsies. This analysis and conventional clinical observation cannot identify biochemically altered tissues predisposed to malignancy if no microstructural changes are detectable. With this in mind, detailed biochemical characterization of normal tissues and their differentiation features on healthy individuals is important in order to recognize biomolecular changes associated with early tissue predisposition to malignant transformation. Raman spectroscopy is a label-free method for characterization of tissue structure and specific composition. In this study, we used Raman spectroscopy to characterize the biochemistry of in vivo oral tissues of healthy individuals. We investigated this biochemistry based on the vibrational modes related to Raman spectra of four oral subsites (buccal, gingiva, lip and tongue) of ten volunteers as well as with principal component (PC) loadings for the difference between the four types of oral subsites. Therefore, we determined the biochemical characteristics of each type of healthy oral subsite and those corresponding to differentiation of the four types of subsites. In addition, we developed a spectral reference of oral healthy tissues of individuals in the Brazilian population for future diagnosis of early pathological conditions using real-time, noninvasive and label-free techniques such as Raman spectroscopy.

scholarly journals Cell Type Classification and Unsupervised Morphological Phenotyping From Low-Resolution Images Using Deep Learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kai Yao ◽  
Nash D. Rochman ◽  
Sean X. Sun
Keyword(s):  
Cell Density ◽  
Semantic Segmentation ◽  
Cell Types ◽  
Support Network ◽  
Label Free ◽  
Cell Type ◽  
Morphological Heterogeneity ◽  
A Cell ◽  
Clustering Approach ◽  
Type Classification

Abstract Convolutional neural networks (ConvNets) have proven to be successful in both the classification and semantic segmentation of cell images. Here we establish a method for cell type classification utilizing images taken with a benchtop microscope directly from cell culture flasks, eliminating the need for a dedicated imaging platform. Significant flask-to-flask morphological heterogeneity was discovered and overcome to support network generalization to novel data. Cell density was found to be a prominent source of heterogeneity even when cells are not in contact. For the same cell types, expert classification was poor for single-cell images and better for multi-cell images, suggesting experts rely on the identification of characteristic phenotypes within subsets of each population. We also introduce Self-Label Clustering (SLC), an unsupervised clustering method relying on feature extraction from the hidden layers of a ConvNet, capable of cellular morphological phenotyping. This clustering approach is able to identify distinct morphological phenotypes within a cell type, some of which are observed to be cell density dependent. Finally, our cell classification algorithm was able to accurately identify cells in mixed populations, showing that ConvNet cell type classification can be a label-free alternative to traditional cell sorting and identification.

scholarly journals Differentiation of Closely Related Oak-Associated Gram-Negative Bacteria by Label-Free Surface Enhanced Raman Spectroscopy (SERS)

2021 ◽  
Vol 9 (9) ◽  
pp. 1969
Author(s):  
Dorotėja Vaitiekūnaitė ◽  
Valentinas Snitka
Keyword(s):  
Raman Spectroscopy ◽  
Principal Component ◽  
High Specificity ◽  
Surface Enhanced Raman Spectroscopy ◽  
Rrna Gene ◽  
Label Free ◽  
Biochemical Tests ◽  
Spectra Analysis ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

Due to the harmful effects of chemical fertilizers and pesticides, the need for an eco-friendly solution to improve soil fertility has become a necessity, thus microbial biofertilizer research is on the rise. Plant endophytic bacteria inhabiting internal tissues represent a novel niche for research into new biofertilizer strains. However, the number of species and strains that need to be differentiated and identified to facilitate faster screening in future plant-bacteria interaction studies, is enormous. Surface enhanced Raman spectroscopy (SERS) may provide a platform for bacterial discrimination and identification, which, compared with the traditional methods, is relatively rapid, uncomplicated and ensures high specificity. In this study, we attempted to differentiate 18 bacterial isolates from two oaks via morphological, physiological, biochemical tests and SERS spectra analysis. Previous 16S rRNA gene fragment sequencing showed that three isolates belong to Paenibacillus, 3—to Pantoea and 12—to Pseudomonas genera. Additional tests were not able to further sort these bacteria into strain-specific groups. However, the obtained label-free SERS bacterial spectra along with the high-accuracy principal component (PCA) and discriminant function analyses (DFA) demonstrated the possibility to differentiate these bacteria into variant strains. Furthermore, we collected information about the biochemical characteristics of selected isolates. The results of this study suggest a promising application of SERS in combination with PCA/DFA as a rapid, non-expensive and sensitive method for the detection and identification of plant-associated bacteria.

scholarly journals Raman spectroscopy and group and basis-restricted non negative matrix factorisation identifies radiation induced metabolic changes in human cancer cells

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kirsty Milligan ◽  
Xinchen Deng ◽  
Phillip Shreeves ◽  
Ramie Ali-Adeeb ◽  
Quinn Matthews ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Raman Spectra ◽  
Cell Lines ◽  
Human Cancer ◽  
Principal Component ◽  
Cell Types ◽  
Response Dynamics ◽  
Human Cancer Cell Lines ◽  
Human Cancer Cells ◽  
Matrix Factorisation

AbstractThis work combines single cell Raman spectroscopy (RS) with group and basis restricted non-negative matrix factorisation (GBR-NMF) to identify individual biochemical changes associated with radiation exposure in three human cancer cell lines. The cell lines analysed were derived from lung (H460), breast (MCF7) and prostate (LNCaP) tissue and are known to display varying degrees of radio sensitivity due to the inherent properties of each cell type. The GBR-NMF approach involves the deconstruction of Raman spectra into component biochemical bases using a library of Raman spectra of known biochemicals present in the cells. Subsequently, scores are obtained on each of these bases which can be directly correlated with the contribution of each chemical to the overall Raman spectrum. We validated GBR-NMF through the correlation of GBR-NMF-derived glycogen scores with scores that were previously observed using principal component analysis (PCA). Phosphatidylcholine, glucose, arginine and asparagine showed a distinct differential score pattern between radio-resistant and radio-sensitive cell types. In summary, the GBR-NMF approach allows for the monitoring of individual biochemical radiation-response dynamics previously unattainable with more traditional PCA-based approaches.

Characteristic of Five Subpopulation Leukocytes in Single-Cell Levels Based on Partial Principal Component Analysis Coupled with Raman Spectroscopy

2020 ◽  
Vol 74 (12) ◽  
pp. 1463-1472
Author(s):  
Wenxue Li ◽  
Liu Wang ◽  
Chuan Luo ◽  
Zhiqiang Zhu ◽  
Jianlong Ji ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Wavelet Transform ◽  
Single Cell ◽  
Low Frequency ◽  
Principal Component ◽  
Component Analysis ◽  
Label Free ◽  
Source Of Infection ◽  
Biochemical Features

Characteristics of five subpopulation leukocytes in single-cell levels based on partial principal component analysis coupled with Raman spectroscopy were proposed to recognize the biochemical features of five subpopulation leukocytes. Using wavelet transform, the reconstructed spectra of the low-frequency wavelet coefficients were used to perform multiple principal component analysis based on segmented spectral data wreathing cover at 720–800 cm–1, 840–994 cm–1, and 1010–1070 cm–1 wavenumbers, respectively. Our approach is promising since it enables to establish a better understanding of the underlying molecular difference between the subtypes of leukocytes in a label-free manner and to estimate the source of infection.

Dendritic cells of the human epidermis: immuno-electron microscopic studies of la antigen reactivity

1978 ◽  
Vol 36 (2) ◽  
pp. 644-645
Author(s):  
G. Rowden ◽  
M. G. Lewis ◽  
T. M. Phillips
Keyword(s):  
Dendritic Cells ◽  
Langerhans Cells ◽  
Cell Types ◽  
Cell Type ◽  
Electron Microscopic ◽  
La Antigen ◽  
Microscopic Studies ◽  
A Cell ◽  
C3 Receptors ◽  
Antigen Reactivity

Langerhans cells of mammalian stratified squamous epithelial have proven to be an enigma since their discovery in 1868. These dendritic suprabasal cells have been considered as related to melanocytes either as effete cells, or as post divisional products. Although grafting experiments seemed to demonstrate the independence of the cell types, much confusion still exists. The presence in the epidermis of a cell type with morphological features seemingly shared by melanocytes and Langerhans cells has been especially troublesome. This so called "indeterminate", or " -dendritic cell" lacks both Langerhans cells granules and melanosomes, yet it is clearly not a keratinocyte. Suggestions have been made that it is related to either Langerhans cells or melanocyte. Recent studies have unequivocally demonstrated that Langerhans cells are independent cells with immune function. They display Fc and C3 receptors on their surface as well as la (immune region associated) antigens.

Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer

2021 ◽  
pp. 000370282110329
Author(s):  
Ling Wang ◽  
Mario O. Vendrell-Dones ◽  
Chiara Deriu ◽  
Sevde Doğruer ◽  
Peter de B. Harrington ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Principal Component ◽  
Surface Enhanced Raman Spectroscopy ◽  
Quantitative Detection ◽  
Least Squares Regression ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Sers Detection ◽  
Current Screening

Recently there has been upsurge in reports that illicit seizures of cocaine and heroin have been adulterated with fentanyl. Surface-enhanced Raman spectroscopy (SERS) provides a useful alternative to current screening procedures that permits detection of trace levels of fentanyl in mixtures. Samples are solubilized and allowed to interact with aggregated colloidal nanostars to produce a rapid and sensitive assay. In this study, we present the quantitative determination of fentanyl in heroin and cocaine using SERS, using a point-and-shoot handheld Raman system. Our protocol is optimized to detect pure fentanyl down to 0.20 ± 0.06 ng/mL and can also distinguish pure cocaine and heroin at ng/mL levels. Multiplex analysis of mixtures is enabled by combining SERS detection with principal component analysis and super partial least squares regression discriminate analysis (SPLS-DA), which allow for the determination of fentanyl as low as 0.05% in simulated seized heroin and 0.10% in simulated seized cocaine samples.

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