Optical Analysis of Glioma: Fourier-Transform Infrared Spectroscopy Reveals the IDH1 Mutation Status

Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation

BACKGROUND

Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation.

METHODS

Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations.

RESULTS

The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of β-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas.

CONCLUSIONS

The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of β-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.

Application of spectrochemical analysis with chemometrics to profile biochemical alterations in nanoplastic-exposed HepG2 cells

Humans are routinely exposed to nanoplastics (NPs) in various ways, and this exposure presents a significant health risk. Nevertheless, there remain gaps in our knowledge, particularly in the mechanisms of toxicity of NPs with different surface charges at very low environmental concentrations. Herein, a spectrochemical approach was used to profile the cytotoxicity of NPs with different surface charges in HepG2 cells. It was found that all three NPs can cause some biomolecular alterations in cells, affecting cellular lipids, proteins, amino acids, and genetic material. Of these, PS and PS-COOH led to a non-linear dose-response, which may be related to a biphasic dose-response, whereas PS-NH2 led to a linear dose-response with a gradual increase in toxicity with increasing exposure concentration. In addition, the spectroscopic results showed that surface modifications led to cellular biochemical changes and caused adverse biological effects, with PS-NH2 exhibiting higher toxicity compared to PS or PS-COOH along with an inhibition of cell proliferation. Surprisingly PS-COOH, although considered the least toxic NP, appears to cause DNA damage. Overall, the toxic effects of different surface-modified NPs in cells were detected for the first time by applying spectrochemical techniques, and these findings provide important data towards understanding the emerging widespread environmental pollution of NPs and their effects on humans.

Differentiation of Urothelial Carcinoma and Normal Bladder Tissues by Means of Fiber-Based ATR IR Spectroscopy

Surgical treatment is widely applied curative approach for bladder cancer. White light cystoscopy (WLC) is currently used for intraoperative diagnostics of malignant lesions but has relatively high false-negative rate. Here we represent an application of label free fiber-based attenuated total reflection infrared spectroscopy (ATR IR) for freshly resected human bladder tissue examination for 54 patients. Defined molecular spectral markers allow to identify normal and urothelial carcinoma tissues. While methods of statistical analysis (Hierarchical cluster analysis (HCA) and Principal component analysis (PCA)) used for spectral data treatment allow to discriminate tissue types with 91% sensitivity and 96-98% specificity. In the present study the described method was applied for tissue examination under ex vivo conditions. However, after method validation the equipment could be translated from laboratory studies to in situ or even in vivo studies in operating room.

A new approach for clinical translation of infrared spectroscopy: exploitation of the signature of glioblastoma for general brain tumor recognition

PURPOSE

Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition.

METHODS

Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors.

RESULTS

The model correctly classified 82% (517/628) of specimens as "tumor" or "non-tumor", respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified.

CONCLUSION

The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Label-free differentiation of human pancreatic cancer, pancreatitis, and normal pancreatic tissue by molecular spectroscopy

SIGNIFICANCE

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer deaths with a best median survival of only 40 to 50 months for localized disease despite multimodal treatment. The standard tissue differentiation method continues to be pathology with histological staining analysis. Microscopic discrimination between inflammatory pancreatitis and malignancies is demanding.

AIM

We aim to accurately distinguish native pancreatic tissue using infrared (IR) spectroscopy in a fast and label-free manner.

APPROACH

Twenty cryopreserved human pancreatic tissue samples were collected from surgical resections. In total, more than 980,000 IR spectra were collected and analyzed using aMATLAB package. For differentiation of PDAC, pancreatitis, and normal tissue, a three-class training set for supervised classification was created with 25,000 spectra and the principal component analysis (PCA) score values for each cohort. Cross-validation was performed using the leaveone- out method. Validation of the algorithm was accomplished with 13 independent test samples.

RESULTS

Reclassification of the training set and the independent test samples revealed an overall accuracy of more than 90% using a discrimination algorithm.

CONCLUSION

IR spectroscopy in combination with PCA and supervised classification is an efficient analytical method to reliably distinguish between benign and malignant pancreatic tissues. It opens up a wide research field for oncological and surgical applications.

Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence

BACKGROUND

Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources.

OBJECTIVE

To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence.

METHODS

We used a fiber laser-based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set.

RESULTS

SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images.

CONCLUSION

SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.

Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping

This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one-dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN-CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm^-1 , 1539 cm^-1 and 1234 cm^-1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two-dimensional MIR mapping at a single frequency can be used by the CNN-CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single-frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.

Safe Surgery for Glioblastoma: Recent Advances and Modern Challenges

One of the major challenges during glioblastoma surgery is balancing between maximizing extent of resection and preventing neurological deficits. Several surgical techniques and adjuncts have been developed to help identify eloquent areas both preoperatively (fMRI, nTMS, MEG, DTI) and intraoperatively (imaging (ultrasound, iMRI), electrostimulation (mapping), cerebral perfusion measurements (fUS)), and visualization (5-ALA, fluoresceine)). In this review, we give an update of the state-of-the-art management of both primary and recurrent glioblastomas. We will review the latest surgical advances, challenges, and approaches that define the onco-neurosurgical practice in a contemporary setting and give an overview of the current prospective scientific efforts.

Emerging Role of TCA Cycle-Related Enzymes in Human Diseases

The tricarboxylic acid (TCA) cycle is the main source of cellular energy and participates in many metabolic pathways in cells. Recent reports indicate that dysfunction of TCA cycle-related enzymes causes human diseases, such as neurometabolic disorders and tumors, have attracted increasing interest in their unexplained roles. The diseases which develop as a consequence of loss or dysfunction of TCA cycle-related enzymes are distinct, suggesting that each enzyme has a unique function. This review aims to provide a comprehensive overview of the relationship between each TCA cycle-related enzyme and human diseases. We also discuss their functions in the context of both mitochondrial and extra-mitochondrial (or cytoplasmic) enzymes.

Application of serum mid-infrared spectroscopy combined with an ensemble learning method in rapid diagnosis of gliomas

The diffuse growth of glioma cells leads to gliomatosis, which has less cure rate and high mortality. As the severity deepens, the treatment difficulty and mortality of glioma patients gradually increase. Therefore, a rapid and non-invasive diagnostic technique is very important for glioma patients. The target of this study is to classify contract subjects and glioma patients by serum mid-infrared spectroscopy combined with an ensemble learning method. The spectra were normalized and smoothed, and principal component analysis (PCA) was utilized for dimensionality reduction. Particle swarm optimization-support vector machine (PSO-SVM), decision tree (DT), logistic regression (LR) as well as random forest (RF) were used as base classifiers, and AdaBoost integrated learning was introduced. AdaBoost-SVM, AdaBoost-LR, AdaBoost-RF and AdaBoost-DT models were established to discriminate glioma patients. The single classification accuracy of the four models for the test set was 87.14%, 90.00%, 92.00% and 90.86%, respectively. For the purpose of further improving the prediction accuracy, the four models were fused at decision level, and the final classification accuracy of the test set reached 94.29%. Experiments show that serum infrared spectroscopy combined with the ensemble learning method algorithm shows wonderful potential in non-invasive, fast and precise identification of glioma patients, and can also be used for reference in intelligent diagnosis of other diseases.

Human serum mid-infrared spectroscopy combined with machine learning algorithms for rapid detection of gliomas

Glioma has a low cure rate and a high mortality rate. Therefore, correct diagnosis and treatment are essential for patients. This research aims to use mid-infrared spectroscopy combined with machine learning algorithms to identify patients with glioma. The glioma infrared spectra and the control group serum are smoothed and normalized, then the principal component analysis (PCA) algorithm is used to reduce the data dimensionality, and finally, the particle swarm optimization-support vector machine (PSO-SVM), backpropagation (BP) neural network and decision tree (DT) model are established. The classification accuracy of the three models was 92.00 %, 91.83 %, 87.20 %, and the AUC values were 0.919, 0.945, and 0.866, respectively. The results show that PCA-PSO-SVM has a better classification effect. This study shows that mid-infrared spectroscopy combined with machine learning algorithms has great potential in the application of non-invasive, rapid and accurate identification of glioma patients.

Label-free brain tumor imaging using Raman-based methods

INTRODUCTION

Label-free Raman-based imaging techniques create the possibility of bringing chemical and histologic data into the operation room. Relying on the intrinsic biochemical properties of tissues to generate image contrast and optical tissue sectioning, Raman-based imaging methods can be used to detect microscopic tumor infiltration and diagnose brain tumor subtypes.

METHODS

Here, we review the application of three Raman-based imaging methods to neurosurgical oncology: Raman spectroscopy, coherent anti-Stokes Raman scattering (CARS) microscopy, and stimulated Raman histology (SRH).

RESULTS

Raman spectroscopy allows for chemical characterization of tissue and can differentiate normal and tumor-infiltrated tissue based on variations in macromolecule content, both ex vivo and in vivo. To improve signal-to-noise ratio compared to conventional Raman spectroscopy, a second pulsed excitation laser can be used to coherently drive the vibrational frequency of specific Raman active chemical bonds (i.e. symmetric stretching of -CH₂ bonds). Coherent Raman imaging, including CARS and stimulated Raman scattering microscopy, has been shown to detect microscopic brain tumor infiltration in fresh brain tumor specimens with submicron image resolution. Advances in fiber-laser technology have allowed for the development of intraoperative SRH as well as artificial intelligence algorithms to facilitate interpretation of SRH images. With molecular diagnostics becoming an essential part of brain tumor classification, preliminary studies have demonstrated that Raman-based methods can be used to diagnose glioma molecular classes intraoperatively.

CONCLUSIONS

These results demonstrate how label-free Raman-based imaging methods can be used to improve the management of brain tumor patients by detecting tumor infiltration, guiding tumor biopsy/resection, and providing images for histopathologic and molecular diagnosis.

Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy

Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

Morphological and Biochemical Properties of Human Astrocytes, Microglia, Glioma, and Glioblastoma Cells Using Fourier Transform Infrared Spectroscopy

BACKGROUND With infiltration, high-grade glioma easily causes the boundary between tumor tissue and adjacent tissue to become unclear and results in tumor recurrence at or near the resection margin according to the incomplete surgical resection. Fourier transform infrared spectroscopy (FTIR) technique has been demonstrated to be a useful tool that yields a molecular fingerprint and provides rapid, nondestructive, high-throughput and clinically relevant diagnostic information. MATERIAL AND METHODS FTIR was used to investigate the morphological and biochemical properties of human astrocytes (HA), microglia (HM1900), glioma cells (U87), and glioblastoma cells (BT325) cultured in vitro to simulate the infiltration area, with the use of multi-peak fitting and principal component analysis (PCA) of amide I of FTIR spectra and the use of hierarchical cluster analysis (HCA). RESULTS We found that the secondary structures of the 4 types of cells were significantly different. The contents of a-helix structure in glial cells was significantly higher than in the glioma cells, but the levels of ß-sheet, ß-turn, and random coil structures were lower. The 4 types of cells could be clearly separated with 85% for PC1 and 12.2% for PC2. CONCLUSIONS FTIR can be used to distinguish between human astrocytes, microglia, glioma, and glioblastoma cells in vitro. The protein secondary structure can be used as an indicator to distinguish tumor cells from glial cells. Further tissue-based and in vivo studies are needed to determine whether FTIR can identify cerebral glioma.

In vivo detection of murine glioblastoma through Raman and reflectance fiber-probe spectroscopies

Significance: Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults. With a worldwide incidence rate of 2 to 3 per 100,000 people, it accounts for more than 60% of all brain cancers; currently, its 5-year survival rate is < 5 % . GBM treatment relies mainly on surgical resection. In this framework, multimodal optical spectroscopy could provide a fast and label-free tool for improving tumor detection and guiding the removal of diseased tissues. Aim: Discriminating healthy brain from GBM tissues in an animal model through the combination of Raman and reflectance spectroscopies. Approach: EGFP-GL261 cells were injected into the brains of eight laboratory mice for inducing murine GBM in these animals. A multimodal optical fiber probe combining fluorescence, Raman, and reflectance spectroscopy was used to localize in vivo healthy and tumor brain areas and to collect their spectral information. Results: Tumor areas were localized through the detection of EGFP fluorescence emission. Then, Raman and reflectance spectra were collected from healthy and tumor tissues, and later analyzed through principal component analysis and linear discriminant analysis in order to develop a classification algorithm. Raman and reflectance spectra resulted in 92% and 93% classification accuracy, respectively. Combining together these techniques allowed improving the discrimination between healthy and tumor tissues up to 97%. Conclusions: These preliminary results demonstrate the potential of multimodal fiber-probe spectroscopy for in vivo label-free detection and delineation of brain tumors, and thus represent an additional, encouraging step toward clinical translation and deployment of fiber-probe spectroscopy.

Pathological identification of brain tumors based on the characteristics of molecular fragments generated by laser ablation combined with a spiking neural network

Quick and accurate diagnosis helps shorten intraoperative waiting time and make a correct plan for the brain tumor resection. The common cryostat section method costs more than 10 minutes and the diagnostic accuracy depends on the sliced and frozen process and the experience of the pathologist. We propose the use of molecular fragment spectra (MFS) in laser-induced breakdown spectroscopy (LIBS) to identify different brain tumors. Formation mechanisms of MFS detected from brain tumors could be generalized into 3 categories, for instance, combination, reorganization and break. Four kinds of brain tumors (glioma, meningioma, hemangiopericytoma, and craniopharyngioma) from different patients were used as investigated samples. The spiking neural network (SNN) classifier was proposed to combine with the MFS (MFS-SNN) for the identification of brain tumors. SNN performed better than conventional machine learning methods for the analysis of similar and limited MFS information. With the ratio data type, the identification accuracy achieved 88.62% in 2 seconds.

Detection of human brain tumours via evaluation of their biochemical composition using ATR-FTIR spectroscopy

The biochemical composition of normal human brain tissue in comparison with that of brain-tumour tissue was studied and diagnosed by means of the attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy technique. IR spectroscopy is a potential histopathological tool for detecting and diagnosing cancer and other diseases. In the study, the amounts of lipids, protein, and water in different brain-tissue specimens from patients of various ages were determined from their ATR-FTIR spectra upon analysing a combination of the pure-component spectra. A higher level of biocomponents was observed in the normal tissue, and in particular, more fluid (water) was contained in benign tumours. The age of patients was found to play an important role; patient age exhibited a direct correlation with the concentration of biocomponents, with increasing age corresponding to a reduction in lipids and proteins. These results demonstrate the diagnostic potential of ATR-FTIR spectroscopy for evaluating brain tumours in terms of its ability to distinguish between normal tissue and tumours in vivo and afford real-time intraoperative diagnosis.

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

BACKGROUND

The molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines.

METHODS

Fresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing.

RESULTS

Fresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%-94% sensitivity and 90%-100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min.

CONCLUSION

These data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.

Biochemical detection of fatal hypothermia and hyperthermia in affected rat hypothalamus tissues by Fourier transform infrared spectroscopy

It is difficult to determinate the cause of death from exposure to fatal hypothermia and hyperthermia in forensic casework. Here, we present a state-of-the-art study that employs Fourier-transform infrared (FTIR) spectroscopy to investigate the hypothalamus tissues of fatal hypothermic, fatal hyperthermic and normothermic rats to determine forensically significant biomarkers related to fatal hypothermia and hyperthermia. Our results revealed that the spectral variations in the lipid, protein, carbohydrate and nucleic acid components are highly different for hypothalamuses after exposure to fatal hypothermic, fatal hyperthermic and normothermic conditions. In comparison with the normothermia group, the fatal hypothermia and hyperthermia groups contained higher total lipid amounts but were lower in unsaturated lipids. Additionally, their cell membranes were found to have less motional freedom. Among these three groups, the fatal hyperthermia group contained the lowest total proteins and carbohydrates and the highest aggregated and dysfunctional proteins, while the fatal hypothermia group contained the highest level of nucleic acids. In conclusion, this study demonstrates that FTIR spectroscopy has the potential to become a reliable method for the biochemical characterization of fatal hypothermia and hyperthermia hypothalamus tissues, and this could be used as a postmortem diagnostic feature in fatal hypothermia and hyperthermia deaths.

Surgical Adjuncts to Increase the Extent of Resection: Intraoperative MRI, Fluorescence, and Raman Histology

In low-grade glioma surgery, depicting tumor margins is challenging. 7 - Bowden 2018 - Sodium Fluorescein Facilitates Guided Sampling of Diagnostic Tumor Tissue.pdf Several tools have emerged to assist surgical decision-making. Intraoperative MRI, albeit expensive and time-consuming, can provide useful information during surgery. Fluorescence-guidance with 5-aminolevulinic acid (5-ALA) helps provide real-time information during surgery regardless of brain-shift, assists in finding anaplastic foci in low-grade tumors, and enables diagnosis of malignant tissue. Raman histology has potential for detecting viable tumor in biopsied tissue and for identifying tumor infiltration in vivo. This article analyzes and discusses these surgical adjuncts.

The clinical use of IDH1 and IDH2 mutations in gliomas

Introduction: Mutations in the genes isocitrate dehydrogenase (IDH) 1 and 2 have been reported in a limited number of tumors. In gliomas, IDH mutations are primarily detected in WHO grade II-III tumors and represent a major biomarker with diagnostic, prognostic, and predictive implications. The recent development of IDH inhibitors and vaccines suggests that the IDH mutation is also an appealing target for therapy. Areas covered: This review focuses on the role of IDH mutations in diffuse gliomas. Besides discussing their role in gliomagenesis, we will emphasize the role of IDH mutations in clinical practice as a diagnostic, prognostic and predictive biomarker, and as a potential therapeutic target. Noninvasive detection of the IDH mutation by means of liquid biopsy and MR spectroscopy will also be discussed. Expert commentary: While IDH mutation is a consolidated diagnostic and prognostic biomarker in clinical practice, its role in oncogenesis is far from being elucidated, and there are several pending issues. The routine use of noninvasive techniques for detection and monitoring of the IDH status remains challenging. Although the IDH mutation is a very early alteration in gliomagenesis, it may then be omitted during tumor progression. This observation has important implications when designing targeted clinical trials.

Shedding Light on IDH1 Mutation in Gliomas

IDH mutation is of central importance in the diagnosis and treatment of gliomas. Fourier-transform infrared spectroscopy, in combination with a supervised machine-learning approach, can be used to detect metabolic alterations induced by IDH1 mutations in a fraction of the time of conventional techniques. Clin Cancer Res; 24(11); 2467-9. ©2018 AACRSee related article by Uckermann et al., p. 2530.

IDH1 mutation in human glioma induces chemical alterations that are amenable to optical Raman spectroscopy

INTRODUCTION

Mutations in the isocytrate dehydrogenase 1 (IDH1) gene are early genetic events in glioma pathogenesis and cause profound metabolic changes. Because this genotype is found in virtually every tumor cell, therapies targeting mutant IDH1 protein are being developed. The intraoperative administration of those therapies would require fast technologies for the determination of IDH1 genotype. As of today, there is no such diagnostic test available. Recently, infrared spectroscopy was shown to bridge this gap. Here, we tested Raman spectroscopy for analysis of IDH1 genotype in glioma, which constitutes an alternative contact-free technique with the potential of being applicable in situ.

METHODS

Human glioma samples (n = 36) were obtained during surgery and cryosections were prepared. IDH1 mutations were assessed using DNA sequencing and 100 Raman spectra were obtained for each sample.

RESULTS

Analysis of Raman spectra revealed increased intensities in spectral bands related to DNA in IDH1 mutant glioma while bands assigned to molecular vibrations of lipids were significantly decreased. Moreover, intensities of Raman bands assigned to proteins differed in IDH1 mutant and IDH1 wild-type glioma, suggesting alterations in the protein profile. The selection of five bands (498, 826, 1003, 1174 and 1337 cm^-1) allowed the classification of Raman spectra according to IDH1 genotype with a correct rate of 89%.

CONCLUSION

Raman spectroscopy constitutes a simple, rapid and safe procedure for determination of the IDH1 mutation that shows great promise for clinically relevant in situ diagnostics.