Virtual spectral histopathology of colon cancer - biomedical applications of Raman spectroscopy and imaging

Rapid, label-free detection of colorectal cancer with an aberration-free line scanning confocal Raman imager

A self-developed aberration-free line scanning confocal Raman imager (AFLSCRI) with a spectral resolution of 0.12 nm and a spatial resolution of 2 μm is utilized to diagnose colorectal cancer. The tissues were categorized into four subgroups (typical tissue, lipid-rich tissue, fat-rich tissue, and collagen-rich tissue) and were successfully distinguished with our Raman imaging results. Compared to traditional point-scanning Raman spectroscopy, this imager offers a much faster speed with high spectral resolution while maintaining a similar spatial resolution. The Raman spectroscopy results of the same sample of colorectal cancer remain stable and unaffected even measured after six months. The molecular composition of the tissues was analyzed, and potential biomarkers such as carotenoids and protein structures were identified for four different types of colorectal tissues. When combined with machine learning algorithms, an accuracy of 92.8% was achieved in identifying 14 pairs of normal/cancer samples. These results highlight the great potential of the AFLSCRI in label-free, rapid, and non-invasive medical diagnosis.

Raman spectroscopy for classification of neoplastic and non-neoplastic CAM colon tumors

This paper demonstrates the potential of Raman spectroscopy for differentiating neoplastic from non-neoplastic colon tumors, obtained with the CAM (chicken chorioallantoic membrane) model. For the CAM model two human cell lines were used to generate two types of tumors, the RKO cell line for neoplastic colon tumors and the NCM460 cell line for non-neoplastic colon tumors. The Raman spectra were acquired with a 785 nm excitation laser. The measured Raman spectra from the CAM samples (n = 14) were processed with several methods for baseline correction and to remove artifacts. The corrected spectra were analyzed with PCA (principal component analysis). Additionally, machine learning based algorithms were used to create a model capable of classifying neoplastic and non-neoplastic tumors. The principal component scores showed a clear differentiation between neoplastic and non-neoplastic colon tumors. The classification model had an accuracy of 93 %. Thus, a complete methodology to process and analyze Raman spectra was validated, using a rapid, accessible, and well-established tumor model that mimics the human tumor pathology with minor ethical concerns.

Comparative study of Raman spectroscopy techniques in blood plasma-based clinical diagnostics: A demonstration on Alzheimer's disease

Nowadays, there are still many diseases with limited or no reliable methods of early diagnosis. A popular approach in clinical diagnostic research is Raman spectroscopy, as a relatively simple, cost-effective, and high-throughput method for searching for disease-specific alterations in the composition of blood plasma. However, the high variability of the experimental designs, targeted diseases, or statistical processing in the individual studies makes it challenging to compare and compile the results to critically assess the applicability of Raman spectroscopy in real clinical practice. This study aimed to compare data from a single series of blood plasma samples of patients with Alzheimer's disease and non-demented elderly controls obtained by four different techniques/experimental setups - Raman spectroscopy with excitation at 532 and 785 nm, Raman optical activity, and surface-enhanced Raman scattering spectroscopy. The obtained results showed that the spectra from each Raman spectroscopy technique contain different information about biomolecules of blood plasma or their conformation and may, therefore, offer diverse points of view on underlying biochemical processes of the disease. The classification models based on the datasets generated by the three non-chiroptical variants of Raman spectroscopy exhibited comparable diagnostic performance, all reaching an accuracy close to or equal to 80%. Raman optical activity achieved only 60% classification accuracy, suggesting its limited applicability in the specific case of Alzheimer's disease diagnostics. The described differences in the outputs of the four utilized techniques/setups of Raman spectroscopy imply that their choice may crucially affect the acquired results and thus should be approached carefully concerning the specific purpose.

A new modality for cholesterol impact tracking in colon cancer development - Raman imaging, fluorescence and AFM studies combined with chemometric analysis

Colorectal cancer (CRC) is the third most common cancer worldwide. Obesity, alcohol consumption, smoking, high consumption of red or processed meat and a diet with low fibre, fruit, and vegetable intake increase CRC risk. Despite advances in surgery (the basic treatment for recovery), chemotherapy, and radiotherapy, CRC remains the second leading cause of cancer-related deaths in the world. Therefore the social importance of this problem stimulates research aimed at developing new tools for rapid CRC diagnosis and analysis of CRC risk factors. Considering the association between the cholesterol level and CRC, we hypothesize that cholesterol spectroscopic and AFM (atomic force microscopy) studies combined with chemometric analysis can be new, powerful tools used to visualize the cholesterol distribution, estimate cholesterol content and determine its influence on the biochemical and nanomechanical properties of colon cells. Our paper presents the analysis of human colon tissues: normal and cancer and human colon single cells normal CCD18-Co and cancer CaCo-2 in the physiological state and CaCo-2 upon mevastatin supplementation. Based on vibrational features we have shown that Raman spectroscopy and imaging allow cholesterol content in human colon tissues and human colon single cells of both types to be tracked and allow the effectiveness of mevastatin in the mevalonate pathway modulation and disruption of the cholesterol level to be proven. All observations have been confirmed by chemometric analysis including principal component analysis (PCA) and partial least squares discriminant analysis (PLSDA). The positive impact of statins on cholesterol content has also been studied by using fluorescence microscopy and atomic force microscopy (AFM). A significant increase in Young modulus as a mechanomarker for CaCo-2 human cancer colon cells upon mevastatin supplementation compared to CCD18-Co human normal colon cells was observed. This paper is one of the first reports about the use of Raman spectroscopic techniques in cholesterol investigations and the first one about cholesterol investigation using Raman spectroscopy (RS) on human cells ex vivo in the context of colon cancer development.

Biomineralogical signatures of breast microcalcifications

Microcalcifications, primarily biogenic apatite, occur in cancerous and benign breast pathologies and are key mammographic indicators. Outside the clinic, numerous microcalcification compositional metrics (e.g., carbonate and metal content) are linked to malignancy, yet microcalcification formation is dependent on microenvironmental conditions, which are notoriously heterogeneous in breast cancer. We interrogate multiscale heterogeneity in 93 calcifications from 21 breast cancer patients using an omics-inspired approach: For each microcalcification, we define a "biomineralogical signature" combining metrics derived from Raman microscopy and energy-dispersive spectroscopy. We observe that (i) calcifications cluster into physiologically relevant groups reflecting tissue type and local malignancy; (ii) carbonate content exhibits substantial intratumor heterogeneity; (iii) trace metals including zinc, iron, and aluminum are enhanced in malignant-localized calcifications; and (iv) the lipid-to-protein ratio within calcifications is lower in patients with poor composite outcome, suggesting that there is potential clinical value in expanding research on calcification diagnostic metrics to include "mineral-entrapped" organic matrix.

Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database

Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning techniques can be used to reduce the workload of experts and to produce quick solutions. The nuclei found in the histopathology dataset are an essential parameter in disease detection. The nucleus segmentation was performed using the colorectal histology MNIST dataset for nucleus detection in this study. The graph theory, PSO, watershed, and random walker algorithms were used for the segmentation process. In addition, we present the 10-class MedCLNet visual dataset consisting of the NCT-CRC-HE-100 K dataset, LC25000 dataset, and GlaS dataset that can be used in transfer learning studies from deep learning techniques. The study proposes a transfer learning technique using the MedCLNet database. Deep neural networks pre-trained with the proposed transfer learning method were used in the classification with the colorectal histology MNIST dataset in the experimental process. DenseNet201, DenseNet169, InceptionResNetV2, InceptionV3, ResNet152V2, ResNet101V2, and Xception deep learning algorithms were used in transfer learning and the classification studies. The proposed approach was analyzed before and after transfer learning with different methods (DenseNet169 + SVM, DenseNet169 + GRU). In the performance measurement, using the colorectal histology MNIST dataset, 94.29% accuracy was obtained in the DenseNet169 model, which was initiated with random weights in the multi-classification study, and 95.00% accuracy after transfer learning was applied. In comparison with the results obtained from empirical studies, it was demonstrated that the proposed method produced satisfactory outcomes. The application is expected to provide a secondary evaluation for physicians in colon cancer detection and the segmentation.

Molecular Planarization of Raman Probes to Avoid Background Interference for High-Precision Intraoperative Imaging of Tumor Micrometastases and Lymph Nodes

The intraoperative imaging applications of a large number of Raman probes are hampered by the overlap of their signals with the background Raman signals generated by biological tissues. Here, we describe a molecular planarization strategy for adjusting the Raman shift of these Raman probes to avoid interference. Using this strategy, we modify the backbone of thiophene polymer-poly(3-hexylthiophene) (P3HT), and obtain the adjacent thiophene units planarized polycyclopenta[2,1-b;3,4-b']dithiophene (PCPDT). Compared with P3HT whose signal is disturbed by the Raman signal of lipids in tissues, PCPDT exhibits a 60 cm^-1 blueshift in its characteristic signal. Therefore, the PCPDT probe successfully avoids the signal of lipids, and achieves intraoperative imaging of lymph nodes and tumor micrometastasis as small as 0.30 × 0.36 mm. In summary, our study presents a concise molecular planarization strategy for regulating the signal shift of Raman probes, and brings a tunable thiophene polymer probe for high-precision intraoperative Raman imaging.

Biochemistry and Nanomechanical Properties of Human Colon Cells upon Simvastatin, Lovastatin, and Mevastatin Supplementations: Raman Imaging and AFM Studies

One of the most important areas of medical science is oncology, which is responsible for both the diagnostics and treatment of cancer diseases. Over the years, there has been an intensive development of cancer diagnostics and treatment. This paper shows the comparison of normal (CCD-18Co) and cancerous (CaCo-2) cell lines of the human gastrointestinal tract on the basis of nanomechanical and biochemical properties to obtain information on cancer biomarkers useful in oncological diagnostics. The research techniques used were Raman spectroscopy and imaging and atomic force microscopy (AFM). In addition, the studies also included the effect of the statin compounds—mevastatin, lovastatin, and simvastatin—and their influence on biochemical and nanomechanical changes of cell properties using Raman imaging and AFM techniques. The cytotoxicity of statins was determined using XTT tests.

Phenotype Identification of HeLa Cells Knockout CDK6 Gene Based on Label-Free Raman Imaging

Identifying cell phenotypes is essential for understanding the function of biological macromolecules and molecular biology. We developed a noninvasive, label-free, single-cell Raman imaging analysis platform to distinguish between the cell phenotypes of the HeLa cell wild type (WT) and cyclin-dependent kinase 6 (CDK6) gene knockout (KO) type. Via large-scale Raman spectral and imaging analysis, two phenotypes of the HeLa cells were distinguished by their intrinsic biochemical profiles. A significant difference was found between the two cell lines: large lipid droplets formed in the knockout HeLa cells but were not observed in the WT cells, which was confirmed by Oil Red O staining. The band ratio of the Raman spectrum of saturated/unsaturated fatty acids was identified as the Raman spectral marker for HeLa cell WT or gene knockout type differentiation. The interaction between organelles involved in lipid metabolism was revealed by Raman imaging and Lorentz fitting, where the distribution intensity of the mitochondria and the endoplasmic reticulum membrane decreased. At the same time, lysosomes increased after the CDK6 gene knockout. The parameters obtained from Raman spectroscopy are based on hierarchical cluster analysis and one-way ANOVA, enabling highly accurate cell classification.

CDK6 increases glycolysis and suppresses autophagy by mTORC1-HK2 pathway activation in cervical cancer cells

Cervical carcinoma is a leading malignant tumor among women worldwide, characterized by the dysregulation of cell cycle. Cyclin-dependent kinase 6 (CDK6) plays important roles in the cell cycle progression, cell differentiation, and tumorigenesis. However, the role of CDK6 in cervical cancer remains controversial. Here, we found that loss of CDK6 in cervical adenocarcinoma HeLa cell line inhibited cell proliferation but induced apoptosis as well as autophagy, accompanied by attenuated expression of mammalian target of rapamycin complex 1 (mTORC1) and hexokinase 2 (HK2), reduced glycolysis, and production of protein, nucleotide, and lipid. Similarly, we showed that CDK6 knockout inhibited the survival of CDK6-high CaSki but not CDK6-low SiHa cervical cancer cells by regulation of glycolysis and autophagy process. Collectively, our studies indicate that CDK6 is a critical regulator of human cervical cancer cells, especially with high CDK6 level, through its ability to regulate cellular apoptosis and metabolism. Thus, inhibition of CDK6 kinase activity could be a powerful therapeutic avenue used to treat cervical cancers.

Mevastatin in colon cancer by spectroscopic and microscopic methods - Raman imaging and AFM studies

One of the most important areas of medical science is oncology, which is responsible for both the diagnostics and treatment of cancer diseases. Simultaneously one of the main challenges of oncology is the development of modern drugs effective in the fight against cancer. Statins are a group of biologically active compounds with the activity of 3-hydroxy-3-methyl glutaryl-CoA reductase inhibitors, an enzyme catalyzing the reduction of 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) to mevalonic acid. By acting on this enzyme, statins inhibit the endogenous cholesterol synthesis which in turn causes the reduction of its systemic concentrations. However, in vitro and in vivo studies confirm also the cytostatic and cytotoxic effects of statins against various types of cancer cells including colon cancer. In the presented studies the influence of mevastatin on cancerous colon cells CaCo-2 by Raman spectroscopy and imaging is discussed and compared with biochemistry characteristic for normal colon cells CCD-18Co. Based on vibrational features of colon cells: normal cells CCD-18Co, cancerous cells CaCo-2 and cancerous cells CaCo-2 treated by mevastatin in different concentrations and incubation times we have confirmed the influence of this statin on biochemistry composition of cancerous human colon cells. Moreover, the spectroscopic results for colon normal cells and cancerous cells based on data typical for nucleic acids, proteins, lipids have been compared. The cytotoxisity of mevastatin was determined by using XTT tests.

Non-invasive diagnosis of colorectal cancer by Raman spectroscopy: Recent developments in liquid biopsy and endoscopy approaches

Colorectal cancer (CRC) is the third most common cancer diagnosed globally and is also one of the leading causes of cancer deaths in both men and women. The progression of CRC is slow and is often contained in colon but the risk increases with age. Based on the high certainty that the net benefit of screening in an age group is substantial, screening for CRC is recommended beginning at the age of 50. Currently, most of the incidence is concentrated in developed countries but the rate is increasing rapidly in developing geographies. Detecting CRC at an early stage is critical to reduce morbidity and mortality. Colonoscopy is the most preferred screening method but not very widely implemented due to practical considerations such as cost involved, lack of personnel and facility. To address these concerns, Raman spectroscopy (RS) has been suggested as a viable alternative due to its potential as a rapid non-invasive diagnostic tool. Recently, several studies have been reported but many variations of RS applications in CRC exists and are not well understood by non-specialists. This review focuses particularly on developments of Raman based liquid biopsy and endoscopic studies in order to throw light on each of their significance and limitations. Necessary developments in the future to translate RS into a clinical tool for screening and diagnosis of CRC are also briefly presented.

Synergy Effect of Combined Near and Mid-Infrared Fibre Spectroscopy for Diagnostics of Abdominal Cancer

Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers, with the highest contribution from colon and rectal cancers (12% of the human population), followed by stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate resection margins to avoid local recurrences due to minimal residual disease. The presence of functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue samples in combination with chemometric models constitutes a promising approach for more efficient and precise tumour margin identification. Additionally, this technique provides a real-time tumour identification approach not only for intraoperative application but also during endoscopic diagnosis of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has advantages compared to individual methods for the clinical implementation of this technique as a diagnostic tool.