Molecular-Level Characterization of Normal, Benign, and Malignant Breast Tissues Using FTIR Spectroscopy

Classification of healthy and cancerous colon cells by Fourier transform infrared spectroscopy

Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm-1 (low wavenumber range, LWR) and 2700-3700 cm-1 (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.

Spectroscopic insight into breast cancer: profiling small extracellular vesicles lipids via infrared spectroscopy for diagnostic precision

Breast cancer, a leading cause of female mortality due to delayed detection owing to asymptomatic nature and limited early diagnostic tools, was investigated using a multi-modal approach. Plasma-derived small EVs from breast cancer patients (BrCa, n = 74) and healthy controls (HC, n = 30) were analyzed. Small EVs (n = 104), isolated through chemical precipitation, underwent characterization via transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA). Validation involved antibody-based tests (TSG101, CD9, CD81, CD63). Infrared spectra of small EVs were obtained, revealing significant differences in lipid acyl chains, particularly in the C-H stretching of CH3. The study focused on the lipid region (3050-2900 cm-1), identifying peaks (3015 cm-1, 2960 cm-1, 2929 cm-1) as distinctive lipid characteristics. Spectroscopic lipid-to-lipid ratios [(I3015/I2929), (I2960/I2929)] emerged as prominent breast cancer markers. Exploration of protein, nucleic acid, and carbohydrate ratios indicated variations in alpha helices, asymmetric C-H stretching vibrations, and C-O stretching at 1033 cm-1. Principal component analysis (PCA) successfully differentiated BrCa and HC small EVs, and heatmap analysis and receiver operating characteristic (ROC) curve evaluations underscored the discriminatory power of lipid ratios. Notably, (I2960/I2929) exhibited 100% sensitivity and specificity, highlighting its potential as a robust BrCa sEV marker for breast cancer detection.

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics-area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)-were averaged for comparison. The NN models were compared to six (6) machine learning models-logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)-for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C-OH C-OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample's spectrum using NN.

Discrimination of Different Breast Cell Lines on Glass Substrate by Means of Fourier Transform Infrared Spectroscopy

Fourier transform infrared (FTIR) micro-spectroscopy has been attracting the interest of many cytologists and histopathologists for several years. This is related to the possibility of FTIR translation in the clinical diagnostic field. In fact, FTIR spectra are able to detect changes in biochemical cellular components occurring when the cells pass to a pathological state. Recently, this interest has increased because it has been shown that FTIR spectra carried out just in the high wavenumber spectral range (2500-4000 cm^-1), where information mainly relating to lipids and proteins can be obtained, are able to discriminate cell lines related to different tissues. This possibility allows to perform IR absorption measurements of cellular samples deposited onto microscopy glass slides (widely used in the medical environment) which are transparent to IR radiation only for wavenumber values larger than 2000 cm^-1. For these reasons, we show that FTIR spectra in the 2800-3000 cm^-1 spectral range can discriminate three different cell lines from breast tissue: a non-malignant cell line (MCF10A), a non-metastatic adenocarcinoma cell line (MCF7) and a metastatic adenocarcinoma cell line (MDA). All the cells were grown onto glass slides. The spectra were discriminated by means of a principal component analysis, according to the PC1 component, whose values have the opposite sign in the pairwise score plots. This result supports the wide studies that are being carried out to promote the translation of the FTIR technique in medical practice, as a complementary diagnostic tool.

Thermal Liquid Biopsy (TLB) of Blood Plasma as a Potential Tool to Help in the Early Diagnosis of Multiple Sclerosis

Background: Multiple sclerosis (MS) is frequently characterized by a variety of clinical signs, often exhibiting little specificity. The diagnosis requires a combination of medical observations and instrumental tests, and any support for its objective assessment is helpful. Objective: Herein, we describe the application of thermal liquid biopsy (TLB) of blood plasma samples, a methodology for predicting the occurrence of MS with a noninvasive, quick blood test. Methods: TLB allows one to define an index (TLB score), which provides information about overall real-time alterations in plasma proteome that may be indicative of MS. Results: This pilot study, based on 85 subjects (45 MS patients and 40 controls), showed good performance indexes (sensitivity and specificity both around 70%). The diagnostic methods better discriminate between early stage and low-burden MS patients, and it is not influenced by gender, age, or assumption of therapeutic drugs. TLB is more accurate for patients having low disability level (≤ 3.0, measured by the expanded disability status scale, EDSS) and a relapsing–remitting diagnosis. Conclusion: Our results suggest that TLB can be applied to MS, especially in an initial phase of the disease when diagnosis is difficult and yet more important (in such cases, accuracy of prediction is close to 80%), as well as in personalized patient periodic monitoring. The next step will be determining its utility in differentiating between MS and other disorders, in particular in inflammatory diseases.

A Comparison between FTIR Spectra from HUKE and SH-SY5Y Cell Lines Grown on Different Substrates

In recent years, Fourier Transform Infrared (FTIR) micro-spectroscopy has shown promising potential in medical diagnostics at the cellular level. In fact, FTIR spectra can provide information related to DNA, protein, and lipid content and how such a content changes when a pathological state arises. Most of these information is included in the so-called fingerprint region (1000–1800 cm−1), consisting of several spectral peaks related to vibrational modes occurring inside cellular components. Unfortunately, the slides commonly used in cytology (as the glass microscopy slides and coverslips) are opaque to IR radiation in the fingerprint region, whereas they are transparent for wavenumber values larger than 2000 cm−1, where few and broad spectral absorption bands, mainly due to lipids and proteins, are present. Nonetheless, here we show that FTIR spectra performed in the high wavenumber range 2750–3000 cm−1 can be used to discriminate two different types of cells, one from a normal cell line (Human Keratinocyte, HUKE) and the other from a cancer one (SH-SY5Y). The spectra are discriminated by means of their Principal Component Analysis, according to the PC1 component, and by means of ratiometric analysis, according to the ratio of the intensity of the peak at 2956 cm−1 and that of the peak at 2924 cm−1. The PC1 score values of the HUKE are statistically different from the PC1 score values of SH-SY5Y, whereas the intensity ratio results larger for SH-SY5Y than for HUKE cells. Such results occur for different substrates over which the cells have been grown, including the thick glass slides used for cytological analysis. This result is a further step toward the application of FTIR microspectroscopy in the cytological routine diagnosis.

Third Harmonic Generation microscopy distinguishes malignant cell grade in human breast tissue biopsies

The ability to distinguish and grade malignant cells during surgical procedures in a fast, non-invasive and staining-free manner is of high importance in tumor management. To this extend, Third Harmonic Generation (THG), Second Harmonic Generation (SHG) and Fourier-Transform Infrared (FTIR) spectroscopy were applied to discriminate malignant from healthy cells in human breast tissue biopsies. Indeed, integration of non-linear processes into a single, unified microscopy platform offered complementary structural information within individual cells at the submicron level. Using a single laser beam, label-free THG imaging techniques provided important morphological information as to the mean nuclear and cytoplasmic area, cell volume and tissue intensity, which upon quantification could not only distinguish cancerous from benign breast tissues but also define disease severity. Simultaneously, collagen fibers that could be detected by SHG imaging showed a well structured continuity in benign tumor tissues, which were gradually disoriented along with disease severity. Combination of THG imaging with FTIR spectroscopy could provide a clearer distinction among the different grades of breast cancer, since FTIR analysis showed increased lipid concentrations in malignant tissues. Thus, the use of non-linear optical microscopy can be considered as powerful and harmless tool for tumor cell diagnostics even during real time surgery procedures.

Plasma membrane proteins: A new probe for the characterization of breast cancer

This work aimed to characterize normal, benign and malignant excised breast tissues through the analysis of the FTIR spectra of their plasma membrane proteins. Tissue characterization parameters such as peak position, peak intensity, area under the peak, relative peak intensity and relative area under peak were evaluated mainly for protein spectral peaks; 1150 cm^-1, Amide I, Amide II, Amide III, and Amide A. The sensitivity, specificity and diagnostic accuracy for each parameter were obtained and Receiver Operating Characteristic (ROC) Curves were plotted. Results showed significant spectral differences between normal and benign tissues compared to malignant tissues at 1536 and 1645 cm^-1. The three tissues could be distinguished at 2900 cm^-1, where the malignant peak uniquely split into two separate peaks. ROC curves showed that the Amide A peak position yielded a higher accuracy compared to all other investigated characterization parameters. The deconvolution of Amide I revealed the conformational changes in plasma proteins characterizing the transformation to malignancy (a decrease in the percentage of alpha helix accompanied by an increase in the percentage of beta sheets). The use of the present structure-based analysis in conjunction with histopathological examination of excised breast tissues would offer an enhanced characterization that might reduce possible personal diagnostic mistakes.

Fourier transform infrared spectroscopy based spectral biomarkers of metastasized breast cancer progression

Breast cancer is a global health issue and the second leading cause of cancer death in women. Breast cancer tends to migrate to bone and causes bone metastases which is ultimately the cause of death. Here, we report the use of FTIR to identify spectral biomarkers of cancer progression on 3D in vitro model of breast cancer bone metastasis. Our results indicate that the following spectral biomarkers can monitor cancer progression, for example, lipids (CH2 asymmetric/CH2 symmetric stretch), Amide I/Amide II, and RNA/DNA. Principal component analysis also confirmed the involvement of protein, lipids and nucleic acids in cancer progression on sequential culture. The collective observations from this study suggest successful application of FTIR as a non-invasive and accurate method to identify biochemical changes in cancer cells during the progression of breast cancer bone metastasis.