What do we know about dynamic glucose-enhanced (DGE) MRI and how close is it to the clinics? Horizon 2020 GLINT consortium report

Cancer is one of the most devastating diseases that the world is currently facing, accounting for 10 million deaths in 2020 (WHO). In the last two decades, advanced medical imaging has played an ever more important role in the early detection of the disease, as it increases the chances of survival and the potential for full recovery. To date, dynamic glucose-enhanced (DGE) MRI using glucose-based chemical exchange saturation transfer (glucoCEST) has demonstrated the sensitivity to detect both d-glucose and glucose analogs, such as 3-oxy-methyl-d-glucose (3OMG) uptake in tumors. As one of the recent international efforts aiming at pushing the boundaries of translation of the DGE MRI technique into clinical practice, a multidisciplinary team of eight partners came together to form the “glucoCEST Imaging of Neoplastic Tumors (GLINT)” consortium, funded by the Horizon 2020 European Commission. This paper summarizes the progress made to date both by these groups and others in increasing our knowledge of the underlying mechanisms related to this technique as well as translating it into clinical practice.

CEST MRI provides amide/amine surrogate biomarkers for treatment-naïve glioma sub-typing

Purpose Accurate glioma classification affects patient management and is challenging on non- or low-enhancing gliomas. This study investigated the clinical value of different chemical exchange saturation transfer (CEST) metrics for glioma classification and assessed the diagnostic effect of the presence of abundant fluid in glioma subpopulations. Methods Forty-five treatment-naïve glioma patients with known isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status received CEST MRI (B1rms = 2μT, Tsat = 3.5 s) at 3 T. Magnetization transfer ratio asymmetry and CEST metrics (amides: offset range 3–4 ppm, amines: 1.5–2.5 ppm, amide/amine ratio) were calculated with two models: ‘asymmetry-based’ (AB) and ‘fluid-suppressed’ (FS). The presence of T2/FLAIR mismatch was noted. Results IDH-wild type had higher amide/amine ratio than IDH-mutant_1p/19qcodel (p < 0.022). Amide/amine ratio and amine levels differentiated IDH-wild type from IDH-mutant (p < 0.0045) and from IDH-mutant_1p/19qret (p < 0.021). IDH-mutant_1p/19qret had higher amides and amines than IDH-mutant_1p/19qcodel (p < 0.035). IDH-mutant_1p/19qret with AB/FS mismatch had higher amines than IDH-mutant_1p/19qret without AB/FS mismatch ( < 0.016). In IDH-mutant_1p/19qret, the presence of AB/FS mismatch was closely related to the presence of T2/FLAIR mismatch (p = 0.014). Conclusions CEST-derived biomarkers for amides, amines, and their ratio can help with histomolecular staging in gliomas without intense contrast enhancement. T2/FLAIR mismatch is reflected in the presence of AB/FS CEST mismatch. The AB/FS CEST mismatch identifies glioma subgroups that may have prognostic and clinical relevance.

Characterization of opposing responses to phenol by Bacillus subtilis chemoreceptors

Bacillus subtilis employs ten chemoreceptors to move in response to chemicals in its environment. While the sensing mechanisms have been determined for many attractants, little is known about the sensing mechanisms for repellents. In this work, we investigated phenol chemotaxis in B. subtilis . Phenol is an attractant at low, micromolar concentrations, and a repellent at high, millimolar concentrations. McpA was found to be the principal chemoreceptor governing the repellent response to phenol and other related aromatic compounds. In addition, the chemoreceptors McpC and HemAT were found to govern the attractant response to phenol and related compounds. Using chemoreceptor chimeras, McpA was found to sense phenol using its signaling domain rather than its sensing domain. These observations were substantiated in vitro, where direct binding of phenol to the signaling domain of McpA was observed using saturation-transfer difference nuclear magnetic resonance. These results further advance our understanding of B. subtilis chemotaxis and further demonstrate that the signaling domain of B. subtilis chemoreceptors can directly sense chemoeffectors. IMPORTANCE Bacterial chemotaxis is commonly thought to employ a sensing mechanism involving the extracellular sensing domain of chemoreceptors. Some ligands, however, appear to be sensed by the signaling domain. Phenolic compounds, commonly found in soil and root exudates, provide environmental cues for soil microbes like Bacillus subtilis . We show that phenol is sensed both as an attractant and a repellent. While the mechanism for sensing phenol as an attractant is still unknown, we found that phenol is sensed as a repellent by the signaling domain of the chemoreceptor McpA. This study furthers our understanding of the unconventional sensing mechanisms employed by the B. subtilis chemotaxis pathway.

Saturation Transfer MRI for Detection of Metabolic and Microstructural Impairments Underlying Neurodegeneration in Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common causes of dementia and difficult to study as the pool of subjects is highly heterogeneous. Saturation transfer (ST) magnetic resonance imaging (MRI) methods are quantitative modalities with potential for non-invasive identification and tracking of various aspects of AD pathology. In this review we cover ST-MRI studies in both humans and animal models of AD over the past 20 years. A number of magnetization transfer (MT) studies have shown promising results in human brain. Increased computing power enables more quantitative MT studies, while access to higher magnetic fields improves the specificity of chemical exchange saturation transfer (CEST) techniques. While much work remains to be done, results so far are very encouraging. MT is sensitive to patterns of AD-related pathological changes, improving differential diagnosis, and CEST is sensitive to particular pathological processes which could greatly assist in the development and monitoring of therapeutic treatments of this currently incurable disease.

Can Chemical Exchange Saturation Transfer (CEST MRI) be used as biomarker of disease progression in Prion Disease?

Human prion diseases are fatal neurodegenerative disorders that may have prolonged asymptomatic incubation periods. However, the underlying mechanism by which prions cause brain damage remains unclear. In turn, characterization of early pathological aspects would be of benefit for the diagnosis and potential treatment of these progressive neurodegenerative disorders. We investigated chemical exchange saturation transfer (CEST) MRI based on its exquisite sensitivity to cytosol protein content as a surrogate for prion disease pathology. Three groups of prion-infected mice at different stages of the disease underwent conventional magnetic resonance imaging and CEST MRI at 9.4T. For each mouse, chemical exchange contrasts were measured by applying five RF powers at various frequency offsets using magnetization transfer asymmetries. Relayed Nuclear Overhauser effects (NOE*) and amide proton transfer (APT*) were also assessed. For comparison, CEST MRI measurements were also made in healthy control mice brains. Here we show that alterations in CEST signal were detected before structural modifications or any clinical signs of prion disease. The detected CEST signal displayed different patterns at different stages of the disease indicating its potential for use as a longitudinal marker of disease progression. Highly significant correlations were found between CEST metrics and histopathological findings. A decline in NOE signal was positively correlated with abnormal prion protein deposition (R2 = 0.91) in the thalami of prion infected mice. Moreover, the NOE signal was negatively correlated with astrogliosis (R2 = 0.71) in the thalamus. No significant correlations were detected between NOE signals and spongiosis. MTR asymmetry at 3.5 ppm was also correlated with astrogliosis (R2 = 0.59), and prion protein deposition (R2 = 0.63) in thalamus. No significant changes were detected in APT* between prion-infected and control mice at all stages of the disease. Finally, MTR asymmetry between 2.8 and 3.2 ppm was correlated with prion protein deposition (R2 = 0.47) in the thalamus of prion -infected mice. To conclude, CEST MRI has potential utility as a biomarker of neurodegenerative processes in prion disease

Towards autonomous analysis of Chemical Exchange Saturation Transfer experiments using Deep Neural Networks

Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3 - 60 ms, the range most frequently observed via experiment. The work presented here focuses on the 1H CEST class of methods that are further complicated, in relation to applications to other nuclei, by anti-phase features. The developed DNNs accurately predict the chemical shifts of nuclei in the exchanging species directly from anti-phase 1HN CEST profiles, along with an uncertainty associated with the predictions. The performance of the DNN was quantitatively assessed using both synthetic and experimental anti-phase CEST profiles. The assessments show that the DNN accurately determines chemical shifts and their associated uncertainties. The DNNs developed here do not contain any parameters for the end-user to adjust and the method therefore allows for autonomous analysis of complex NMR data that report on conformational exchange.