Real-time diagnosis of chemically induced hepatocellular carcinoma using a novel mass spectrometry-based technique

Diagnostic Accuracy of Ambient Mass Spectrometry with Blood Plasma in a Murine Glioma Model Using Machine Learning

OBJECTIVE

Malignant glioma progresses rapidly and shows poor prognosis, but clinically applicable blood plasma-based biochemical tumor markers remain lacking. This study aimed to develop a diagnostic system using probe electrospray ionization mass spectrometry (PESI-MS) and a machine-learning logistic regression model to detect plasma changes at various time points in a murine glioma model.

METHODS

We used a syngeneic intracranial orthotopic murine model with GL261 glioma cells. Blood plasmas were collected before and 3, 7, and 14 days after intracranial transplantation of glioma cells (tumor group, n = 7) or injection of phosphate-buffered saline (control group, n = 8). Mass spectra from those samples were obtained using PESI-MS and compared between control and tumor groups. We explored changes in mass spectra at the 3 time points (3, 7, and 14 days) after transplantation. The performance of machine-learning logistic regression-based diagnosis algorithm was evaluated to clarify the potential utility for early diagnosis.

RESULTS

Sixteen significant mass spectrum peaks were identified between the tumor and control groups. Multiple logistic regression analysis revealed 5 key mass spectra, achieving sensitivity of 0.875 and specificity of 0.943 for tumor discrimination. The area under the receiver operating characteristic curve was 0.981, outperforming analyses of individual spectra.

CONCLUSIONS

These results indicate that PESI-MS combined with machine learning-based diagnostics in blood plasma could be a promising approach to accurate detection of malignant glioma.

Ambient Mass Spectrometry and Machine Learning-Based Diagnosis System for Acute Coronary Syndrome

Aims: The purpose of this study is to establish a novel diagnosis system in early acute coronary syndrome (ACS) using probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning (ML) and to validate the diagnostic accuracy. Methods: A total of 32 serum samples derived from 16 ACS patients and 16 control patients were analyzed by PESI-MS. The acquired mass spectrum dataset was subsequently analyzed by partial least squares (PLS) regression to find the relationship between the two groups. A support vector machine, an ML method, was applied to the dataset to construct the diagnostic algorithm. Results: Control and ACS groups were separated into the two clusters in the PLS plot, indicating ACS patients differed from the control in the profile of serum composition obtained by PESI-MS. The sensitivity, specificity, and accuracy of our diagnostic system were all 93.8%, and the area under the receiver operating characteristic curve showed 0.965 (95% CI: 0.84-1). Conclusion: The PESI-MS and ML-based diagnosis system are likely an optimal solution to assist physicians in ACS diagnosis with its remarkably predictive accuracy.

Diagnostic method of mass spectrometry for detecting lymph node metastasis of non-small cell lung cancer

BACKGROUND

Histopathology by pathologists is essential in the diagnosis of non-small cell lung cancer (NSCLC). However, auxiliary diagnostic procedures for malignant tumor have continued to evolve. Despite the poor prognosis of patients with NSCLC, the application of the latest procedures and technologies to the field of lung cancer has lagged. Mass spectrometry was used to detect trace amounts of peptides in human tissue with high accuracy. The aim of this study was to establish a method for diagnostic mass spectrometry to identify lymph node metastasis by detecting cytokeratin (CK)19, a useful biomarker in lung cancer.

METHODS

We collected 81 lymph nodes with positive expression of CK19 in patients who underwent radical surgical resection in the Department of Thoracic Surgery at Iwate Medical University between May 2020 and December 2022. An X500R instrument was used for sample analysis. A positive result for lymph node metastasis as the detection at least two product ions (FGPGVAFR and ILGATIENSR) from CK19 was defined.

RESULTS

Our study indicated a high diagnostic efficiency for mass spectrometry, with 87.5% sensitivity and 91.2% specificity. The mutual concordance of mass spectrometry methods and histopathological diagnosis was 90.1%.

CONCLUSIONS

Mass spectrometry offers high diagnostic accuracy and can be clinically applied to auxiliary diagnostic procedures for lymph node metastasis from NSCLC.

Probe electrospray ionization mass spectrometry-based rapid diagnosis of liver tumors

BACKGROUND AND AIM

Prompt differential diagnosis of liver tumors is clinically important and sometimes difficult. A new diagnostic device that combines probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning may help provide the differential diagnosis of liver tumors.

METHODS

We evaluated the diagnostic accuracy of this new PESI-MS device using tissues obtained and stored from previous surgically resected specimens. The following cancer tissues (with collection dates): hepatocellular carcinoma (HCC, 2016-2019), intrahepatic cholangiocellular carcinoma (ICC, 2014-2019), and colorectal liver metastasis (CRLM, 2014-2019) from patients who underwent hepatic resection were considered for use in this study. Non-cancerous liver tissues (NL) taken from CRLM cases were also incorporated into the analysis. Each mass spectrum provided by PESI-MS was tested using support vector machine, a type of machine learning, to evaluate the discriminatory ability of the device.

RESULTS

In this study, we used samples from 91 of 139 patients with HCC, all 24 ICC samples, and 103 of 202 CRLM samples; 80 NL from CRLM cases were also used. Each mass spectrum was obtained by PESI-MS in a few minutes and was evaluated by machine learning. The sensitivity, specificity, and diagnostic accuracy of the PESI-MS device for discriminating HCC, ICC, and CRLM from among a mix of all three tumors and from NL were 98.9%, 98.1%, and 98.3%; 87.5%, 93.1%, and 92.6%; and 99.0%, 97.9%, and 98.3%, respectively.

CONCLUSION

This study demonstrated that PESI-MS and machine learning could discriminate liver tumors accurately and rapidly.

Ambient Ionization Mass Spectrometry: Application and Prospective

Mass spectrometry (MS) is a formidable analytical tool for the analysis of non-polar to polar compounds individually and/or from mixtures, providing information on the molecular weights and chemical structures of the analytes. During the last more than one-decade, ambient ionization mass spectrometry (AIMS) has developed quickly, producing a wide range of platforms and proving scientific improvements in a variety of domains, from biological imaging to quick quality control. These methods have made it possible to detect target analytes in real time without sample preparation in an open environment, and they can be connected to any MS system with an atmospheric pressure interface. They also have the ability to analyze explosives, illicit drugs, disease diagnostics, drugs in biological samples, adulterants in food and agricultural products, reaction progress, and environmental monitoring. The development of novel ambient ionization techniques, such as probe electrospray ionization, paper spray ionization, and fiber spray ionization, employed even at picolitre to femtolitre solution levels to provide femtogram to attogram levels of the target analytes. The special characteristic of this ambient ion source, which has been extensively used, is the noninvasive property of PESI of examination of biological real samples. The results in the current review supports the idea that AIMS has emerged as a pioneer in MS-based approaches and that methods will continue to be developed along with improvements to existing ones in the near future.

Versatile Mass Spectrometry-Based Intraoperative Diagnosis of Liver Tumor in a Multiethnic Cohort

Currently used techniques for intraoperative assessment of tumor resection margins are time-consuming and laborious and, more importantly, lack specificity. Moreover, pathological diagnosis during surgery does not often give a clear outcome. Recent advances in mass spectrometry (MS) and instrumentation have made it possible to obtain detailed molecular information from tissue specimens in real-time, with minimal sample pre-treatment. Probe Electro Spray Ionization MS (PESI-MS), combined with artificial intelligence (AI), has demonstrated its effectiveness in distinguishing liver cancer tissues from healthy tissues in a large Italian population group. As the MS profile can reflect the patient’s ethnicity, dietary habits, or particular operating room procedures, the AI algorithm must be well trained to distinguish different groups. We used a large dataset composed of liver tumor and healthy specimens, from the Italian and Japanese populations, to develop a versatile algorithm free from ethnic bias. The system can classify tissues with discrepancies <5% from the pathologist’s diagnosis. These results demonstrate the potential of the PESI-MS system to distinguish tumor from surrounding non-tumor tissues in patients, with minimal bias from race/ethnicity or etiological characteristics or operating room procedures.

Utility of mass spectrometry and artificial intelligence for differentiating primary lung adenocarcinoma and colorectal metastatic pulmonary tumor

Background

Rapid intraoperative diagnosis for unconfirmed pulmonary tumor is extremely important for determining the optimal surgical procedure (lobectomy or sublobar resection). Attempts to diagnose malignant tumors using mass spectrometry (MS) have recently been described. This study evaluated the usefulness of MS and artificial intelligence (AI) for differentiating primary lung adenocarcinoma (PLAC) and colorectal metastatic pulmonary tumor.

Methods

Pulmonary samples from 40 patients who underwent pulmonary resection for PLAC (20 tumors, 20 normal lungs) or pulmonary metastases originating from colorectal metastatic pulmonary tumor (CRMPT) (20 tumors, 20 normal lungs) were collected and analyzed retrospectively by probe electrospray ionization‐MS. AI using random forest (RF) algorithms was employed to evaluate the accuracy of each combination.

Results

The accuracy of the machine learning algorithm applied using RF to distinguish malignant tumor (PLAC or CRMPT) from normal lung was 100%. The algorithms offered 97.2% accuracy in differentiating PLAC and CRMPT.

Conclusions

MS combined with an AI system demonstrated high accuracy not only for differentiating cancer from normal tissue, but also for differentiating between PLAC and CRMPT with a short working time. This method shows potential for application as a support tool facilitating rapid intraoperative diagnosis to determine the surgical procedure for pulmonary resection.

New strategy for evaluating pancreatic tissue specimens from endoscopic ultrasound-guided fine needle aspiration and surgery

Background and Aim

Preoperative histological evaluation of pancreatic neoplasms is important for guiding the resection strategy and preventing postoperative adverse events. However, conventional endoscopic methods have technical limitations that reduce the accuracy of the histopathological examination. Probe electrospray ionization mass spectrometry (PESI‐MS) may be a useful technique for rapidly evaluating small specimens.

Methods

This single‐center prospective study included patients with pancreatic neoplasms between October 2018 and December 2019. Pancreatic ductal adenocarcinoma (PDAC) specimens were obtained via endoscopic ultrasound‐guided fine needle aspiration (EUS‐FNA) and non‐neoplastic tissue was obtained via surgery. Specimens were subjected to PESI‐MS and the mass spectra were analyzed using partial least squares regression‐discriminant analysis.

Results

The study included 40 patients with 20 nonneoplastic specimens and 19 PDAC specimens (1 case of neuroendocrine carcinoma was omitted). All nonneoplastic specimens were sufficient for PESI‐MS analysis, although only 7 of 19 PDAC specimens were sufficient for PESI‐MS analysis because of poor sample quality or insufficient quantity (<1 mg). Among the 27 analyzed cases, the mass spectra clearly differentiated between the PDAC and nonneoplastic specimens.

Conclusions

This study revealed that PESI‐MS could differentiate between PDAC and nonneoplastic specimens, even in cases where EUS‐FNA produced very small specimens.

Intraoperative Mass Spectrometry Platform for IDH Mutation Status Prediction, Glioma Diagnosis, and Estimation of Tumor Cell Infiltration

BACKGROUND

Surgical tumor resection is the primary treatment option for diffuse glioma, the most common malignant brain cancer. The intraoperative diagnosis of gliomas from tumor core samples can be improved by use of molecular diagnostics. Further, residual tumor at surgical margins is a primary cause of tumor recurrence and malignant progression. This study evaluates a desorption electrospray ionization mass spectrometry (DESI-MS) system for intraoperative isocitrate dehydrogenase (IDH) mutation assessment, estimation of tumor cell infiltration as tumor cell percentage (TCP), and disease status. This information could be used to enhance the extent of safe resection and so potentially improve patient outcomes.

METHODS

A mobile DESI-MS instrument was modified and used in neurosurgical operating rooms (ORs) on a cohort of 49 human subjects undergoing craniotomy with tumor resection for suspected diffuse glioma. Small tissue biopsies (ntotal = 203) from the tumor core and surgical margins were analyzed by DESI-MS in the OR and classified using univariate and multivariate statistical methods.

RESULTS

Assessment of IDH mutation status using DESI-MS/MS to measure 2-hydroxyglutarate (2-HG) ion intensities from tumor cores yielded a sensitivity, specificity, and overall diagnostic accuracy of 89, 100, and 94%, respectively (ncore = 71). Assessment of TCP (categorized as low or high) in tumor margin and core biopsies using N-acetyl-aspartic acid (NAA) intensity provided a sensitivity, specificity, and accuracy of 91, 76, and 83%, respectively (ntotal = 203). TCP assessment using lipid profile deconvolution provided sensitivity, specificity, and accuracy of 76, 85, and 81%, respectively (ntotal = 203). Combining the experimental data and using PCA-LDA predictions of disease status, the sensitivity, specificity, and accuracy in predicting disease status are 63%, 83%, and 74%, respectively (ntotal = 203).

CONCLUSIONS

The DESI-MS system allowed for identification of IDH mutation status, glioma diagnosis, and estimation of tumor cell infiltration intraoperatively in a large human glioma cohort. This methodology should be further refined for clinical diagnostic applications.

Applying Probe Electrospray Ionization Mass Spectrometry to Cytological Diagnosis: A Preliminary Study by Using Cultured Lung Cancer Cells

OBJECTIVES

Cytology and histology are 2 indispensable diagnostic tools for cancer diagnosis, which are rapidly increasing in importance with aging populations. We applied mass spectrometry (MS) as a rapid approach for swiftly acquiring nonmorphological information of interested cells. Conventional MS, which primarily rely on promoting ionization by pre-applying a matrix to cells, has the drawback of time-consuming both on data acquisition and analysis. As an emerging method, probe electrospray ionization-MS (PESI-MS) with a dedicated probe is capable to pierce sample and measure specimen in small amounts, either liquid or solid, without the requirement for sample pretreatment. Furthermore, PESI-MS is timesaving compared to the conventional MS. Herein, we investigated the capability of PESI-MS to characterize the cell types derived from the respiratory tract of human tissues.

STUDY DESIGN

PESI-MS analyses with DPiMS-2020 were performed on various type of cultured cells including 5 lung squamous cell carcinomas, 5 lung adenocarcinomas, 5 small-cell carcinomas, 4 malignant mesotheliomas, and 2 normal controls.

RESULTS

Several characteristic peaks were detected at around m/z 200 and 800 that were common in all samples. As expected, partial least squares-discriminant analysis of PESI-MS data distinguished the cancer cell types from normal control cells. Moreover, distinct clusters divided squamous cell carcinoma from adenocarcinoma.

CONCLUSION

PESI-MS presented a promising potential as a novel diagnostic modality for swiftly acquiring specific cytological information.

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis

BACKGROUND

Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples, and is a promising novel diagnostic tool when combined with machine learning which discriminates malignant spectrum patterns from others. The present study was performed to evaluate the utility of this device for rapid diagnosis of colorectal liver metastasis (CRLM).

METHODS

A prospectively planned study using retrospectively obtained tissues was performed. In total, 103 CRLM samples and 80 non-cancer liver tissues cut from surgically extracted specimens were analyzed using PESI-MS. Mass spectra obtained by PESI-MS were classified into cancer or non-cancer groups by using logistic regression, a kind of machine learning. Next, to identify the exact molecules responsible for the difference between CRLM and non-cancerous tissues, we performed liquid chromatography-electrospray ionization-MS (LC-ESI-MS), which visualizes sample molecular composition in more detail.

RESULTS

This diagnostic system distinguished CRLM from non-cancer liver parenchyma with an accuracy rate of 99.5%. The area under the receiver operating characteristic curve reached 0.9999. LC-ESI-MS analysis showed higher ion intensities of phosphatidylcholine and phosphatidylethanolamine in CRLM than in non-cancer liver parenchyma (P < 0.01, respectively). The proportion of phospholipids categorized as monounsaturated fatty acids was higher in CRLM (37.2%) than in non-cancer liver parenchyma (10.7%; P < 0.01).

CONCLUSION

The combination of PESI-MS and machine learning distinguished CRLM from non-cancer tissue with high accuracy. Phospholipids categorized as monounsaturated fatty acids contributed to the difference between CRLM and normal parenchyma and might also be a useful diagnostic biomarker and therapeutic target for CRLM.

Endoscopic Ultrasound-Guided Sampling for Personalized Pancreatic Cancer Treatment

Pancreatic cancer is the most lethal solid malignancy, and the number of patients with pancreatic cancer is increasing. Systemic chemotherapies are often ineffective for such patients, and there is an urgent need for personalized medicine. Unlike other types of cancer, personalized treatments for pancreatic cancer are still in development. Consequently, pancreatic cancer is less sensitive to anticancer drugs and is often refractory to common treatments. Therefore, advances in personalized medicine for pancreatic cancer are necessary. This review examined advances in personalized medicine for pancreatic cancer, including the use of endoscopic ultrasound (EUS)-guided sampling. EUS-guided sampling is widely used for diagnosing pancreatic tumors and is expected to be applied to sampled tissues. Additionally, there has been an increase in clinical research using EUS-guided sampling. The combination of precision medicine using genomic testing and pharmacological profiles based on high-throughput drug sensitivity testing using patient-derived organoids is expected to revolutionize pancreatic cancer treatment.

Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

BACKGROUND AND AIMS

Complete surgical resection with negative margin is one of the pillars in treatment of liver tumours. However, current techniques for intra-operative assessment of tumour resection margins are time-consuming and empirical. Mass spectrometry (MS) combined with artificial intelligence (AI) is useful for classifying tissues and provides valuable prognostic information. The aim of this study was to develop a MS-based system for rapid and objective liver cancer identification and classification.

METHODS

A large dataset derived from 222 patients with hepatocellular carcinoma (HCC, 117 tumours and 105 non-tumours) and 96 patients with mass-forming cholangiocarcinoma (MFCCC, 50 tumours and 46 non-tumours) were analysed by Probe Electrospray Ionization (PESI) MS. AI by means of support vector machine (SVM) and random forest (RF) algorithms was employed. For each classifier, sensitivity, specificity and accuracy were calculated.

RESULTS

The overall diagnostic accuracy exceeded 94% in both the AI algorithms. For identification of HCC vs non-tumour tissue, RF was the best, with 98.2% accuracy, 97.4% sensitivity and 99% specificity. For MFCCC vs non-tumour tissue, both algorithms gave 99.0% accuracy, 98% sensitivity and 100% specificity.

CONCLUSIONS

The herein reported MS-based system, combined with AI, permits liver cancer identification with high accuracy. Its bench-top size, minimal sample preparation and short working time are the main advantages. From diagnostics to therapeutics, it has the potential to influence the decision-making process in real-time with the ultimate aim of improving cancer patient cure.

Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer

Background

Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratumoural phenotypic heterogeneity and result in tumour progression and treatment failure. However, the precise diagnosis for tumour areas containing subclones with cytokine-induced malignant properties remains clinically challenging.

Methods

We established a rapid diagnostic system based on the combination of probe electrospray ionisation-mass spectrometry (PESI-MS) and machine learning without the aid of immunohistological and biochemical procedures to identify tumour areas with heterogeneous TGF-β signalling status in head and neck squamous cell carcinoma (HNSCC). A total of 240 and 90 mass spectra were obtained from TGF-β-unstimulated and -stimulated HNSCC cells, respectively, by PESI-MS and were used for the construction of a diagnostic system based on lipidome.

Results

This discriminant algorithm achieved 98.79% accuracy in discrimination of TGF-β1-stimulated cells from untreated cells. In clinical human HNSCC tissues, this approach achieved determination of tumour areas with activated TGF-β signalling as efficiently as a conventional histopathological assessment using phosphorylated-SMAD2 staining. Furthermore, several altered peaks on mass spectra were identified as phosphatidylcholine species in TGF-β-stimulated HNSCC cells.

Conclusions

This diagnostic system combined with PESI-MS and machine learning encourages us to clinically diagnose intratumoural phenotypic heterogeneity induced by TGF-β.

Application of probe electrospray ionization-tandem mass spectrometry to ultra-rapid determination of glufosinate and glyphosate in human serum

Glufosinate and glyphosate, which are non-selective herbicides that include an amino acid moiety in their structures, are frequently used worldwide to control unwanted vegetation. Unfortunately, these readily available herbicides are also used by people to commit suicide, and thus represent important chemicals of interest in the fields of clinical medicine and forensics. Because of the high water solubility of these herbicides, most analytical methods for their detection require a derivatization step, which results in longer analysis times. Therefore, derivatization-based methods do not currently contribute to judgements on treatment decisions in emergency medicine. In this study, we addressed this limiting factor by developing an ultra-rapid and simple analytical technique using a combination of probe electrospray ionization (PESI) and tandem mass spectrometry (MS/MS), which gives quantitative results within 0.3 min. Herbicide standards were added to human serum that was then subjected to analysis (N = 5 per concentration). The analysis was repeated daily over eight consecutive days. The limit of detection (LOD) was 0.59 μg/mL for glufosinate and 0.20 μg/mL for glyphosate. The limit of quantitation (LOQ), i.e., the lowest point on the calibration curves, was 1.56 μg/mL for both the herbicides. The matrix effects were observed at three different concentrations (between 95.7%-104% for glufosinate, and between 90.7%-95.7% for glyphosate). When applied to samples taken from actual poisoning cases (six samples for each herbicide), the present method gave almost the same quantitative values as those obtained by conventional high-performance liquid chromatography with fluorescence detection. Thus, we believe that PESI-MS/MS could emerge as a rapid diagnosis method in the clinical emergency field.

Real-time analysis of living animals and rapid screening of human fluid samples using remote sampling electrospray ionization mass spectrometry

Real-time and in-situ mass-spectrometry analyses of living animal and biological sample were performed using a novel remote sampling electrospray ionization (RS-ESI) probe. Unlike conventional ESI, in which injection or syringe loading is required for sample introduction, the RS-ESI probe ionizes the samples when the sampling capillary is in contact with the sample. As the sampling capillary is electrically held at ground potential, the safety of the animal and operator is assured. The liquid sample is aspirated to the ESI emitter at the other end of the capillary by the Venturi effect. Subsequently, the electrospray is generated when a high voltage is applied to the counter electrode placed inside the ion source chamber. The probe unit is attached to the mass spectrometer with a long flexible tube and its position can be freely manipulated during the analysis. In this report, we demonstrate a real-time analysis of a living mouse liver and an automatic analysis of 138 serum samples using this new technique.

Detection of potential new biomarkers of atherosclerosis by probe electrospray ionization mass spectrometry

INTRODUCTION

Atherosclerotic diseases are the leading cause of death worldwide. Biomarkers of atherosclerosis are required to monitor and prevent disease progression. While mass spectrometry is a promising technique to search for such biomarkers, its clinical application is hampered by the laborious processes for sample preparation and analysis.

METHODS

We developed a rapid method to detect plasma metabolites by probe electrospray ionization mass spectrometry (PESI-MS), which employs an ambient ionization technique enabling atmospheric pressure rapid mass spectrometry. To create an automatic diagnosis system of atherosclerotic disorders, we applied machine learning techniques to the obtained spectra.

RESULTS

Using our system, we successfully discriminated between rabbits with and without dyslipidemia. The causes of dyslipidemia (genetic lipoprotein receptor deficiency or dietary cholesterol overload) were also distinguishable by this method. Furthermore, after induction of atherosclerosis in rabbits with a cholesterol-rich diet, we were able to detect dynamic changes in plasma metabolites. The major metabolites detected by PESI-MS included cholesterol sulfate and a phospholipid (PE18:0/20:4), which are promising new biomarkers of atherosclerosis.

CONCLUSION

We developed a remarkably fast and easy method to detect potential new biomarkers of atherosclerosis in plasma using PESI-MS.

Construction of mass spectra database and diagnosis algorithm for head and neck squamous cell carcinoma

OBJECTIVES

Intraoperative identification of tumor margins is essential to achieving complete tumor resection. However, the process of intraoperative pathological diagnosis involves cumbersome procedures, such as preparation of cryosections and microscopic examination, thus requiring more than 30 min. Moreover, intraoperative diagnoses made by examining cryosections are occasionally inconsistent with postoperative diagnoses made by examining paraffin-embedded sections because the former are of poorer quality. We sought to establish a more rapid accurate method of intraoperative assessment.

MATERIALS AND METHODS

A diagnostic algorithm of head and neck squamous cell carcinoma (HNSCC) using machine learning was constructed by mass spectra obtained from 15 non-cancerous and 19 HNSCC specimens by probe electrospray ionization mass spectrometry (PESI-MS). The clinical validity of this system was evaluated using intraoperative specimens of HNSCC and normal mucosa.

RESULTS

A total of 114 and 141 mass spectra were acquired from non-cancerous and cancerous specimens, respectively, using both positive- and negative-ion modes of PESI-MS. These data were fed into partial least squares-logistic regression (PLS-LR) to discriminate tumor-specific spectral patterns. Leave-one-patient-out cross validation of this algorithm in positive- and negative-ion modes showed accuracies in HNSCC diagnosis of 90.48% and 95.35%, respectively. In intraoperative specimens of HNSCC, this algorithm precisely defined the borders of the cancerous regions; these corresponded with those determined by examining histologic sections. The procedure took approximately 5 min.

CONCLUSION

This diagnostic system, based on machine learning, enables accurate discrimination of cancerous regions and has the potential to provide rapid intraoperative assessment of HNSCC margins.

Ambient mass spectrometry in metabolomics

Since the introduction of desorption electrospray ionization (DESI) mass spectrometry (MS), ambient MS methods have seen increased use in a variety of fields from health to food science. Increasing its popularity in metabolomics, ambient MS offers limited sample preparation, rapid and direct analysis of liquids, solids, and gases, in situ and in vivo analysis, and imaging. The metabolome consists of a constantly changing collection of small (<1.5 kDa) molecules. These include endogenous molecules that are part of primary metabolism pathways, secondary metabolites with specific functions such as signaling, chemicals incorporated in the diet or resulting from environmental exposures, and metabolites associated with the microbiome. Characterization of the responsive changes of this molecule cohort is the principal goal of any metabolomics study. With adjustments to experimental parameters, metabolites with a range of chemical and physical properties can be selectively desorbed and ionized and subsequently analyzed with increased speed and sensitivity. This review covers the broad applications of a variety of ambient MS techniques in four primary fields in which metabolomics is commonly employed.

Clinical Application of Ambient Ionization Mass Spectrometry

Ambient ionization allows mass spectrometry analysis directly on the sample surface under atmospheric pressure with almost zero sample pretreatment. Since the development of desorption electrospray ionization (DESI) in 2004, many other ambient ionization techniques were developed. Due to their simplicity and low operation cost, rapid and on-site clinical mass spectrometry analysis becomes real. In this review, we will highlight some of the most widely used ambient ionization mass spectrometry approaches and their applications in clinical study.

Desorption in Mass Spectrometry

In mass spectrometry, analytes must be released in the gas phase. There are two representative methods for the gasification of the condensed samples, i.e., ablation and desorption. While ablation is based on the explosion induced by the energy accumulated in the condensed matrix, desorption is a single molecular process taking place on the surface. In this paper, desorption methods for mass spectrometry developed in our laboratory: flash heating/rapid cooling, Leidenfrost phenomenon-assisted thermal desorption (LPTD), solid/solid friction, liquid/solid friction, electrospray droplet impact (EDI) ionization/desorption, and probe electrospray ionization (PESI), will be described. All the methods are concerned with the surface and interface phenomena. The concept of how to desorb less-volatility compounds from the surface will be discussed.

Ambient ionization mass spectrometric analysis of human surgical specimens to distinguish renal cell carcinoma from healthy renal tissue

Touch spray-mass spectrometry (TS-MS) is an ambient ionization technique (ionization of unprocessed samples in the open air) that may find intraoperative applications in quickly identifying the disease state of cancerous tissues and in defining surgical margins. In this study, TS-MS was performed on fresh kidney tissue (∼1-5 cm(3)), within 1 h of resection, from 21 human subjects afflicted by renal cell carcinoma (RCC). The preliminary diagnostic value of TS-MS data taken from freshly resected tissue was evaluated. Principal component analysis (PCA) of the negative ion mode (m/z 700-1000) data provided the separation between RCC (16 samples) and healthy renal tissue (13 samples). Linear discriminant analysis (LDA) on the PCA-compressed data estimated sensitivity (true positive rate) and specificity (true negative rate) of 98 and 95 %, respectively, based on histopathological evaluation. The results indicate that TS-MS might provide rapid diagnostic information in spite of the complexity of unprocessed kidney tissue and the presence of interferences such as urine and blood. Desorption electrospray ionization-MS imaging (DESI-MSI) in the negative ionization mode was performed on the tissue specimens after TS-MS analysis as a reference method. The DESI imaging experiments provided phospholipid profiles (m/z 700-1000) that also separated RCC and healthy tissue in the PCA space, with PCA-LDA sensitivity and specificity of 100 and 89 %, respectively. The TS and DESI loading plots indicated that different ions contributed most to the separation of RCC from healthy renal tissue (m/z 794 [PC 34:1 + Cl](-) and 844 [PC 38:4 + Cl](-) for TS vs. m/z 788 [PS 36:1 - H](-) and 810 [PS 38:4 - H](-) for DESI), while m/z 885 ([PI 38:4 - H](-)) was important in both TS and DESI. The prospect, remaining hurdles, and future work required for translating TS-MS into a method of intraoperative tissue diagnosis are discussed. Graphical abstract Touch spray-mass spectrometry used for lipid profiling of fresh human renal cell carcinoma. Left) Photograph of the touch spray probe pointed at the MS inlet. Right) Average mass spectra of healthy renal tissue (blue) and RCC (red).

Development of Non-proximate Probe Electrospray Ionization for Real-Time Analysis of Living Animal

Ambient ionization mass spectrometry is one of the most challenging analytical tools in the field of biomedical research. We previously demonstrated that probe electrospray ionization mass spectrometry (PESI-MS) could potentially be used in the rapid diagnosis of cancer. Although this technique does not require a tedious sample pretreatment process, it was not possible for our previously reported setup to be applied to cases involving the direct sampling of tissues from living animal and large animal subjects, because there would not be enough room to accommodate the larger bodies juxtaposed to the ion inlet. To make PESI-MS more applicable for the real-time analysis of living animals, a long auxiliary ion sampling tube has been connected to the ion inlet of the mass spectrometer to allow for the collection of ions and charged droplets from the PESI source (hereafter, referred to as non-proximate PESI). Furthermore, an additional ion sampling tube was connected to a small diaphragm pump to increase the uptake rate of air carrying the ions and charged droplets to the ion inlet. This modification allows for the extended ion sampling orifice to be positioned closer to the specimens, even when they are too large to be placed inside the ionization chamber. In this study, we have demonstrated the use of non-proximate PESI-MS for the real-time analysis for biological molecules and pharmacokinetic parameters from living animals.

Probe electrospray ionization (PESI) mass spectrometry with discontinuous atmospheric pressure interface (DAPI)

Probe electrospray ionization (PESI) using a 0.2 mm outside diameter titanium wire was performed and the generated ions were introduced into the mass spectrometer via a discontinuous atmospheric pressure interface using a pinch valve. Time-lapse PESI mass spectra were acquired by gradually increasing delay time for the pinch valve opening with respect to the start of each electrospray event when a high voltage was applied. The opening time of the pinch valve was 20 ms. Time-resolved PESI mass spectra showed marked differences for 10 mM NaCl, 10(-5) M gramicidin S and insulin in H(2)O/CH(3)OH/CH(3)COOH/CH(3)COONH(4) (65/35/1) with and without the addition of 10 mM CH(3)COONH(4). This was ascribed to the pH change of the liquid attached to the needle caused by electrochemical reactions taking place at the interface between the metal probe and the solution. NaCl cluster ions appeared only after the depletion of analytes. For the mixed solution of 10(-5) M cytochrome c, insulin, and gramicidin S in H(2)O/CH(3)OH/CH(3)COOH (65/35/1), a sequential appearance of analyte ions in the order of cytochrome c→insulin→gramicidin S was observed. The present technique was applied to three narcotic samples; methamphetamine, morphine and codeine. Limits of detection for these compounds were 10 ppb in H(2)O/CH(3)OH (1/1) for the single sampling with a pinch valve opening time of 200 ms.

Nanomanipulation-coupled nanospray mass spectrometry as an approach for single cell analysis

Electrospray mass spectrometry is now a widely used technique for observing cell content of various biological tissues. However, electrospray techniques (liquid chromatography and direct infusion) often involve lysing a group of cells and extracting the biomolecules of interest, rather than a sensitive, individual cell method to observe local chemistry. Presented here is an approach of combining a nanomanipulator workstation with nanospray mass spectrometry, which allows for extraction of a single cell, followed by rapid mass analysis that can provide a detailed metabolic profile. Triacylglycerol content was profiled with this tool coupled to mass spectrometry to investigate heterogeneity between healthy and tumorous tissues as well as lipid droplet containing adipocytes in vitro as proof of concept. This selective approach provides cellular resolution and complements existing bioanalytical techniques with minimal invasion to samples. In addition, the coupling of nanomanipulation and mass spectrometry holds the potential to be used in a great number of applications for individual organelles, diseased tissues, and in vitro cell cultures for observing heterogeneity even amongst cells and organelles of the same tissue.

Electrospray Ionization on Solid Substrates

Development of electrospray ionization on solid substrates (solid-substrate ESI) avoids the clogging problem encountered in conventional capillary-based ESI, allows more convenient sampling and permits new applications. So far, solid-substrate ESI with various materials, e.g., metals, paper, wood, fibers and biological tissue, has been developed, and applications ranging from analysis of pure compounds to complex mixtures as well as in vivo study were demonstrated. Particularly, the capability of solid-substrate ESI in direct analysis of complex samples, e.g., biological fluids and foods, has significantly facilitated mass spectrometric analysis in real-life applications and led to increasingly important roles of these techniques nowadays. In this review, various solid-substrate ESI techniques and their applications are summarized and the prospects in this field are discussed.

Fatty acid elongation in non-alcoholic steatohepatitis and hepatocellular carcinoma

Non-alcoholic steatohepatitis (NASH) represents a risk factor for the development of hepatocellular carcinoma (HCC) and is characterized by quantitative and qualitative changes in hepatic lipids. Since elongation of fatty acids from C16 to C18 has recently been reported to promote both hepatic lipid accumulation and inflammation we aimed to investigate whether a frequently used mouse NASH model reflects this clinically relevant feature and whether C16 to C18 elongation can be observed in HCC development. Feeding mice a methionine and choline deficient diet to model NASH not only increased total hepatic fatty acids and cholesterol, but also distinctly elevated the C18/C16 ratio, which was not changed in a model of simple steatosis (ob/ob mice). Depletion of Kupffer cells abrogated both quantitative and qualitative methionine-and-choline deficient (MCD)-induced alterations in hepatic lipids. Interestingly, mimicking inflammatory events in early hepatocarcinogenesis by diethylnitrosamine-induced carcinogenesis (48 h) increased hepatic lipids and the C18/C16 ratio. Analyses of human liver samples from patients with NASH or NASH-related HCC showed an elevated expression of the elongase ELOVL6, which is responsible for the elongation of C16 fatty acids. Taken together, our findings suggest a detrimental role of an altered fatty acid pattern in the progression of NASH-related liver disease.