Raman spectroscopy based diagnosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma is currently the 12th most frequent form of cancer worldwide, characterized by a very low 5-year survival rate. Although several therapeutic approaches have been proposed to treat this form of pancreatic cancer, surgical resection is still commonly recognized as the most effective technique to slow down the disease progression and maximize the 5-year survival rate. Analogously, one critical issue is the ability of current diagnostic methodologies to distinguish between irregular growth of the tumor mass and surrounding inflammatory tissues. In this pilot study, we apply Raman spectroscopy, supported by a series of machine learning techniques, to distinguish among healthy, pancreatitis and ductal adenocarcinoma tissues, respectively, for a total of 15 cases. Raman spectroscopy is a label-free, non-destructive spectral technique exploiting Raman scattering. In turn, by applying a combination of principal component analysis and random forest classifier on the Raman spectral dataset, we achieved a maximum accuracy of up to ∼ 96%. Our findings clearly indicate that Raman spectroscopy could become a powerful spectral technique to support pathologists in improving pancreatic cancer diagnosis.

Machine learning with label-free Raman microscopy to investigate ferroptosis in comparison with apoptosis and necroptosis

Human and animal health rely on balancing cell division and cell death to maintain normal homeostasis. This process is accomplished by regulated cell death (RCD), whose imbalance can lead to disease. Currently, the most frequently used method for analyzing RCD is fluorescence microscopy. This method has limitations and potential side effects due to the presence of fluorescent labels. Furthermore, fluorescence often lacks specificity and may have side effects. In the quest to overcome such difficulties, label-free approaches have come into focus.Here, Raman microscopy in combination with machine learning is used to investigate RCDs, where biochemical molecular "fingerprints" are investigated with a focus on the vibrations of atoms in molecules. Three different and unique RCD types with different genetic and biochemical machinery, namely, ferroptosis is studied in comparison with apoptosis, and necroptosis in the murine fibroblast line L929sAhFas. Interestingly, during ferroptosis, a decrease in the wavenumber at 939 cm-1 was observed, which is associated with a potential reduction in the expression of collagen - a compound essential in multiple diseases. Data analysis was performed by machine learning (ML), here SVMs, where the model utilizing the spectra directly into a support vector machine (SVM) outperforms other SVM strategies correctly predicting 73% of all spectra. Other methods: PCA-SVM (principal component analysis-SVM), peak fitting-AUC-SVM (area under the curve) and peak fitting-spectral reconstruction-SVM rendered prediction accuracies of ~52%, ~43%, and 61%, respectively. Peak fitting has the additional benefit of enabling the biological interpretation of Raman scattering peaks by using the area under the curve, although at a loss of general accuracy. The potential of Raman microscopy in biology, in combination with machine learning pipelines, can be applied to a broader field of cell biology, not limited to regulated cell death.

Wavenumber Calibration Protocol for Raman Spectrometers Using Physical Modelling and a Fast Search Algorithm

A wavenumber calibration protocol is proposed that replaces polynomial fitting to relate the detector axis and the wavenumber shift. The physical model of the Raman spectrometer is used to derive a mathematical expression relating the detector plane to the wavenumber shift, in terms of the system parameters including the spectrograph focal length, the grating angle, and the laser wavelength; the model is general to both reflection and transmission gratings. A fast search algorithm detects the set of parameters that best explains the position of spectral lines recorded on the detector for a known reference standard. Using three different reference standards, four different systems, and hundreds of spectra recorded with a rotating grating, we demonstrate the superior accuracy of the technique, especially in bands outside of the outermost reference peaks when compared with polynomial fitting. We also provide a thorough review of wavenumber calibration for Raman spectroscopy and we introduce several new evaluation metrics to this field borrowed from chemometrics, including leave-one-out and leave-half-out cross-validation.

Monitoring Metabolic Changes in Response to Chemotherapies in Cancer with Raman Spectroscopy and Metabolomics

Resistance to clinical therapies remains a major barrier in cancer management. There is a critical need for rapid and highly sensitive diagnostic tools that enable early prediction of treatment response to allow accurate clinical decisions. Here, Raman spectroscopy was employed to monitor changes in key metabolites as early predictors of response in KRAS-mutant colorectal cancer (CRC) cells, HCT116, treated with chemotherapies. We show at the single cell level that HCT116 is resistant to cetuximab (CTX), the first-line treatment in CRC, but this resistance can be overcome with pre-sensitization of cells with oxaliplatin (OX). In combination treatment of CTX + OX, sequential delivery of OX followed by CTX rather than simultaneous administration of drugs was observed to be critical for effective therapy. Our results demonstrated that metabolic changes are well aligned to cellular mechanical changes where Young's modulus decreased after effective treatment, indicating that both changes in mechanical properties and metabolism in cells are likely responsible for cancer proliferation. Raman findings were verified with mass spectrometry (MS) metabolomics, and both platforms showed changes in lipids, nucleic acids, and amino acids as predictors of resistance/response. Finally, key metabolic pathways enriched were identified when cells are resistant to CTX but downregulated with effective treatment. This study highlights that drug-induced metabolic changes both at the single cell level (Raman) and ensemble level (MS) have the potential to identify mechanisms of response to clinical cancer therapies.

Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification

Simple Summary

We demonstrate an automated Raman cytology system designed for high-throughput and reproducibility. The system uses a Raman spectroscopy system integrated into a conventional microscope, all controlled electronically via and open source software, Micro-Manager. The system can automatically identify and probe epithelial cell nuclei for Raman spectroscopy. 6426 HT1197 (high-grade bladder cancer) cell spectra, and 7499 RT112 (low-grade bladdercancer) cell spectra were recorded. The data was subsequently culled and processed for denoising and artifact removal. We demonstrate, using multivariate statistical analysis, that the cells can be distinguished, using a variety of approaches with accuracy, sensitivity and specificity in excess of 95%.

Abstract

Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.

A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and non-linear optics in cancer research as a gateway to tumor cell identity

Introduction: Currently, intensely developing of linear and non-linear optical methods for cancer detection provides a valuable tool to improve sensitivity and specificity. One of the main reasons for insufficient progress in cancer diagnostics is related to the fact that most cancer types are not only heterogeneous in their genetic composition but also reside in varying microenvironments and interact with different cell types. Until now, no technology has been fully proven for effective detecting of invasive cancer, which infiltrating the extracellular matrix.Areas covered: This review investigates the current status of Raman spectroscopy and Raman imaging for brain and breast cancer diagnostics. Moreover, the review provides a comprehensive overview of the applicability of atomic force microscopy (AFM), linear and non-linear optics in cancer research as a gateway to tumor cell identity.Expert commentary: A combination of linear and non-linear optics, particularly Raman-driven methods, has many additional advantages to identify alterations in cancer cells that are crucial for their proliferation and that distinguish them from normal cells.