Identification of remodeled collagen fibers in tumor stroma by FTIR Micro-spectroscopy: A new approach to recognize the colon carcinoma

Santis, Serena De; Porcelli, Francesco; Sotgiu, Giovanni; Crescenzi, Anna; Ceccucci, Anita; Verri, Martina; Caricato, Marco; Taffon, Chiara; Orsini, Monica

doi:10.1016/j.bbadis.2021.166279

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…In particular, the absorption values of cancerous cells were larger than those of healthy ones and the differences were statistically significant. Such a result is in good agreement with that reported by S. De Santis et al regarding FTIR microspectroscopy of collagen from human colon specimens which was surgically removed after diagnosis of adenocarcinoma [44]: they found larger FTIR spectral signals from malignant tissue than normal tissue in the amide III spectral range [44]. Even B. Brozen-Pluska reported that protein-related peaks in the Raman spectra of Caco-2 cells were characterized by greater intensities with respect to the corresponding peaks in the Raman spectra of noncancerous colon cells [45].…”

Section: Dong Et Al Regarding Colon Tissuesupporting

confidence: 93%

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2023

Applied Sciences

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Colorectal cancer was one of the most frequent causes of death due to cancer in 2020. Current diagnostic methods, based on colonoscopy and histological analysis of biopsy specimens, are partly dependent on the operator’s skills and expertise. In this study, we used Fourier transform infrared (FTIR) spectroscopy and different machine learning algorithms to evaluate the performance of such method as a complementary tool to reliably diagnose colon cancer. We obtained FTIR spectra of FHC and CaCo-2 cell lines originating from healthy and cancerous colon tissue, respectively. The analysis, based on the intensity values of specific spectral structures, suggested differences mainly in the content of lipid and protein components, but it was not reliable enough to be proposed as diagnostic tool. Therefore, we built six machine learning algorithms able to classify the two different cell types: CN2 rule induction, logistic regression, classification tree, support vector machine, k nearest neighbours, and neural network. Such models achieved classification accuracy values ranging from 87% to 100%, sensitivity from 88.1% to 100%, and specificity from 82.9% to 100%. By comparing the experimental data, the neural network resulted to be the model with the best performance parameters, having excellent values of accuracy, sensitivity, and specificity both in the low-wavenumber range (1000–1760 cm−1) and in the high-wavenumber range (2700–3700 cm−1). These results are encouraging for the application of the FTIR technique, assisted by machine learning algorithms, as a complementary diagnostic tool for cancer detection.

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Section: Dong Et Al Regarding Colon Tissuesupporting

confidence: 93%

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Lasalvia,

Gallo,

Capozzi

et al. 2023

Applied Sciences

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“…The amide III is characterized by three main signals (1291, 1241, and 1998 cm −1 ) associated with CN stretching, NH bending, CC stretching, and CH bending vibrations. The band at 1241 cm −1 showed the highest intensity and is related to the NH group involved in hydrogen bonds with CO of the adjacent α ‐chain 77 …”

Section: Resultsmentioning

confidence: 99%

“…In showed the highest intensity and is related to the N H group involved in hydrogen bonds with C O of the adjacent α-chain. 77 A study on the comparative composition of PVA, PCL, and PCL-Col_1/PVA core-shell fibers was carried out by using FTIR spectroscopy. The infrared spectra of these materials presented in Figure 6 show the characteristic bands for pristine collagen, PVA, PCL, and PCL-Col_1/PVA (before and after release/degradation in saline solution at 37 C for 28 days).…”

Section: Core-shell Fiber Chemical Composition Assessmentmentioning

confidence: 99%

Novel polycaprolactone (PCL)‐type I collagen core‐shell electrospun nanofibers for wound healing applications

et al. 2022

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Type I collagen (Col_1) is one of the main proteins present in the skin extracellular matrix, serving as support for skin regeneration and maturation in its granulation stage. Electrospun materials have been intensively studied as the next generation of skin wound dressing mainly due to their high surface area and fibrous porosity. However, the electrospinning of collagen‐based solutions causes degradation of its structure. In this work, a coaxial electrospinning process was proposed to overcome this limitation. The production of mats of polycaprolactone (PCL)–Col_1/PVA (collagen/poly(vinyl alcohol)) composed of core‐shell nanofibers was investigated. PCL solution was used as the core solution, while Col_1/PVA was used as the shell solution. PVA was used to improve the processability of collagen, while PCL was employed to improve the mechanical properties and morphology of Col_1/PVA fibers. The morphology and the cytotoxicity of the fibers were highly dependent on the processing parameters. Defect‐free core‐shell nanofibers were obtained with a shell/core flow rates ratio = 4, flight distance of 12 cm, and an applied voltage of 16 kV. Using this strategy, the triple helix structure characteristic of the collagen molecule was preserved. Moreover, the common post‐processing of solvent removal could be suppressed, simplifying the manufacturing processing of these biomaterials. The nanostructured mats showed no cytotoxicity, high liquid absorption, structural stability, hydrophilic character, and collagen release capacity, making them a potential novel dressing for skin damage regeneration, in special in the case of chronic wounds treatment, in which exogenous collagen delivery is necessary.

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“…Conversely, in OP osteoporotic samples, two sets of spectra were identified representative of collagen‐rich areas (OP+) and collagen‐poor ones (OP–) (named respectively OP+ cortical, OP+ trabecular, OP– cortical and OP– trabecular, based on the region of interest). The analysis of the average IR spectra, reported in Figure 4 both in absorbance (continuous colored lines) and second derivative mode (dotted black lines), highlighted the following bands: ~1340 cm −1 (CH 2 wagging of proline side chains) 29 , 30 ;~1320 cm −1 (a‐helix secondary structures) 31 , 32 ; ~1284 cm −1 and ~1240 cm −1 , (collagen triple helix) 33 ; ~1264 cm −1 (random secondary structures), 33 ~1204 cm −1 (amino acids lateral chains) 34 and ~ 1160 cm −1 (C‐OH moiety of carbohydrates). 18 , 35 , 36 …”

Section: Resultsmentioning

confidence: 99%

“…Conversely, in OP osteoporotic samples, two sets of spectra were identified representative of collagen-rich areas (OP+) and collagen-poor ones (OP-) (named respectively OP+ cortical, OP+ trabecular, OP-cortical and OP-trabecular, based on the region of interest). The analysis of the average IR spectra, reported in Figure 4 both in absorbance (continuous colored lines) and second derivative mode (dotted black lines), highlighted the following bands: ~1340 cm À1 (CH 2 wagging of proline side chains) 29,30 ;~1320 cm À1 (a-helix secondary structures) 31,32 ; ~1284 cm À1 and ~1240 cm À1 , (collagen triple helix) 33 ; ~1264 cm À1 (random secondary structures), 33 ~1204 cm À1 (amino acids lateral chains) 34 and ~1160 cm À1 (C-OH moiety of carbohydrates). 18,35,36 The following band area ratios diagnostic for collagen composition and structure were calculated and statistically analyzed (Figure 5): Coll/Tot (total collagen), 1340/1160 (collagen's proline), 1320/1160 (a-helices), 1284/1160 (triple helices), 1264/1160 (random structures), 1240/1160 (triple helices), 1204/1160 (amino acids' lateral chains), and Random/Folded (calculated as the ratio between the areas of the 1264 cm À1 band and the sum of the areas of the bands at 1320, 1284 -, and 1240 cm À1 ).…”

Section: Type I Collagen Structure and Localization Were Altered In D...mentioning

confidence: 94%

Altered type I collagen networking in osteoporotic human femoral head revealed by histomorphometric and Fourier transform infrared imaging correlated analyses

et al. 2022

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Bone homeostasis is the equilibrium between organic and inorganic components of the extracellular matrix (ECM) and cells. Alteration of this balance has consequences on bone mass and architecture, resulting in conditions such as osteoporosis (OP). Given ECM protein mutual regulation and their effects on bone structure and mineralization, further insight into their expression is crucial to understanding bone biology under normal and pathological conditions. This study focused on Type I Collagen, which is mainly responsible for structural properties and mineralization of bone, and selected proteins implicated in matrix composition, mineral deposition, and cell‐matrix interaction such as Decorin, Osteocalcin, Osteopontin, Bone Sialoprotein 2, Osteonectin and Transforming Growth Factor beta. We developed a novel multidisciplinary approach in order to assess bone matrix in healthy and OP conditions more comprehensively by exploiting the Fourier Transform Infrared Imaging (FTIRI) technique combined with histomorphometry, Sirius Red staining, immunohistochemistry, and Western Blotting. This innovatory procedure allowed for the analysis of superimposed tissue sections and revealed that the alterations in OP bone tissue architecture were associated with warped Type I Collagen structure and deposition but not with changes in the total protein amount. The detected changes in the expression and/or cooperative or antagonist role of Decorin, Osteocalcin, Osteopontin, and Bone Sialoprotein‐2 indicate the deep impact of these NCPs on collagen features of OP bone. Overall, our strategy may represent a starting point for designing targeted clinical strategies aimed at bone mass preservation and sustain the FTIRI translational capability as upcoming support for traditional diagnostic methods.

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Identification of remodeled collagen fibers in tumor stroma by FTIR Micro-spectroscopy: A new approach to recognize the colon carcinoma

Cited by 10 publications

References 36 publications

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Discrimination of Healthy and Cancerous Colon Cells Based on FTIR Spectroscopy and Machine Learning Algorithms

Novel polycaprolactone (PCL)‐type I collagen core‐shell electrospun nanofibers for wound healing applications

Altered type I collagen networking in osteoporotic human femoral head revealed by histomorphometric and Fourier transform infrared imaging correlated analyses

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