Browsing by Subject "HRMAS NMR"
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Item Open Access Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy(Bilkent University, 2021-07) Çakmakçı, DorukComplete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and ma-lign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted anal-ysis for the existence of known tumor biomarkers can be made and this requires atechnicianwithchemistrybackground, andapathologistwithknowledgeon tumor metabolism to be present during surgery. Here, we show that we can accu-rately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier and validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an impor-tant role in distinguishing tumor and normal cells and suggest new biomarker regions.Item Open Access Metabolomic characterization of human hippocampus from drug-resistant epilepsy with mesial temporal seizure(Wiley-Blackwell Publishing, 2018-03) Detour, J.; Bund, C.; Behr, C.; Cebula, H.; Çiçek, A. Ercüment; Valenti-Hirsch, Maria-Paola; Lannes, B.; Lhermitte, B.; Nehlig, A.; Kehrli, P.; Proust, F.; Hirsch, E.; Namer, Izzie-JacquesWithin a complex systems biology perspective, we wished to assess whether hippocampi with established neuropathological features have distinct metabolome. Apparently normal hippocampi with no signs of sclerosis (noHS), were compared to hippocampal sclerosis (HS) type 1 (HS1) and/or type 2 (HS2). Hippocampus metabolome from patients with epilepsy-associated neuroepithelial tumors (EANTs), namely, gangliogliomas (GGs) and dysembryoplastic neuroepithelial tumors (DNTs), was also compared to noHS epileptiform tissue. Methods: All patients underwent standardized temporal lobectomy. We applied 1H high-resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) spectroscopy to 48 resected human hippocampi. NMR spectra allowed quantification of 21 metabolites. Data were analyzed using multivariate analysis based on mutual information. Results: Clear distinct metabolomic profiles were observed between all studied groups. Sixteen and 18 expected metabolite levels out of 21 were significantly different for HS1 and HS2, respectively, when compared to noHS. Distinct concentration variations for glutamine, glutamate, and N-acetylaspartate (NAA) were observed between HS1 and HS2. Hippocampi from GG and DNT patients showed 7 and 11 significant differences in metabolite concentrations when compared to the same group, respectively. GG and DNT had a clear distinct metabolomic profile, notably regarding choline compounds, glutamine, glutamate, aspartate, and taurine. Lactate and acetate underwent similar variations in both groups. Significance: HRMAS NMR metabolomic analysis was able to disentangle metabolic profiles between HS, noHS, and epileptic hippocampi associated with EANT. HRMAS NMR metabolomic analysis may contribute to a better identification of abnormal biochemical processes and neuropathogenic combinations underlying mesial temporal lobe epilepsy.Item Open Access Metabolomic profile of aggressive meningiomas by using highresolution magic angle spinning nuclear magnetic resonance(American Chemical Society, 2020) Bender, L.; Somme, F.; Ruhland, E.; Çiçek, A. Ercüment; Bund, C.; Namer, I. J.Meningiomas are in most cases benign brain tumors. The WHO 2016 classification defines three grades of meningiomas. This classification had a prognosis value because grade III meningiomas have a worse prognosis value compared to grades I and II meningiomas. However, some benign or atypical meningiomas can have a clinical aggressive behavior. There are currently no reliable markers which allow distinguishing between the meningiomas with a good prognosis and those which may recur. High-resolution magic angle spinning (HRMAS) spectrometry is a noninvasive method able to determine the metabolite profile of a tissue sample. We retrospectively analyzed 62 meningioma samples by using HRMAS spectrometry (43 metabolites). We described a metabolic profile defined by a high concentration for acetate, threonine, N-acetyl-lysine, hydroxybutyrate, myoinositol, ascorbate, scylloinositol, and total choline and a low concentration for aspartate, glucose, isoleucine, valine, adenosine, arginine, and alanine. This metabolomic signature was associated with poor prognosis histological markers [Ki-67 ≥ 40%, high histological grade and negative progesterone receptor (PR) expression]. We also described a similar metabolomic spectrum between grade III and grade I meningiomas. Moreover, all grade I meningiomas with a low Ki-67 expression and a positive PR expression did not have the same metabolomic profile. Metabolomic analysis could be used to determine an aggressive meningioma in order to discuss a personalized treatment. Further studies are needed to confirm these results and to correlate this metabolic profile with survival data.Item Open Access Metabolomics approaches in experimental allergic encephalomyelitis(Elsevier, 2018) Battini, B.; Bund, C.; Moussallieh, F. M.; Çiçek, A. Ercüment; De Sèze, J.; Namer, I. J.A myelin basic protein (MBP)-induced experimental allergic encephalomyelitis (EAE) involves paraplegia due to a reversible thoracolumbar spinal cord impairment. The aims of this study were thus to find significant metabolic biomarkers of inflammation and identify the site of inflammation in the central nervous system (CNS) during the acute signs in of the disease using metabolomics. All the EAE samples were associated with higher levels of lactate, ascorbate, glucose and amino acids, and decreased level of N-acetyl-aspartate (NAA) compared to the control group. A decreased NAA level has been particularly shown in lumbar spinal cord in relationship with the clinical signs.Item Open Access Predicting carbon spectrum in Heteronuclear Single Quantum Coherence Spectroscopy for online feedback during surgery(IEEE, 2020) Karakaşlar, E. Onur; Coşkun, B.; Outilaft, H.; Namer, I. J.; Çiçek, A. Ercüment1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (1H-13C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1H and 13C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the 13C dimension by just performing 1H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97 percent accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.Item Open Access Predicting carbon spectrum in heteronuclear single quantum coherence spectroscopy for online feedback during surgery(Bilkent University, 2020-09) Karakaşlar, Emin Onur1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (1H-13C). Unfortunately, this analysis requires much longer time and prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1H and 13C dimensions. Learning is possible with small sample sizes and without the need for performing the HSQC analysis, we can predict the 13C dimension by just performing 1H HRMAS NMR experiment. We show on a rat model of central nervous system tissues (80 samples, 5 tissues) that our methods achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 groups of 39 metabolites with 97% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension. In practice, this information can provide valuable feedback to the surgeon to further resect the cavity to avoid potential recurrence.