Raman Spectroscopic Analysis Reveals Abnormal Fatty Acid Composition in Tumor Micro- and Macroenvironments in Human Breast and Rat Mammary Cancer

Label-Free Prediction of Tumor Metastatic Potential via Ramanome

Assessing metastatic potential is crucial for cancer treatment strategies. However, current methods are time-consuming, labor-intensive, and have limited sample accessibility. Therefore, this study aims to investigate the urgent need for rapid and accurate approaches by proposing a Ramanome-based metastasis index (RMI) using machine learning of single-cell Raman spectra to rapidly and accurately assess tumor cell metastatic potential. Validation with various cultured tumor cells and a mouse orthotopic model of pancreatic ductal adenocarcinoma show a Kendall rank correlation coefficient of 1 compared to Transwell experiments and histopathological assessments. Significantly, lipid-related Raman peaks are most influential in determining RMI. The lipidomic analysis confirmed strong correlations between metastatic potential and phosphatidylcholine, phosphatidylethanolamine, cholesteryl ester, ceramide, and bis(monoacylglycero)phosphate, crucial in cell membrane composition or signal transduction. Therefore, RMI is a valuable tool for predicting tumor metastatic potential and providing insights into metastasis mechanisms.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue

With the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised deep learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a multiple instance learning-based classifier for the identification of regions of interest in coarsely labeled images and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal of exploring unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.

In situ Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer

SIGNIFICANCE

As many as 60% of patients with early stage breast cancer undergo breast-conserving surgery. Of those, 20% to 35% need a second surgery because of incomplete resection of the lesions. A technology allowing in situ detection of cancer could reduce re-excision procedure rates and improve patient survival.

AIM

Raman spectroscopy was used to measure the spectral fingerprint of normal breast and cancer tissue ex-vivo. The aim was to build a machine learning model and to identify the biomolecular bands that allow one to detect invasive breast cancer.

APPROACH

The system was used to interrogate specimens from 20 patients undergoing lumpectomy, mastectomy, or breast reduction surgery. This resulted in 238 ex-vivo measurements spatially registered with standard histology classifying tissue as cancer, normal, or fat. A technique based on support vector machines led to the development of predictive models, and their performance was quantified using a receiver-operating-characteristic analysis.

RESULTS

Raman spectroscopy combined with machine learning detected normal breast from ductal or lobular invasive cancer with a sensitivity of 93% and a specificity of 95%. This was achieved using a model based on only two spectral bands, including the peaks associated with C-C stretching of proteins around 940    cm - 1 and the symmetric ring breathing at 1004    cm - 1 associated with phenylalanine.

CONCLUSIONS

Detection of cancer on the margins of surgically resected breast specimen is feasible with Raman spectroscopy.

Polarized Micro-Raman Spectroscopy and 2D Convolutional Neural Network Applied to Structural Analysis and Discrimination of Breast Cancer

Raman spectroscopy has been efficiently used to recognize breast cancer tissue by detecting the characteristic changes in tissue composition in cancerization. In addition to chemical composition, the change in bio-structure may be easily obtained via polarized micro-Raman spectroscopy, aiding in identifying the cancerization process and diagnosis. In this study, a polarized Raman spectral technique is employed to obtain rich structural features and, combined with deep learning technology, to achieve discrimination of breast cancer tissue. The results reconfirm that the orientation of collagen fibers changes from parallel to vertical during breast cancerization, and there are significant structural differences between cancerous and normal tissues, which is consistent with previous reports. Optical anisotropy of collagen fibers weakens in cancer tissue, which is closely related with the tumor's progression. To distinguish breast cancer tissue, a discrimination model is established based on a two-dimensional convolutional neural network (2D-CNN), where the input is a matrix containing the Raman spectra acquired at a set of linear polarization angles varying from 0° to 360°. As a result, an average discrimination accuracy of 96.01% for test samples is achieved, better than that of the KNN classifier and 1D-CNN that are based on non-polarized Raman spectra. This study implies that polarized Raman spectroscopy combined with 2D-CNN can effectively detect changes in the structure and components of tissues, innovatively improving the identification and automatic diagnosis of breast cancer with label-free probing and analysis.

NAT10: An RNA cytidine transferase regulates fatty acid metabolism in cancer cells

Background

N‐4 cytidine acetylation (ac4C) is an epitranscriptomics modification catalyzed by N‐acetyltransferase 10 (NAT10); important for cellular mRNA stability, rRNA biogenesis, cell proliferation and epithelial to mesenchymal transition (EMT). However, whether other crucial pathways are regulated by NAT10‐dependent ac4C modification in cancer cells remains unclear. Therefore, in this study, we explored the impact of NAT10 depletion in cancer cells using unbiased RNA‐seq.

Methods

High‐throughput sequencing of knockdown NAT10 in cancer cells was conducted to identify enriched pathways. Acetylated RNA immunoprecipitation‐seq (acRIP‐seq) and RIP‐PCR were used to map and determine ac4C levels of RNA. Exogenous palmitate uptake assay was conducted to assess NAT10 knockdown cancer cells using Oil Red O staining and lipid content analysis. Gas‐chromatography–tandem mass spectroscopy (GC/MS) was used to perform untargeted lipidomics.

Results

High‐throughput sequencing of NAT10 knockdown in cancer cells revealed fatty acid (FA) metabolism as the top enriched pathway through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis in differentially downregulated genes. FA metabolic genes such as ELOLV6, ACSL1, ACSL3, ACSL4, ACADSB and ACAT1 were shown to be stabilised via NAT10‐dependent ac4C RNA acetylation. Additionally, NAT10 depletion was shown to significantly reduce the levels of overall lipid content, triglycerides and total cholesterol. Further, NAT10 depletion in palmitate‐loaded cancer cells showed decrease in ac4C levels across the RNA transcripts of FA metabolic genes. In untargeted lipidomics, 496 out of 2 279 lipids were statistically significant in NAT10 depleted cancer cells, of which pathways associated with FA metabolism are the most enriched.

Conclusions

Conclusively, our results provide novel insights into the impact of NAT10‐mediated ac4C modification as a crucial regulatory factor during FA metabolism and showed the benefit of targeting NAT10 for cancer treatment.

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.

Studying the pathological and biochemical features in breast cancer progression by confocal Raman microspectral imaging of excised tissue samples

Confocal Raman microspectral imaging (CRMI) has been used to detect the spectra-pathological features of ductal carcinoma in situ (DCIS) and lobular hyperplasia (LH) compared with the heathy (H) breast tissue. A total of 15-20 spectra were measured from healthy tissue, LH tissue, and DCIS tissue. One-way ANOVA and Tukey's honest significant difference (HSD) post hoc multiple tests were used to evaluate the peak intensity variations in all three tissue types. Besides that, linear discrimination analysis (LDA) algorithm was adopted in combination with principal component analysis (PCA) to classify the spectral features from tissues at different stages along the continuum to breast cancer. Moreover, by using the point-by-point scanning methodology, spectral datasets were obtained and reconstructed for further pathologic visualization by multivariate imaging methods, including K-mean clustering analysis (KCA) and PCA. Univariate imaging of individual Raman bands was also used to describe the differences in the distribution of specific molecular components in the scanning area. After a detailed spectral feature analysis from 800 to 1800 cm^-1 and 2800 to 3000 cm^-1 for all the three tissue types, the histopathological features were visualized based on the content and structural variations of lipids, proteins, phenylalanine, carotenoids and collagen, as well as the calcification phenomena. The results obtained not only allowed a detailed Raman spectroscopy-based understanding of the malignant transformation process of breast cancer, but also provided a solid spectral data support for developing Raman based breast cancer clinical diagnostic techniques.

Iron-binding cellular profile of transferrin using label-free Raman hyperspectral imaging and singular value decomposition (SVD)

Serum transferrin (Tf) is the essential iron transport protein in the body. Transferrin is responsible for the sequestration of free iron in serum and the delivery of iron throughout the body and into cells, where iron is released inside a mildly acidified endosome. Altered iron distributions are associated with diseases such as iron-overload, cancer, and cardiovascular disease. The presence of free iron is linked to deleterious redox reactions, inside and outside cells and organelles. As Tf iron release is pH dependent, any changes in intraorganelle and extracellular pH, often associated with disease progression, could inhibit normal iron delivery or accelerate iron release in the wrong compartment. However, imaging approaches to monitor changes in the iron-bound state of Tf are lacking. Recently, Raman spectroscopy has been shown to measure iron-bound forms of Tf in solution, intact cells and tissue samples. Here, a biochemical Raman assay has been developed to identify iron-release from Tf following modification of chemical environment. Quantitative singular value decomposition (SVD) method has been applied to discriminate between iron-bound Tf samples during endocytic trafficking in intact cancer cells subjected to Raman hyperspectral confocal imaging. We demonstrate the strength of the SVD method to monitor pH-induced Tf iron-release using Raman hyperspectral imaging, providing the redox biology field with a novel tool that facilitates subcellular investigation of the iron-binding profile of transferrin in various disease models.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Identification of Molecular Basis for Objective Discrimination of Breast Cancer Cells (MCF-7) from Normal Human Mammary Epithelial Cells by Raman Microspectroscopy and Multivariate Curve Resolution Analysis

Raman spectroscopy (RS), a non-invasive and label-free method, has been suggested to improve accuracy of cytological and even histopathological diagnosis. To our knowledge, this novel technique tends to be employed without concrete knowledge of molecular changes in cells. Therefore, identification of Raman spectral markers for objective diagnosis is necessary for universal adoption of RS. As a model study, we investigated human mammary epithelial cells (HMEpC) and breast cancer cells (MCF-7) by RS and employed various multivariate analyses (MA) including principal components analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) to estimate diagnostic accuracy. Furthermore, to elucidate the underlying molecular changes in cancer cells, we utilized multivariate curve resolution analysis–alternating least squares (MCR-ALS) with non-negative constraints to extract physically meaningful spectra from complex cellular data. Unsupervised PCA and supervised MA, such as LDA and SVM, classified HMEpC and MCF-7 fairly well with high accuracy but without revealing molecular basis. Employing MCR-ALS analysis we identified five pure biomolecular spectra comprising DNA, proteins and three independent unsaturated lipid components. Relative abundance of lipid 1 seems to be strictly regulated between the two groups of cells and could be the basis for excellent discrimination by chemometrics-assisted RS. It was unambiguously assigned to linoleate rich glyceride and therefore serves as a Raman spectral marker for reliable diagnosis. This study successfully identified Raman spectral markers and demonstrated the potential of RS to become an excellent cytodiagnostic tool that can both accurately and objectively discriminates breast cancer from normal cells.

Falcarindiol Purified From Carrots Leads to Elevated Levels of Lipid Droplets and Upregulation of Peroxisome Proliferator-Activated Receptor-γ Gene Expression in Cellular Models

Falcarindiol (FaDOH) is a cytotoxic and anti-inflammatory polyacetylenic oxylipin found in food plants of the carrot family (Apiaceae). FaDOH has been shown to activate PPARγ and to increase the expression of the cholesterol transporter ABCA1 in cells, both of which play an important role in lipid metabolism. Thus, a common mechanism of action of the anticancer and antidiabetic properties of FaDOH may be due to a possible effect on lipid metabolism. In this study, the effect of sub-toxic concentration (5 μM) of FaDOH inside human mesenchymal stem cells (hMSCs) was studied using white light microscopy and Raman imaging. Our results show that FaDOH increases lipid content in the hMSCs cells as well as the number of lipid droplets (LDs) and that this can be explained by increased expression of PPARγ2 as shown in human colon adenocarcinoma cells. Activation of PPARγ can lead to increased expression of ABCA1. We demonstrate that ABCA1 is upregulated in colorectal neoplastic rat tissue, which indicates a possible role of this transporter in the redistribution of lipids and increased formation of LDs in cancer cells that may lead to endoplasmic reticulum stress and cancer cell death.

Quantitative label-free imaging of iron-bound transferrin in breast cancer cells and tumors

Transferrin (Tf) is an essential serum protein which delivers iron throughout the body via transferrin-receptor (TfR)-mediated uptake and iron release in early endosomes. Currently, there is no robust method to assay the population of iron-bound Tf in intact cells and tissues. Raman hyperspectral imaging detected spectral peaks that correlated with iron-bound Tf in intact cells and tumor xenografts sections (~1270-1300 cm-1). Iron-bound (holo) and iron-free (apo) human Tf forms were endocytosed by MDAMB231 and T47D human breast cancer cells. The Raman iron-bound Tf peak was identified in cells treated with holo-Tf, but not in cells incubated with apo-Tf. A reduction in the Raman peak intensity between 5 and 30 min of Tf internalization was observed in T47D, but not in MDAMB231, suggesting that T47D can release iron from Tf more efficiently than MDAMB231. MDAMB231 may display a disrupted iron homeostasis due to iron release delays caused by alterations in the pH or ionic milieu of the early endosomes. In summary, we have demonstrated that Raman hyperspectral imaging can be used to identify iron-bound Tf in cell cultures and tumor xenografts and detect iron release behavior of Tf in breast cancer cells.

Multiplex fatty acid imaging inside cells by Raman microscopy

Visualizing intracellular fatty acids (including free and esterified form) is very useful for understanding how and where such molecules are incorporated, stored, and metabolized within cells. However, techniques of imaging multiple intracellular fatty acids have been limited by their small size, making it difficult to label and track without changing their biological and biophysical characteristics. Here, we present a new method for simultaneously visualizing up to five atomically labeled intracellular fatty acid species. For this, we utilized the distinctive Raman spectra depending on the labeling patterns and created a new, extensible opensource software to perform by-pixel analysis of extracting original spectra from mixed ones. Our multiplex imaging method revealed that fatty acids with more double bonds tend to concentrate more efficiently at lipid droplets. This novel approach contributes to reveal not only the spatial dynamics of fatty acids, but also of any other metabolites inside cells.

Raman Spectroscopy for Rapid Evaluation of Surgical Margins during Breast Cancer Lumpectomy

Failure to precisely distinguish malignant from healthy tissue has severe implications for breast cancer surgical outcomes. Clinical prognoses depend on precisely distinguishing healthy from malignant tissue during surgery. Laser Raman spectroscopy (LRS) has been previously shown to differentiate benign from malignant tissue in real time. However, the cost, assembly effort, and technical expertise needed for construction and implementation of the technique have prohibited widespread adoption. Recently, Raman spectrometers have been developed for non-medical uses and have become commercially available and affordable. Here we demonstrate that this current generation of Raman spectrometers can readily identify cancer in breast surgical specimens. We evaluated two commercially available, portable, near-infrared Raman systems operating at excitation wavelengths of either 785 nm or 1064 nm, collecting a total of 164 Raman spectra from cancerous, benign, and transitional regions of resected breast tissue from six patients undergoing mastectomy. The spectra were classified using standard multivariate statistical techniques. We identified a minimal set of spectral bands sufficient to reliably distinguish between healthy and malignant tissue using either the 1064 nm or 785 nm system. Our results indicate that current generation Raman spectrometers can be used as a rapid diagnostic technique distinguishing benign from malignant tissue during surgery.

Tumor Macroenvironment: An Update

Modes of tumor cell interaction include autocrine stimulation, secretion of paracrine growth factors and inhibitors, as well as interaction with the tumor macroenvironment. This evolving concept in tumor pathobiology describes the interaction of a malignant tumor with its host as an extension and addition to its local interaction with tumor cells and surrounding nontransformed cells, the tumor microenvironment. Angiogenesis, which is considered part of the tumor microenvironment, also allows reciprocal interactions between cancer cells and other organs and systems. Well-known examples of tumor endocrine signaling are the paraneoplastic syndromes. In addition, cachexia, a severe complication of tumor growth, results from the systemic reprogramming of the host metabolism as a result of tumor growth and progression. Moreover, recent reports indicate that cancer cells may secrete factors that might play a role in forming premetastatic niches at distant sites. In addition, cancer cells seem to be able to secrete factors influencing and resetting endogenous circadian organizers. The importance of understanding the whole complex interaction of a malignant tumor and its host - the tumor macroenvironment - is of great importance for the better management and treatment of cancer patients.

Toward Single-Organelle Lipidomics in Live Cells

Detailed studies of lipids in biological systems, including their role in cellular structure, metabolism, and disease development, comprise an increasingly prominent discipline called lipidomics. However, the conventional lipidomics tools, such as mass spectrometry, cannot investigate lipidomes until they are extracted, and thus they cannot be used for probing the lipid distribution nor for studying in live cells. Furthermore, conventional techniques rely on the lipid extraction from relatively large samples, which averages the data across the cellular populations and masks essential cell-to-cell variations. Further advancement of the discipline of lipidomics critically depends on the capability of high-resolution lipid profiling in live cells and, potentially, in single organelles. Here we report a micro-Raman assay designed for single-organelle lipidomics. We demonstrate how Raman microscopy can be used to measure the local intracellular biochemical composition and lipidome hallmarks-lipid concentration and unsaturation level, cis/trans isomer ratio, sphingolipids and cholesterol levels in live cells-with a sub-micrometer resolution, which is sufficient for profiling of subcellular structures. These lipidome data were generated by a newly developed biomolecular component analysis software, which provides a shared platform for data analysis among different research groups. We outline a robust, reliable, and user-friendly protocol for quantitative analysis of lipid profiles in subcellular structures. This method expands the capabilities of Raman-based lipidomics toward the analysis of single organelles within either live or fixed cells, thus allowing an unprecedented measure of organellar lipid heterogeneity and opening new quantitative ways to study the phenotypic variability in normal and diseased cells.

The peculiarities of cancer cell metabolism: A route to metastasization and a target for therapy

The last decade has witnessed the peculiarities of metabolic reprogramming in tumour onset and progression, and their relevance in cancer therapy. Also, it has been indicated that the metastatic process may depend on the metabolic rewiring and adaptation of cancer cells to the pressure of tumour microenvironment and limiting nutrient availability. The present review gatherers the existent knowledge on the influence of tumour microenvironment and metabolic routes driving metastasis. A focus will be given to glycolysis, fatty acid metabolism, glutaminolysis, and amino acid handling. In addition, the role of metabolic waste driving metastasization will be explored. Finally, we discuss the status of cancer treatment approaches targeting metabolism. This knowledge revision will highlight the critical metabolic targets in metastasis and the chemicals already used in preclinical studies and clinical trials, providing clues that would be further exploited in medicinal chemistry research.

Histological coherent Raman imaging: a prognostic review

Histopathology plays a central role in diagnosis of many diseases including solid cancers. Efforts are underway to transform this subjective art to an objective and quantitative science. Coherent Raman imaging (CRI), a label-free imaging modality with sub-cellular spatial resolution and molecule-specific contrast possesses characteristics which could support the qualitative-to-quantitative transition of histopathology. In this work we briefly survey major themes related to modernization of histopathology, review applications of CRI to histopathology and, finally, discuss potential roles for CRI in the transformation of histopathology that is already underway.

Angiogenesis - a crucial step in breast cancer growth, progression and dissemination by Raman imaging

Combined micro-Raman imaging and AFM imaging are efficient methods for analyzing human tissue due to their high spatial and spectral resolution as well as sensitivity to subtle chemical, structural and topographical changes. The aim of this study was to determine biochemical composition and mechanical topography around blood vessels in the tumor mass of human breast tissue. Significant alterations of the chemical composition and structural architecture around the blood vessel were found compared to the normal breast tissue. A pronounced increase of collagen-fibroblast-glycocalyx network, as well as enhanced lactic acid, and glycogen activity in patients affected by breast cancer were reported.

The biochemical, nanomechanical and chemometric signatures of brain cancer

Raman spectroscopy and imaging combined with AFM topography and mechanical indentation by AFM have been shown to be an effective tool for analysis and discrimination of human brain tumors from normal structures. Raman methods have potential to be applied in clinical practice as they allow for identification of tumor margins during surgery. In this study, we investigate medulloblastoma (grade IV WHO) (n=5) and the tissue from the negative margins used as normal controls. We compare a high grade medulloblastoma (IV grade), and non-tumor samples from human central nervous system (CNS) tissue. Based on the properties of the Raman vibrational spectra and Raman images we provide a real-time feedback that is label-free method to monitor tumor metabolism that reveals reprogramming of biosynthesis of lipids, and proteins. We have found that the high-grade tumors of central nervous system (medulloblastoma) exhibit enhanced level of β-sheet conformation and down-regulated level of α-helix conformation when comparing against normal tissue. We have shown that the ratio of Raman intensities I2930/I2845 at 2930 and 2845cm^-1 is a good source of information on the ratio of lipid and protein contents. We have found that the ratio reflects the lipid and protein contents of tumorous brain tissue compared to the non-tumor tissue. Almost all brain tumors have the Raman intensity ratios significantly higher (1.99±0.026) than that found in non-tumor brain tissue, which is 1.456±0.02, and indicates that the relative amount of lipids compared to proteins is significantly higher in the normal brain tissue. Mechanical indentation using AFM on sliced human brain tissues (medulloblastoma, grade IV) revealed that the mechanical properties of this tissue are strongly heterogeneous, between 1.8 and 75.7kPa, and the mean of 27.16kPa. The sensitivity and specificity obtained directly from PLSDA and cross validation gives a sensitivity and specificity of 98.5% and 96% and 96.3% and 92% for cross-validation, respectively. The high sensitivity and specificity demonstrates usefulness for a proper decision for a Raman diagnostic test on biochemical alterations monitored by Raman spectroscopy related to brain cancer development.

Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment

The biannual International Conference on Biophotonics was recently held on April 30 to May 1, 2017, in Fremantle, Western Australia. This continuing conference series brought together key opinion leaders in biophotonics to present their latest results and, importantly, to participate in discussions on the future of the field and what opportunities exist when we collectively work together for using biophotonics for biological discovery and medical applications. One session in this conference, entitled "Tumor Margin Identification: Critiquing Technologies," challenged invited speakers and attendees to review and critique representative label-free optical imaging technologies and their application for intraoperative assessment and guidance in surgical oncology. We are pleased to share a summary in this outlook paper, with the intent to motivate more research inquiry and investigations, to challenge these and other optical imaging modalities to evaluate and improve performance, to spur translation and adoption, and ultimately, to improve the care and outcomes of patients.