Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

AI-assisted mass spectrometry imaging with in situ image segmentation for subcellular metabolomics analysis

Subcellular metabolomics analysis is crucial for understanding intracellular heterogeneity and accurate drug-cell interactions. Unfortunately, the ultra-small size and complex microenvironment inside the cell pose a great challenge to achieving this goal. To address this challenge, we propose an artificial intelligence-assisted subcellular mass spectrometry imaging (AI-SMSI) strategy with in situ image segmentation. Based on the nanometer-resolution MSI technique, the protonated guanine and threonine ions were respectively employed as the nucleus and cytoplasmic markers to complete image segmentation at the subcellular level, avoiding mutual interference of signals from various compartments in the cell. With advanced AI models, the metabolites within the different regions could be further integrated and profiled. Through this method, we decrypted the distinct action mechanism of isomeric drugs, doxorubicin (DOX) and epirubicin (EPI), only with a stereochemical inversion at C-4'. Within the cytoplasmic region, fifteen specific metabolites were discovered as biomarkers for distinguishing the drug action difference between DOX and EPI. Moreover, we identified that the downregulations of glutamate and aspartate in the malate-aspartate shuttle pathway may contribute to the higher paratoxicity of DOX. Our current AI-SMSI approach has promising applications for subcellular metabolomics analysis and thus opens new opportunities to further explore drug-cell specific interactions for the long-term pursuit of precision medicine.

Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

Proteomic Analysis Revealed the Potential Role of MAGE-D2 in the Therapeutic Targeting of Triple-Negative Breast Cancer

Among all the molecular subtypes of breast cancer, triple-negative breast cancer (TNBC) is the most aggressive one. Currently, the clinical prognosis of TNBC is poor because there is still no effective therapeutic target. Here, we carried out a combined proteomic analysis involving bioinformatic analysis of the proteome database, label-free quantitative proteomics, and immunoprecipitation (IP) coupled with mass spectrometry (MS) to explore potential therapeutic targets for TNBC. The results of bioinformatic analysis showed an overexpression of MAGE-D2 (melanoma antigen family D2) in TNBC. In vivo and in vitro experiments revealed that MAGE-D2 overexpression could promote cell proliferation and metastasis. Furthermore, label-free quantitative proteomics revealed that MAGE-D2 acted as a cancer-promoting factor by activating the PI3K-AKT pathway. Moreover, the outcomes of IP-MS and cross-linking IP-MS demonstrated that MAGE-D2 could interact with Hsp70 and prevent Hsp70 degradation, but evidence for their direct interaction is still lacking. Nevertheless, MAGE-D2 is a potential therapeutic target for TNBC, and blocking MAGE-D2 may have important therapeutic implications.

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.

Aptamer-based sample purification for mass spectrometric quantification of trastuzumab in human serum

In this study, we developed a simple liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay to quantify trastuzumab in human serum using aptamers for sample purification. Trastuzumab was extracted from serum samples using the capture probe based on its aptamer CH1S-3, followed by reduction, alkylation, trypsin digestion, and quantification using LC-MS/MS. Additionally, a unique peptide, FTISADTSK, was employed as a surrogate peptide and quantified, and *FTISADTSK (¹³C₉¹⁵N-labeled phenylalanine) was used as an internal standard to minimize variability in detection among the samples. The detection range for this method was 0.5-250 μg/mL, with a high correlation coefficient (r² > 0.99). The intra- and inter-day precision (%CV, the coefficient of variation) of the quality control samples was less than 12.7%, and the accuracy (%bias) was below 8.64%. After optimization and verification, this assay was used to determine trastuzumab levels in clinical human serum samples. The results indicated that the trastuzumab concentrations had an approximate 4-fold difference among ten patients (range: 11.80-41.90 μg/mL). This study provides a novel approach for the accurate and quantitative monitoring of the mAb-trastuzumab.

Multimodal high-resolution nano-DESI MSI and immunofluorescence imaging reveal molecular signatures of skeletal muscle fiber types

The skeletal muscle is a highly heterogeneous tissue comprised of different fiber types with varying contractile and metabolic properties. The complexity in the analysis of skeletal muscle fibers associated with their small size (30-50 μm) and mosaic-like distribution across the tissue tnecessitates the use of high-resolution imaging to differentiate between fiber types. Herein, we use a multimodal approach to characterize the chemical composition of skeletal fibers in a limb muscle, the gastrocnemius. Specifically, we combine high-resolution nanospray desorption electrospray ionization (nano-DESI) mass spectrometry imaging (MSI) with immunofluorescence (IF)-based fiber type identification. Computational image registration and segmentation approaches are used to integrate the information obtained with both techniques. Our results indicate that the transition between oxidative and glycolytic fibers is associated with shallow chemical gradients (<2.5 fold change in signals). Interestingly, we did not find any fiber type-specific molecule. We hypothesize that these findings might be linked to muscle plasticity thereby facilitating a switch in the metabolic properties of fibers in response to different conditions such as exercise and diet, among others. Despite the shallow chemical gradients, cardiolipins (CLs), acylcarnitines (CAR), monoglycerides (MGs), fatty acids, highly polyunsaturated phospholipids, and oxidized phospholipids, were identified as molecular signatures of oxidative metabolism. In contrast, histidine-related compounds were found as molecular signatures of glycolytic fibers. Additionally, the presence of highly polyunsaturated acyl chains in phospholipids was found in oxidative fibers whereas more saturated acyl chains in phospholipids were found in glycolytic fibers which suggests an effect of the membrane fluidity on the metabolic properties of skeletal myofibers.

Simultaneous quantification of multiple single nucleotide variants in PIK3CA ctDNA using mass-tagged LCR probe sets

Circulating tumor DNA (ctDNA) in blood carries genetic variations associated with tumors. There is evidence indicating that the abundance of single nucleotide variant (SNV) in ctDNA is correlated well with cancer progression and metastasis. Thus, accurate and quantitative detection of SNVs in ctDNA may benefit clinical practice. However, most current methods are unsuitable for the quantification of SNV in ctDNA that usually differentiates from wild-type DNA (wtDNA) only by a single base. In this setting, ligase chain reaction (LCR) coupled with mass spectrometry (MS) was developed to simultaneously quantify multiple SNVs using PIK3CA ctDNA as a model. Mass-tagged LCR probe set for each SNV including mass-tagged probe and three DNA probes was firstly designed and prepared. Then, LCR was initiated to discriminate SNVs specifically and amplify the signal of SNVs in ctDNA selectively. Afterward, a biotin-streptavidin reaction system was used to separate the amplified products, and photolysis was initiated to release mass tags. Finally, mass tags were monitored and quantified by MS. After optimizing conditions and verifying performance, this quantitative system was applied for blood samples from breast cancer patients, and risk stratification for breast cancer metastasis was also performed. This study is among the first to quantify multiple SNVs in ctDNA in a signal amplification and conversion manner, and also highlights the potential of SNV in ctDNA as a liquid biopsy marker to monitor cancer progression and metastasis.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Steric Hindrance On-Off Mass-Tagged Probe Set Enables Detection of Protein Homodimer in Living Cells

The major challenge in the detection of protein homodimers is that the identical monomers in a homodimer are indistinguishable using most conventional methods and cannot be sequentially recognized. In this study, a steric hindrance on-off mass-tagged probe set strategy was developed for the quantification of HER2 homodimer in living cells. The probe set contained a hindrance probe and a detection probe. The hindrance probe had a DNA dendrimer as a hindrance group to achieve the steric hindrance on-off function and thus the assignment of monomer identity. The detection probe contained a mass tag released for mass spectrometric quantification. Using the steric hindrance on-off mass-tagged probe set, the level of HER2 homodimer in various breast cancer cell lines was quantified. This is the first report to determine the quantity of protein homodimers, and the steric hindrance on-off probe set developed herein can facilitate the illustration of protein function in cancer.

Ultrasensitive detection of β-lactamase-associated drug-resistant bacteria using a novel mass-tagged probe-mediated cascaded signal amplification strategy

The emergence and spread of drug-resistant bacteria (DRB) is a global health threat. Early and accurate detection of DRB is a critical step in the treatment of DRB infection. However, traditional assays for DRB detection are time-consuming and have inferior analytical sensitivity and quantification capability. Herein, a mass-tagged probe (MP-CMSA)-mediated enzyme- and light-assisted cascaded signal amplification strategy was developed for the ultrasensitive detection of β-lactamase (BLA), an enzyme closely associated with most DRB. Each MP-CMSA probe contained multiple poly(amidoamine) (PAMAM) dendrimer molecules immobilized on a streptavidin agarose bead via a BLA-cleavable linker, and each dendrimer was modified with multiple mass tags via a photo-cleavable linker. In BLA detection, BLA could cleave the BLA-cleavable linker, leading to dendrimers shedding from the MP-CMSA probe to achieve enzyme-assisted signal amplification. Then, each dendrimer can further release mass tags under UV light to achieve light-assisted signal amplification. After this cascaded signal amplification, the released mass tags were ultimately quantified by mass spectrometry. Consequently, the sensitivity of BLA detection can be significantly enhanced by four orders of magnitude with a detection limit of 50.0 fM. Finally, this approach was applied to the blood samples from patients with DRB. This platform provides a potential strategy for the sensitive, rapid and quantitative detection of DRB infection.

Construction of a Mass-Tagged Oligo Probe Set for Revealing Protein Ratiometric Relationship Associated with EGFR-HER2 Heterodimerization in Living Cells

Protein dimerization, as the most common form of protein-protein interaction, can manifest more significant roles in cellular signaling than individual monomers. For example, excessive formation of EGFR-HER2 dimer has been implicated in cancer development and therapeutic resistance in addition to the overexpression of EGFR and HER2 proteins. Thus, quantitative evaluation of these heterodimers in living cells and revelation of their ratiometric relationship with protein monomers in dimerization may provide insights into clinical cancer management. To achieve this goal, the prerequisite is protein heterodimer quantification. Given the current lack of quantitative methods, we constructed a mass-tagged oligo nanoprobe set for quantification of EGFR-HER2 dimer in living cells. The mass-tagged oligo nanoprobe set contained two targeting probes (nucleic acid aptamers), a connector probe, a hairpin probe, and a photocleavable mass-tagged probe. Two distinct aptamers can recognize target protein monomers and initiate the subsequent hybridization cascade involving binding to the connector probe, formation of an initiator strand, opening of a hairpin probe, and ensuing hybridization with a photocleavable mass-tagged probe. Ultimately, the mass tag was released under ultraviolet light and then subjected to mass spectrometric analysis. In this way, the information regarding the interaction between two protein monomers was successfully converted to the quantitative signal of the mass tag. Using the assay, the expression level of EGFR-HER2 dimer and its relationship with individual protein monomers were determined in four breast cancer cell lines. We are among the first to obtain the absolute level of protein heterodimer, and this quantitative information may be vital in understanding the molecular basis of cancer.

Self-supervised clustering of mass spectrometry imaging data using contrastive learning

Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. One of key challenges in molecular colocalization is that complex MSI data are too large for manual annotation but too small for training deep neural networks. Herein, we introduce a self-supervised clustering approach based on contrastive learning, which shows an excellent performance in clustering of MSI data. We train a deep convolutional neural network (CNN) using MSI data from a single experiment without manual annotations to effectively learn high-level spatial features from ion images and classify them based on molecular colocalizations. We demonstrate that contrastive learning generates ion image representations that form well-resolved clusters. Subsequent self-labeling is used to fine-tune both the CNN encoder and linear classifier based on confidently classified ion images. This new approach enables autonomous and high-throughput identification of co-localized species in MSI data, which will dramatically expand the application of spatial lipidomics, metabolomics, and proteomics in biological research.

SPME-LC/MS-based serum metabolomic phenotyping for distinguishing ovarian cancer histologic subtypes: a pilot study

Epithelial ovarian cancer (EOC) is the most common cause of death from gynecological cancer. The outcomes of EOC are complicated, as it is often diagnosed late and comprises several heterogenous subtypes. As such, upfront treatment can be highly challenging. Although many significant advances in EOC management have been made over the past several decades, further work must be done to develop early detection tools capable of distinguishing between the various EOC subtypes. In this paper, we present a sophisticated analytical pipeline based on solid-phase microextraction (SPME) and three orthogonal LC/MS acquisition modes that facilitates the comprehensive mapping of a wide range of analytes in serum samples from patients with EOC. PLS-DA multivariate analysis of the metabolomic data was able to provide clear discrimination between all four main EOC subtypes: serous, endometrioid, clear cell, and mucinous carcinomas. The prognostic performance of discriminative metabolites and lipids was confirmed via multivariate receiver operating characteristic (ROC) analysis (AUC value > 88% with 20 features). Further pathway analysis using the top 57 dysregulated metabolic features showed distinct differences in amino acid, lipid, and steroids metabolism among the four EOC subtypes. Thus, metabolomic profiling can serve as a powerful tool for complementing histology in classifying EOC subtypes.

Biomarkers of disease in human nails: a comprehensive review

Diagnostic, monitoring, response, predictive, risk, and prognostic biomarkers of disease are all widely studied, for the most part in biological fluids or tissues, but there is steadily growing interest in alternative matrices such as nails. Here we comprehensively review studies dealing with molecular or elemental biomarkers of disease, as opposed to semiological, pharmacological, toxicological, or biomonitoring studies. Nails have a long history of use in medicine as indicators of pathological processes and have also been used extensively as a matrix for monitoring exposure to environmental pollution. Nail clippings are simple to collect noninvasively as well as to transport and store, and the matrix itself is relatively stable. Nails incorporate, and are influenced by, circulating molecules and elements over their several months of growth, and it is widely held that markers of biological processes will remain in the nail, even when their levels in blood have declined. Nails thus offer the possibility to not only look back into a subject's metabolic history but also to study biomarkers of processes that operate over a longer time scale such as the post-translational modification of proteins. Reports on ungual biomarkers of metabolic and endocrine diseases, cancer, and psychological and neurological disorders will be presented, and an overview of the sampling and analytical techniques provided.