Glioma progression is shaped by genetic evolution and microenvironment interactions.

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions were associated with an increase in proliferating neoplastic cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their neoplastic cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a myeloid cell state defined by specific ligand-receptor interactions with neoplastic cells. Collectively, these recurrence-associated phenotypes represent potential targets to alter disease progression.

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

Translational repression of pre-formed cytokine-encoding mRNA prevents chronic activation of memory T cells.

Memory T cells are critical for the immune response to recurring infections. Their instantaneous reactivity to pathogens is empowered by the persistent expression of cytokine-encoding mRNAs. How the translation of proteins from pre-formed cytokine-encoding mRNAs is prevented in the absence of infection has remained unclear. Here we found that protein production in memory T cells was blocked via a 3' untranslated region (3' UTR)-mediated process. Germline deletion of AU-rich elements (AREs) in the Ifng-3' UTR led to chronic cytokine production in memory T cells. This aberrant protein production did not result from increased expression and/or half-life of the mRNA. Instead, AREs blocked the recruitment of cytokine-encoding mRNA to ribosomes; this block depended on the ARE-binding protein ZFP36L2. Thus, AREs mediate repression of translation in mouse and human memory T cells by preventing undesirable protein production from pre-formed cytokine-encoding mRNAs in the absence of infection.

Intestinal tissue-resident memory T cells maintain distinct identity from circulating memory T cells after in vitro restimulation.

Resident memory T (TRM) cells have been recently established as an important subset of memory T cells that provide early and essential protection against reinfection in the absence of circulating memory T cells. Recent findings showing that TRM expand in vivo after repeated antigenic stimulation indicate that these memory T cells are not terminally differentiated. This suggests an opportunity for in vitro TRM expansion to apply in an immunotherapy setting. However, it has also been shown that TRM may not maintain their identity and form circulating memory T cells after in vivo restimulation. Therefore, we set out to determine how TRM respond to antigenic activation in culture. Using Listeria monocytogenes and LCMV infection models, we found that TRM from the intraepithelial compartment of the small intestine expand in vitro after antigenic stimulation and subsequent resting in homeostatic cytokines. A large fraction of the expanded TRM retained their phenotype, including the expression of key TRM markers CD69 and CD103 (ITGAE). The optimal culture of TRM required low O2 pressure to maintain the expression of these and other TRM-associated molecules. Expanded TRM retained their effector capacity to produce cytokines after restimulation, but did not acquire a highly glycolytic profile indicative of effector T cells. The proteomic analysis confirmed TRM profile retention, including expression of TRM-related transcription factors, tissue retention factors, adhesion molecules, and enzymes involved in fatty acid metabolism. Collectively, our data indicate that limiting oxygen conditions supports in vitro expansion of TRM cells that maintain their TRM phenotype, at least in part, suggesting an opportunity for therapeutic strategies that require in vitro expansion of TRM.

Longitudinal molecular trajectories of diffuse glioma in adults.

The evolutionary processes that drive universal therapeutic resistance in adult patients with diffuse glioma remain unclear1,2. Here we analysed temporally separated DNA-sequencing data and matched clinical annotation from 222 adult patients with glioma. By analysing mutations and copy numbers across the three major subtypes of diffuse glioma, we found that driver genes detected at the initial stage of disease were retained at recurrence, whereas there was little evidence of recurrence-specific gene alterations. Treatment with alkylating agents resulted in a hypermutator phenotype at different rates across the glioma subtypes, and hypermutation was not associated with differences in overall survival. Acquired aneuploidy was frequently detected in recurrent gliomas and was characterized by IDH mutation but without co-deletion of chromosome arms 1p/19q, and further converged with acquired alterations in the cell cycle and poor outcomes. The clonal architecture of each tumour remained similar over time, but the presence of subclonal selection was associated with decreased survival. Finally, there were no differences in the levels of immunoediting between initial and recurrent gliomas. Collectively, our results suggest that the strongest selective pressures occur during early glioma development and that current therapies shape this evolution in a largely stochastic manner.

A hierarchy of linguistic predictions during natural language comprehension.

Understanding spoken language requires transforming ambiguous acoustic streams into a hierarchy of representations, from phonemes to meaning. It has been suggested that the brain uses prediction to guide the interpretation of incoming input. However, the role of prediction in language processing remains disputed, with disagreement about both the ubiquity and representational nature of predictions. Here, we address both issues by analyzing brain recordings of participants listening to audiobooks, and using a deep neural network (GPT-2) to precisely quantify contextual predictions. First, we establish that brain responses to words are modulated by ubiquitous predictions. Next, we disentangle model-based predictions into distinct dimensions, revealing dissociable neural signatures of predictions about syntactic category (parts of speech), phonemes, and semantics. Finally, we show that high-level (word) predictions inform low-level (phoneme) predictions, supporting hierarchical predictive processing. Together, these results underscore the ubiquity of prediction in language processing, showing that the brain spontaneously predicts upcoming language at multiple levels of abstraction.

Conceptual Associations Generate Sensory Predictions.

A crucial ability of the human brain is to learn and exploit probabilistic associations between stimuli to facilitate perception and behavior by predicting future events. Although studies have shown how perceptual relationships are used to predict sensory inputs, relational knowledge is often between concepts rather than percepts (e.g., we learned to associate cats with dogs, rather than specific images of cats and dogs). Here, we asked if and how sensory responses to visual input may be modulated by predictions derived from conceptual associations. To this end we exposed participants of both sexes to arbitrary word-word pairs (e.g., car-dog) repeatedly, creating an expectation of the second word, conditional on the occurrence of the first. In a subsequent session, we exposed participants to novel word-picture pairs, while measuring fMRI BOLD responses. All word-picture pairs were equally likely, but half of the pairs conformed to the previously formed conceptual (word-word) associations, whereas the other half violated this association. Results showed suppressed sensory responses throughout the ventral visual stream, including early visual cortex, to pictures that corresponded to the previously expected words compared with unexpected words. This suggests that the learned conceptual associations were used to generate sensory predictions that modulated processing of the picture stimuli. Moreover, these modulations were tuning specific, selectively suppressing neural populations tuned toward the expected input. Combined, our results suggest that recently acquired conceptual priors are generalized across domains and used by the sensory brain to generate category-specific predictions, facilitating processing of expected visual input.SIGNIFICANCE STATEMENT Perceptual predictions play a crucial role in facilitating perception and the integration of sensory information. However, little is known about whether and how the brain uses more abstract, conceptual priors to form sensory predictions. In our preregistered study, we show that priors derived from recently acquired arbitrary conceptual associations result in category-specific predictions that modulate perceptual processing throughout the ventral visual hierarchy, including early visual cortex. These results suggest that the predictive brain uses prior knowledge across various domains to modulate perception, thereby extending our understanding of the extensive role predictions play in perception.

Memory CD8+ T cells upregulate glycolysis and effector functions under limiting oxygen conditions.

Memory CD8+ T cells are indispensable for maintaining long-term immunity against intracellular pathogens and tumors. Despite their presence at oxygen-deprived infected tissue sites or in tumors, the impact of local oxygen pressure on memory CD8+ T cells remains largely unclear. We sought to elucidate how oxygen pressure impacts memory CD8+ T cells arising after infection with Listeria monocytogenes-OVA. Our data revealed that reduced oxygen pressure during in vitro culture switched CD8+ T cell metabolism from oxidative phosphorylation to a glycolytic phenotype. Quantitative proteomic analysis showed that limiting oxygen conditions increased the expression of glucose transporters and components of the glycolytic pathway, while decreasing TCA cycle and mitochondrial respiratory chain proteins. The altered CD8+ T cell metabolism did not affect the expansion potential, but enhanced the granzyme B and IFN-γ production capacity. In vivo, memory CD8+ T cells cultured under low oxygen pressure provided protection against bacterial rechallenge. Taken together, our study indicates that strategies of cellular immune therapy may benefit from reducing oxygen during culture to develop memory CD8+ T cells with superior effector functions.

Synthesis and Evaluation of Glycosyl Luciferins.

Measuring glycosidase activity is important to monitor any aberrations in carbohydrate hydrolase activity, but also for the screening of potential glycosidase inhibitors. To this end, synthetic substrates are needed which provide an enzyme-dependent read-out upon hydrolysis by the glycosidase. Herein, we present two new routes for the synthesis of caged luminescent carbohydrates, which can be used for determining glycosidase activity with a luminescent reporter molecule. The substrates were validated with glycosidase and revealed a clear linear range and enzyme-dependent signal upon the in situ generation of the luciferin moiety from the corresponding nitrile precursors. Besides, we showed that these compounds could directly be synthesized from unprotected glycosyl-α-fluorides in a two-step procedure with yields up to 75 %. The intermediate methyl imidate appeared a key intermediate which also reacted with d-cysteine to give the corresponding d-luciferin substrate rendering this a highly attractive method for synthesizing glycosyl luciferins in good yields.

Myostatin and CXCL11 promote nervous tissue macrophages to maintain osteoarthritis pain.

Pain is the most debilitating symptom of knee osteoarthritis (OA) that can even persist after total knee replacement. The severity and duration of pain do not correlate well with joint tissue alterations, suggesting other mechanisms may drive pain persistence in OA. Previous work identified that macrophages accumulate in the dorsal root ganglia (DRG) containing the somas of sensory neurons innervating the injured knee joint in a mouse OA model and acquire a M1-like phenotype to maintain pain. Here we aimed to unravel the mechanisms that govern DRG macrophage accumulation and programming. The accumulation of F4/80+iNOS+ (M1-like) DRG macrophages was detectable at day 3 after mono-iodoacetate (MIA)-induced OA in the mouse. Depletion of macrophages prior to induction of OA resolved pain-like behaviors by day 7 without affecting the initial development of pain-like behaviors. Analysis of DRG transcript identified CXCL11 and myostatin. CXCL11 and myostatin were increased at 3 weeks post OA induction, with CXCL11 expression partially localized in satellite glial cells and myostatin in sensory neurons. Blocking CXCL11 or myostatin prevented the persistence of OA pain, without affecting the initiation of pain. CXCL11 neutralization reduced the number of total and F4/80+iNOS+ DRG macrophages, whilst myostatin inhibition diminished the programming of F4/80+iNOS+ DRG macrophages. Intrathecal injection of recombinant CXCL11 did not induce pain-associated behaviors. In contrast, intrathecal myostatin increased the number of F4/80+iNOS+ DRG macrophages concurrent with the development of mechanical hypersensitivity that was prevented by macrophages depletion or CXCL11 blockade. Finally, myostatin inhibition during established OA, resolved pain and F4/80+iNOS+ macrophage accumulation in the DRG. In conclusion, DRG macrophages maintain OA pain, but are not required for the induction of OA pain. Myostatin is a key ligand in neuro-immune communication that drives the persistence of pain in OA through nervous tissue macrophages and represent a novel therapeutic target for the treatment of OA pain.

Maximal Systolic Acceleration and Near-Infrared Fluorescence Imaging With Indocyanine Green as Predictors for Successful Lower Extremity Revascularization.

BACKGROUND: Patients with lower extremity arterial disease (LEAD) frequently require revascularization procedures. Currently used diagnostic methods are insufficient in predicting successful outcomes and focus on macrovascular rather than microvascular state. Several promising modalities to increase diagnostic accuracy are emerging, including maximal systolic acceleration (ACCmax), measured by duplex ultrasound (DUS). For the assessment of tissue perfusion, near-infrared fluorescence (NIR) imaging using indocyanine green (ICG) demonstrates promising results. This study aims to identify the usefulness of combining these two methods for macrovascular and microvascular perfusion assessment to predict successful clinical outcomes. METHODS: A retrospective study was performed collecting preinterventional and postinterventional DUS and ICG NIR fluorescence imaging measurements from LEAD patients undergoing revascularization. The correlation between the preinterventional and postinterventional perfusion parameters, described as the delta (Δ) ACCmax and ΔICG NIR fluorescence parameters, were analyzed. Improvements in perfusion parameters were compared to clinical outcomes, defined as improvement in pain-free walking distance, freedom from rest pain, or tendency toward wound and ulcer healing. RESULTS: A total of 38 patients (42 limbs) were included. ACCmax and ICG NIR fluorescence perfusion parameters improved significantly after revascularization (p<0.001). Patients with a poor clinical outcome had a significantly lower improvement of both parameters after revascularization (p<0.001-0.016). Lack of correlation was found between the delta of ACCmax and ICG NIR fluorescence imaging. Multiple non-congruent improvements of macrovascular parameters (ACCmax) and perfusion (ICG NIR fluorescence) were seen within patients. However, for all patients with a successful clinical outcome, at least one parameter improved. CONCLUSION: Combining ACCmax and ICG NIR fluorescence imaging revealed improvement in at least one parameter within all patients with a successful clinical outcome. This study highlights the potential of assessing both the macrovascular state and tissue perfusion following lower extremity revascularization, as both appear to reflect different aspects of vascularization. CLINICAL IMPACT: Numerous techniques have been developed to assess tissue perfusion to predict clinical outcomes following revascularization in patients with peripheral artery disease. However, none are widely implemented in clinical practice. This study emphasized the importance of employing multiple modalities from different perspectives for more accurate prediction. By focusing on both the macrovascular state and tissue perfusion, clinicians can better guide themselves in their treatment strategies.

Humans predict the forest, not the trees: statistical learning of spatiotemporal structure in visual scenes.

The human brain is capable of using statistical regularities to predict future inputs. In the real world, such inputs typically comprise a collection of objects (e.g. a forest constitutes numerous trees). The present study aimed to investigate whether perceptual anticipation relies on lower-level or higher-level information. Specifically, we examined whether the human brain anticipates each object in a scene individually or anticipates the scene as a whole. To explore this issue, we first trained participants to associate co-occurring objects within fixed spatial arrangements. Meanwhile, participants implicitly learned temporal regularities between these displays. We then tested how spatial and temporal violations of the structure modulated behavior and neural activity in the visual system using fMRI. We found that participants only showed a behavioral advantage of temporal regularities when the displays conformed to their previously learned spatial structure, demonstrating that humans form configuration-specific temporal expectations instead of predicting individual objects. Similarly, we found suppression of neural responses for temporally expected compared with temporally unexpected objects in lateral occipital cortex only when the objects were embedded within expected configurations. Overall, our findings indicate that humans form expectations about object configurations, demonstrating the prioritization of higher-level over lower-level information in temporal expectation.

Perceptual confirmation bias and decision bias underlie adaptation to sequential regularities.

Our perception does not depend exclusively on the immediate sensory input. It is also influenced by our internal predictions derived from prior observations and the temporal regularities of the environment, which can result in choice history biases. However, it is unclear how this flexible use of prior information to predict the future influences perceptual decisions. Prior information may bias decisions independently of the current sensory input, or it may modulate the weight of current sensory input based on its consistency with the expectation. To address this question, we used a visual decision-making task and manipulated the transitional probabilities between successive noisy grating stimuli. Using a reverse correlation analysis, we evaluated the contribution of stimulus-independent decision bias and stimulus-dependent sensitivity modulations to choice history biases. We found that both effects coexist, whereby there was increased bias to respond in line with the predicted orientation alongside modulations in perceptual sensitivity to favor perceptual information consistent with the prediction, akin to selective attention. Furthermore, at the individual differences level, we investigated the relationship between autistic-like traits and the adaptation of choice history biases to the sequential statistics of the environment. Over two studies, we found no convincing evidence of reduced adaptation to sequential regularities in individuals with high autistic-like traits. In sum, we present robust evidence for both perceptual confirmation bias and decision bias supporting adaptation to sequential regularities in the environment.

Amodal completion across the brain: The impact of structure and knowledge.

This study investigates the phenomenon of amodal completion within the context of naturalistic objects, employing a repetition suppression paradigm to disentangle the influence of structure and knowledge cues on how objects are completed. The research focuses on early visual cortex (EVC) and lateral occipital complex (LOC), shedding light on how these brain regions respond to different completion scenarios. In LOC, we observed suppressed responses to structure and knowledge-compatible stimuli, providing evidence that both cues influence neural processing in higher-level visual areas. However, in EVC, we did not find evidence for differential responses to completions compatible or incompatible with either structural or knowledge-based expectations. Together, our findings suggest that the interplay between structure and knowledge cues in amodal completion predominantly impacts higher-level visual processing, with less pronounced effects on the early visual cortex. This study contributes to our understanding of the complex mechanisms underlying visual perception and highlights the distinct roles played by different brain regions in amodal completion.

Updating Contextual Sensory Expectations for Adaptive Behavior.

The brain has the extraordinary capacity to construct predictive models of the environment by internalizing statistical regularities in the sensory inputs. The resulting sensory expectations shape how we perceive and react to the world; at the neural level, this relates to decreased neural responses to expected than unexpected stimuli ("expectation suppression"). Crucially, expectations may need revision as context changes. However, existing research has often neglected this issue. Further, it is unclear whether contextual revisions apply selectively to expectations relevant to the task at hand, hence serving adaptive behavior. The present fMRI study examined how contextual visual expectations spread throughout the cortical hierarchy as we update our beliefs. We created a volatile environment: two alternating contexts contained different sequences of object images, thereby producing context-dependent expectations that needed revision when the context changed. Human participants of both sexes attended a training session before scanning to learn the contextual sequences. The fMRI experiment then tested for the emergence of contextual expectation suppression in two separate tasks, respectively, with task-relevant and task-irrelevant expectations. Effects of contextual expectation emerged progressively across the cortical hierarchy as participants attuned themselves to the context: expectation suppression appeared first in the insula, inferior frontal gyrus, and posterior parietal cortex, followed by the ventral visual stream, up to early visual cortex. This applied selectively to task-relevant expectations. Together, the present results suggest that an insular and frontoparietal executive control network may guide the flexible deployment of contextual sensory expectations for adaptive behavior in our complex and dynamic world.SIGNIFICANCE STATEMENT The world is structured by statistical regularities, which we use to predict the future. This is often accompanied by suppressed neural responses to expected compared with unexpected events ("expectation suppression"). Crucially, the world is also highly volatile and context-dependent: expected events may become unexpected when the context changes, thus raising the crucial need for belief updating. However, this issue has generally been neglected. By setting up a volatile environment, we show that expectation suppression emerges first in executive control regions, followed by relevant sensory areas, only when observers use their expectations to optimize behavior. This provides surprising yet clear evidence on how the brain controls the updating of sensory expectations for adaptive behavior in our ever-changing world.

Brief Stimuli Cast a Persistent Long-Term Trace in Visual Cortex.

Visual processing is strongly influenced by recent stimulus history, a phenomenon termed adaptation. Prominent theories cast adaptation as a consequence of optimized encoding of visual information by exploiting the temporal statistics of the world. However, this would require the visual system to track the history of individual briefly experienced events, within a stream of visual input, to build up statistical representations over longer timescales. Here, using an openly available dataset from the Allen Brain Observatory, we show that neurons in the early visual cortex of the mouse indeed maintain long-term traces of individual past stimuli that persist despite the presentation of several intervening stimuli, leading to long-term and stimulus-specific adaptation over dozens of seconds. Long-term adaptation was selectively expressed in cortical, but not in thalamic, neurons, which only showed short-term adaptation. Early visual cortex thus maintains concurrent stimulus-specific memory traces of past input, enabling the visual system to build up a statistical representation of the world to optimize the encoding of new information in a changing environment.SIGNIFICANCE STATEMENT In the natural world, previous sensory input is predictive of current input over multisecond timescales. The visual system could exploit these predictabilities by adapting current visual processing to the long-term history of visual input. However, it is unclear whether the visual system can track the history of individual briefly experienced images, within a stream of input, to build up statistical representations over such long timescales. Here, we show that neurons in early visual cortex of the mouse brain exhibit remarkably long-term adaptation to brief stimuli, persisting over dozens of seconds, and despite the presentation of several intervening stimuli. The visual cortex thus maintains long-term traces of individual briefly experienced past images, enabling the formation of statistical representations over extended timescales.

Stubborn Predictions in Primary Visual Cortex.

Perceivers can use past experiences to make sense of ambiguous sensory signals. However, this may be inappropriate when the world changes and past experiences no longer predict what the future holds. Optimal learning models propose that observers decide whether to stick with or update their predictions by tracking the uncertainty or "precision" of their expectations. However, contrasting theories of prediction have argued that we are prone to misestimate uncertainty-leading to stubborn predictions that are difficult to dislodge. To compare these possibilities, we had participants learn novel perceptual predictions before using fMRI to record visual brain activity when predictive contingencies were disrupted-meaning that previously "expected" events became objectively improbable. Multivariate pattern analyses revealed that expected events continued to be decoded with greater fidelity from primary visual cortex, despite marked changes in the statistical structure of the environment, which rendered these expectations no longer valid. These results suggest that our perceptual systems do indeed form stubborn predictions even from short periods of learning-and more generally suggest that top-down expectations have the potential to help or hinder perceptual inference in bounded minds like ours.

NMR Discrimination of d- and l-α-Amino Acids at Submicromolar Concentration via Parahydrogen-Induced Hyperpolarization.

Differentiation of enantiomers represents an important research area for pharmaceutical, chemical, and food industries. However, enantiomer separation is a laborious task that demands complex analytical techniques, specialized equipment, and expert personnel. In this respect, discrimination and quantification of d- and l-α-amino acids is no exception, generally requiring extensive sample manipulation, including isolation, functionalization, and chiral separation. This complex sample treatment results in high time costs and potential biases in the quantitative determination. Here, we present an approach based on the combination of non-hydrogenative parahydrogen-induced hyperpolarization and nuclear magnetic resonance that allows detection, discrimination, and quantification of d- and l-α-amino acids in complex mixtures such as biofluids and food extracts down to submicromolar concentrations. Importantly, this method can be directly applied to the system under investigation without any prior isolation, fractionation, or functionalization step.

Solid-Phase Synthesis of Caged Luminescent Peptides via Side Chain Anchoring.

The synthesis of caged luminescent peptide substrates remains challenging, especially when libraries of the substrates are required. Most currently available synthetic methods rely on a solution-phase approach, which is less suited for parallel synthesis purposes. We herein present a solid-phase peptide synthesis (SPPS) method for the synthesis of caged aminoluciferin peptides via side chain anchoring of the P1 residue. After the synthesis of a preliminary test library consisting of 40 compounds, the synthetic method was validated and optimized for up to >100 g of resin. Subsequently, two separate larger peptide libraries were synthesized either having a P1 = lysine or arginine residue containing in total 719 novel peptide substrates. The use of a more stable caged nitrile precursor instead of caged aminoluciferin rendered our parallel synthetic approach completely suitable for SPPS and serine protease profiling was demonstrated using late-stage aminoluciferin generation.

Analysis of Complex Mixtures by Chemosensing NMR Using para-Hydrogen-Induced Hyperpolarization.

Nuclear magnetic resonance (NMR) is a powerful technique for chemical analysis. The use of NMR to investigate dilute analytes in complex systems is, however, hampered by its relatively low sensitivity. An additional obstacle is represented by the NMR signal overlap. Because solutes in a complex mixture are usually not isotopically labeled, NMR studies are often limited to 1H measurements, which, because of the modest dispersion of the 1H resonances (typically ∼10 ppm), can result in challenging signal crowding. The low NMR sensitivity issue can be alleviated by nuclear spin hyperpolarization (i.e., transiently increasing the differences in nuclear spin populations), which determines large NMR signal enhancements. This has been demonstrated for hyperpolarization methods such as dynamic nuclear polarization, spin-exchange optical pumping and para-hydrogen-induced polarization (PHIP). In particular, PHIP has grown into a fast, efficient, and versatile technique since the recent discovery of non-hydrogenative routes to achieve nuclear spin hyperpolarization.For instance, signal amplification by reversible exchange (SABRE) can generate proton as well as heteronuclear spin hyperpolarization in a few seconds in compounds that are able to transiently bind to an iridium catalyst in the presence of para-hydrogen in solution. The hyperpolarization transfer catalyst acts as a chemosensor in the sense that it is selective for analytes that can coordinate to the metal center, such as nitrogen-containing aromatic heterocycles, sulfur heteroaromatic compounds, nitriles, Schiff bases, diaziridines, carboxylic acids, and amines. We have demonstrated that the signal enhancement achieved by SABRE allows rapid NMR detection and quantification of a mixture of substrates down to low-micromolar concentration. Furthermore, in the transient complex, the spin configuration of p-H2 can be easily converted to spin hyperpolarization to produce up to 1000-fold enhanced NMR hydride signals. Because the hydrides' chemical shifts are highly sensitive to the structure of the analyte associating with the iridium complex, they can be employed as hyperpolarized "probes" to signal the presence of specific compounds in the mixture. This indirect detection of the analytes in solution provides important benefits in the case of complex systems, as hydrides resonate in a region of the 1H spectrum (at ca. -20 ppm) that is generally signal-free. The enhanced sensitivity provided by non-hydrogenative PHIP (nhPHIP), together with the absence of interference from the complex matrix (usually resonating between 0 and 10 ppm), set the detection limit for this NMR chemosensor down to sub-µM concentrations, approximately 3 orders of magnitude lower than for conventional NMR. This nhPHIP approach represents, therefore, a powerful tool for NMR analysis of dilute substrates in complex mixtures as it addresses at once the issues of signal crowding and NMR sensitivity. Importantly, being performed at high field inside the NMR spectrometer, the method allows for rapid acquisition of multiple scans, multidimensional hyperpolarized NMR spectra, in a fashion comparable to that of standard NMR measurements.In this Account, we focus on our chemosensing NMR technology, detailing its principles, advantages, and limitations and presenting a number of applications to real systems such as biofluids, beverages, and natural extracts.