MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation

In situ analysis of metabolic changes under hypoxic-ischemic encephalopathy via MALDI mass spectrometry imaging

Hypoxic-ischemic encephalopathy (HIE) is a leading cause of neurological disability and even more serious fetal or neonatal asphyxia death. As the therapeutic time window is limited and timely intervention could have a better prognosis, elucidating the mechanisms underlying HIE and developing novel therapeutic strategies is of great importance. In the present study, 1, 5-Diaminonaphthalene hydrochloride (1, 5-DANHCl) assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) was applied to the neonatal rat model of HIE to investigate metabolic changes during hypoxic-ischemic period. Seventy-three metabolites involved in various metabolic pathways such as glycolysis, tricarboxylic acid (TCA) cycle, nucleoside metabolism, lipid metabolism, oxidative stress and ionic homeostasis demonstrated significant changes. It is worth mentioning that we have detected neutral triglycerides (TGs) that are difficult to ionize and observed their accumulation in the ischemic region, which has been rarely reported in previous studies. The results not only help us discover biomarkers but also provide new insights into its mechanism for us to understand the pathological and physiological processes of the disease.

Advances in Imaging Mass Spectrometry for Biomedical and Clinical Research

Imaging mass spectrometry (IMS) allows for the untargeted mapping of biomolecules directly from tissue sections. This technology is increasingly integrated into biomedical and clinical research environments to supplement traditional microscopy and provide molecular context for tissue imaging. IMS has widespread clinical applicability in the fields of oncology, dermatology, microbiology, and others. This review summarizes the two most widely employed IMS technologies, matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), and covers technological advancements, including efforts to increase spatial resolution, specificity, and throughput. We also highlight recent biomedical applications of IMS, primarily focusing on disease diagnosis, classification, and subtyping.

PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas

MOTIVATION

Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based tumor pathology prediction, their model complexity predictive performance is limited by the small dataset sizes. Furthermore, the information conveyed by the feedback provided on the tumor tissue could be improved both in terms of content and accuracy.

RESULTS

In this study, we propose a metabolic pathway-informed deep learning model (PiDeeL) to perform survival analysis and pathology assessment based on metabolite concentrations. We show that incorporating pathway information into the model architecture substantially reduces parameter complexity and achieves better survival analysis and pathological classification performance. With these design decisions, we show that PiDeeL improves tumor pathology prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve by 3.38% and the Area Under the Precision-Recall Curve by 4.06%. Similarly, with respect to the time-dependent concordance index (c-index), PiDeeL achieves better survival analysis performance (improvement of 4.3%) when compared to the state-of-the-art. Moreover, we show that importance analyses performed on input metabolite features as well as pathway-specific neurons of PiDeeL provide insights into tumor metabolism. We foresee that the use of this model in the surgery room will help surgeons adjust the surgery plan on the fly and will result in better prognosis estimates tailored to surgical procedures.

AVAILABILITY AND IMPLEMENTATION

The code is released at https://github.com/ciceklab/PiDeeL. The data used in this study are released at https://zenodo.org/record/7228791.

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

Extensive effort has been devoted to the discovery, development, and validation of biomarkers for early disease diagnosis and prognosis as well as rapid evaluation of the response to therapeutic interventions. Genomic and transcriptomic profiling are well-established means to identify disease-associated biomarkers. However, analysis of disease-associated peptidomes can also identify novel peptide biomarkers or signatures that provide sensitive and specific diagnostic and prognostic information for specific malignant, chronic, and infectious diseases. Growing evidence also suggests that peptidomic changes in liquid biopsies may more effectively detect changes in disease pathophysiology than other molecular methods. Knowledge gained from peptide-based diagnostic, therapeutic, and imaging approaches has led to promising new theranostic applications that can increase their bioavailability in target tissues at reduced doses to decrease side effects and improve treatment responses. However, despite major advances, multiple factors can still affect the utility of peptidomic data. This review summarizes several remaining challenges that affect peptide biomarker discovery and their use as diagnostics, with a focus on technological advances that can improve the detection, identification, and monitoring of peptide biomarkers for personalized medicine.

Metabolomics in acromegaly: a systematic review

The therapeutic response heterogeneity in acromegaly persists, despite the medical-surgical advances of recent years. Thus, personalized medicine implementation, which focuses on each patient, is justified. Metabolomics would decipher the molecular mechanisms underlying the therapeutic response heterogeneity. Identification of altered metabolic pathways would open new horizons in the therapeutic management of acromegaly. This research aimed to evaluate the metabolomic profile in acromegaly and metabolomics' contributions to understanding disease pathogenesis. A systematic review was carried out by querying four electronic databases and evaluating patients with acromegaly through metabolomic techniques. In all, 21 studies containing 362 patients were eligible. Choline, the ubiquitous metabolite identified in growth hormone (GH)-secreting pituitary adenomas (Pas) by in vivo magnetic resonance spectroscopy (MRS), negatively correlated with somatostatin receptors type 2 expression and positively correlated with magnetic resonance imaging T2 signal and Ki-67 index. Moreover, elevated choline and choline/creatine ratio differentiated between sparsely and densely granulated GH-secreting PAs. MRS detected low hepatic lipid content in active acromegaly, which increased after disease control. The panel of metabolites of acromegaly deciphered by mass spectrometry (MS)-based techniques mainly included amino acids (especially branched-chain amino acids and taurine), glyceric acid, and lipids. The most altered pathways in acromegaly were the metabolism of glucose (particularly the downregulation of the pentose phosphate pathway), linoleic acid, sphingolipids, glycerophospholipids, arginine/proline, and taurine/hypotaurine. Matrix-assisted laser desorption/ionization coupled with MS imaging confirmed the functional nature of GH-secreting PAs and accurately discriminated PAs from healthy pituitary tissue.

Systematic study of tissue section thickness for MALDI MS profiling and imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) has become a powerful method for studying the spatial distribution of molecules. Preparation of tissue sections is a critical step for obtaining high-quality imaging data. The thickness of the slice of tissue affects the feature quality of MALDI MSI. However, few studies involved in-depth and systematic examination of slice thickness. Herein, we investigate the effect of tissue slice thickness on MALDI MSI detection. We found that the thicker the slice, the worse the results obtained by MALDI MS, which we attributed to the charging effect. The optimal slice thickness of brain tissue obtained in this work is 2-6 μm. Comparisons of the effects of slice thickness on atmospheric pressure and vacuum MALDI assays indicated that the ion signals and imaging quality of vacuum MALDI were more seriously affected by the thickness, with atmospheric pressure (AP) MALDI having a greater tolerance for slice thickness than vacuum MALDI. The MALDI MSI of peptides after enzymatic digestion of tissue sections of different thicknesses was also studied, revealing that the most suitable tissue thickness for enzyme digestion is about 10 μm. Finally, we optimized the slice thicknesses of six tissues in mice to provide a reference for MALDI MSI studies. It is worth mentioning that in our study the values of slice thickness range from the nanometer level (400 nm) at the minimum to 150 μm at the maximum, values which were unprecedented. Detailed in-depth and systematic studies of slice thickness will promote the development of sample preparation technology of AP and vacuum MALDI MSI, which will provide important references for the selection of tissue section thickness.

A noise-robust deep clustering of biomolecular ions improves interpretability of mass spectrometric images

MOTIVATION

Mass Spectrometry Imaging (MSI) analyzes complex biological samples such as tissues. It simultaneously characterizes the ions present in the tissue in the form of mass spectra, and the spatial distribution of the ions across the tissue in the form of ion images. Unsupervised clustering of ion images facilitates the interpretation in the spectral domain, by identifying groups of ions with similar spatial distributions. Unfortunately, many current methods for clustering ion images ignore the spatial features of the images, and are therefore unable to learn these features for clustering purposes. Alternative methods extract spatial features using deep neural networks pre-trained on natural image tasks; however, this is often inadequate since ion images are substantially noisier than natural images.

RESULTS

We contribute a deep clustering approach for ion images that accounts for both spatial contextual features and noise. In evaluations on a simulated dataset and on four experimental datasets of different tissue types, the proposed method grouped ions from the same source into a same cluster more frequently than existing methods. We further demonstrated that using ion image clustering as a pre-processing step facilitated the interpretation of a subsequent spatial segmentation as compared to using either all the ions or one ion at a time. As a result, the proposed approach facilitated the interpretability of MSI data in both the spectral domain and the spatial domain.

AVAILABILITYAND IMPLEMENTATION

The data and code are available at https://github.com/DanGuo1223/mzClustering.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Analyses and Localization of Serotonin and L-DOPA in Ocular Tissues by Imaging Mass Spectrometry

Imaging mass spectrometry (IMS) allows for visualization of the spatial distribution of proteins, lipids, and other metabolites in a targeted or untargeted approach. The identification of compounds through mass spectrometry combined with the mapping of compound distribution in the sample establishes IMS as a powerful tool for metabolomics. IMS analysis for serotonin will allow researchers to pinpoint areas of deficiencies or accumulations associated with ocular disorders such as serotonin selective reuptake inhibitor optic neuropathy. Furthermore, L-DOPA has shown great promise as a therapeutic approach for disorders such as age-related macular degeneration, and IMS allows for localization, and relative magnitudes, of L-DOPA in the eye. We describe here an end-to-end approach of IMS from sample preparation to data analysis for serotonin and L-DOPA analysis.

Imaging the Hypothalamo-Neurohypophysial System

The hypothalamo-neurohypophysial system (HNS) is a brain peptidergic neurosecretory apparatus which is composed of arginine vasopressin (AVP) and oxytocin (OXT) magnocellular neurones and their neuronal processes in the posterior pituitary (PP). In response to specific stimuli, AVP and OXT are secreted into the systemic circulation at the neurovascular interface of the PP, where they act as hormones, but they can also behave as neurotransmitters when released at the somatodendritic compartment or by axon collaterals to other brain regions. Because these peptides are crucial for several physiological processes, including fluid homoeostasis and reproduction, it is of great importance to map the HNS connectome in its entirety in order to understand its functions. In recent years, advances in imaging technologies have provided considerable new information about the HNS. These approaches include the use of reporter proteins under the control of specific promoters, viral tracers, brain-clearing methods, genetically encoded indicators, sniffer cells, mass spectrometry imaging, and spatially resolved transcriptomics. In this review, we illustrate how these latest approaches have enhanced our understanding of the structure and function of the HNS and how they might contribute further in the coming years.

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.

Targeted metabolomics analyses for brain tumor margin assessment during surgery

MOTIVATION

Identification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy-based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique. While machine learning techniques that analyze the NMR spectrum in an untargeted manner (i.e. using the full raw signal) have been shown to effectively automate this feedback mechanism, high dimensional and noisy structure of the NMR signal limits the attained performance.

RESULTS

In this study, we show that identifying informative regions in the HRMAS NMR spectrum and using them for tumor margin assessment improves the prediction power. We use the spectra normalized with the ERETIC (electronic reference to access in vivo concentrations) method which uses an external reference signal to calibrate the HRMAS NMR spectrum. We train models to predict quantities of metabolites from annotated regions of this spectrum. Using these predictions for tumor margin assessment provides performance improvements up to 4.6% the Area Under the ROC Curve (AUC-ROC) and 2.8% the Area Under the Precision-Recall Curve (AUC-PR). We validate the importance of various tumor biomarkers and identify a novel region between 7.97 ppm and 8.09 ppm as a new candidate for a glioma biomarker.

AVAILABILITY AND IMPLEMENTATION

The code is released at https://github.com/ciceklab/targeted_brain_tumor_margin_assessment. The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.5781769.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Development of an Automatic Ultrasonic Matrix Sprayer for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) has emerged as a powerful tool for studying the spatial distribution of various types of molecules. Matrix deposition is a critical step for obtaining high-quality imaging data. An automatic device named SoniCoat was fabricated based on an ultrasonic nozzle driven by a pizeoelectric ceramic for matrix coating in the present study. Compared with the minihumidifier developed previously by our group for matrix deposition, SoniCoat realized automation, and the ultrasonic nozzle of this device is easy to clean and less likely to get clogged, indicating a longer life. Compared with commercial and homemade instruments such as the TM-Sprayer and electrospray matrix coating device, our newly developed device achieved a good nebulization effect without the need for high-pressure gas flow and high voltage. Matrix deposition by SoniCoat generated more homogeneous matrix crystals with diameters of about 10 μm and reduced the occurrence of molecular diffusion to a greater extent, compared with the results by TM-Sprayer. Repeatable high-spatial-resolution MS imaging data were obtained with the help of SoniCoat. Furthermore, the newly developed device can also be successfully applied for in situ derivatization.

Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20–30% of cases are deemed “indeterminate for malignancy” and undergo surgery. However, after thyroidectomy, 70–80% of these nodules are benign. The identification of tools for improving FNA’s diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (n = 70), with 100.0% (95% CI = 83.2–100.0%) sensitivity and 96.0% (95% CI = 86.3–99.5%) specificity. The external validation (n = 170) showed a specificity of 82.9% (95% CI = 74.3–89.5%) and a sensitivity of 43.1% (95% CI = 30.9–56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8–89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up.

Advances in MALDI Mass Spectrometry Imaging Single Cell and Tissues

Compared with conventional optical microscopy techniques, mass spectrometry imaging (MSI) or imaging mass spectrometry (IMS) is a powerful, label-free analytical technique, which can sensitively and simultaneously detect, quantify, and map hundreds of biomolecules, such as peptides, proteins, lipid, and other organic compounds in cells and tissues. So far, although several soft ionization techniques, such as desorption electrospray ionization (DESI) and secondary ion mass spectrometry (SIMS) have been used for imaging biomolecules, matrix-assisted laser desorption/ionization (MALDI) is still the most widespread MSI scanning method. Here, we aim to provide a comprehensive review of MALDI-MSI with an emphasis on its advances of the instrumentation, methods, application, and future directions in single cell and biological tissues.

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation

MOTIVATION

Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computational challenges in its analysis. The complexity of the data arises from its large size, high-dimensionality and spectral nonlinearity. Preprocessing, including peak picking, has been used to reduce raw data complexity; however, peak picking is sensitive to parameter selection that, perhaps prematurely, shapes the downstream analysis for tissue classification and ensuing biological interpretation.

RESULTS

We propose a deep learning model, massNet, that provides the desired qualities of scalability, nonlinearity and speed in MSI data analysis. This deep learning model was used, without prior preprocessing and peak picking, to classify MSI data from a mouse brain harboring a patient-derived tumor. The massNet architecture established automatically learning of predictive features, and automated methods were incorporated to identify peaks with potential for tumor delineation. The model's performance was assessed using cross-validation, and the results demonstrate higher accuracy and a substantial gain in speed compared to the established classical machine learning method, support vector machine.

AVAILABILITY AND IMPLEMENTATION

https://github.com/wabdelmoula/massNet. The data underlying this article are available in the NIH Common Fund's National Metabolomics Data Repository (NMDR) Metabolomics Workbench under project id (PR001292) with http://dx.doi.org/10.21228/M8Q70T.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review

OBJECTIVE

A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.

METHODS

We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies.

RESULTS

Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively.

CONCLUSION

Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible.

SIGNIFICANCE

By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

Applications of spatially resolved omics in the field of endocrine tumors

Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.

Data parsing in mass spectrometry imaging using R Studio and Cardinal: A tutorial

Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.

Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis

Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed.

Chemical analysis of the human brain by imaging mass spectrometry

Analysis of the chemical makeup of the brain enables a deeper understanding of several neurological processes. Molecular imaging that deciphers the spatial distribution of neurochemicals with high specificity and sensitivity is an exciting avenue in this aspect. The past two decades have witnessed a significant surge of mass spectrometry imaging (MSI) that can simultaneously map the distribution of hundreds to thousands of biomolecules in the tissue specimen at a fairly high resolution, which is otherwise beyond the scope of other molecular imaging techniques. In this review, we have documented the evolution of MSI technologies in imaging the anatomical distribution of neurochemicals in the human brain in the context of several neuro diseases. This review also addresses the potential of MSI to be a next-generation molecular imaging technique with its promising applications in neuropathology.

MASS SPECTROMETRY TECHNOLOGIES TO ADVANCE CARE FOR CANCER PATIENTS IN CLINICAL AND INTRAOPERATIVE USE

Developments in mass spectrometry technologies have driven a widespread interest and expanded their use in cancer-related research and clinical applications. In this review, we highlight the developments in mass spectrometry methods and instrumentation applied to direct tissue analysis that have been tailored at enhancing performance in clinical research as well as facilitating translation and implementation of mass spectrometry in clinical settings, with a focus on cancer-related studies. Notable studies demonstrating the capabilities of direct mass spectrometry analysis in biomarker discovery, cancer diagnosis and prognosis, tissue analysis during oncologic surgeries, and other clinically relevant problems that have the potential to substantially advance cancer patient care are discussed. Key challenges that need to be addressed before routine clinical implementation including regulatory efforts are also discussed. Overall, the studies highlighted in this review demonstrate the transformative potential of mass spectrometry technologies to advance clinical research and care for cancer patients. © 2020 Wiley Periodicals, Inc. Mass Spec Rev.

Clinical Translation and Evaluation of a Handheld and Biocompatible Mass Spectrometry Probe for Surgical Use

BACKGROUND

Intraoperative tissue analysis and identification are critical to guide surgical procedures and improve patient outcomes. Here, we describe the clinical translation and evaluation of the MasSpec Pen technology for molecular analysis of in vivo and freshly excised tissues in the operating room (OR).

METHODS

An Orbitrap mass spectrometer equipped with a MasSpec Pen interface was installed in an OR. A "dual-path" MasSpec Pen interface was designed and programmed for the clinical studies with 2 parallel systems that facilitated the operation of the MasSpec Pen. The MasSpec Pen devices were autoclaved before each surgical procedure and were used by surgeons and surgical staff during 100 surgeries over a 12-month period.

RESULTS

Detection of mass spectral profiles from 715 in vivo and ex vivo analyses performed on thyroid, parathyroid, lymph node, breast, pancreatic, and bile duct tissues during parathyroidectomies, thyroidectomies, breast, and pancreatic neoplasia surgeries was achieved. The MasSpec Pen enabled gentle extraction and sensitive detection of various molecular species including small metabolites and lipids using a droplet of sterile water without causing apparent tissue damage. Notably, effective molecular analysis was achieved while no limitations to sequential histologic tissue analysis were identified and no device-related complications were reported for any of the patients.

CONCLUSIONS

This study shows that the MasSpec Pen system can be successfully incorporated into the OR, allowing direct detection of rich molecular profiles from tissues with a seconds-long turnaround time that could be used to inform surgical and clinical decisions without disrupting tissue analysis workflows.

Bringing Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to the Clinics

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is an emerging analytical technique that promises to change tissue-based diagnostics. This article provides a brief introduction to MALDI MSI as well as clinical diagnostic workflows and opportunities to apply this powerful approach. It describes various MALDI MSI applications, from more clinically mature applications such as cancer to emerging applications such as infectious diseases and drug distribution. In addition, it discusses the analytical considerations that need to be considered when bringing these approaches to different diagnostic problems and settings.

Mapping the distribution of perfluoroalkyl substances in zebrafishes by liquid extraction surface analysis mass spectrometry

Investigation on the distribution of persistent organic pollutants (POPs) in aquatic organisms is of great importance for exploring the biological toxicity and health risks of environmental pollutants. In this study, a liquid extraction surface analysis mass spectrometry (LESA-MS) method was developed for rapid and in situ analysis of the spatial distribution of perfluoroalkyl substances (PFASs) in zebrafish. By combining the high-precision automated moving platform of LESA device and the high-resolution MS, quantitative analysis of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) in zebrafish tissue section were easily achieved. A tissue-specific ionization efficiency factor (TSF) strategy was also proposed to correct the matrix effect in different parts of zebrafish tissue. By using the developed method, high sensitive and efficient imaging of PFOA and PFOS in zebrafish tissue was achieved, and the distributions of PFOA and PFOS in descending order were gills, organs, roes, pelvic fin, muscle, and brain. The experimental results demonstrated that the coupling of LESA-MS method with TFS strategy is an efficient and reliable approach for monitoring the content distribution of environmental pollutants in biological tissues.

Cholesterol was identified as a biomarker in human melanocytic nevi using DESI and DESI/PI mass spectrometry imaging

The rapid differentiation between diseased tissue and healthy normal tissue is of great importance for the intraoperative diagnosis. Herein, desorption electrospray ionization (DESI) and DESI/post-photoionization (DESI/PI) mass spectrometry imaging were combined to in situ visualize the distribution of biochemicals within the tissue regions of human melanocytic nevi under the ambient condition with a spatial resolution of around 200 μm. Plenty of polar and nonpolar lipids were found to be specifically distributed in melanocytic nevi with statistical significance and could be used to differentiate the healthy normal tissue and melanocytic nevi. Cholesterol was further confirmed to be a potential biomarker for melanocytic nevi diagnosis by multivariate statistical analysis and immunohistochemistry of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and translocator protein (TSPO) enzymes. This work provides a visual way for the diagnosis of human melanocytic nevi by lipid profiling, which benefits the understanding of the pathological mechanism of melanocytic nevi and provides a new insight to control melanin growth from the synthesis, transport, and metabolism of cholesterol.

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations

MOTIVATION

Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, which takes as input MSI spectra, and assigns class labels to subtissue locations. Unfortunately, developing such classifiers is hindered by the limited availability of training sets with subtissue labels as the ground truth. Subtissue labeling is prohibitively expensive, and only rough annotations of the entire tissues are typically available. Classifiers trained on data with approximate labels have sub-optimal performance.

RESULTS

To alleviate this challenge, we contribute a semi-supervised approach mi-CNN. mi-CNN implements multiple instance learning with a convolutional neural network (CNN). The multiple instance aspect enables weak supervision from tissue-level annotations when classifying subtissue locations. The convolutional architecture of the CNN captures contextual dependencies between the spectral features. Evaluations on simulated and experimental datasets demonstrated that mi-CNN improved the subtissue classification as compared to traditional classifiers. We propose mi-CNN as an important step toward accurate subtissue classification in MSI, enabling rapid distinction between tissue types and disease states.

AVAILABILITY AND IMPLEMENTATION

The data and code are available at https://github.com/Vitek-Lab/mi-CNN_MSI.

Biological Applications for LC-MS-Based Proteomics

Since its inception, liquid chromatography-mass spectrometry (LC-MS) has been continuously improved upon in many aspects, including instrument capabilities, sensitivity, and resolution. Moreover, the costs to purchase and operate mass spectrometers and liquid chromatography systems have decreased, thus increasing affordability and availability in sectors outside of academic and industrial research. Processing power has also grown immensely, cutting the time required to analyze samples, allowing more data to be feasibly processed, and allowing for standardized processing pipelines. As a result, proteomics via LC-MS has become popular in many areas of biological sciences, forging an important seat for itself in targeted and untargeted assays, pure and applied science, the laboratory, and the clinic. In this chapter, many of these applications of LC-MS-based proteomics and an outline of how they can be executed will be covered. Since the field of personalized medicine has matured alongside proteomics, it has also come to rely on various mass spectrometry methods and will be elaborated upon as well. As time goes on and mass spectrometry evolves, there is no doubt that its presence in these areas, and others, will only continue to grow.

Pneumatically Sprayed Gold Nanoparticles for Mass Spectrometry Imaging of Neurotransmitters

Using citrate-capped gold nanoparticles (AuNPs) for laser desorption ionization mass spectrometry (LDI-MS) is an approach that has demonstrated broad applicability to ionization of different classes of molecules. Here, we show a simple AuNP-based approach for the ionization of neurotransmitters. Specifically, the detection of acetylcholine, dopamine, epinephrine, glutamine, 4-aminobutyric acid, norepinephrine, octopamine, and serotonin was achieved at physiologically relevant concentrations in serum and homogenized tissue. Additionally, pneumatic spraying of AuNPs onto tissue sections facilitated mass spectrometry imaging (MSI) of rabbit brain tissue sections, zebrafish embryos, and neuroblastoma cells for several neurotransmitters simultaneously using this quick and simple sample preparation. AuNP LDI-MS achieved mapping of neurotransmitters in fine structures of zebrafish embryos and neuroblastoma cells at a lateral spatial resolution of 5 μm. The use of AuNPs to ionize small aminergic neurotransmitters in situ provides a fast, high-spatial resolution method for simultaneous detection of a class of molecules that typically evade comprehensive detection with traditional matrixes.

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

Complete 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 malign 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 analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately 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. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning

BACKGROUND

Machine learning has emerged as a viable asset in the setting of pituitary surgery. In the past decade, the number of machine learning models developed to aid in the diagnosis of pituitary lesions and predict intraoperative and postoperative complications following transsphenoidal surgery has increased exponentially. As computational processing power continues to increase, big data sets continue to expand, and learning algorithms continue to surpass gold standard predictive tools, machine learning will serve to become an important component in improving patient care and outcomes.

METHODS

Relevant studies were identified based on a literature search in PubMed and MEDLINE databases, as well as from other sources including reference lists of published articles.

RESULTS

Radiomics and artificial neural networks comprise the majority of machine learning-based applications in pituitary surgery. Radiomics serves to quantify specific imaging features, which can then be used to noninvasively identify tumor characteristics and make definitive diagnoses, circumventing presurgical biopsy altogether. Neural networks can be adapted to predict intraoperative changes in visual evoked potentials or cerebral spinal fluid leak. In addition, these algorithms may be combined with others to predict tumor aggressiveness, gross total resection, recurrence and remission, and even total cost burden.

CONCLUSIONS

The field of machine learning is broad, with radiomics and artificial neural networks comprising 2 commonly used supervised learning methods in pituitary surgery. Given the large heterogeneity of pituitary and sellar lesions, the promise of machine learning lies in its ability to identify relationships and patterns that are otherwise hidden from standard statistical methods. While machine learning has great potential as a clinical adjunct during the surgical preplanning process and in predicting complications and outcomes, challenges moving forward include standardization and validation of these paradigms.

Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions

PURPOSE

To provide an overview of fundamental concepts in machine learning (ML), review the literature on ML applications in imaging analysis of pituitary tumors for the last 10 years, and highlight the future directions on potential applications of ML for pituitary tumor patients.

METHOD

We presented an overview of the fundamental concepts in ML, its various stages used in healthcare, and highlighted the key components typically present in an imaging-based tumor analysis pipeline. A search was conducted across four databases (PubMed, Ovid, Embase, and Google Scholar) to gather research articles from the past 10 years (2009-2019) involving imaging related to pituitary tumor and ML. We grouped the studies by imaging modalities and analyzed the ML tasks in terms of the data inputs, reference standards, methodologies, and limitations.

RESULTS

Of the 16 studies included in our analysis, 10 appeared in 2018-2019. Most of the studies utilized retrospective data and followed a semi-automatic ML pipeline. The studies included use of magnetic resonance imaging (MRI), facial photographs, surgical microscopic video, spectrometry, and spectroscopy imaging. The objectives of the studies covered 14 distinct applications and majority of the studies addressed a binary classification problem. Only five of the 11 MRI-based studies had an external validation or a holdout set to test the performance of a final trained model.

CONCLUSION

Through our concise evaluation and comparison of the studies using the concepts presented, we highlight future directions so that potential ML applications using different imaging modalities can be developed to benefit the clinical care of pituitary tumor patients.

Mass spectrometry imaging-based metabolomics to visualize the spatially resolved reprogramming of carnitine metabolism in breast cancer

New insights into tumor-associated metabolic reprogramming have provided novel vulnerabilities that can be targeted for cancer therapy. Here, we propose a mass spectrometry imaging (MSI)-based metabolomic strategy to visualize the spatially resolved reprogramming of carnitine metabolism in heterogeneous breast cancer. Methods: A wide carnitine coverage MSI method was developed to investigate the spatial alternations of carnitines in cancer tissues of xenograft mouse models and human samples. Spatial expression of key metabolic enzymes that are closely associated with the altered carnitines was examined in adjacent cancer tissue sections. Results: A total of 17 carnitines, including L-carnitine, 6 short-chain acylcarnitines, 3 middle-chain acylcarnitines, and 7 long-chain acylcarnitines were imaged. L-carnitine and short-chain acylcarnitines are significantly reprogrammed in breast cancer. A classification model based on the carnitine profiles of 170 cancer samples and 128 normal samples enables an accurate identification of breast cancer. CPT 1A, CPT 2, and CRAT, which are extensively involved in carnitine system-mediated fatty acid β-oxidation pathway were also found to be abnormally expressed in breast cancer. Remarkably, the expressions of CPT 2 and CRAT were found for the first time to be altered in breast cancer. Conclusion: These data not only expand our understanding of the complex tumor metabolic reprogramming, but also provide the first evidence that carnitine metabolism is reprogrammed at both the metabolite and enzyme levels in breast cancer.

The choice of tissue fixative is a key determinant for mass spectrometry imaging based tumor metabolic reprogramming characterization

The application of mass spectrometry imaging (MSI) for the study of spatiotemporal alterations of the metabolites in tumors has brought a number of significant biological results. At present, metabolite profiling based on MSI is typically performed on frozen tissue sections; however, the majority of clinical specimens need to be fixed in tissue fixative to avoid autolysis and to preserve antigenicity. In this study, we present the global impacts of different fixatives on the MS imaging of gastric cancer tissue metabolites. The MSI performances of 17 kinds of metabolites, such as amino acids, polyamines, cholines, organic acids, polypeptides, nucleotides, nucleosides, nitrogen bases, cholesterols, fatty acids, and phospholipids, in untreated, 10% formalin-, 4% paraformaldehyde-, acetone-, and 95% ethanol-fixed gastric cancer tissues were thoroughly explored for the first time. Furthermore, we also investigated the spatial expressions of 6 metabolic enzymes, namely, GLS, FASN, CHKA, PLD2, cPLA2, and EGFR, closely related to tumor-associated metabolites. Immunohistochemical staining carried out on the same tissue sections' which have undergone MSI analysis' suggests that enzymatic characterization is feasible after metabolite imaging. Combining the spatial signatures of metabolites and pathway-related metabolic enzymes in heterogeneous tumor tissues offers an insight to understand the complex tumor metabolism. Graphical abstract.

1,1'-binaphthyl-2,2'-diamine as a novel MALDI matrix to enhance the in situ imaging of metabolic heterogeneity in lung cancer

Profile the spatial distributions of endogenous metabolites in heterogeneous tissues is critical to elucidate the complex metabolic mechanisms during pathological progression. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a label-free technique for tissue imaging that allows simultaneous localisation and quantification of metabolites in different histological regions of interest. In the present study, 1,1'-binaphthyl-2,2'-diamine (BNDM) was developed as a novel MALDI matrix for the detection and imaging of metabolites because of its low background interference, high sensitivity, and applicability in both positive and negative ion modes. 301 negative metabolite ions and 175 positive metabolite ions, including amino acids, organic acids, nucleosides, nucleotides, nitrogenous bases, cholesterols, peptides, fatty acids, cholines, carnitines, polyamines, creatine, phospholipids, etc., were imaged in rat brain when BDMN was used as matrix. Furthermore, BNDM-assisted MALDI-MSI of mouse lung cancer tissue successfully characterized the spatial features of numerous metabolites in viable, necrotic, and connective tissue areas. Importantly, our results demonstrate that the viable area of lung cancer tissue contained a higher abundance of K⁺ adducts, while the necrotic area showed a stronger Na⁺ adducts intensity. Data-driven segmentation analysis based on the in situ tissue metabolic fingerprints clearly visualized the underlying metabolic heterogeneity of lung cancer, which may provide new insights into the profiling of tumor microenvironment. All these results suggest that the newly developed matrix has great potential application in the field of biomedical research.

Ins and Outs of Interpreting Lipidomic Results

Membrane lipids are essential for life; however, research on how cells regulate cell lipid composition has been falling behind for quite some time. One reason was the difficulty in establishing analytical methods able to cope with the cell lipid repertoire. Development of a diversity of mass spectrometry-based technologies, including imaging mass spectrometry, has helped to demonstrate beyond doubt that the cell lipidome is not only greatly cell type dependent but also highly sensitive to any pathophysiological alteration such as differentiation or tumorigenesis. Interestingly, the current popularization of metabolomic studies among numerous disciplines has led many researchers to rediscover lipids. Hence, it is important to underscore the peculiarities of these metabolites and their metabolism, which are both radically different from protein and nucleic acid metabolism. Once differences in lipid composition have been established, researchers face a rather complex scenario, to investigate the signaling pathways and molecular mechanisms accounting for their results. Thus, a detail often overlooked, but of crucial relevance, is the complex networks of enzymes involved in controlling the level of each one of the lipid species present in the cell. In most cases, these enzymes are redundant and promiscuous, complicating any study on lipid metabolism, since the modification of one particular lipid enzyme impacts simultaneously on many species. Altogether, this review aims to describe the difficulties in delving into the regulatory mechanisms tailoring the lipidome at the activity, genetic, and epigenetic level, while conveying the numerous, stimulating, and sometimes unexpected research opportunities afforded by this type of studies.

Rapid MALDI mass spectrometry imaging for surgical pathology

Matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) is an emerging analytical technique, which generates spatially resolved proteomic and metabolomic images from tissue specimens. Conventional MALDI MSI processing and data acquisition can take over 30 min, limiting its clinical utility for intraoperative diagnostics. We present a rapid MALDI MSI method, completed under 5 min, including sample preparation and analysis, providing a workflow compatible with the clinical frozen section procedure.

A systematic review on machine learning in sellar region diseases: quality and reporting items

INTRODUCTION

Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies.

METHODS

PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance.

RESULTS

Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing's disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results.

CONCLUSIONS

Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.

Novel methods in adrenal research: a metabolomics approach

Metabolic alterations have implications in a spectrum of tissue functions and disease. Aided by novel molecular biological and computational tools, our understanding of physiological and pathological processes underpinning endocrine and endocrine-related disease has significantly expanded over the last decade. Herein, we focus on novel metabolomics-related methodologies in adrenal research: in situ metabolomics by mass spectrometry imaging, steroid metabolomics by gas and liquid chromatography-mass spectrometry, energy pathway metabologenomics by liquid chromatography-mass spectrometry-based metabolomics of Krebs cycle intermediates, and cellular reprogramming to generate functional steroidogenic cells and hence to modulate the steroid metabolome. All four techniques to assess and/or modulate the metabolome in biological systems provide tremendous opportunities to manage neoplastic and non-neoplastic disease of the adrenal glands in the era of precision medicine. In this context, we discuss emerging clinical applications and/or promising metabolic-driven research towards diagnostic, prognostic, predictive and therapeutic biomarkers in tumours arising from the adrenal gland and extra-adrenal paraganglia as well as modern approaches to delineate and reprogram adrenal metabolism.

Whole Cell MALDI Fingerprinting Is a Robust Tool for Differential Profiling of Two-Component Mammalian Cell Mixtures

MALDI fingerprinting was first described two decades ago as a technique to identify microbial cell lines. Microbial fingerprinting has since evolved into an automated platform for microorganism identification and classification, which is now routinely used in clinical and environmental sectors. The extension of fingerprinting to mammalian cells has yet to progress partly due to compartmentalization of eukaryotic cells and overall higher cellular complexity. A number of publications on mammalian whole cell fingerprinting suggest that the method could be useful for classification of different cell types, cell states, and monitoring cell differentiation. We report the optimization of MALDI fingerprinting workflow parameters for mammalian cells and its application for differential profiling of mammalian cell lines and two-component cell line mixtures. Murine fallopian tube cells and high-grade ovarian carcinoma cell lines and their mixtures are used as model mammalian cell lines. Two-component cell mixtures serve to determine the method's feasibility for complex biological samples as the ability to detect cancer cells in a mixed cell population. The level of detection of cancer cells in the two-component mixture by principle component analysis (PCA) starts to deteriorate at 5% but with application of a different statistical approach, Wilcoxon rank sum test, the level of detection was determined to be 1%. The ability to differentiate heterogeneous cell mixtures will help further extend whole cell MALDI fingerprinting to complex biological systems. Graphical Abstract.

Proteome Imaging: From Classic to Modern Mass Spectrometry-Based Molecular Histology

In order to overcome the limitations of classic imaging in Histology during the actually era of multiomics, the multi-color "molecular microscope" by its emerging "molecular pictures" offers quantitative and spatial information about thousands of molecular profiles without labeling of potential targets. Healthy and diseased human tissues, as well as those of diverse invertebrate and vertebrate animal models, including genetically engineered species and cultured cells, can be easily analyzed by histology-directed MALDI imaging mass spectrometry. The aims of this review are to discuss a range of proteomic information emerging from MALDI mass spectrometry imaging comparative to classic histology, histochemistry and immunohistochemistry, with applications in biology and medicine, concerning the detection and distribution of structural proteins and biological active molecules, such as antimicrobial peptides and proteins, allergens, neurotransmitters and hormones, enzymes, growth factors, toxins and others. The molecular imaging is very well suited for discovery and validation of candidate protein biomarkers in neuroproteomics, oncoproteomics, aging and age-related diseases, parasitoproteomics, forensic, and ecotoxicology. Additionally, in situ proteome imaging may help to elucidate the physiological and pathological mechanisms involved in developmental biology, reproductive research, amyloidogenesis, tumorigenesis, wound healing, neural network regeneration, matrix mineralization, apoptosis and oxidative stress, pain tolerance, cell cycle and transformation under oncogenic stress, tumor heterogeneity, behavior and aggressiveness, drugs bioaccumulation and biotransformation, organism's reaction against environmental penetrating xenobiotics, immune signaling, assessment of integrity and functionality of tissue barriers, behavioral biology, and molecular origins of diseases. MALDI MSI is certainly a valuable tool for personalized medicine and "Eco-Evo-Devo" integrative biology in the current context of global environmental challenges.

Extracellularly oxidative activation and inactivation of matured prodrug for cryptic self-resistance in naphthyridinomycin biosynthesis

Understanding how antibiotic-producing bacteria deal with highly reactive chemicals will ultimately guide therapeutic strategies to combat the increasing clinical resistance crisis. Here, we uncovered a distinctive self-defense strategy featured by a secreted oxidoreductase NapU to perform extracellularly oxidative activation and conditionally overoxidative inactivation of a matured prodrug in naphthyridinomycin (NDM) biosynthesis from Streptomyces lusitanus NRRL 8034. It was suggested that formation of NDM first involves a nonribosomal peptide synthetase assembly line to generate a prodrug. After exclusion and prodrug maturation, we identified a pharmacophore-inactivated intermediate, which required reactivation by NapU via oxidative C-H bond functionalization extracellularly to afford NDM. Beyond that, NapU could further oxidatively inactivate the NDM pharmacophore to avoid self-cytotoxicity if they coexist longer than necessary. This discovery represents an amalgamation of sophisticatedly temporal and spatial shielding mode conferring self-resistance in antibiotic biosynthesis from Gram-positive bacteria.