MALDI-Mass Spectrometric Imaging Revealing Hypoxia-Driven Lipids and Proteins in a Breast Tumor Model

Characterization of Humanized Mouse Model of Organophosphate Poisoning and Detection of Countermeasures via MALDI-MSI

Organophosphoate (OP) chemicals are known to inhibit the enzyme acetylcholinesterase (AChE). Studying OP poisoning is difficult because common small animal research models have serum carboxylesterase, which contributes to animals' resistance to OP poisoning. Historically, guinea pigs have been used for this research; however, a novel genetically modified mouse strain (KIKO) was developed with nonfunctional serum carboxylase (Es1 KO) and an altered acetylcholinesterase (AChE) gene, which expresses the amino acid sequence of the human form of the same protein (AChE KI). KIKO mice were injected with 1xLD50 of an OP nerve agent or vehicle control with or without atropine. After one to three minutes, animals were injected with 35 mg/kg of the currently fielded Reactivator countermeasure for OP poisoning. Postmortem brains were imaged on a Bruker RapifleX ToF/ToF instrument. Data confirmed the presence of increased acetylcholine in OP-exposed animals, regardless of treatment or atropine status. More interestingly, we detected a small amount of Reactivator within the brain of both exposed and unexposed animals; it is currently debated if reactivators can cross the blood-brain barrier. Further, we were able to simultaneously image acetylcholine, the primary affected neurotransmitter, as well as determine the location of both Reactivator and acetylcholine in the brain. This study, which utilized sensitive MALDI-MSI methods, characterized KIKO mice as a functional model for OP countermeasure development.

Application of clustering strategy for automatic segmentation of tissue regions in mass spectrometry imaging

RATIONALE

Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel in MSI consists of a mass spectrum that reflects the molecule feature of the tissue spot. Because MSI contains high-dimensional datasets, it is highly desired to develop computational methods for data mining and constructing tissue segmentation maps.

METHODS

To visualize different tissue regions based on mass spectrum features and improve the efficiency in processing enormous data, we proposed a computational strategy that consists of four procedures including preprocessing, data reduction, clustering, and quantitative validation.

RESULTS

In this study, we examined the combination of t-distributed stochastic neighbor embedding (t-SNE) and hierarchical clustering (HC) for MSI data analysis. Using publicly available MSI datasets, one dataset of mouse urinary bladder, and one dataset of human colorectal cancer, we demonstrated that the generated tissue segmentation maps from this combination were superior to other data reduction and clustering algorithms. Using the staining image as a reference, we assessed the performance of clustering algorithms with external and internal clustering validation measures, including purity, adjusted Rand index (ARI), Davies-Bouldin index (DBI), and spatial aggregation index (SAI). The result indicated that SAI delivered excellent performance for automatic segmentation of tissue regions in MSI.

CONCLUSIONS

We used a clustering algorithm to construct tissue automatic segmentation in MSI datasets. The performance was evaluated by comparing it with the stained image and calculating clustering validation indexes. The results indicated that SAI is important for automatic tissue segmentation in MSI, different from traditional clustering validation measures. Compared to the reports that used internal clustering validation measures such as DBI, our method offers more effective evaluation of clustering results for MSI segmentation. We envision that the proposed automatic image segmentation strategy can facilitate deep learning in molecular feature extraction and biomarker discovery for the biomedical applications of MSI.

Quantitative Analysis of the Concentration of Trifluridine in Tumor Hypoxic Regions Using a Novel Platform Combining Functional Endoscopy and Mass Spectrometry

Hypoxic regions in solid tumors are highly resistant to drugs and thus represents an obstacle in drug discovery. Currently, however, there are technical barriers in sampling human hypoxic tumors and examining drug delivery with high sensitivity and accuracy. Herein, we present a new platform combining functional endoscopy and highly sensitive liquid chromatography-mass spectrometry (LC-MS) to assess drug delivery to hypoxic regions. Because oxygen saturation endoscopic imaging (OXEI), a functional endoscopy, can evaluate lesions and hypoxia in real-time by simultaneously acquiring a pseudocolor map of oxygen saturation and conventional endoscopic images, this platform can be used to evaluate drug delivery with human samples from hypoxic regions. As the first clinical application of this platform, the relationship between hypoxic regions and the concentration of trifluridine (FTD) incorporated into DNA was evaluated in patients with advanced gastric cancer treated with FTD/tipiracil (FTD/TPI; n = 13) by obtaining and analysis of tissue samples by OXEI and LC-MS and vascular maturity index by CD31/α-SMA staining ex vivo. The results showed that the concentration of FTD was significantly higher in the normoxic region than in the hypoxic region (P < 0.05) and there were significantly more immature vessels in hypoxic regions than in normoxic regions (P < 0.05). These results indicate that the platform was sufficiently sensitive to evaluate differences in drug anabolism in different oxygenic regions of human tumor tissue. This new platform allows quantitative drug analysis in hypoxic regions and is expected to initiate a new era of drug discovery and development.

FluoMALDI Microscopy: Matrix Co-Crystallization Simultaneously Enhances Fluorescence and MALDI Imaging

Here, the authors report that co-crystallization of fluorophores with matrix-assisted laser desorption/ionization (MALDI) imaging matrices significantly enhances fluorophore brightness up to 79-fold, enabling the amplification of innate tissue autofluorescence. This discovery facilitates FluoMALDI, the imaging of the same biological sample by both fluorescence microscopy and MALDI imaging. The approach combines the high spatial resolution and specific labeling capabilities of fluorescence microscopy with the inherently multiplexed, versatile imaging capabilities of MALDI imaging. This new paradigm simplifies registration by avoiding physical changes between fluorescence and MALDI imaging, allowing to image the exact same cells in tissues with both modalities. Matrix-fluorophore co-crystallization also facilitates applications with insufficient fluorescence brightness. The authors demonstrate  feasibility of FluoMALDI imaging with endogenous and exogenous fluorophores and autofluorescence-based FluoMALDI of brain and kidney tissue sections. FluoMALDI will advance structural-functional microscopic imaging in cell biology, biomedicine, and pathology.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

Emerging Computational Methods in Mass Spectrometry Imaging

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

Generation and multiomic profiling of a TP53/CDKN2A double-knockout gastroesophageal junction organoid model

Inactivation of the tumor suppressor genes tumor protein p53 (TP53) and cyclin-dependent kinase inhibitor 2A (CDKN2A) occurs early during gastroesophageal junction (GEJ) tumorigenesis. However, because of a paucity of GEJ-specific disease models, cancer-promoting consequences of TP53 and CDKN2A inactivation at the GEJ have not been characterized. Here, we report the development of a wild-type primary human GEJ organoid model and a CRISPR-edited transformed GEJ organoid model. CRISPR-Cas9-mediated TP53 and CDKN2A knockout (TP53/CDKN2AKO) in GEJ organoids induced morphologic dysplasia and proneoplastic features in vitro and tumor formation in vivo. Lipidomic profiling identified several platelet-activating factors (PTAFs) among the most up-regulated lipids in CRISPR-edited organoids. PTAF/PTAF receptor (PTAFR) abrogation by siRNA knockdown or a pharmacologic inhibitor (WEB2086) reduced proliferation and other proneoplastic features of TP53/CDKN2AKO GEJ organoids in vitro and tumor formation in vivo. In addition, murine xenografts of Eso26, an established human esophageal adenocarcinoma cell line, were suppressed by WEB2086. Mechanistically, TP53/CDKN2A dual inactivation disrupted both the transcriptome and the DNA methylome, likely mediated by key transcription factors, particularly forkhead box M1 (FOXM1). FOXM1 activated PTAFR transcription by binding to the PTAFR promoter, further amplifying the PTAF-PTAFR pathway. Together, these studies established a robust model system for investigating early GEJ neoplastic events, identified crucial metabolic and epigenomic changes occurring during GEJ model tumorigenesis, and revealed a potential cancer therapeutic strategy. This work provides insights into proneoplastic mechanisms associated with TP53/CDKN2A inactivation in early GEJ neoplasia, which may facilitate early diagnosis and prevention of GEJ neoplasms.

Protein Profiling of Breast Cancer for Treatment Decision-Making

The increasing use of neoadjuvant therapy has resulted in therapeutic decisions being made on the basis of diagnostic needle core biopsy. For many patients, this method might yield the only fragment of tumor available for biomarker analysis, necessitating judicious use. Many multiplex protein analytic methods have been developed that employ fluorescence or other tags to overcome the limitations of immunohistochemistry while still retaining the spatial annotation. Interpretation of the data can be difficult because of the limitations of the human eye. Computational deconvolution of the signals may be necessary for some of these methods to enable identification of cell-specific localization and coexpression of biomarkers. Herein, we present the different methods that are coming of age and their application in cancer research, with a focus on breast cancer. We also discuss the limitations, which include high costs and long turnaround times. The methods are also based on the premise that preanalytical factors will have identical impact on all proteins analyzed. There is a need to establish standards to normalize the data and enable cross-sample comparisons. In spite of these limitations, the multiplex technologies are extremely valuable discovery tools and can provide novel insights into the biology of cancer and mechanisms of drug resistance.

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.

Tissue sample preparations for preclinical research determined by molecular imaging mass spectrometry using MALDI

Matrix-assisted laser desorption/ionization - imaging mass spectrometry is an alternative tool, which can be implemented in order to obtain and visualize the "omic" signature of tissue samples. Its application to clinical study enables simultaneous imaging-based morphological observations and mass spectrometry analysis. Application of fully informative material like tissue, allows to obtain the complex and unique profile of analyzed samples. This knowledge leads to diagnose disease, study the mechanism of cancer development, select the potential biomarkers as well as correlating obtained image with prognosis. Nevertheless, it is worth to notice that this method is found to be objective but the result of analysis is mainly influenced by the sample preparation protocol, included collection of biological material, its preservation and processing. However, application of this approach requires a special sample preparation procedure. The main goal of the study is to present the current knowledge on the clinical application of matrix-assisted laser desorption/ionization - imaging mass spectrometry in cancer research, with particular emphasis on the sample preparation step. For this purpose, several protocols based on cryosections and formalin-fixed paraffin embedded tissue were compiled and compared, taking into account the measured metabolites of potential diagnostic importance for a given type of cancer. This article is protected by copyright. All rights reserved.

Reduced Hemoglobin Signal and Improved Detection of Endogenous Proteins in Blood-Rich Tissues for MALDI Mass Spectrometry Imaging

Mass spectrometry imaging provides a powerful approach for the direct analysis and spatial visualization of molecules in tissue sections. Using matrix-assisted laser desorption/ionization mass spectrometry, intact protein imaging has been widely investigated for biomarker analysis and diagnosis in a variety of tissue types and diseases. However, blood-rich or highly vascular tissues present a challenge in molecular imaging due to the high ionization efficiency of hemoglobin, which leads to ion suppression of endogenous proteins. Here, we describe a protocol to selectively reduce hemoglobin signal in blood-rich tissues that can easily be integrated into mass spectrometry imaging workflows.

Immune and stromal scoring system associated with tumor microenvironment and prognosis: a gene-based multi-cancer analysis

Background

Tumor microenvironment (TME) is associated with tumor progression and prognosis. Previous studies provided tools to estimate immune and stromal cell infiltration in TME. However, there is still a lack of single index to reflect both immune and stromal status associated with prognosis and immunotherapy responses.

Methods

A novel immune and stromal scoring system named ISTMEscore was developed. A total of 15 datasets were used to train and validate this system, containing 2965 samples from lung adenocarcinoma, skin cutaneous melanoma and head and neck squamous cell carcinoma.

Results

The patients with high immune and low stromal scores (HL) were associated with low ratio of T cell co-inhibitory/stimulatory molecules and low levels of angiogenesis markers, while the patients with low immune and high stromal scores (LH) had the opposite characteristics. The HL patients had immune-centered networks, while the patients with low immune and low stromal scores (LL) had desert-like networks. Moreover, copy number alteration burden was decreased in the HL patients. For the clinical characteristics, our TME classification was an independent prognostic factor. In the 5 cohorts with immunotherapy, the LH patients were linked to the lowest response rate.

Conclusions

ISTMEscore system could reflect the TME status and predict the prognosis. Compared to previous TME scores, our ISTMEscore was superior in the prediction of prognosis and immunotherapy response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03002-1.

[Mass spectrometry imaging technology and its application in breast cancer research]

乳腺癌是女性最常见的恶性肿瘤,其发病率在世界范围内呈现上升趋势,是威胁女性健康的重要疾病之一。随着现代医学技术的快速发展,早期有效的诊断和筛查方法能够改善乳腺癌患者生存率和提高其生活质量。由于乳腺癌肿瘤具有非常显著的异质性,这对于诊断和筛查带来了较大困难,亟须在肿瘤演进时间信息中,继续引入生物分子的空间信息,从而对其异质性、肿瘤微环境等进行准确的追踪。质谱成像技术,可在免标记的前提下利用离子质荷比的特性发现生物组织中的各种分子,并研究这些分子的时间和空间信息,对其进行准确的定性、定量和空间定位。目前,通过质谱成像技术可直接获取药物及其代谢物、内源性代谢物、脂质、多肽和蛋白质等在组织中的空间分布信息,为肿瘤分子分型诊断和确认以及相关抗肿瘤药物的筛选提供了新的思路和研究方向。该综述以乳腺癌相关的生物样品制备和研究进展为主要内容,从小分子样本、大分子样本、石蜡包埋样本、基质喷涂方式、常用离子源等方面阐述质谱成像中样本制备的重要性以及样品制备过程中存在的难点问题。同时,以细胞模型、动物模型和临床肿瘤标本为研究对象,汇总了质谱成像技术在乳腺癌方面的应用进展,并进行了展望,为开展癌症精准分型研究和药物药效的快速筛查提供了重要依据。

Quantitative In Vivo Monitoring of Hypoxia and Vascularization of Patient-Derived Murine Xenografts of Mantle Cell Lymphoma Using Photoacoustic and Ultrasound Imaging

Tumor oxygenation and vascularization are important parameters that determine the aggressiveness of the tumor and its resistance to cancer therapies. We introduce dual-modality ultrasound and photoacoustic imaging (US-PAI) for the direct, non-invasive real-time in vivo evaluation of oxygenation and vascularization of patient-derived xenografts (PDXs) of B-cell mantle cell lymphomas. The different optical properties of oxyhemoglobin and deoxyhemoglobin make it possible to determine oxygen saturation (sO₂) in tissues using PAI. High-frequency color Doppler imaging enables the visualization of blood flow with high resolution. Tumor oxygenation and vascularization were studied in vivo during the growth of three different subcutaneously implanted patient-derived xenograft (PDX) lymphomas (VFN-M1, VFN-M2 and VFN-M5 R1). Similar values of sO₂ (sO₂ Vital), determined from US-PAI volumetric analysis, were obtained in small and large VFN-M1 tumors ranging from 37.9 ± 2.2 to 40.5 ± 6.0 sO₂ Vital (%) and 37.5 ± 4.0 to 35.7 ± 4.6 sO₂ Vital (%) for small and large VFN-M2 PDXs. In contrast, the higher sO₂ Vital values ranging from 57.1 ± 4.8 to 40.8 ± 5.7 sO₂ Vital (%) (small to large) of VFN-M5 R1 tumors corresponds with the higher aggressiveness of that PDX model. The different tumor percentage vascularization (assessed as micro-vessel areas) of VFN-M1, VFN-M2 and VFN-M5 R1 obtained by color Doppler (2.8 ± 0.1%, 3.8 ± 0.8% and 10.3 ± 2.7%) in large-stage tumors clearly corresponds with their diverse growth and aggressiveness. The data obtained by color Doppler were validated by histology. In conclusion, US-PAI rapidly and accurately provided relevant and reproducible information on tissue oxygenation in PDX tumors in real time without the need for a contrast agent.

Rapid Imaging of Unsaturated Lipids at an Isomeric Level Achieved by Controllable Oxidation

Lipid imaging plays an important role in the research of some diseases, such as cancers. Unsaturated lipids are often present as isomers that can have different functions; however, traditional tandem mass spectrometry imaging (MSI) cannot differentiate between different isomers, which presents difficulties for the pathological study of lipids. Herein, we propose a method for the MSI of the C═C double-bond isomers of unsaturated lipids based on oxidative reactions coupled with air flow-assisted desorption electrospray ionization, which can conveniently achieve rapid MSI of unsaturated lipids at an isomeric level. Using this method, tissue sections can be scanned directly with MSI after only 10 min of accelerated oxidation. This method was used for the imaging of mouse lung cancer tissues, revealing a distributional difference in the unsaturated lipid isomers of normal and pathological regions. Through the MSI of unsaturated lipids at an isomeric level in tissues infected with cancer cells, the regions where the isomers were enriched were exhibited, indicating that these regions were the most concentrated regions of cancer cells. This method provides a convenient platform for studying the functional effects of the isomers of unsaturated lipids in pathological tissues.

Three dimensional engineered models to study hypoxia biology in breast cancer

Breast cancer is the second leading cause of mortality among women worldwide. Despite the available therapeutic regimes, variable treatment response is reported among different breast cancer subtypes. Recently, the effects of the tumor microenvironment on tumor progression as well as treatment responses have been widely recognized. Hypoxia and hypoxia inducible factors in the tumor microenvironment have long been known as major players in tumor progression and survival. However, the majority of our understanding of hypoxia biology has been derived from two dimensional (2D) models. Although many hypoxia-targeted therapies have elicited promising results in vitro and in vivo, these results have not been successfully translated into clinical trials. These limitations of 2D models underscore the need to develop and integrate three dimensional (3D) models that recapitulate the complex tumor-stroma interactions in vivo. This review summarizes role of hypoxia in various hallmarks of cancer progression. We then compare traditional 2D experimental systems with novel 3D tissue-engineered models giving accounts of different bioengineering platforms available to develop 3D models and how these 3D models are being exploited to understand the role of hypoxia in breast cancer progression.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer

BACKGROUND

Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue.

RECENT FINDINGS

Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers.

CONCLUSION

MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

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

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

Imaging of cancer lipid metabolism in response to therapy

Lipids represent a diverse array of molecules essential to the cell's structure, defense, energy, and communication. Lipid metabolism can often become dysregulated during tumor development. During cancer therapy, targeted inhibition of cell proliferation can likewise cause widespread and drastic changes in lipid composition. Molecular imaging techniques have been developed to monitor altered lipid profiles as a biomarker for cancer diagnosis and treatment response. For decades, MRS has been the dominant non-invasive technique for studying lipid metabolite levels. Recent insights into the oncogenic transformations driving changes in lipid metabolism have revealed new mechanisms and signaling molecules that can be exploited using optical imaging, mass spectrometry imaging, and positron emission tomography. These novel imaging modalities have provided researchers with a diverse toolbox to examine changes in lipids in response to a wide array of anticancer strategies including chemotherapy, radiation therapy, signal transduction inhibitors, gene therapy, immunotherapy, or a combination of these strategies. The understanding of lipid metabolism in response to cancer therapy continues to evolve as each therapeutic method emerges, and this review seeks to summarize the current field and areas of unmet needs.

Mass Spectrometric Imaging Reveals Temporal and Spatial Dynamics of Bioactive Lipids in Arteries Undergoing Restenosis

Restenosis, or renarrowing of the arterial lumen, is a common recurrent disease following balloon angioplasty and stenting treatments for cardiovascular disease. A major technical barrier for deciphering restenotic mechanisms is the dynamic, spatial profiling of bioactive lipids in the arterial wall, especially in small animals. Here, applying matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI), we conducted the first lipidomic study of temporal-spatial profiling in a small animal model of angioplasty-induced restenosis. Cross sections were collected 3, 7, and 14 days after balloon angioplasty of rat carotid arteries. MALDI-MSI analyses showed that diacylglycerols (DAGs), signaling lipids associated with restenosis, and lysophosphatidylcholines (LysoPCs), whose function was uncharacterized in restenosis, dramatically increased at postangioplasty day 7 and day 14 in the neointimal layer of balloon-injured arteries compared to uninjured controls. In contrast, sphingomyelins (SMs) did not increase, but rather decreased at day 3, day 7, and day 14 in injured arteries versus the uninjured control arteries. These results revealed previously unexplored distinct temporal-spatial lipid dynamics in the restenotic arterial wall. Additionally, we employed time-of-flight secondary ion mass spectrometry (TOF-SIMS) tandem MS imaging for both molecular identification and imaging at high spatial resolution. These imaging modalities provide powerful tools for unraveling novel mechanisms of restenosis involving lipids or small signaling molecules.

Specific N-Linked Glycosylation Patterns in Areas of Necrosis in Tumor Tissues

Tissue necrosis is a form of cell death common in advanced and aggressive solid tumors, and is associated with areas of intratumoral chronic ischemia. The histopathology of necrotic regions appear as a scaffold of cellular membrane remnants, reflective of the hypoxia and cell degradation events associated with this cellular death pathway. Changes in the glycosylation of cell surface proteins is another common feature of cancer progression. Using a recently developed mass spectrometry imaging approach to evaluate N-linked glycan distributions in human formalin-fixed clinical cancer tissues, differences in the glycan structures of regions of tumor, stroma and necrosis were evaluated. While the structural glycan classes detected in the tumor and stromal regions are typically classified as high mannose or branched glycans, the glycans found in necrotic regions displayed limited branching, contained sialic acid modifications and lack fucose modifications. While this phenomenon was initially classified in breast cancer tissues, it has been also seen in cervical, thyroid and liver cancer samples. These changes in glycosylation within the necrotic regions could provide further mechanistic insight to necrotic changes in cancer tissue and provide new research directions for identifying prognostic markers of necrosis.

Analysis of the Metabolic Characteristics of Serum Samples in Patients With Multiple Myeloma

Aims: This study aimed to identify potential, non-invasive biomarkers for diagnosis and monitoring of the progress in multiple myeloma (MM) patients. Methods: MM patients and age-matched healthy controls (HC) were recruited in Discovery phase and Validation phase, respectively. MM patients were segregated into active group (AG) and responding group (RG). Serum samples were collected were conducted to non-targeted metabolomics analyses. Metabolites which were significantly changed (SCMs) among groups were identified in Discovery phase and was validated in Validation phase. The signaling pathways of these SCMs were enriched. The ability of SCMs to discriminate among groups in Validation phase was analyzed through receiver operating characteristic curve. The correlations between SCMs and clinical features, between SCMs and survival period of MM patients were analyzed. Results: Total of 23 SCMs were identified in AG compared with HC both in Discovery phase and Validation phase. Those SCMs were significantly enriched in arginine and proline metabolism and glycerophospholipid metabolism. 4 SCMs had the discriminatory ability between MM patients and healthy controls in Validation phase. Moreover, 12 SCMs had the ability to discriminate between the AG patients and RG patients in Validation phase. 10 out of 12 SCMs correlated with advanced features of MM. Moreover, 8 out of 12 SCMs had the negative impact on the survival of MM. 5'-Methylthioadenosine may be the only independent prognostic factor in survival period of MM. Conclusion: 10 SCMs identified in our study, which correlated with advanced features of MM, could be potential, novel, non-invasive biomarkers for active disease in MM.

Laser post-ionisation combined with a high resolving power orbitrap mass spectrometer for enhanced MALDI-MS imaging of lipids

Coupling laser post-ionisation with a high resolving power MALDI Orbitrap mass spectrometer has realised an up to ∼100-fold increase in the sensitivity and enhanced the chemical coverage for MALDI-MS imaging of lipids relative to conventional MALDI. This could constitute a major breakthrough for biomedical research.

Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution

Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment.

Tissue imaging with in situ solid-phase extraction micro-funnel based spray ionization mass spectrometry

Current imaging mass spectrometry techniques are faced with a major challenge related to ion suppression effect. Data regarding low-abundance components or low-polarity compounds cannot be normally obtained presumably because of the discrimination effect of easily ionized chemical components on desorption/ionization process. In this study, a new method was proposed to obtain images of chemical components in biological tissues or sections through in situ solid-phase extraction in sorbent mounted micro-funnel based spray ionization mass spectrometry. An imprint of a strawberry section was formed by gently pressing against a 2D array of micro-funnels. The sorbent mounted micro-funnels were then subjected to in situ single-pixel solid-phase extraction to alleviate the matrix-related ion suppression effect. The achievable spatial resolution is approximately 250 µm. The imaging of the spatial distribution of low-abundance or low-polarity chemicals in the strawberry imprint could be obtained by using a gradient elution strategy. Results demonstrated that the "not observed" remark does not necessarily indicate that a specific compound is non-existent when traditional imaging mass spectrometry techniques are used. The proposed method can be applied to conduct low-abundance chemical imaging through in situ single-pixel sample pretreatment.

Oxygen-Dependent Lipid Profiles of Three-Dimensional Cultured Human Chondrocytes Revealed by MALDI-MSI

Articular cartilage is exposed to a gradient of oxygen levels ranging from 5% at the surface to 1% in the deepest layers. While most cartilage research is performed in supraphysiological oxygen levels (19–21%), culturing chondrocytes under hypoxic oxygen levels (≤8%) promotes the chondrogenic phenotype. Exposure of cells to various oxygen levels alters their lipid metabolism, but detailed studies examining how hypoxia affects lipid metabolism in chondrocytes are lacking. To better understand the chondrocyte’s behavior in response to oxygen, we cultured 3D pellets of human primary chondrocytes in normoxia (20% oxygen) and hypoxia (2.5% oxygen) and employed matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) in order to characterize the lipid profiles and their spatial distribution. In this work we show that chondrocytes cultured in hypoxia and normoxia can be differentiated by their lipid profiles. Among other species, phosphatidylglycerol species were increased in normoxic pellets, whereas phosphatidylinositol species were the most prominent lipids in hypoxic pellets. Moreover, spatial mapping revealed that phospahtidylglyycerol species were less prominent in the center of pellets where the oxygen level is lower. Additional analysis revealed a higher abundance of the mitochondrial-specific lipids, cardiolipins, in normoxic conditions. In conclusion MALDI-MSI described specific lipid profiles that could be used as sensors of oxygen level changes and may especially be relevant for retaining the chondrogenic phenotype, which has important implications for the treatment of bone and cartilage diseases.

Clinical applications of MALDI imaging technologies in cancer and neurodegenerative diseases

Matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) imaging mass spectrometry (IMS) enables localization of analytes of interest along with histology. More specifically, MALDI-IMS identifies the distributions of proteins, peptides, small molecules, lipids, and drugs and their metabolites in tissues, with high spatial resolution. This unique capacity to directly analyze tissue samples without the need for lengthy sample preparation reduces technical variability and renders MALDI-IMS ideal for the identification of potential diagnostic and prognostic biomarkers and disease gradation. MALDI-IMS has evolved rapidly over the last decade and has been successfully used in both medical and basic research by scientists worldwide. In this review, we explore the clinical applications of MALDI-IMS, focusing on the major cancer types and neurodegenerative diseases. In particular, we re-emphasize the diagnostic potential of IMS and the challenges that must be confronted when conducting MALDI-IMS in clinical settings. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Rapid Detection of Necrosis in Breast Cancer with Desorption Electrospray Ionization Mass Spectrometry

Identification of necrosis in tumors is of prognostic value in treatment planning, as necrosis is associated with aggressive forms of cancer and unfavourable outcomes. To facilitate rapid detection of necrosis with Mass Spectrometry (MS), we report the lipid MS profile of necrotic breast cancer with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) imaging validated with statistical analysis and correlating pathology. This MS profile is characterized by (1) the presence of the ion of m/z 572.48 [Cer(d34:1) + Cl]⁻ which is a ceramide absent from the viable cancer subregions; (2) the absence of the ion of m/z 391.25 which is present in small abundance only in viable cancer subregions; and (3) a slight increase in the relative intensity of known breast cancer biomarker ions of m/z 281.25 [FA(18:1)-H]⁻ and 303.23 [FA(20:4)-H]⁻. Necrosis is accompanied by alterations in the tissue optical depolarization rate, allowing tissue polarimetry to guide DESI-MS analysis for rapid MS profiling or targeted MS imaging. This workflow, in combination with the MS profile of necrosis, may permit rapid characterization of necrotic tumors from tissue slices. Further, necrosis-specific biomarker ions are detected in seconds with single MS scans of necrotic tumor tissue smears, which further accelerates the identification workflow by avoiding tissue sectioning and slide preparation.

Key Players in Choline Metabolic Reprograming in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC), defined as lack of estrogen and progesterone receptors in the absence of protein overexpression/gene amplification of human epidermal growth factor receptor 2, is still a clinical challenge despite progress in breast cancer care. ¹H magnetic resonance spectroscopy allows identification and non-invasive monitoring of TNBC metabolic aberrations and elucidation of some key mechanisms underlying tumor progression. Thus, it has the potential to improve in vivo diagnosis and follow-up and also to identify new targets for treatment. Several studies have shown an altered phosphatidylcholine (PtdCho) metabolism in TNBCs, both in patients and in experimental models. Upregulation of choline kinase-alpha, an enzyme of the Kennedy pathway that phosphorylates free choline (Cho) to phosphocholine (PCho), is a major contributor to the increased PCho content detected in TNBCs. Phospholipase-mediated PtdCho headgroup hydrolysis also contributes to the build-up of a PCho pool in TNBC cells. The oncogene-driven PtdCho cycle appears to be fine tuned in TNBC cells in at least three ways: by modulating the choline import, by regulating the activity or expression of specific metabolic enzymes, and by contributing to the rewiring of the entire metabolic network. Thus, only by thoroughly dissecting these mechanisms, it will be possible to effectively translate this basic knowledge into further development and implementation of Cho-based imaging techniques and novel classes of therapeutics.

Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry imaging

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a robust tool for spatially resolved analysis of biomolecules in situ. Recent advances in high ionization-efficiency MALDI matrices, new matrix deposition procedures, and the development of high spatial-resolution and high sensitivity MS instruments continue to drive new applications of MALDI-MSI, along with other MSI techniques, which allow us to visualize and determine the regio-specific and temporal changes in proteins, peptides, lipids, drug molecules, and metabolites within the tissues, cells and microorganisms. These provide researchers with a new route to the discovery of potential biomarkers of human disease and elucidation of the underlying biology of metabolic regulation, thus bringing our understanding of human health to a new level.

MALDI mass spectrometry imaging: A cutting-edge tool for fundamental and clinical histopathology

Histopathological diagnoses have been done in the last century based on hematoxylin and eosin staining. These methods were complemented by histochemistry, electron microscopy, immunohistochemistry (IHC), and molecular techniques. Mass spectrometry (MS) methods allow the thorough examination of various biocompounds in extracts and tissue sections. Today, mass spectrometry imaging (MSI), and especially matrix-assisted laser desorption ionization (MALDI) imaging links classical histology and molecular analyses. Direct mapping is a major advantage of the combination of molecular profiling and imaging. MSI can be considered as a cutting edge approach for molecular detection of proteins, peptides, carbohydrates, lipids, and small molecules in tissues. This review covers the detection of various biomolecules in histopathological sections by MSI. Proteomic methods will be introduced into clinical histopathology within the next few years.

Increased arachidonic acid-containing phosphatidylcholine is associated with reactive microglia and astrocytes in the spinal cord after peripheral nerve injury

Peripheral nerve injury (PNI) triggers cellular and molecular changes in the spinal cord. However, little is known about how the polyunsaturated fatty acid-containing phosphatidylcholines (PUFA-PCs) are regulated in the spinal cord after PNI and the association of PUFA-PCs with the non-neuronal cells within in the central nervous system (CNS). In this study, we found that arachidonic acid-containing phosphatidylcholine (AA-PC), [PC(16:0/20:4)+K](+), was significantly increased in the ipsilateral ventral and dorsal horns of the spinal cord after sciatic nerve transection, and the increased expression of [PC(16:0/20:4)+K](+) spatiotemporally resembled the increase of reactive microglia and the astrocytes. From the lipidomics point of view, we conclude that [PC(16:0/20:4)+K](+) could be the main phospholipid in the spinal cord influenced by PNI, and the regulation of specific phospholipid molecule in the CNS after PNI is associated with the reactive microglia and astrocytes.

Identification of Biomarkers of Necrosis in Xenografts Using Imaging Mass Spectrometry

Xenografts are commonly used to test the effect of new drugs on human cancer. However, because of their heterogeneity, analysis of the results is often controversial. Part of the problem originates in the existence of tumor cells at different metabolic stages: from metastatic to necrotic cells, as it happens in real tumors. Imaging mass spectrometry is an excellent solution for the analysis of the results as it yields detailed information not only on the composition of the tissue but also on the distribution of the biomolecules within the tissue. Here, we use imaging mass spectrometry to determine the distribution of phosphatidylcholine (PC), phosphatidylethanolamine (PE), and their plasmanyl- and plasmenylether derivatives (PC-P/O and PE-P/O) in xenografts of five different tumor cell lines: A-549, NCI-H1975, BX-PC3, HT29, and U-87 MG. The results demonstrate that the necrotic areas showed a higher abundance of Na(+) adducts and of PC-P/O species, whereas a large abundance of PE-P/O species was found in all the xenografts. Thus, the PC/PC-ether and Na(+)/K(+) ratios may highlight the necrotic areas while an increase on the number of PE-ether species may be pointing to the existence of viable tumor tissues. Furthermore, the existence of important changes in the concentration of Na(+) and K(+) adducts between different tissues has to be taken into account while interpreting the imaging mass spectrometry results. Graphical Abstract ᅟ.

Optimized Protocol To Analyze Changes in the Lipidome of Xenografts after Treatment with 2-Hydroxyoleic Acid

Xenografts are a popular model for the study of the action of new antitumor drugs. However, xenografts are highly heterogeneous structures, and therefore it is sometimes difficult to evaluate the effects of the compounds on tumor metabolism. In this context, imaging mass spectrometry (IMS) may yield the required information, due to its inherent characteristics of sensitivity and spatial resolution. To the best of our knowledge, there is still no clear analysis protocol to properly evaluate the changes between samples due to the treatment. Here we present a protocol for the evaluation of the effect of 2-hydroxyoleic acid (2-OHOA), an antitumor compound, on xenografts lipidome based on IMS. Direct treated/control comparison did not show conclusive results. As we will demonstrate, a more sophisticated protocol was required to evaluate these changes including the following: (1) identification of different areas in the xenograft, (2) classification of these areas (necrotic/viable) to compare similar types of tissues, (3) suppression of the effect of the variation of adduct formation between samples, and (4) normalization of the variables using the standard deviation to eliminate the excessive impact of the stronger peaks in the statistical analysis. In this way, the 36 lipid species that experienced the largest changes between treated and control were identified. Furthermore, incorporation of 2-hydroxyoleic acid to a sphinganine base was also confirmed by MS/MS. Comparison of the changes observed here with previous results obtained with different techniques demonstrates the validity of the protocol.

More from less: high-throughput dual polarity lipid imaging of biological tissues

Here, we reveal the increased biochemical and spatial information acquired using high-speed MALDI-MSI and sequential acquisitions of positive and negative lipid-MSI data from single tissue sections.

Nanobiosensors in diagnostics

Medical diagnosis has been greatly improved thanks to the development of new techniques capable of performing very sensitive detection and quantifying certain parameters. These parameters can be correlated with the presence of specific molecules and their quantity. Unfortunately, these techniques are demanding, expensive, and often complicated. On the other side, progress in other fields of science and technology has contributed to the rapid growth of nanotechnology. Although being an emerging discipline, nanotechnology has raised huge interest and expectations. Most of the enthusiasm comes from new possibilities and properties of nanomaterials. Biosensors (simple, robust, sensitive, cost-effective) combined with nanomaterials, also called nanobiosensors, are serving as bridge between advanced detection/diagnostics and daily/routine tests. Here we review some of the latest applications of nanobiosensors in diagnostics field.

Use of advantageous, volatile matrices enabled by next-generation high-speed matrix-assisted laser desorption/ionization time-of-flight imaging employing a scanning laser beam

RATIONALE

In mass spectrometry imaging (MSI) it is often desirable to analyse the same sample in both polarities to extract the most information. However, many matrices that produce high-quality spectra in matrix-assisted laser desorption/ionization (MALDI) are volatile, greatly limiting their use in long imaging experiments. We demonstrate that using a new high speed MALDI-MSI instrument, volatile matrices, including those that produce intense lipid signals in both positive and negative ion mode, can now be effectively used in MSI.

METHODS

A prototype Bruker rapifleX MALDI Tissuetyper™ time-of-flight (TOF) instrument was used for high-speed imaging. This allows acquisition rates up to 50 pixels/s made possible by use of a 10 kHz laser and two rotating mirrors that allow the laser beam to be moved over, and synchronised with, the rapidly moving sample. MSI experiments were performed on mouse brain sections using non-vacuum stable dithranol and 2,6-dihydroxyacetophenone (DHA) matrices with pixel sizes ranging from 10 × 10 µm(2) to 50 × 50 µm(2).

RESULTS

Both DHA and dithranol produced rich, complementary lipid spectra in both positive and negative ion modes. Due to the rapid acquisition speed of the instrument, both matrices could be effectively used for MSI despite their volatility. For example, an entire mouse brain could be imaged consecutively in both positive and negative ion mode with 50 × 50 µm(2) pixels in ~35 min. We demonstrate that these speeds make possible both faster and higher resolution imaging of biological tissues on practical timescales.

CONCLUSIONS

These high acquisition speeds now make possible whole new classes of matrices that are unstable under high vacuum for MALDI-MSI studies. This provides researchers with far greater range and flexibility in choosing the best matrix for the given sample and analytes that they wish to detect. In addition, such instruments allow MSI to be performed at higher resolution across larger areas on practical time scales.

High Throughput In Situ DDA Analysis of Neuropeptides by Coupling Novel Multiplex Mass Spectrometric Imaging (MSI) with Gas-Phase Fractionation

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) is a powerful tool to map the spatial distribution of biomolecules on tissue sections. Recent developments of hybrid MS instruments allow combination of different types of data acquisition by various mass analyzers into a single MSI analysis, which reduces experimental time and sample consumptions. Here, using the well-characterized crustacean nervous system as a test-bed, we explore the utility of high resolution and accurate mass (HRAM) MALDI Orbitrap platform for enhanced in situ characterization of the neuropeptidome with improved chemical information. Specifically, we report on a multiplex-MSI method, which combines HRAM MSI with data dependent acquisition (DDA) tandem MS analysis in a single experiment. This method enables simultaneous mapping of neuropeptide distribution, sequence validation, and novel neuropeptide discovery in crustacean neuronal tissues. To enhance the dynamic range and efficiency of in situ DDA, we introduced a novel approach of fractionating full m/z range into several sub-mass ranges and embedding the setup using the multiplex-DDA-MSI scan events to generate pseudo fractionation before MS/MS scans. The division of entire m/z into multiple segments of m/z sub-ranges for MS interrogation greatly decreased the complexity of molecular species from tissue samples and the heterogeneity of the distribution and variation of intensities of m/z peaks. By carefully optimizing the experimental conditions such as the dynamic exclusion, the multiplex-DDA-MSI approach demonstrates better performance with broader precursor coverage, less biased MS/MS scans towards high abundance molecules, and improved quality of tandem mass spectra for low intensity molecular species. Graphical Abstract ᅟ.