Spatial probabilistic mapping of metabolite ensembles in mass spectrometry imaging.

Mass spectrometry imaging vows to enable simultaneous spatially resolved investigation of hundreds of metabolites in tissues, but it primarily relies on traditional ion images for non-data-driven metabolite visualization and analysis. The rendering and interpretation of ion images neither considers nonlinearities in the resolving power of mass spectrometers nor does it yet evaluate the statistical significance of differential spatial metabolite abundance. Here, we outline the computational framework moleculaR ( https://github.com/CeMOS-Mannheim/moleculaR ) that is expected to improve signal reliability by data-dependent Gaussian-weighting of ion intensities and that introduces probabilistic molecular mapping of statistically significant nonrandom patterns of relative spatial abundance of metabolites-of-interest in tissue. moleculaR also enables cross-tissue statistical comparisons and collective molecular projections of entire biomolecular ensembles followed by their spatial statistical significance evaluation on a single tissue plane. It thereby fosters the spatially resolved investigation of ion milieus, lipid remodeling pathways, or complex scores like the adenylate energy charge within the same image.

Tryptophan metabolism drives dynamic immunosuppressive myeloid states in IDH-mutant gliomas.

The dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype: in isocitrate dehydrogenase (IDH)-mutant tumors, differentiation of infiltrating myeloid cells is blocked, resulting in an immature phenotype. In late-stage gliomas, monocyte-derived macrophages drive tolerogenic alignment of the microenvironment, thus preventing T cell response. We define the IDH-dependent tumor education of infiltrating macrophages to be causally related to a complex re-orchestration of tryptophan metabolism, resulting in activation of the aryl hydrocarbon receptor. We further show that the altered metabolism of IDH-mutant gliomas maintains this axis in bystander cells and that pharmacological inhibition of tryptophan metabolism can reverse immunosuppression. In conclusion, we provide evidence of a glioma genotype-dependent intratumoral network of resident and recruited myeloid cells and identify tryptophan metabolism as a target for immunotherapy of IDH-mutant tumors.

Quantitative Mass Spectrometry Imaging Reveals Mutation Status-independent Lack of Imatinib in Liver Metastases of Gastrointestinal Stromal Tumors.

Mass spectrometry imaging (MSI) is an enabling technology for label-free drug disposition studies at high spatial resolution in life science- and pharmaceutical research. We present the first extensive clinical matrix-assisted laser desorption/ionization (MALDI) quantitative mass spectrometry imaging (qMSI) study of drug uptake and distribution in clinical specimen, analyzing 56 specimens of tumor and corresponding non-tumor tissues from 27 imatinib-treated patients with the biopsy-proven rare disease gastrointestinal stromal tumors (GIST). For validation, we compared MALDI-TOF-qMSI with conventional UPLC-ESI-QTOF-MS-based quantification from tissue extracts and with ultra-high resolution MALDI-FTICR-qMSI. We introduced a novel generalized nonlinear calibration model of drug quantities based on computational evaluation of drug-containing areas that enabled better data fitting and assessment of the inherent method nonlinearities. Imatinib tissue spatial maps revealed striking inefficiency in drug penetration into GIST liver metastases even though the corresponding healthy liver tissues in the vicinity showed abundant imatinib levels beyond the limit of quantification (LOQ), thus providing evidence for secondary drug resistance independent of mutation status. Taken together, these findings underscore the important application of MALDI-qMSI in studying the spatial distribution of molecularly targeted therapeutics in oncology, namely to serve as orthogonal post-surgical approach to evaluate the contribution of anticancer drug disposition to resistance against treatment.

An intelligent healthcare management system: a new approach in work-order prioritization for medical equipment maintenance requests.

The effective maintenance management of medical technology influences the quality of care delivered and the profitability of healthcare facilities. Medical equipment maintenance in Jordan lacks an objective prioritization system; consequently, the system is not sensitive to the impact of equipment downtime on patient morbidity and mortality. The current work presents a novel software system (EQUIMEDCOMP) that is designed to achieve valuable improvements in the maintenance management of medical technology. This work-order prioritization model sorts medical maintenance requests by calculating a priority index for each request. Model performance was assessed by utilizing maintenance requests from several Jordanian hospitals. The system proved highly efficient in minimizing equipment downtime based on healthcare delivery capacity, and, consequently, patient outcome. Additionally, a preventive maintenance optimization module and an equipment quality control system are incorporated. The system is, therefore, expected to improve the reliability of medical equipment and significantly improve safety and cost-efficiency.