Application of desorption electrospray ionization mass spectrometry imaging in breast cancer margin analysis

Direct Sampling Mass Spectrometry Analysis for the Assessment of Wounds: A Systematic Review

Mass spectrometry is increasingly utilised in medicine to identify and quantify small biomarkers for diagnostic and prognostic purposes. Conventional mass spectrometry, however, requires time-consuming sample preparation, hindering its clinical application. Direct sampling mass spectrometry, which allows for direct analysis of patient samples with minimal preparation, offers potential for clinical use. This systematic review examines the utility of direct sampling mass spectrometry for the assessment of external wounds and explores its translational applications in wound care. Out of 2 930 screened abstracts, six studies were included employing various direct sampling mass spectrometry technologies. These studies focused on burn wounds (n = 3), pressure ulcers (n = 2), and acute surgical wounds (n = 1). Both targeted and untargeted molecular profiling methods were used to examine biomarkers related to inflammatory and healing processes, including various proteins, lipid species, and other metabolites. Direct sampling mass spectrometry was found to complement conventional methods such as histology, providing additional insights into the spatial localisation and accumulation of metabolites within wounds. Additionally, imaging techniques equipped with this technology can spatially map wound surfaces and reveal dynamic changes in wounds as they age or progress through different healing processes, with specific metabolite and protein accumulations potentially aiding in prognostication.

Illustrate the metabolic regulatory effects of Ganoderma Lucidum polysaccharides on cognitive dysfunction in formaldehyde-exposed mouse brain by mass spectrometry imaging

Long-term formaldehyde (FA) exposure causes cognitive dysfunction, often associated with metabolic disorders. While some studies suggest Ganoderma lucidum polysaccharides (GLP) can improve cognitive function, such as Alzheimer's disease. However, the effects of GLP on FA-exposed cognitive dysfunction and the regulation of GLP on brain metabolic disturbances caused by FA remain unclear. In our study, we revealed that GLP significantly reversed FA-exposed spatial cognitive deficits in mice by using Morris Water Maze and Histological analysis. Furthermore, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) found that exposure of FA can caused dysregulated expression of 35 metabolites. Following GLP treatment, there was a significant restoration of the imbalance of choline and acetylcholine, carnitine and acetylcarnitine, and spermidine and spermine, which were all involved in choline metabolism, carnitine metabolism, and polyamine metabolism. Our results suggested that GLP alleviated FA-exposed cognitive dysfunction, likely through modulation of metabolic pathways, providing a potential therapeutic approach for FA-related cognitive dysfunction.

Harnessing the Power of Metabolomics for Precision Oncology: Current Advances and Future Directions

Metabolic reprogramming is a hallmark of cancer, with cancer cells acquiring many unique metabolic traits to support malignant growth, and extensive intra- and inter-tumour metabolic heterogeneity. Understanding these metabolic characteristics presents opportunities in precision medicine for both diagnosis and therapy. However, despite its potential, metabolic phenotyping has lagged behind genetic, transcriptomic, and immunohistochemical profiling in clinical applications. This is partly due to the lack of a single experimental technique capable of profiling the entire metabolome, necessitating the use of multiple technologies and approaches to capture the full range of cancer metabolic plasticity. This review examines the repertoire of tools available for profiling cancer metabolism, demonstrating their applications in preclinical and clinical settings. It also presents case studies illustrating how metabolomic profiling has been integrated with other omics technologies to gain insights into tumour biology and guide treatment strategies. This information aims to assist researchers in selecting the most effective tools for their studies and highlights the importance of combining different metabolic profiling techniques to comprehensively understand tumour metabolism.

Untargeted Swab Touch Spray-Mass Spectrometry Analysis with Machine Learning for On-Site Breast Surgical Margin Assessment

Direct sampling mass spectrometry (MS) has rapidly advanced with the development of ambient ionization MS techniques. Swab touch-spray (TS)-MS has shown promise for rapid clinical diagnostics. However, commercially available swabs are notorious for their high background signals, particularly in the positive ionization mode. Although changes to MS methods or precleaning of the swabs can serve as workarounds, this inherent issue still limits the clinical application of swab TS-MS. In this study, we report the use of the sterile-packaged OmniSwab as an alternative material for untargeted swab TS-MS analysis. As a proof of concept, breast surgical margins were swabbed in vivo during surgeries and analyzed using a compact mass spectrometer within the hospital. Subsequently, various machine learning algorithms were applied to the acquired MS spectra to determine the optimal model for classifying margins as normal or tumor. The Least Absolute Shrinkage and Selection Operator (LASSO) model yielded the highest prediction performance, with accuracies exceeding 90% in both testing and validation data sets. Notably, three out of four surgical margins involved with cancer cells were accurately identified. The entire workflow, from swab TS-MS analysis to margin prediction, can be completed within 5 min with high accuracy, demonstrating the feasibility of swab TS-MS to assist intraoperative decision-making.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

The evolving tumor-associated adipose tissue microenvironment in breast cancer: from cancer initiation to metastatic outgrowth

Adipocytes represent a significant proportion of breast tissue, comprising between 3.7 and 37% of stromal tissue. They play a pivotal role in metabolic regulation, energy supply, metabolic regulation, support effects, and cytokine release within the breast. In breast cancer (BC) tissue, adipocytes engage in intricate crosstalk with BC cells, playing a key role in tumor proliferation, invasion, metastasis formation, and metabolic remodeling. This is due to the provision of hormones, adipokines, and fatty acids to tumor cells by the adipocytes. With the initiation of metastatic outgrowth of BC, the peritumoral adipose tissue exhibits abundant and intricate changes based on its original construction and function, which convert it into a tumor-associated adipose tissue microenvironment (TAAME). It includes some specific adipocytes: adipose-derived stem cells (ASCs), cancer-associated adipocytes (CAAs), adipocyte-derived fibroblasts (ADFs), etc. From a mechanistic standpoint, specific adipocytes can facilitate the proliferation, invasion, metastasis, and angiogenesis of BC cells by secreting a multitude of cytokines (IL-6) and adipokines (leptin), which collectively create an environment conducive to BC progression. It is of paramount importance to recognize the TAAME as a crucial target for the diagnosis, treatment, and drug resistance of BC. Consequently, the review presents an overview of the characteristics and interactions of specific adipocytes within TAAME cell populations. This will facilitate the development of more effective personalized therapies against BC progression, relapse, and metastasis.

New perspective on central nervous system disorders: focus on mass spectrometry imaging

An abnormally organized brain spatial network is linked to the development of various central nervous system (CNS) disorders, including neurodegenerative diseases and neuropsychiatric disorders. However, the complicated molecular mechanisms of these diseases remain unresolved, making the development of treatment strategies difficult. A novel molecular imaging technique, called mass spectrometry imaging (MSI), captures molecular information on the surface of samples in situ. With MSI, multiple compounds can be simultaneously visualized in a single experiment. The high spatial resolution enables the simultaneous visualization of the spatial distribution and relative content of various compounds. The wide application of MSI in biomedicine has facilitated extensive studies on CNS disorders in recent years. This review provides a concise overview of the processes, applications, advantages, and disadvantages, as well as mechanisms of the main types of MSI. Meanwhile, this review summarizes the main applications of MSI in studying CNS diseases, including Alzheimer's disease (AD), CNS tumors, stroke, depression, Huntington's disease (HD), and Parkinson's disease (PD). Finally, this review comprehensively discusses the synergistic application of MSI with other advanced imaging modalities, its utilization in organoid models, its integration with spatial omics techniques, and provides an outlook on its future potential in single-cell analysis.

Non-coding RNAs in oral cancer: Emerging biomarkers and therapeutic frontier

Around the world, oral cancer (OC) is a major public health problem, resulting in a significant number of deaths each year. Early detection and treatment are crucial for improving patient outcomes. Recent progress in DNA sequencing and transcriptome profiling has revealed extensive non-coding RNAs (ncRNAs) transcription, underscoring their regulatory importance. NcRNAs influence genomic transcription and translation and molecular signaling pathways, making them valuable for various clinical applications. Combining spatial transcriptomics (ST) and spatial metabolomics (SM) with single-cell RNA sequencing provides deeper insights into tumor microenvironments, enhancing diagnostic and therapeutic precision for OC. Additionally, the exploration of salivary biomarkers offers a non-invasive diagnostic avenue. This article explores the potential of ncRNAs as diagnostic and therapeutic tools for OC.

Reflection mode polarimetry guides laser mass spectrometry to diagnostically important regions of human breast cancer tissue

To enhance the clinical utility of mass spectrometry (MS), lengthy dwell times on less informative regions of patient specimens (e.g., adipose tissue in breast) must be minimized. Additionally, a promising variant of MS known as picosecond infrared laser MS (PIRL-MS) faces further challenges, namely, lipid contamination when probing adipose tissue. Here we demonstrate on several thick non-sectioned resected human breast specimens (healthy and malignant) that reflection-mode polarimetric imaging can robustly guide PIRL-MS toward regions devoid of significant fat content to (1) avoid signal contamination and (2) shorten overall MS analysis times. Through polarimetric targeting of non-fat regions, PIRL-MS sampling revealed feature-rich spectral signatures including several known breast cancer markers. Polarimetric guidance mapping was enabled by circular degree-of-polarization (DOP) imaging via both Stokes and Mueller matrix polarimetry. These results suggest a potential synergistic hybrid approach employing polarimetry as a wide-field-imaging guidance tool to optimize efficient probing of tissue molecular content using MS.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Imaging with mass spectrometry: Which ionization technique is best?

The use of mass spectrometry (MS) to acquire molecular images of biological tissues and other substrates has developed into an indispensable analytical tool over the past 25 years. Imaging mass spectrometry technologies are widely used today to study the in situ spatial distributions for a variety of analytes. Early MS images were acquired using secondary ion mass spectrometry and matrix-assisted laser desorption/ionization. Researchers have also designed and developed other ionization techniques in recent years to probe surfaces and generate MS images, including desorption electrospray ionization (DESI), nanoDESI, laser ablation electrospray ionization, and infrared matrix-assisted laser desorption electrospray ionization. Investigators now have a plethora of ionization techniques to select from when performing imaging mass spectrometry experiments. This brief perspective will highlight the utility and relative figures of merit of these techniques within the context of their use in imaging mass spectrometry.

Mass Spectrometric Imaging of Anionic Phospholipids Desorbed from Human Hippocampal Sections: Discrimination between Temporal and Nontemporal Lobe Epilepsies

Temporal lobe epilepsy (TLE) is one of the most common neurological disorders, often accompanied by hippocampal sclerosis. The molecular processes underlying this epileptogenesis are poorly understood. To examine the lipid profile, 39 fresh frozen sections of the human hippocampus obtained from epilepsy surgery for TLE (n = 14) and non-TLE (control group; n = 25) patients were subjected to desorption electrospray ionization mass spectrometry imaging in the negative ion mode. In contrast to our earlier report that showed striking downregulation of positively charged phospholipids (e.g., phosphatidylcholine and phosphatidylethanolamine, etc.) in the TLE hippocampus, this study finds complementary upregulation of negatively charged phospholipids, notably, phosphatidylserine and phosphatidylglycerol. This result may point to an active metabolic pool in the TLE hippocampus that produces these anionic phospholipids at the expense of the cationic phospholipids. This metabolic shift could be due to the dysregulation of the Kennedy and CDP-DG pathways responsible for biosynthesizing these lipids. Thus, this study further opens up opportunities to investigate the molecular hallmarks and potential therapeutic targets for TLE.

Intraoperative Evaluation of Breast Tissues During Breast Cancer Operations Using the MasSpec Pen

Importance

Surgery with complete tumor resection remains the main treatment option for patients with breast cancer. Yet, current technologies are limited in providing accurate assessment of breast tissue in vivo, warranting development of new technologies for surgical guidance.

Objective

To evaluate the performance of the MasSpec Pen for accurate intraoperative assessment of breast tissues and surgical margins based on metabolic and lipid information.

Design, Setting, and Participants

In this diagnostic study conducted between February 23, 2017, and August 19, 2021, the mass spectrometry-based device was used to analyze healthy breast and invasive ductal carcinoma (IDC) banked tissue samples from adult patients undergoing breast surgery for ductal carcinomas or nonmalignant conditions. Fresh-frozen tissue samples and touch imprints were analyzed in a laboratory. Intraoperative in vivo and ex vivo breast tissue analyses were performed by surgical staff in operating rooms (ORs) within 2 different hospitals at the Texas Medical Center. Molecular data were used to build statistical classifiers.

Main Outcomes and Measures

Prediction results of tissue analyses from classification models were compared with gross assessment, frozen section analysis, and/or final postoperative pathology to assess accuracy.

Results

All data acquired from the 143 banked tissue samples, including 79 healthy breast and 64 IDC tissues, were included in the statistical analysis. Data presented rich molecular profiles of healthy and IDC banked tissue samples, with significant changes in relative abundances observed for several metabolic species. Statistical classifiers yielded accuracies of 95.6%, 95.5%, and 90.6% for training, validation, and independent test sets, respectively. A total of 25 participants enrolled in the clinical, intraoperative study; all were female, and the median age was 58 years (IQR, 44-66 years). Intraoperative testing of the technology was successfully performed by surgical staff during 25 breast operations. Of 273 intraoperative analyses performed during 25 surgical cases, 147 analyses from 22 cases were subjected to statistical classification. Testing of the classifiers on 147 intraoperative mass spectra yielded 95.9% agreement with postoperative pathology results.

Conclusions and Relevance

The findings of this diagnostic study suggest that the mass spectrometry-based system could be clinically valuable to surgeons and patients by enabling fast molecular-based intraoperative assessment of in vivo and ex vivo breast tissue samples and surgical margins.

The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations

Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.

Saturated Fatty Acid Emulsions Open the Blood-Brain Barrier and Promote Drug Delivery in Rat Brains

We performed this study to evaluate whether saturated fatty acid (SFA) emulsions affect the BBB and determine the duration of BBB opening, thereby promoting drug delivery to the brain. Butyric, valeric, caproic, enanthic, and caprylic acid emulsions were infused into the carotid artery of the rat model. We evaluated the BBB opening and drug delivery over time. The trypan blue and doxorubicin delivery studies were repeated from 30 min to 6 h. In the 1 h rats in each group, transmission electron microscopy (TEM) was performed to morphologically evaluate tight junctions, and the delivery of temozolomide was assessed by desorption electrospray ionization mass spectrometry. The ipsilateral hemisphere was positive for trypan blue staining in all the five SFA emulsion groups. In the valeric, enanthic, and caprylic acid emulsion groups, RGB ratios were significantly higher at 30 min and decreased thereafter. Doxorubicin delivery increased in all emulsion groups at all time points. Tight junctions were observed to be open in all groups. TMZ delivery was significantly higher in the ipsilateral hemisphere. In conclusion, intra-arterially infused SFA emulsions opened the BBB and promoted drug delivery within 30 min, which decreased thereafter. Therefore, SFA emulsions may aid BBB research and promote drug delivery to the brain.

Noninvasive Detection of Skin Cancer by Imprint Desorption Electrospray Ionization Mass Spectrometry Imaging

We report a technique for the noninvasive detection of skin cancer by imprint desorption electrospray ionization mass spectrometry imaging (DESI-MSI) using a transfer agent that is pressed against the tissue of interest. By noninvasively pressing a tape strip against human skin, metabolites, fatty acids, and lipids on the skin surface are transferred to the tape with little spatial distortion. Running DESI-MSI on the tape strip provides chemical images of the molecules on the skin surface, which are valuable for distinguishing cancer from healthy skin. Chemical components of the tissue imprint on the tape strip and the original basal cell carcinoma (BCC) section from the mass spectra show high consistency. By comparing MS images (about 150-μm resolution) of same molecules from the tape strip and from the BCC section, we confirm that chemical patterns are successfully transferred to the tape stripe. We also used the technique to distinguish cherry angiomas from normal human skin by comparing the molecular patterns from a tape strip. These results demonstrate the potential of the imprint DESI-MSI technique for the noninvasive detection of skin cancers as well as other skin diseases before and during clinical surgery.

Advanced progress of spatial metabolomics in head and neck cancer research

Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.

Metabolic heterogeneity in cancer

Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.

Intraoperative Assessment of Resection Margin in Oral Cancer: The Potential Role of Spectroscopy

In parallel with the increasing number of oncological cases, the need for faster and more efficient diagnostic tools has also appeared. Different diagnostic approaches are available, such as radiological imaging or histological staining methods, but these do not provide adequate information regarding the resection margin, intraoperatively, or are time consuming. The purpose of this review is to summarize the current knowledge on spectrometric diagnostic modalities suitable for intraoperative use, with an emphasis on their relevance in the management of oral cancer. The literature agrees on the sensitivity, specificity, and accuracy of spectrometric diagnostic modalities, but further long-term prospective, multicentric clinical studies are needed, which may standardize the intraoperative assessment of the resection margin and the use of real-time spectroscopic approaches.

Molecular characterization of human peripheral nerves using desorption electrospray ionization mass spectrometry imaging

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is a molecular imaging method that can be used to elucidate the small-molecule composition of tissues and map their spatial information using two-dimensional ion images. This technique has been used to investigate the molecular profiles of variety of tissues, including within the central nervous system, specifically the brain and spinal cord. To our knowledge, this technique has yet to be applied to tissues of the peripheral nervous system (PNS). Data generated from such analyses are expected to advance the characterization of these structures. The study aimed to: (i) establish whether DESI-MSI can discriminate the molecular characteristics of peripheral nerves and distinguish them from surrounding tissues and (ii) assess whether different peripheral nerve subtypes are characterized by unique molecular profiles. Four different nerves for which are known to carry various nerve fiber types were harvested from a fresh cadaveric donor: mixed, motor and sensory (sciatic and femoral); cutaneous, sensory (sural); and autonomic (vagus). Tissue samples were harvested to include the nerve bundles in addition to surrounding connective tissue. Samples were flash-frozen, embedded in optimal cutting temperature compound in cross-section, and sectioned at 14 μm. Following DESI-MSI analysis, identical tissue sections were stained with hematoxylin and eosin. In this proof-of-concept study, a combination of multivariate and univariate statistical methods was used to evaluate molecular differences between the nerve and adjacent tissue and between nerve subtypes. The acquired mass spectral profiles of the peripheral nerve samples presented trends in ion abundances that seemed to be characteristic of nerve tissue and spatially corresponded to the associated histology of the tissue sections. Principal component analysis (PCA) supported the separation of the samples into distinct nerve and adjacent tissue classes. This classification was further supported by the K-means clustering analysis, which showed separation of the nerve and background ions. Differences in ion expression were confirmed using ANOVA which identified statistically significant differences in ion expression between the nerve subtypes. The PCA plot suggested some separation of the nerve subtypes into four classes which corresponded with the nerve types. This was supported by the K-means clustering. Some overlap in classes was noted in these two clustering analyses. This study provides emerging evidence that DESI-MSI is an effective tool for metabolomic profiling of peripheral nerves. Our results suggest that peripheral nerves have molecular profiles that are distinct from the surrounding connective tissues and that DESI-MSI may be able to discriminate between nerve subtypes. DESI-MSI of peripheral nerves may be a valuable technique that could be used to improve our understanding of peripheral nerve anatomy and physiology. The ability to utilize ambient mass spectrometry techniques in real time could also provide an unprecedented advantage for surgical decision making, including in nerve-sparing procedures in the future.

Medium-chain and long-chain fatty acids are associated with diarrheal predominant irritable bowel syndrome revealed by DESI-MSI

BACKGROUND

Irritable bowel syndrome (IBS) is one of the most common functional bowel disorders, but its pathogenesis remains unknown. Its development may be linked to intestinal dysmetabolism, directly and indirectly. The present study aimed to screen the differentially expressed small molecular substances in the mucosa of the colon between IBS with diarrhea (IBS-D) patients and healthy subjects and explore the pathogenesis of IBS-D.

METHODS

In this pilot study, the metabolites of colonic mucosa in ten patients with IBS-D and six healthy controls (HC) were analyzed by DESI-MSI. We also mapped the spatial distribution of the screened differential metabolites from samples of the IBS-D group and HC group.

RESULTS

The results showed that 20 metabolites in the colonic mucosa of IBS-D were significantly more abundant, while the other 77 substances were significantly reduced. Enrichment analysis of 97 differential metabolites and KEGG pathway analysis revealed that six medium-chain and long-chain fatty acids were determined to be most overrepresented in the IBS-D group compared to the HC group. Four of these six fatty acids are all PUFAs. The DESI-MSI results suggested that these fatty acids were localized in the colonic mucosa and confirmed the differences in these fatty acids between IBS-D and HC.

CONCLUSIONS

Medium-chain and long-chain fatty acids localized in the colonic mucosa are likely to be potential indicators for the differentiation of IBS-D from healthy subjects which may have implications in the mechanisms and possible preventive measures against IBS.

CLINICAL TRIAL REGISTRY REGISTRATION NUMBER

ChiCTR2200060224.

Next-generation pathology practices with mass spectrometry imaging

Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.

Advancements in Intraoperative Near-Infrared Fluorescence Imaging for Accurate Tumor Resection: A Promising Technique for Improved Surgical Outcomes and Patient Survival

Clear surgical margins for solid tumor resection are essential for preventing cancer recurrence and improving overall patient survival. Complete resection of tumors is often limited by a surgeon's ability to accurately locate malignant tissues and differentiate them from healthy tissue. Therefore, techniques or imaging modalities are required that would ease the identification and resection of tumors by real-time intraoperative visualization of tumors. Although conventional imaging techniques such as positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), or radiography play an essential role in preoperative diagnostics, these cannot be utilized in intraoperative tumor detection due to their large size, high cost, long imaging time, and lack of cancer specificity. The inception of several imaging techniques has paved the way to intraoperative tumor margin detection with a high degree of sensitivity and specificity. Particularly, molecular imaging using near-infrared fluorescence (NIRF) based nanoprobes provides superior imaging quality due to high signal-to-noise ratio, deep penetration to tissues, and low autofluorescence, enabling accurate tumor resection and improved survival rates. In this review, we discuss the recent developments in imaging technologies, specifically focusing on NIRF nanoprobes that aid in highly specific intraoperative surgeries with real-time recognition of tumor margins.

Enzyme-triggered deep tumor penetration of a dual-drug nanomedicine enables an enhanced cancer combination therapy

Cancer cells could be eradicated by promoting generation of excessive intracellular reactive oxygen species (ROS) via emerging nanomedicines. However, tumor heterogeneity and poor penetration of nanomedicines often lead to diverse levels of ROS production in the tumor site, and ROS at a low level promote tumor cell growth, thus diminishing the therapeutic effect of these nanomedicines. Herein, we construct an amphiphilic and block polymer-dendron conjugate-derived nanomedicine (Lap@pOEGMA-b-p(GFLG-Dendron-Ppa), GFLG-DP/Lap NPs) that incorporates a photosensitizer, Pyropheophorbide a (Ppa), for ROS therapy and Lapatinib (Lap) for molecular targeted therapy. Lap, an epidermal growth factor receptor (EGFR) inhibitor that plays a role in inhibiting cell growth and proliferation, is hypothesized to synergize with ROS therapy for effectively killing cancer cells. Our results suggest that the enzyme-sensitive polymeric conjugate, pOEGMA-b-p(GFLG-Dendron-Ppa) (GFLG-DP), releases in response to cathepsin B (CTSB) after entering the tumor tissue. Dendritic-Ppa has a strong adsorption capacity to tumor cell membranes, which promotes efficient penetration and long-term retention. Lap can also be efficiently delivered to internal tumor cells to play its role due to the increased vesicle activity. Laser irradiation of Ppa-containing tumor cells results in production of intracellular ROS that is sufficient for inducing cell apoptosis. Meanwhile, Lap efficiently inhibits proliferation of remaining viable cells even in deep tumor regions, thus generating a significant synergistic anti-tumor therapeutic effect. This novel strategy can be extended to the development of efficient membrane lipid-based therapies to effectively combat tumors.

Mass spectrometry imaging technology in metabolomics: A systematic review

Mass spectrometry imaging (MSI) is a powerful label-free analysis technique that can provide simultaneous spatial distribution of multiple compounds in a single experiment. By combining the sensitive and rapid screening of high-throughput MS with spatial chemical information, metabolite analysis and morphological characteristics are presented in a single image. MSI can be used for qualitative and quantitative analysis of metabolic profiles and it can provide visual analysis of spatial distribution information of complex biological and microbial systems. Matrix-assisted laser desorption ionization, laser ablation electrospray ionization and desorption electrospray ionization are commonly used in MSI. Here, we summarize and compare these three technologies, as well as the applications and prospects of MSI in metabolomics.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning

PURPOSE

Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes.

MATERIALS AND METHODS

Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues.

RESULTS

From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC.

CONCLUSION

Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Characterisation of hepatic lipid signature distributed across the liver zonation using mass spectrometry imaging

Background & Aims

Lipid metabolism plays an important role in liver pathophysiology. The liver lobule asymmetrically distributes oxygen and nutrition, resulting in heterogeneous metabolic functions. Periportal and pericentral hepatocytes have different metabolic functions, which lead to generating liver zonation. We developed spatial metabolic imaging using desorption electrospray ionisation mass spectrometry to investigate lipid distribution across liver zonation with high reproducibility and accuracy.

Methods

Fresh frozen livers from healthy mice with control diet were analysed using desorption electrospray ionisation mass spectrometry imaging. Imaging was performed at 50 μm × 50 μm pixel size. Regions of interest (ROIs) were manually created by co-registering with histological data to determine the spatial hepatic lipids across liver zonation. The ROIs were confirmed by double immunofluorescence. The mass list of specific ROIs was automatically created, and univariate and multivariate statistical analysis were performed to identify statistically significant lipids across liver zonation.

Results

A wide range of lipid species was identified, including fatty acids, phospholipids, triacylglycerols, diacylglycerols, ceramides, and sphingolipids. We characterised hepatic lipid signatures in three different liver zones (periportal zone, midzone, and pericentral zone) and validated the reproducibility of our method for measuring a wide range of lipids. Fatty acids were predominantly detected in the periportal region, whereas phospholipids were distributed in both the periportal and pericentral zones. Interestingly, phosphatidylinositols, PI(36:2), PI(36:3), PI(36:4), PI(38:5), and PI(40:6) were located predominantly in the midzone (zone 2). Triacylglycerols and diacylglycerols were detected mainly in the pericentral region. De novo triacylglycerol biosynthesis appeared to be the most influenced pathway across the three zones.

Conclusions

The ability to accurately assess zone-specific hepatic lipid distribution in the liver could lead to a better understanding of lipid metabolism during the progression of liver disease.

Impact and Implications

Zone-specific hepatic lipid metabolism could play an important role in lipid homoeostasis during disease progression. Herein, we defined the zone-specific references of hepatic lipid species in the three liver zones using molecular imaging. The de novo triacylglycerol biosynthesis was highlighted as the most influenced pathway across the three zones.

Development of mass spectrometry imaging techniques and its latest applications

Mass spectrometry imaging (MSI) is a novel molecular imaging technology that collects molecular information from the surface of samples in situ. The spatial distribution and relative content of various compounds can be visualized simultaneously with high spatial resolution. The prominent advantages of MSI promote the active development of ionization technology and its broader applications in diverse fields. This article first gives a brief introduction to the vital parts of the processes during MSI. On this basis, provides a comprehensive overview of the most relevant MS-based imaging techniques from their mechanisms, pros and cons, and applications. In addition, a critical issue in MSI, matrix effects is also discussed. Then, the representative applications of MSI in biological, forensic, and environmental fields in the past 5 years have been summarized, with a focus on various types of analytes (e.g., proteins, lipids, polymers, etc.) Finally, the challenges and further perspectives of MSI are proposed and concluded.

Mass Spectrometry Imaging of Lumpectomy Specimens Deciphers Diacylglycerols as Potent Biomarkers for the Diagnosis of Breast Cancer

Detecting breast tumor markers with a fast turnaround time from frozen sections should foster intraoperative histopathology in breast-conserving surgery, reducing the need for a second operation. Hence, rapid label-free discrimination of the spatially resolved molecular makeup between cancer and adjacent normal breast tissue is of growing importance. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of fresh-frozen excision specimens, including cancer and paired adjacent normal sections, obtained from the lumpectomy of 73 breast cancer patients. The results demonstrate that breast cancer tissue posits sharp metabolic upregulation of diacylglycerol, a lipid second messenger that activates protein kinase C for promoting tumor growth. We identified four specific sn-1,2-diacylglycerols that outperformed all other lipids simultaneously mapped by the positive ion mode DESI-MSI for distinguishing cancers from adjacent normal specimens. This result contrasts with several previous DESI-MSI studies that probed metabolic dysregulation of glycerophospholipids, sphingolipids, and free fatty acids for cancer diagnoses. A random forest-based supervised machine learning considering all detected ion signals also deciphered the highest diagnostic potential of these four diacylglycerols with the top four importance scores. This led us to construct a classifier with 100% overall prediction accuracy of breast cancer by using the parsimonious set of four diacylglycerol biomarkers only. The metabolic pathway analysis suggested that increased catabolism of phosphatidylcholine in breast cancer contributes to diacylglycerol overexpression. These results open up opportunities for mapping diacylglycerol signaling in breast cancer in the context of novel therapeutic and diagnostic developments, including the intraoperative assessment of breast cancer margin status.

Imaging the metabolic reprograming of fatty acid synthesis pathway enables new diagnostic and therapeutic opportunity for breast cancer

BACKGROUND

Reprogrammed metabolic network is a key hallmark of cancer. Profiling cancer metabolic alterations with spatial signatures not only provides clues for understanding cancer biochemical heterogeneity, but also helps to decipher the possible roles of metabolic reprogramming in cancer development.

METHODS

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to characterize the expressions of fatty acids in breast cancer tissues. Specific immunofluorescence staining was further carried out to investigate the expressions of fatty acid synthesis-related enzymes.

RESULTS

The distributions of 23 fatty acids in breast cancer tissues have been mapped, and the levels of most fatty acids in cancer tissues are significantly higher than those in adjacent normal tissues. Two metabolic enzymes, fatty acid synthase (FASN) and acetyl CoA carboxylase (ACC), which being involved in the de novo synthesis of fatty acid were found to be up-regulated in breast cancer. Targeting the up-regulation of FASN and ACC is an effective approach to limiting the growth, proliferation, and metastasis of breast cancer cells.

CONCLUSIONS

These spatially resolved findings enhance our understanding of cancer metabolic reprogramming and give an insight into the exploration of metabolic vulnerabilities for better cancer treatment.

Rapid Molecular Evaluation of Human Kidney Tissue Sections by In Situ Mass Spectrometry and Machine Learning to Classify the Nephrotic Syndrome

Nephrotic syndrome (NS) is classified based on morphological changes of glomeruli in biopsied kidney tissues evaluated by time-consuming microscopy methods. In contrast, we employed desorption electrospray ionization mass spectrometry (DESI-MS) directly on renal biopsy specimens obtained from 37 NS patients to rapidly differentiate lipid profiles of three prevalent forms of NS: IgA nephropathy (n = 9), membranous glomerulonephritis (n = 7), and lupus nephritis (n = 8), along with other types of glomerular diseases (n = 13). As we noted molecular heterogeneity in regularly spaced renal tissue regions, multiple sections from each biopsy specimen were collected, providing a total of 973 samples for investigation. Using multivariate analysis, we report differential expressions of glycerophospholipids, sphingolipids, and glycerolipids among the above four classes of NS kidneys, which were otherwise overlooked in several past studies correlating lipid abnormalities with glomerular diseases. We developed machine learning (ML) models with the top 100 features using the support vector machine, which enabled us to discriminate the concerned glomerular diseases with 100% overall accuracy in the training, validation, and holdout test set. This DESI-MS/ML-based tissue analysis can be completed in a few minutes, in sharp contrast to a daylong procedure followed in the conventional histopathology of NS.

Desorption electrospray ionization mass spectrometry imaging in discovery and development of novel therapies

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) is one of the least specimen destructive ambient ionization mass spectrometry tissue imaging methods. It enables rapid simultaneous mapping, measurement, and identification of hundreds of molecules from an unmodified tissue sample. Over the years, since its first introduction as an imaging technique in 2005, DESI-MSI has been extensively developed as a tool for separating tissue regions of various histopathologic classes for diagnostic applications. Recently, DESI-MSI has also emerged as a versatile technique that enables drug discovery and can guide the efficient development of drug delivery systems. For example, it has been increasingly employed for uncovering unique patterns of in vivo drug distribution, the discovery of potentially treatable biochemical pathways, revealing novel druggable targets, predicting therapeutic sensitivity of diseased tissues, and identifying early tissue response to pharmacological treatment. These and other recent advances in implementing DESI-MSI as the tool for the development of novel therapies are highlighted in this review.

The Relationship between Histological Composition and Metabolic Profile in Breast Tumors and Peritumoral Tissue Determined with 1H HR-MAS NMR Spectroscopy

Breast tumors constitute the complex entities composed of cancer cells and stromal components. The compositional heterogeneity should be taken into account in bulk tissue metabolomics studies. The aim of this work was to find the relation between the histological content and ¹H HR-MAS (high-resolution magic angle spinning nuclear magnetic resonance) metabolic profiles of the tissue samples excised from the breast tumors and the peritumoral areas in 39 patients diagnosed with invasive breast carcinoma. The total number of the histologically verified specimens was 140. The classification accuracy of the OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis) model differentiating the cancerous from non-involved samples was 87% (sensitivity of 72.2%, specificity of 92.3%). The metabolic contents of the epithelial and stromal compartments were determined from a linear regression analysis of the levels of the evaluated compounds against the cancer cell fraction in 39 samples composed mainly of cancer cells and intratumoral fibrosis. The correlation coefficients between the levels of several metabolites and a tumor purity were found to be dependent on the tumor grade (I vs II/III). The comparison of the levels of the metabolites in the intratumoral fibrosis (obtained from the extrapolation of the regression lines to 0% cancer content) to those levels in the fibrous connective tissue beyond the tumors revealed a profound metabolic reprogramming in the former tissue. The joint analysis of the metabolic profiles of the stromal and epithelial compartments in the breast tumors contributes to the increased understanding of breast cancer biology.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Long-chain polyunsaturated lipids associated with responsiveness to anti-PD-1 therapy are colocalized with immune infiltrates in the tumor microenvironment

The programmed cell death protein-1 (PD-1) is highly expressed on the surface of antigen-specific exhausted T cells and, upon interaction with its ligand PD-L1, can result in inhibition of the immune response. Anti-PD-1 treatment has been shown to extend survival and result in durable responses in several cancers, yet only a subset of patients benefit from this therapy. Despite the implication of metabolic alteration following cancer immunotherapy, mechanistic associations between antitumor responses and metabolic changes remain unclear. Here, we used desorption electrospray ionization mass spectrometry imaging to examine the lipid profiles of tumor tissue from three syngeneic murine models with varying treatment sensitivity at the baseline and at three time points post-anti-PD-1 therapy. These imaging experiments revealed specific alterations in the lipid profiles associated with the degree of response to treatment and allowed us to identify a significant increase of long-chain polyunsaturated lipids within responsive tumors following anti-PD-1 therapy. Immunofluorescence imaging of tumor tissues also demonstrated that the altered lipid profile associated with treatment response is localized to dense regions of tumor immune infiltrates. Overall, these results indicate that effective anti-PD-1 therapy modulates lipid metabolism in tumor immune infiltrates, and we thereby propose that further investigation of the related immune-metabolic pathways may be useful for better understanding success and failure of anti-PD-1 therapy.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Lipid Metabolism Heterogeneity and Crosstalk with Mitochondria Functions Drive Breast Cancer Progression and Drug Resistance

Breast cancer (BC) is a heterogeneous disease that can be triggered by genetic alterations in mammary epithelial cells, leading to diverse disease outcomes in individual patients. The metabolic heterogeneity of BC enhances its ability to adapt to changes in the tumor microenvironment and metabolic stress, but unfavorably affects the patient's therapy response, prognosis and clinical effect. Extrinsic factors from the tumor microenvironment and the intrinsic parameters of cancer cells influence their mitochondrial functions, which consequently alter their lipid metabolism and their ability to proliferate, migrate and survive in a harsh environment. The balanced interplay between mitochondria and fatty acid synthesis or fatty acid oxidation has been attributed to a combination of environmental factors and to the genetic makeup, oncogenic signaling and activities of different transcription factors. Hence, understanding the mechanisms underlying lipid metabolic heterogeneity and alterations in BC is gaining interest as a major target for drug resistance. Here we review the major recent reports on lipid metabolism heterogeneity and bring to light knowledge on the functional contribution of diverse lipid metabolic pathways to breast tumorigenesis and therapy resistance.

The pleiotropic roles of adipocyte secretome in remodeling breast cancer

Background

Breast cancer is the leading female cancer type and the cause of cancer-related mortality worldwide. Adipocytes possess important functions of energy supply, metabolic regulation, and cytokine release, and are also the matrix cell that supports mammary gland tissue. In breast cancer tumor microenvironment (TME), adipocytes are the prominent stromal cells and are implicated in inflammation, metastatic formation, metabolic remodeling, and cancer susceptibility.

Main body

It is well-established that adipocyte secretome is a reservoir engaged in the regulation of tumor cell behavior by secreting a large number of cytokines (IL-6, IL-8, and chemokines), adipokines (leptin, adiponectin, autotaxin, and resistin), lipid metabolites (free fatty acids and β-hydroxybutyrate), and other exosome-encapsulated substances. These released factors influence the evolution and clinical outcome of breast cancer through complex mechanisms. The progression of breast cancer tumors revolves around the tumor-adipose stromal network, which may contribute to breast cancer aggressiveness by increasing the pro-malignant potential of TME and tumor cells themselves. Most importantly, the secretome alterations of adipocytes are regarded as distinctly important targets for breast cancer diagnosis, treatment, and drug resistance.

Conclusion

Therefore, this review will provide a comprehensive description of the specific adipocyte secretome characteristics and interactions within TME cell populations, which will enable us to better tailor strategies for tumor stratification management and treatment.

Association of levels of metabolites with the safe margin of rectal cancer surgery: a metabolomics study

Background

Rectal cancer is one of the most lethal of gastrointestinal malignancies. Metabonomics has gradually developed as a convenient, inexpensive and non-destructive technique for the study of cancers.

Methods

A total of 150 tissue samples from 25 rectal cancer patients were analyzed by liquid chromatography–mass spectrometry (LC–MS), and 6 tissue samples were collected from each patient (group 1: tumor; group 2: 0.5 cm from tumor; group 3:1 cm from tumor; group 4:2 cm from tumor; group 5:3 cm from tumor and group 6:5 cm from tumor). The differential metabolites of tumor tissues and 5 cm from the tumor (normal tissues) were first selected. The differential metabolites between tumor tissues and normal tissues were regrouped by hierarchical clustering analysis, and further selected by discriminant analysis according to the regrouping of clustering results. The potential safe margin of clinical T(cT)1,cT2 stage rectal cancer and cT3,cT4 stage rectal cancer at the metabolomic level was further identified by observing the changes in the level of differential metabolites within the samples from group 1 to group 6.

Results

We found 22 specific metabolites to distinguish tumor tissue and normal tissue. The most significant changes in metabolite levels were observed at 0.5 cm (cT1, cT2) and 2.0 cm (cT3, cT4) from the tumor, while the changes in the tissues afterwards showed a stable trend.

Conclusions

There are differential metabolites between tumor tissues and normal tissues in rectal cancer. Based on our limited sample size, the safe distal incision margin for rectal cancer surgery in metabolites may be 0.5 cm in patients with cT1 and cT2 stage rectal cancer and 2.0 cm in patients with cT3 and cT4 stage rectal cancer.

Rapid Evaporative Ionization Mass Spectrometry-Based Lipidomics for Identification of Canine Mammary Pathology

The present work proposes the use of a fast analytical platform for the mass spectrometric (MS) profiling of canine mammary tissues in their native form for the building of a predictive statistical model. The latter could be used as a novel diagnostic tool for the real-time identification of different cellular alterations in order to improve tissue resection during veterinary surgery, as previously validated in human oncology. Specifically, Rapid Evaporative Ionization Mass Spectrometry (REIMS) coupled with surgical electrocautery (intelligent knife—iKnife) was used to collect MS data from histologically processed mammary samples, classified into healthy, hyperplastic/dysplastic, mastitis and tumors. Differences in the lipid composition enabled tissue discrimination with an accuracy greater than 90%. The recognition capability of REIMS was tested on unknown mammary samples, and all of them were correctly identified with a correctness score of 98–100%. Triglyceride identification was increased in healthy mammary tissues, while the abundance of phospholipids was observed in altered tissues, reflecting morpho-functional changes in cell membranes, and oxidized species were also tentatively identified as discriminant features. The obtained lipidomic profiles represented unique fingerprints of the samples, suggesting that the iKnife technique is capable of differentiating mammary tissues following chemical changes in cellular metabolism.

Comparing DESI-MSI and MALDI-MSI Mediated Spatial Metabolomics and Their Applications in Cancer Studies

Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.

Mass Spectrometry Imaging Spatial Tissue Analysis toward Personalized Medicine

Novel profiling methodologies are redefining the diagnostic capabilities and therapeutic approaches towards more precise and personalized healthcare. Complementary information can be obtained from different omic approaches in combination with the traditional macro- and microscopic analysis of the tissue, providing a more complete assessment of the disease. Mass spectrometry imaging, as a tissue typing approach, provides information on the molecular level directly measured from the tissue. Lipids, metabolites, glycans, and proteins can be used for better understanding imbalances in the DNA to RNA to protein translation, which leads to aberrant cellular behavior. Several studies have explored the capabilities of this technology to be applied to tumor subtyping, patient prognosis, and tissue profiling for intraoperative tissue evaluation. In the future, intercenter studies may provide the needed confirmation on the reproducibility, robustness, and applicability of the developed classification models for tissue characterization to assist in disease management.

Review: New horizons in retinoblastoma treatment: an updated review article

Retinoblastoma (Rb) is a rare childhood intraocular malignancy with an incidence rate of approximately 9000 children per year worldwide. The management of Rb is inherently complex and depends on several factors. The orders of priorities in the treatment of Rb are saving life, globe salvage and vision salvage. Rarity and the young age at diagnosis impede conducting randomized clinical trials (RCTs) for new therapeutic options, and therefore pre-RCTs studies are needed. This review provides an overview of advances in Rb treatment options, focusing on the emergence of new small molecules to treat Rb. Articles related to the management and treatments of Rb were searched in different databases. Several studies and animal models discussing recent advances in the treatment of Rb were included to have a better grasp of the biological mechanisms of Rb. Over the years, the principles of management and treatment of Rb have changed significantly. Innovations in targeted therapies and molecular biology have led to improved patient and ocular survival. However, there is still a need for further evaluation of the long-term effects of these new treatments.

Three-dimensional quantitative mass spectrometry imaging in complex system: From subcellular to whole organism

Mass spectrometry imaging (MSI) has been applied for label-free three-dimensional (3D) imaging from position array across the whole organism, which provides high-dimensional quantitative data of inorganic or organic compounds that may play an important role in the regulation of cellular signaling, including metals, metabolites, lipids, drugs, peptides, and proteins. While MSI is suitable for investigation of the spatial distribution of molecules, it has a limitation with visualization and quantification of multiple molecules. 3D-MSI, however, can be applied toward exploring metabolic pathway as well as the interactions of lipid-protein, protein-protein, and metal-protein in complex systems from subcellular to the whole organism through an untargeted methodology. In this review, we highlight the methods and applications of MS-based 3D imaging to address the complexity of molecular interaction from nano- to micrometer lateral resolution, with particular focus on: (a) common and hybrid 3D-MSI techniques; (b) quantitative MSI methodology, including the methods using a stable isotope labeling internal standard (SILIS) and SILIS-free approaches with tissue extinction coefficient or virtual calibration; (c) reconstruction of the 3D organ; (d) application of 3D-MSI for biomarker screening and environmental toxicological research. 3D-MSI quantitative analysis provides accurate spatial information and quantitative variation of biomolecules, which may be valuable for the exploration of the molecular mechanism of the disease progresses and toxicological assessment of environmental pollutants in the whole organism. Additionally, we also discuss the challenges and perspectives on the future of 3D quantitative MSI.

Identification of Metabolism-Associated Biomarkers for Early and Precise Diagnosis of Oral Squamous Cell Carcinoma

The 5-year survival rate for oral squamous cell carcinoma (OSCC), one of the most common head and neck cancers, has not improved in the last 20 years. Poor prognosis of OSCC is the result of failure in early and precise diagnosis. Metabolic reprogramming, including the alteration of the uptake and utilisation of glucose, amino acids and lipids, is an important feature of OSCC and can be used to identify its biomarkers for early and precise diagnosis. In this review, we summarise how recent findings of rewired metabolic networks in OSCC have facilitated early and precise diagnosis of OSCC.

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).

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.