Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter

Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies

Cancer prognosis remains a critical clinical challenge. Lipidomic analysis via mass spectrometry (MS) offers the potential for objective prognostic prediction, leveraging the distinct lipid profiles of cancer patient-derived specimens. This review aims to systematically summarize the application of MS-based lipidomic analysis in prognostic prediction for cancer patients. Our systematic review summarized 38 studies from the past decade that attempted prognostic prediction of cancer patients through lipidomics. Commonly analyzed cancers included colorectal, prostate, and breast cancers. Liquid (serum and urine) and tissue samples were equally used, with liquid chromatography-tandem MS being the most common analytical platform. The most frequently evaluated prognostic outcomes were overall survival, stage, and recurrence. Thirty-eight lipid markers (including phosphatidylcholine, ceramide, triglyceride, lysophosphatidylcholine, sphingomyelin, phosphatidylethanolamine, diacylglycerol, phosphatidic acid, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, dihydroceramide, prostaglandin, sphingosine-1-phosphate, phosphatidylinosito, fatty acid, glucosylceramide and lactosylceramide) were identified as prognostic factors, demonstrating potential for clinical application. In conclusion, the potential for developing lipidomics in cancer prognostic prediction was demonstrated. However, the field is still nascent, necessitating future studies for validating and establishing lipid markers as reliable prognostic tools in clinical practice.

Acupuncture for brain diseases: Conception, application, and exploration

The brain is probably the most complex organ in the human body. It has been the hot spot and direction of brain science research all over the world to deeply study the pathogenesis of various kinds of brain diseases and find effective treatment methods. Acupuncture is a nonpharmacological therapy of traditional Chinese medicine originating from ancient clinical practice. The research on the treatment of brain diseases by acupuncture has been constantly enriched and updated with the promotion of interdisciplinary research. In order to account for the current achievements in the field of acupuncture for brain diseases, this article reviews it in terms of conception, application, and exploration. Based on the literature review, we found that in the past decades, acupuncture has received widespread attention worldwide and many literatures have reported the clinical efficacy and underlying mechanisms of acupuncture in the treatment of brain diseases. Presently, the conception, application, and exploration of acupuncture in the treatment of brain diseases have evolved from empirical medicine to evidence-based medicine and precision medicine, and are experiencing a deeper understanding of the information about acupuncture regulating the brain function based on interdisciplinary research.

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

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.

Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data

The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents' brain sections. CNN produced classification models with high accuracy and low loss rate in any test data sets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS and of mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. On the basis of the extracted mass spectra pattern features, the histologically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging generated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no significant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment.

Extract Metabolomic Information from Mass Spectrometry Images Using Advanced Data Analysis

Mass spectrometry imaging (MSI) data generally contains large sizes and high-dimensional structures due to their inherent complex chemical and spatial information. A variety of data analysis methods have been developed to comprehensively analyze the MSI experimental results and extract essential information. Here, we describe the protocols of data preprocessing and emerging methods for data analyses, including multivariate analysis, machine learning, and image fusion, that have been applied to the data generated from the Single-probe MSI technique. These strategies and methods can be potentially applied to handling data produced from other MSI techniques.

Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging

Simple Summary

Classic histopathological examination of tissues remains the mainstay for cancer diagnosis and staging. However, in some cases histopathologic analysis yields ambiguous results, leading to inconclusive disease classification. We set out to explore the diagnostic potential of mass spectrometry-based imaging for tumour classification based on proteomic fingerprints. Combining mass spectrometry with supervised machine learning, we were able to distinguish colorectal tumor from normal tissue with an overall accuracy of 98%. In addition, this approach was able to predict the presence of lymph node metastasis in primary tumour of endometrial cancer with an overall accuracy of 80%. These results highlight the potential of this technology to determine the optimal treatment for cancer patients to reduce morbidity and improve patients’ outcomes.

Abstract

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications.

Neuropathology of SCA34 showing widespread oligodendroglial pathology with vacuolar white matter degeneration: a case study

Spinocerebellar ataxia type 34 (SCA34) is an autosomal dominant inherited ataxia due to mutations in ELOVL4, which encodes one of the very long-chain fatty acid elongases. SCA38, another spinocerebellar ataxia, is caused by mutations in ELOVL5, a gene encoding another elongase. However, there have been no previous studies describing the neuropathology of either SCA34 or 38. This report describes the neuropathological findings of an 83-year-old man with SCA34 carrying a pathological ELOVL4 mutation (NM_022726, c.736T>G, p.W246G). Macroscopic findings include atrophies in the pontine base, cerebellum, and cerebral cortices. Microscopically, marked neuronal and pontocerebellar fiber loss was observed in the pontine base. In addition, in the pontine base, accumulation of CD68-positive macrophages laden with periodic acid-Schiff (PAS)-positive material was observed. Many vacuolar lesions were found in the white matter of the cerebral hemispheres and, to a lesser extent, in the brainstem and spinal cord white matter. Immunohistological examination and ultrastructural observations with an electron microscope suggest that these vacuolar lesions are remnants of degenerated oligodendrocytes. Electron microscopy also revealed myelin sheath destruction. Unexpectedly, aggregation of the four-repeat tau was observed in a spatial pattern reminiscent of progressive supranuclear palsy. The tau lesions included glial fibrillary tangles resembling tuft-shaped astrocytes and neurofibrillary tangles and pretangles. This is the first report to illustrate that a heterozygous missense mutation in ELOVL4 leads to neuronal loss accompanied by macrophages laden with PAS-positive material in the pontine base and oligodendroglial degeneration leading to widespread vacuoles in the white matter in SCA34.

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Understanding mass spectrometry images: complexity to clarity with machine learning

The application of artificial intelligence and machine learning to hyperspectral mass spectrometry imaging (MSI) data has received considerable attention over recent years. Various methodologies have shown great promise in their ability to handle the complexity and size of MSI data sets. Advances in this area have been particularly appealing for MSI of biological samples, which typically produce highly complicated data with often subtle relationships between features. There are many different machine learning approaches that have been applied to MSI data over the past two decades. In this review, we focus on a subset of non-linear machine learning techniques that have mostly only been applied in the past 5 years. Specifically, we review the use of the self-organizing map (SOM), SOM with relational perspective mapping (SOM-RPM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). While not their only functionality, we have grouped these techniques based on their ability to produce what we refer to as similarity maps. Similarity maps are color representations of hyperspectral data, in which spectral similarity between pixels-that is, their distance in high-dimensional space-is represented by relative color similarity. In discussing these techniques, we describe, briefly, their associated algorithms and functionalities, and also outline applications in MSI research with a strong focus on biological sample types. The aim of this review is therefore to introduce this relatively recent paradigm for visualizing and exploring hyperspectral MSI, while also providing a comparison between each technique discussed.

Imaging of lysophosphatidylcholine in an induced pluripotent stem cell-derived endothelial cell network

Introduction

Vascular endothelial cell disorders are closely related to cardiovascular disease (CVD) and pulmonary diseases. Abnormal lipid metabolism in the endothelium leads to changes in cell signalling, and the expression of genes related to immunity and inflammation. It is therefore important to investigate the pathophysiology of vascular endothelial disorders in terms of lipid metabolism, using a disease model of endothelium.

Methods

Human induced pluripotent stem cell-derived endothelial cells (iECs) were cultured on a matrigel to form an iEC network. Lipids in the iEC network were investigated by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) analysis. Ion fragments obtained by mass spectrometry were analysed using an infusion method, involving precursor ion scanning with fragment ion.

Results

The MALDI TOF IMS analysis revealed co-localized intensity of peaks at m/z 592.1 and 593.1 in the iEC network. Tandem mass spectrometry (MS/MS) analysis by MALDI-imaging, in conjunction with precursor ion scanning using an infusion method with lipid extracts, identified that these precursor ions were lysophosphatidylcholine (LPC) (22:5) and its isotype.

Conclusion

The MALDI-imaging analysis showed that LPC (22:5) was abundant in an iEC network. As an in vitro test model for disease and potential therapy, present analysis methods using MALDI-imaging combined with, for example, mesenchymal stem cells (MSC) to a disease derived iEC network may be useful in revealing the changes in the amount and distribution of lipids under various stimuli.