Organ-specific isogenic metastatic breast cancer cell lines exhibit distinct Raman spectral signatures and metabolomes

MALDI-MSI of lipids in a model of breast cancer brain metastasis provides a surrogate measure of ischemia/hypoxia

Breast cancer brain metastasis (BCBM) has an incidence of 10-30%. It is incurable and the biological mechanisms that promote its progression remain largely undefined. Consequently, to gain insights into BCBM processes, we have developed a spontaneous mouse model of BCBM and in this study found a 20% penetrance of macro-metastatic brain lesion formation. Considering that lipid metabolism is indispensable to metastatic progression, our goal was the mapping of lipid distributions throughout the metastatic regions of the brain. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) of lipids revealed that, relative to surrounding brain tissue, seven long-chain (13-21 carbons long) fatty acylcarnitines, as well as two phosphatidylcholines, two phosphatidylinositols two diacylglycerols, a long-chain phosphatidylethanolamine, and a long-chain sphingomyelin were highly concentrated in the metastatic brain lesion In broad terms, lipids known to be enriched in brain tissues, such as very long-chain (≥ 22 carbons in length) polyunsaturated fatty acid of phosphatidylcholines, phosphatidylethanolamine, sphingomyelins, sulfatides, phosphatidylinositol phosphates, and galactosylceramides, were not found or only found in trace amounts in the metastatic lesion and instead consistently detected in surrounding brain tissues. The data, from this mouse model, highlights an accumulation of fatty acylcarnitines as possible biological makers of a chaotic inefficient vasculature within the metastasis, resulting in relatively inadequate blood flow and disruption of fatty acid β-oxidation due to ischemia/hypoxia.

Isogenic Cell Lines Derived from Specific Organ Metastases Exhibit Divergent Cytogenomic Aberrations

Aneuploidy, a deviation in chromosome numbers from the normal diploid set, is now recognized as a fundamental characteristic of all cancer types and is found in 70-90% of all solid tumors. The majority of aneuploidies are generated by chromosomal instability (CIN). CIN/aneuploidy is an independent prognostic marker of cancer survival and is a cause of drug resistance. Hence, ongoing research has been directed towards the development of therapeutics aimed at targeting CIN/aneuploidy. However, there are relatively limited reports on the evolution of CIN/aneuploidies within or across metastatic lesions. In this work, we built on our previous studies using a human xenograft model system of metastatic disease in mice that is based on isogenic cell lines derived from the primary tumor and specific metastatic organs (brain, liver, lung, and spine). As such, these studies were aimed at exploring distinctions and commonalities between the karyotypes; biological processes that have been implicated in CIN; single-nucleotide polymorphisms (SNPs); losses, gains, and amplifications of chromosomal regions; and gene mutation variants across these cell lines. Substantial amounts of inter- and intra-heterogeneity were found across karyotypes, along with distinctions between SNP frequencies across each chromosome of each metastatic cell line relative the primary tumor cell line. There were disconnects between chromosomal gains or amplifications and protein levels of the genes in those regions. However, commonalities across all cell lines provide opportunities to select biological processes as druggable targets that could have efficacy against the primary tumor, as well as metastases.

Raman spectroscopy reveals phenotype switches in breast cancer metastasis

The accurate analytical characterization of metastatic phenotype at primary tumor diagnosis and its evolution with time are critical for controlling metastatic progression of cancer. Here, we report a label-free optical strategy using Raman spectroscopy and machine learning to identify distinct metastatic phenotypes observed in tumors formed by isogenic murine breast cancer cell lines of progressively increasing metastatic propensities. Methods: We employed the 4T1 isogenic panel of murine breast cancer cells to grow tumors of varying metastatic potential and acquired label-free spectra using a fiber probe-based portable Raman spectroscopy system. We used MCR-ALS and random forests classifiers to identify putative spectral markers and predict metastatic phenotype of tumors based on their optical spectra. We also used tumors derived from 4T1 cells silenced for the expression of TWIST, FOXC2 and CXCR3 genes to assess their metastatic phenotype based on their Raman spectra. Results: The MCR-ALS spectral decomposition showed consistent differences in the contribution of components that resembled collagen and lipids between the non-metastatic 67NR tumors and the metastatic tumors formed by FARN, 4T07, and 4T1 cells. Our Raman spectra-based random forest analysis provided evidence that machine learning models built on spectral data can allow the accurate identification of metastatic phenotype of independent test tumors. By silencing genes critical for metastasis in highly metastatic cell lines, we showed that the random forest classifiers provided predictions consistent with the observed phenotypic switch of the resultant tumors towards lower metastatic potential. Furthermore, the spectral assessment of lipid and collagen content of these tumors was consistent with the observed phenotypic switch. Conclusion: Overall, our findings indicate that Raman spectroscopy may offer a novel strategy to evaluate metastatic risk during primary tumor biopsies in clinical patients.

Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy

OBJECTIVES

Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple-negative) from normal tissue in surgical specimens.

MATERIALS AND METHODS

Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression.

RESULTS

PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple-negative and triple-negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%.

CONCLUSION

PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.

Label-Free Vibrational and Quantitative Phase Microscopy Reveals Remarkable Pathogen-Induced Morphomolecular Divergence in Tumor-Derived Cells

Delineating the molecular and morphological changes that cancer cells undergo in response to extracellular stimuli is crucial for identifying factors that promote tumor progression. Label-free optical imaging offers a potentially promising route for retrieving such single-cell information by generating detailed visualization of the morphology and determining alterations in biomolecular composition. The potential of such nonperturbative morphomolecular microscopy for analyzing microbiota-cancer cell interactions has been surprisingly underappreciated, despite the growing evidence of the critical role of dysbiosis in malignant transformations. Here, using a model system of breast cancer cells, we show that label-free Raman microspectroscopy and quantitative phase microscopy can detect biomolecular and morphological changes in single cells exposed to Bacteroides fragilis toxin (BFT), a toxin secreted by enterotoxigenicB. fragilis. Remarkably, using machine learning to elucidate subtle, but consistent, cellular differences, we found that the morphomolecular differences between BFT-exposed and control breast cancer cells became more accentuated after in vivo passage, corroborating our findings that a short-term BFT exposure imparts a long-term effect on cancer cells and promotes a more invasive phenotype. Complementing more classical labeling techniques, our label-free platform offers a global detection approach with measurements representative of the overall cellular phenotype, paving the way for further investigations into the multifaceted interactions between the cancer cell and the microbiota.

Raman Spectroscopy and Its Modifications Applied to Biological and Medical Research

Nowadays, there is an interest in biomedical and nanobiotechnological studies, such as studies on carotenoids as antioxidants and studies on molecular markers for cardiovascular, endocrine, and oncological diseases. Moreover, interest in industrial production of microalgal biomass for biofuels and bioproducts has stimulated studies on microalgal physiology and mechanisms of synthesis and accumulation of valuable biomolecules in algal cells. Biomolecules such as neutral lipids and carotenoids are being actively explored by the biotechnology community. Raman spectroscopy (RS) has become an important tool for researchers to understand biological processes at the cellular level in medicine and biotechnology. This review provides a brief analysis of existing studies on the application of RS for investigation of biological, medical, analytical, photosynthetic, and algal research, particularly to understand how the technique can be used for lipids, carotenoids, and cellular research. First, the review article shows the main applications of the modified Raman spectroscopy in medicine and biotechnology. Research works in the field of medicine and biotechnology are analysed in terms of showing the common connections of some studies as caretenoids and lipids. Second, this article summarises some of the recent advances in Raman microspectroscopy applications in areas related to microalgal detection. Strategies based on Raman spectroscopy provide potential for biochemical-composition analysis and imaging of living microalgal cells, in situ and in vivo. Finally, current approaches used in the papers presented show the advantages, perspectives, and other essential specifics of the method applied to plants and other species/objects.

Raman Spectroscopy and Machine Learning Reveals Early Tumor Microenvironmental Changes Induced by Immunotherapy

Cancer immunotherapy provides durable clinical benefit in only a small fraction of patients, and identifying these patients is difficult due to a lack of reliable biomarkers for prediction and evaluation of treatment response. Here, we demonstrate the first application of label-free Raman spectroscopy for elucidating biomolecular changes induced by anti-CTLA4 and anti-PD-L1 immune checkpoint inhibitors (ICI) in the tumor microenvironment (TME) of colorectal tumor xenografts. Multivariate curve resolution-alternating least squares (MCR-ALS) decomposition of Raman spectral datasets revealed early changes in lipid, nucleic acid, and collagen content following therapy. Support vector machine classifiers and random forests analysis provided excellent prediction accuracies for response to both ICIs and delineated spectral markers specific to each therapy, consistent with their differential mechanisms of action. Corroborated by proteomics analysis, our observation of biomolecular changes in the TME should catalyze detailed investigations for translating such markers and label-free Raman spectroscopy for clinical monitoring of immunotherapy response in cancer patients. SIGNIFICANCE: This study provides first-in-class evidence that optical spectroscopy allows sensitive detection of early changes in the biomolecular composition of tumors that predict response to immunotherapy with immune checkpoint inhibitors.

Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common malignancies that account for nearly one-third of all pediatric cancers. The current diagnostic assays are time-consuming, labor-intensive, and require expensive reagents. Here, we report a label-free approach featuring diffraction phase imaging and Raman microscopy that can retrieve both morphological and molecular attributes for label-free optical phenotyping of individual B cells. By investigating leukemia cell lines of early and late stages along with the healthy B cells, we show that phase images can capture subtle morphological differences among the healthy, early, and late stages of leukemic cells. By exploiting its biomolecular specificity, we demonstrate that Raman microscopy is capable of accurately identifying not only different stages of leukemia cells but also individual cell lines at each stage. Overall, our study provides a rationale for employing this hybrid modality to screen leukemia cells using the widefield QPI and using Raman microscopy for accurate differentiation of early and late-stage phenotypes. This contrast-free and rapid diagnostic tool exhibits great promise for clinical diagnosis and staging of leukemia in the near future.

Spectroscopy-Assisted Label-free Molecular Analysis of Live Cell Surface with Vertically Aligned Plasmonic Nanopillars

A generalized label-free platform for surface-selective molecular sensing in living cells can transform the ability to examine complex events in the cell membrane. While vertically aligned semiconductor and metal-semiconductor hybrid nanopillars have rapidly surfaced for stimulating and probing the intracellular environment, the potential of such constructs for selectively interrogating the cell membrane is surprisingly underappreciated. In this work, a new platform, entitled nano-PROD (nano-pillar based Raman optical detection), enables molecular recording by probing fundamental vibrational modes of membrane constituents of cells adherent on a large-area silver-coated silicon nanopillar substrate fabricated using a precursor solution-based nanomanufacturing process. It is shown that the nano-PROD platform sustains live cells in near-physiological conditions, which can be directly profiled using surface-enhanced Raman spectroscopy due to the confined electromagnetic field enhancement. The experimental results highlight the utility of the platform in probing specific cell surface markers for accurately recognizing the phenotypically identical prostate cancer cells, differing only in prostate-specific membrane antigen expression. Due to its tunability, nano-PROD has the promise to be readily extendable to other applications that can leverage its unique combination of nanoscale topographic features and molecular sensing capabilities, from stain-free cytopathology inspection to understanding spatio-mechanical regulation in membrane receptor function.

Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential

Identification of the metastatic potential represents one of the most important tasks for molecular imaging of cancer. While molecular imaging of metastases has witnessed substantial progress as an area of clinical inquiry, determining precisely what differentiates the metastatic phenotype has proven to be more elusive. In this study, we utilize both the morphological and molecular information provided by 3D optical diffraction tomography and Raman spectroscopy, respectively, to propose a label-free route for optical phenotyping of cancer cells at single-cell resolution. By using an isogenic panel of cell lines derived from MDA-MB-231 breast cancer cells that vary in their metastatic potential, we show that 3D refractive index tomograms can capture subtle morphological differences among the parental, circulating tumor cells, and lung metastatic cells. By leveraging its molecular specificity, we demonstrate that coarse Raman microscopy is capable of rapidly mapping a sufficient number of cells for training a random forest classifier that can accurately predict the metastatic potential of cells at a single-cell level. We also perform multivariate curve resolution alternating least squares decomposition of the spectral dataset to demarcate spectra from cytoplasm and nucleus, and test the feasibility of identifying metastatic phenotypes using the spectra only from the cytoplasmic and nuclear regions. Overall, our study provides a rationale for employing coarse Raman mapping to substantially reduce measurement time thereby enabling the acquisition of reasonably large training datasets that hold the key for label-free single-cell analysis and, consequently, for differentiation of indolent from aggressive phenotypes.

Divergent organ-specific isogenic metastatic cell lines identified using multi-omics exhibit differential drug sensitivity

Background

Monitoring and treating metastatic progression remains a formidable task due, in part, to an inability to monitor specific differential molecular adaptations that allow the cancer to thrive within different tissue types. Hence, to develop optimal treatment strategies for metastatic disease, an important consideration is the divergence of the metastatic cancer growing in visceral organs from the primary tumor. We had previously reported the establishment of isogenic human metastatic breast cancer cell lines that are representative of the common metastatic sites observed in breast cancer patients.

Methods

Here we have used proteomic, RNAseq, and metabolomic analyses of these isogenic cell lines to systematically identify differences and commonalities in pathway networks and examine the effect on the sensitivity to breast cancer therapeutic agents.

Results

Proteomic analyses indicated that dissemination of cells from the primary tumor sites to visceral organs resulted in cell lines that adapted to growth at each new site by, in part, acquiring protein pathways characteristic of the organ of growth. RNAseq and metabolomics analyses further confirmed the divergences, which resulted in differential efficacies to commonly used FDA approved chemotherapeutic drugs. This model system has provided data that indicates that organ-specific growth of malignant lesions is a selective adaptation and growth process.

Conclusions

The insights provided by these analyses indicate that the rationale of targeted treatment of metastatic disease may benefit from a consideration that the biology of metastases has diverged from the primary tumor biology and using primary tumor traits as the basis for treatment may not be ideal to design treatment strategies.

Selective Detection of Nano-Sized Diagnostic Markers Using Au-ZnO Nanorod-Based Surface-Enhanced Raman Spectroscopy (SERS) in Ureteral Obstruction Models

Background

This study investigated the diagnosis of renal diseases using a biochip capable of detecting nano-sized biomarkers. Raman measurements from a kidney injury model were taken, and the feasibility of early diagnosis was assessed.

Materials and Methods

Rat models with mild and severe unilateral ureteral obstructions were created, with the injury to the kidney varying according to the tightness of the stricture. After generating the animal ureteral obstruction models, urine was collected from the kidney and bladder.

Results and Discussion

After confirming the presence of renal injury, urine drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods, allowing nano-sized biomarkers that diffused into the nanogaps to be selectively amplified. The Raman signals varied according to the severity of the renal damage, and these differences were statistically confirmed.

Conclusion

These results confirm that ureteral stricture causes kidney injury and that signals in the urine from the release of nano-biomarkers can be monitored using surface-enhanced Raman spectroscopy.

Diagnosis of a model of Duchenne muscular dystrophy in blood serum of mdx mice using Raman hyperspectroscopy

Duchenne muscular dystrophy (DMD) is the most common and severe form of muscular dystrophy and affects boys in infancy or early childhood. Current methods for diagnosing DMD are often laborious, expensive, invasive, and typically diagnose the disease late in its progression. In an effort to improve the accuracy and ease of diagnosis, this study focused on developing a novel method for diagnosing DMD which combines Raman hyperspectroscopic analysis of blood serum with advanced statistical analysis. Partial least squares discriminant analysis was applied to the spectral dataset acquired from blood serum of a mouse model of Duchenne muscular dystrophy (mdx) and control mice. Cross-validation showed 95.2% sensitivity and 94.6% specificity for identifying diseased spectra. These results were verified via external validation, which achieved 100% successful classification accuracy at the donor level. This proof-of-concept study presents Raman hyperspectroscopic analysis of blood serum as an easy, fast, non-expensive, and minimally invasive detection method for distinguishing control and mdx model mice, with a strong potential for clinical diagnosis of DMD.

Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer

Patient survival remains poor even after diagnosis in lung cancer cases, and the molecular events resulting from lung cancer progression remain unclear. Raman spectroscopy could be used to noninvasively and accurately reveal the biochemical properties of biological tissues on the basis of their pathological status. This study aimed at probing biomolecular changes in lung cancer, using Raman spectroscopy as a potential diagnostic tool. Herein, biochemical alterations were evident in the Raman spectra (region of 600-1800 cm^-1) in normal and cancerous lung tissues. The levels of saturated and unsaturated lipids and the protein-to-lipid, nucleic acid-to-lipid, and protein-to-nucleic acid ratios were significantly altered among malignant tissues compared to normal lung tissues. These biochemical alterations in tissues during neoplastic transformation have profound implications in not only the biochemical landscape of lung cancer progression but also cytopathological classification. Based on this spectroscopic approach, classification methods including k-nearest neighbour (kNN) and support vector machine (SVM) were successfully applied to cytopathologically diagnose lung cancer with an accuracy approaching 99%. The present results indicate that Raman spectroscopy is an excellent tool to biochemically interrogate and diagnose lung cancer.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

Label-Free Raman Spectroscopy Reveals Signatures of Radiation Resistance in the Tumor Microenvironment

Delay in the assessment of tumor response to radiotherapy continues to pose a major challenge to quality of life for patients with nonresponsive tumors. Here, we exploited label-free Raman spectroscopic mapping to elucidate radiation-induced biomolecular changes in tumors and uncovered latent microenvironmental differences between treatment-resistant and -sensitive tumors. We used isogenic radiation-resistant and -sensitive A549 human lung cancer cells and human head and neck squamous cell carcinoma (HNSCC) cell lines (UM-SCC-47 and UM-SCC-22B, respectively) to grow tumor xenografts in athymic nude mice and demonstrated the molecular specificity and quantitative nature of Raman spectroscopic tissue assessments. Raman spectra obtained from untreated and treated tumors were subjected to chemometric analysis using multivariate curve resolution-alternating least squares (MCR-ALS) and support vector machine (SVM) to quantify biomolecular differences in the tumor microenvironment. The Raman measurements revealed significant and reliable differences in lipid and collagen content postradiation in the tumor microenvironment, with consistently greater changes observed in the radiation-sensitive tumors. In addition to accurately evaluating tumor response to therapy, the combination of Raman spectral markers potentially offers a route to predicting response in untreated tumors prior to commencing treatment. Combined with its noninvasive nature, our findings provide a rationale for in vivo studies using Raman spectroscopy, with the ultimate goal of clinical translation for patient stratification and guiding adaptation of radiotherapy during the course of treatment. SIGNIFICANCE: These findings highlight the sensitivity of label-free Raman spectroscopy to changes induced by radiotherapy and indicate the potential to predict radiation resistance prior to commencing therapy.

Liquid Biopsies in Lung Cancer: Four Emerging Technologies and Potential Clinical Applications

Background: Liquid biopsies offer a promising alternative to tissue samples, providing non-invasive diagnostic approaches or serial monitoring of disease evolution. However, certain challenges remain, and the full potential of liquid biopsies has yet to be reached. Here we report several methodological approaches to interrogate liquid biopsies using circulating tumour cell (CTC) enumeration and characterisation, transcriptomics, Raman spectroscopy, and copy number instability (CNI) scores using blood samples of lung cancer (LC) patients. Methods: We choose LC; since it still is the most common cause of cancer-related mortality worldwide, and therefore there is a need for development of new non-invasive diagnostic/prognostic technologies. Changes in gene expression were assessed using RNA-seq, and in CTCs using ImageStream, an imaging flow-cytometer. CNI scores, from paired tissue/ctDNA were also explored. Raman spectroscopy was used to provide chemical fingerprints of plasma samples. Results: CTCs were detected in all LC patients (n = 10). We observed a significant increase in CTC levels in LC patients (n = 10) compared to controls (n = 21). A similar CNI was noted in the tissue and plasma of 2 patients, where higher CNI scores corresponded with poorer outcome. Significant changes in Raman spectra (carotenoid concentrations) were noted in LC patients (n = 20) compared to controls (n = 10). RNA-seq revealed differential expression of 21 genes between LC cases and controls in both LC tissue and blood samples. Conclusions: Liquid biopsies can potentially provide a more comprehensive picture of the disease compared to a single tissue biopsy. CTC enumeration is feasible and sensitive for LC patients. Molecular profiling of CTCs is also possible from total blood. CNI scores and Raman spectra require further investigation. Further work is being undertaken to explore these methods of detection in a larger LC cohort.

Raman-Enhanced Spectroscopy Distinguishes Anal Squamous Intraepithelial Lesions in Human Immunodeficiency Virus-Serodiscordant Couples

HIV-positive individuals are at increased risk for precancerous anal squamous intraepithelial lesions (SILs). Anal cytology and digital rectal examination are performed as screening tools, but extensive training and appropriate instruments are required to follow up on an abnormal anal cytology. Thus, novel approaches to SIL evaluation could improve better health care follow-up by efficient and timely diagnosis to offer treatment options. Recently, Raman-enhanced spectroscopy (RESpect) has emerged as a potential new tool for early identification of SIL. RESpect is a noninvasive, label-free, laser-based technique that identifies molecular composition of tissues and cells. HIV-serodiscordant couples had anal biopsies obtained during high-resolution anoscopy. RESpect was performed on the specimens. Principal component analysis of the data identified differences between normal and abnormal tissue as well as HIV-positive and HIV-negative individuals of each couple even with similar pathologies. RESpect has the potential to change the paradigm of anal pathology diagnosis and could provide insight into different pathways leading to SIL in HIV-serodiscordant couples.

Biochemical Changes in Irradiated Oral Mucosa: A FTIR Spectroscopic Study

Radiation exposure during the course of treatment in head and neck cancer (HNC) patients can induce both structural and biochemical anomalies. The present study is focused on utilizing infrared imaging for the identification of the minor biochemical alterations in the oral mucosa. Chemical maps generated using glycoprotein band indicates its differential distribution along the superficial layer. Spectra extracted from this layer suggests changes in overall nucleic acid and protein content in response to the therapeutic irradiation. Discrimination among control and irradiated groups have been achieved using principal component analysis. Findings of this preliminary study further support prospective utilization of Fourier Transform InfraRed (FTIR) imaging as a non-destructive, label-free tool for objective assessment of the oral mucosa in patient groups with or without radiation therapy.

In Vivo Imaging With Confirmation by Histopathology for Increased Rigor and Reproducibility in Translational Research: A Review of Examples, Options, and Resources

Preclinical noninvasive imaging can be an indispensable tool for studying animal models of disease. In vivo imaging to assess anatomical, functional, and molecular features requires verification by a comparison to the macroscopic and microscopic morphological features, since all noninvasive in vivo imaging methods have much lower resolution than standard histopathology. Comprehensive pathological evaluation of the animal model is underutilized; yet, many institutions have veterinary or human pathologists with necessary comparative pathology expertise. By performing a rigorous comparison to gross or histopathology for image interpretation, these trained individuals can assist scientists with the development of the animal model, experimental design, and evaluation of the in vivo imaging data. These imaging and pathology corroboration studies undoubtedly increase scientific rigor and reproducibility in descriptive and hypothesis-driven research. A review of case examples including ultrasound, nuclear, optical, and MRI is provided to illustrate how a wide range of imaging modalities data can be confirmed by gross or microscopic pathology. This image confirmation and authentication will improve characterization of the model and may contribute to decreasing costs and number of animals used and to more rapid translation from preclinical animal model to the clinic.

Differential diagnosis of otitis media with effusion using label-free Raman spectroscopy: A pilot study

Otitis media with effusion (OME) is an important and common condition affecting hearing in pediatric patients characterized by the presence of fluid in the middle ear space. The fluid is normally described as serous or mucoid based on differences in the fluid viscosity. The differential diagnosis of two OMEs, namely serous and mucoid is of significant clinical value because while the former is self-limiting, surgical procedure is commonly required for the latter. However, accurate identification of fluid types remains a challenging target unattainable with current clinical modalities due to unavailability of nonperturbative molecular tools. Here, we report an emerging spectroscopy approach featuring Raman scattering and multivariate analysis of spectral patterns to discern serous and mucoid fluids, obtained from pediatric patients undergoing myringotomy and tube placement, by providing information of differentially expressed molecules with high specificity. We demonstrate the feasibility of Raman spectroscopy-based approach to categorize middle ear effusion based on the characteristic spectral markers, notably of mucin, with classification accuracy of 91% and 93% for serous and mucoid, respectively. Our findings pave the way for further development of such a tool for fully noninvasive application that will lead to objective and accurate diagnosis thereby reducing unnecessary visits and surgical procedures.

Raman spectroscopy for accurately characterizing biomolecular changes in androgen-independent prostate cancer cells

Metastatic prostate cancer resistant to hormonal manipulation is considered the advanced stage of the disease and leads to most cancer-related mortality. With new research focusing on modulating cancer growth, it is essential to understand the biochemical changes in cells that can then be exploited for drug discovery and for improving responsiveness to treatment. Raman spectroscopy has a high chemical specificity and can be used to detect and quantify molecular changes at the cellular level. Collection of large data sets generated from biological samples can be employed to form discriminatory algorithms for detection of subtle and early changes in cancer cells. The present study describes Raman finger printing of normal and metastatic hormone-resistant prostate cancer cells including analyses with principal component analysis and linear discrimination. Amino acid-specific signals were identified, especially loss of arginine band. Androgen-resistant prostate cancer cells presented a higher content of phenylalanine, tyrosine, DNA and Amide III in comparison to PNT2 cells, which possessed greater amounts of L-arginine and had a B conformation of DNA. The analysis utilized in this study could reliably differentiate the 2 cell lines (sensitivity 95%; specificity 88%).

Challenges in application of Raman spectroscopy to biology and materials

Raman spectroscopy has become an essential tool for chemists, physicists, biologists and materials scientists. In this article, we present the challenges in unravelling the molecule-specific Raman spectral signatures of different biomolecules like proteins, nucleic acids, lipids and carbohydrates based on the review of our work and the current trends in these areas. We also show how Raman spectroscopy can be used to probe the secondary and tertiary structural changes occurring during thermal denaturation of protein and lysozyme as well as more complex biological systems like bacteria. Complex biological systems like tissues, cells, blood serum etc. are also made up of such biomolecules. Using mice liver and blood serum, it is shown that different tissues yield their unique signature Raman spectra, owing to a difference in the relative composition of the biomolecules. Additionally, recent progress in Raman spectroscopy for diagnosing a multitude of diseases ranging from cancer to infection is also presented. The second part of this article focuses on applications of Raman spectroscopy to materials. As a first example, Raman spectroscopy of a melt cast explosives formulation was carried out to monitor the changes in the peaks which indicates the potential of this technique for remote process monitoring. The second example presents various modern methods of Raman spectroscopy such as spatially offset Raman spectroscopy (SORS), reflection, transmission and universal multiple angle Raman spectroscopy (UMARS) to study layered materials. Studies on chemicals/layered materials hidden in non-metallic containers using the above variants are presented. Using suitable examples, it is shown how a specific excitation or collection geometry can yield different information about the location of materials. Additionally, it is shown that UMARS imaging can also be used as an effective tool to obtain layer specific information of materials located at depths beyond a few centimeters.

This paper reviews various facets of Raman spectroscopy. This encompasses biomolecule fingerprinting and conformational analysis, discrimination of healthy vs. diseased states, depth-specific information of materials and 3D Raman imaging.

Label-free Raman spectroscopy provides early determination and precise localization of breast cancer-colonized bone alterations

Raman spectral markers offer new routes to recognition of biomolecular alterations at sites of nascent and progressing metastatic cancer in bone.

Breast neoplasms frequently colonize bone and induce development of osteolytic bone lesions by disrupting the homeostasis of the bone microenvironment. This degenerative process can lead to bone pain and pathological bone fracture, a major cause of cancer morbidity and diminished quality of life, which is exacerbated by our limited ability to monitor early metastatic disease in bone and assess fracture risk. Spurred by its label-free, real-time nature and its exquisite molecular specificity, we employed spontaneous Raman spectroscopy to assess and quantify early metastasis driven biochemical alterations to bone composition. As early as two weeks after intracardiac inoculations of MDA-MB-435 breast cancer cells in NOD-SCID mice, Raman spectroscopic measurements in the femur and spine revealed consistent changes in carbonate substitution, overall mineralization as well as crystallinity increase in tumor-bearing bones when compared with their normal counterparts. Our observations reveal the possibility of early stage detection of biochemical changes in the tumor-bearing bones – significantly before morphological variations are captured through radiographic diagnosis. This study paves the way for a better molecular understanding of altered bone remodeling in such metastatic niches, and for further clinical studies with the goal of establishing a non-invasive tool for early metastasis detection and prediction of pathological fracture risk in breast cancer.

The combination of four analytical methods to explore skeletal muscle metabolomics: Better coverage of metabolic pathways or a marketing argument?

OBJECTIVES

Metabolomics is an emerging science based on diverse high throughput methods that are rapidly evolving to improve metabolic coverage of biological fluids and tissues. Technical progress has led researchers to combine several analytical methods without reporting the impact on metabolic coverage of such a strategy. The objective of our study was to develop and validate several analytical techniques (mass spectrometry coupled to gas or liquid chromatography and nuclear magnetic resonance) for the metabolomic analysis of small muscle samples and evaluate the impact of combining methods for more exhaustive metabolite covering.

DESIGN AND METHODS

We evaluated the muscle metabolome from the same pool of mouse muscle samples after 2 metabolite extraction protocols. Four analytical methods were used: targeted flow injection analysis coupled with mass spectrometry (FIA-MS/MS), gas chromatography coupled with mass spectrometry (GC-MS), liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS), and nuclear magnetic resonance (NMR) analysis. We evaluated the global variability of each compound i.e., analytical (from quality controls) and extraction variability (from muscle extracts). We determined the best extraction method and we reported the common and distinct metabolites identified based on the number and identity of the compounds detected with low analytical variability (variation coefficient<30%) for each method. Finally, we assessed the coverage of muscle metabolic pathways obtained.

RESULTS

Methanol/chloroform/water and water/methanol were the best extraction solvent for muscle metabolome analysis by NMR and MS, respectively. We identified 38 metabolites by nuclear magnetic resonance, 37 by FIA-MS/MS, 18 by GC-MS, and 80 by LC-HRMS. The combination led us to identify a total of 132 metabolites with low variability partitioned into 58 metabolic pathways, such as amino acid, nitrogen, purine, and pyrimidine metabolism, and the citric acid cycle. This combination also showed that the contribution of GC-MS was low when used in combination with other mass spectrometry methods and nuclear magnetic resonance to explore muscle samples.

CONCLUSION

This study reports the validation of several analytical methods, based on nuclear magnetic resonance and several mass spectrometry methods, to explore the muscle metabolome from a small amount of tissue, comparable to that obtained during a clinical trial. The combination of several techniques may be relevant for the exploration of muscle metabolism, with acceptable analytical variability and overlap between methods However, the difficult and time-consuming data pre-processing, processing, and statistical analysis steps do not justify systematically combining analytical methods.

Label-free characterization of ultra violet-radiation-induced changes in skin fibroblasts with Raman spectroscopy and quantitative phase microscopy

Minimizing morbidities and mortalities associated with skin cancers requires sustained research with the goal of obtaining fresh insights into disease onset and progression under specific stimuli, particularly the influence of ultraviolet rays. In the present study, label-free profiling of skin fibroblasts exposed to time-bound ultra-violet radiation has been performed using quantitative phase imaging and Raman spectroscopy. Statistically significant differences in quantifiable biophysical parameters, such as matter density and cell dry mass, were observed with phase imaging. Accurate estimation of changes in the biochemical constituents, notably nucleic acids and proteins, was demonstrated through a combination of Raman spectroscopy and multivariate analysis of spectral patterns. Overall, the findings of this study demonstrate the promise of these non-perturbative optical modalities in accurately identifying cellular phenotypes and responses to external stimuli by combining molecular and biophysical information.