Automated sample preparation for electrospray ionization mass spectrometry based on CLOCK-controlled autonomous centrifugal microfluidics.

We report a centrifugal microfluidic device that automatically performs sample preparation under steady-state rotation for clinical applications using mass spectrometry. The autonomous microfluidic device was designed for the control of liquid operation on centrifugal hydrokinetics (CLOCK) paradigm. The reported device was highly stable, with less than 7% variation with respect to the time of each unit operation (sample extraction, mixing, and supernatant extraction) in the preparation process. An agitation mechanism with bubbling was used to mix the sample and organic solvent in this device. We confirmed that the device effectively removed the protein aggregates from the sample, and the performance was comparable to those of conventional manual sample preparation procedures that use high-speed centrifugation. In addition, probe electrospray ionization mass spectrometry (PESI-MS) was performed to compare the device-treated and manually treated samples. The obtained PESI-MS spectra were analyzed by partial least squares discriminant analysis, and the preparation capability of the device was found to be equivalent to that of the conventional method.

Supersulfides support bone growth by promoting chondrocyte proliferation in the growth plates.

OBJECTIVES: While chondrocytes have mitochondria, they receive little O2 from the bloodstream. Sulfur respiration, an essential energy production system in mitochondria, uses supersulfides instead of O2. Supersulfides are inorganic and organic sulfides with catenated sulfur atoms and are primarily produced by cysteinyl tRNA synthetase-2 (CARS2). Here, we investigated the role of supersulfides in chondrocyte proliferation and bone growth driven by growth plate chondrocyte proliferation. METHODS: We examined the effects of NaHS, an HS-/H2S donor, and cystine, the cellular source of cysteine, on the proliferation of mouse primary chondrocytes and growth of embryonic mouse tibia in vitro. We also examined the effect of RNA interference acting on the Cars2 gene on chondrocyte proliferation in the presence of cystine. RESULTS: NaHS (30 µmol/L) enhanced tibia longitudinal growth in vitro with expansion of the proliferating zone of their growth plates. While NaHS (30 µmol/L) also promoted chondrocyte proliferation only under normoxic conditions (20 % O2), cystine (0.5 mmol/L) promoted it under both normoxic and hypoxic (2 % O2) conditions. Cars2 gene knockdown abrogated the ability of cystine (0.5 mmol/L) to promote chondrocyte proliferation under normoxic conditions, indicating that supersulfides produced by CARS2 were responsible for the cystine-dependent promotion of bone growth. CONCLUSIONS: The presented results indicate that supersulfides play a vital role in bone growth achieved by chondrocyte proliferation in the growth plates driven by sulfur respiration.

New Diagnostic Modality Combining Mass Spectrometry and Machine Learning for the Discrimination of Malignant Intraductal Papillary Mucinous Neoplasms.

BACKGROUND: An intraductal papillary mucinous neoplasm (IPMN) is a pancreatic tumor with malignant potential. Although we anticipate a sensitive method to diagnose the malignant conversion of IPMN, an effective strategy has not yet been established. The combination of probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning provides a promising solution for this purpose. METHODS: We prospectively analyzed 42 serum samples obtained from IPMN patients who underwent pancreatic resection between 2020 and 2021. Based on the postoperative pathological diagnosis, patients were classified into two groups: IPMN-low grade dysplasia (n = 17) and advanced-IPMN (n = 25). Serum samples were analyzed by PESI-MS, and the obtained mass spectral data were converted into continuous variables. These variables were used to discriminate advanced-IPMN from IPMN-low grade dysplasia by partial least square regression or support vector machine analysis. The areas under receiver operating characteristics curves were obtained to visualize the difference between the two groups. RESULTS: Partial least square regression successfully discriminated the two disease classes. From another standpoint, we selected 130 parameters from the entire dataset by PESI-MS, which were fed into the support vector machine. The diagnostic accuracy was 88.1%, and the area under the receiver operating characteristics curve was 0.924 by this method. Approximately 10 min were required to perform each method. CONCLUSION: PESI-MS combined with machine learning is an easy-to-use tool with the advantage of rapid on-site analysis. Here, we show the great potential of our system to diagnose the malignant conversion of IPMN, which would be a promising diagnostic tool in clinical settings.

8-Nitro-cGMP suppresses mineralization by mouse osteoblasts.

Nitric oxide and reactive oxygen species regulate bone remodeling, which occurs via bone formation and resorption by osteoblasts and osteoclasts, respectively. Recently, we found that 8-nitro-cGMP, a second messenger of nitric oxide and reactive oxygen species, promotes osteoclastogenesis. Here, we investigated the formation and function of 8-nitro-cGMP in osteoblasts. Mouse calvarial osteoblasts were found to produce 8-nitro-cGMP, which was augmented by tumor necrosis factor-α (10 ng/ml) and interleukin-1ß (1 ng/ml). These cytokines suppressed osteoblastic differentiation in a NO synthase activity-dependent manner. Exogenous 8-nitro-cGMP (30 µmol/L) suppressed expression of osteoblastic phenotypes, including mineralization, in clear contrast to the enhancement of mineralization by osteoblasts induced by 8-bromo-cGMP, a cell membrane-permeable analog of cGMP. It is known that reactive sulfur species denitrates and degrades 8-nitro-cGMP. Mitochondrial cysteinyl-tRNA synthetase plays a crucial role in the endogenous production of RSS. The expression of osteoblastic phenotypes was suppressed by not only exogenous 8-nitro-cGMP but also by silencing of the Cars2 gene, indicating a role of endogenous 8-nitro-cGMP in suppressing the expression of osteoblastic phenotypes. These results suggest that 8-nitro-cGMP is a negative regulator of osteoblastic differentiation.

Low oxygen tension suppresses the death of chondrocyte-like ATDC5 cells induced by interleukin-1ß.

The articular cartilage is an avascular tissue, and oxygen tensions in its superficial and deeper zones are estimated to be 6% and 1%. Degeneration of the articular cartilage begins from the surface zone in osteoarthritis. We previously reported that monocarboxylate transporter-1, a transmembrane transporter for monocarboxylates, played an essential role in the interleukin-1ß-induced expression of NADPH oxidase-2, a reactive oxygen species-producing enzyme, and reactive oxygen species-dependent death of mouse chondrogenic ATDC5 cells cultured in a normal condition (20% oxygen). Here, we investigated the effect of oxygen tension on interleukin-1ß-induced events described above in ATDC5 cells. Interleukin-1ß induced the death of ATDC5 cells under 20% and 6% oxygen but did not under 2% and 1% oxygen. Interleukin-1ß induced Mct1 (monocarboxylate transporter-1 gene) and Nox2 (NADPH oxidase-2 gene) mRNAs' expression under 20% oxygen in 24 h, respectively, but not under 2% oxygen. On the other hand, a 24-h incubation with interleukin-1ß upregulated the expression of Nos2 (inducible nitric oxide synthase gene) mRNA irrespective of oxygen tension. Furthermore, inhibition of I-κB kinase suppressed the interleukin-1ß-induced expression of Mct1 mRNA in the cells cultured under 20% and 2% oxygen, indicating NF-κB plays an essential role in the induction of the Mct1 gene expression. The results suggest that interleukin-1ß induces monocarboxylate transporter-1 in an oxygen tension-dependent manner required for cell death in ATDC5 cells. These results might explain some part of the degenerative process of the articular cartilage, which begins from its superficial zone in the pathogenesis of osteoarthritis.

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.

Extracellular acidification augments sclerostin and osteoprotegerin production by Ocy454 mouse osteocytes.

Osteocytes sense the microenvironmental stimuli, including mechanical stress, and regulate bone resorption by osteoclasts and bone formation by osteoblasts. Diabetes and cancer metastasis to bone raise l-lactic acid in the bone tissue, causing acidification. Here, we investigated the effects of l-lactic acid and extracellular acidification on the function of mouse Ocy454 osteocytes. L- and d-lactic acid with low chiral selectivity and acidification of the medium raised the production of sclerostin and osteoprotegerin by Ocy454 cells. The mRNA expression of their genes increased after either treatment of L- and d-lactic acid or acidification of the medium. Furthermore, the conditioned medium of Ocy454 cells cultured in an acidic environment suppressed the induction of alkaline phosphatase activity in MC3T3-E1 cells, which was recovered by the anti-sclerostin antibody. While it is reported that HDAC5 inhibits the transcription of the sclerostin gene, extracellular acidification reduced the nuclear localization of HDAC5 in Ocy454 cells. While calmodulin kinase II (CaMKII) is known to phosphorylate and induce extranuclear translocation of HDAC5, KN-62, an inhibitor of CaMKII lowered the expression of the sclerostin gene in Ocy454 cells. Collectively, extracellular acidification is a microenvironmental factor that modulates osteocyte functions.

Association of allele-specific methylation of the ASNS gene with asparaginase sensitivity and prognosis in T-ALL.

Asparaginase therapy is a key component of chemotherapy for patients with T-cell acute lymphoblastic leukemia (T-ALL). Asparaginase depletes serum asparagine by deamination into aspartic acid. Normal hematopoietic cells can survive due to asparagine synthetase (ASNS) activity, whereas leukemia cells are supposed to undergo apoptosis due to silencing of the ASNS gene. Because the ASNS gene has a typical CpG island in its promoter, its methylation status in T-ALL cells may be associated with asparaginase sensitivity. Thus, we investigated the significance of ASNS methylation status in asparaginase sensitivity of T-ALL cell lines and prognosis of childhood T-ALL. Sequencing of bisulfite polymerase chain reaction products using next-generation sequencing technology in 22 T-ALL cell lines revealed a stepwise allele-specific methylation of the ASNS gene, in association with an aberrant methylation of a 7q21 imprinted gene cluster. T-ALL cell lines with ASNS hypermethylation status showed significantly higher in vitro l-asparaginase sensitivity in association with insufficient asparaginase-induced upregulation of ASNS gene expression and lower basal ASNS protein expression. A comprehensive analysis of diagnostic samples from pediatric patients with T-ALL in Japanese cohorts (N = 77) revealed that methylation of the ASNS gene was associated with an aberrant methylation of the 7q21 imprinted gene cluster. In pediatric T-ALL patients in Japanese cohorts (n = 75), ASNS hypomethylation status was significantly associated with poor therapeutic outcome, and all cases with poor prognostic SPI1 fusion exclusively exhibited ASNS hypomethylation status. These observations show that ASNS hypomethylation status is associated with asparaginase resistance and is a poor prognostic biomarker in childhood T-ALL.

Establishment of Down's syndrome periodontal ligament cells by transfection with SV40T-Ag and hTERT.

Down's syndrome is one of the most common human congenital genetic diseases and affected patients have increased risk of periodontal disease. To examine involvement of the disease with periodontal disease development, we established immortalized periodontal ligament cells obtained from a Down's syndrome patient by use of SV40T-Ag and hTERT gene transfection. Expressions of SV40T-Ag and hTERT were observed in periodontal ligament cell-derived immortalized cells established from healthy (STPDL) and Down's syndrome patient (STPDLDS) samples. Primary cultured periodontal ligament cells obtained from a healthy subject (pPDL) had a limited number of population doublings (< 40), while STPDL and STPDLDS cells continued to grow with more than 80 population doublings. Primary cultured periodontal ligament cells obtained from the patient showed a chromosome pattern characteristic of Down's syndrome with trisomy 21, whereas STPDLDS samples showed a large number of abnormal chromosomes in those results. Gene expression analysis revealed that expression of DSCR-1 in STPDLDS is greater than that in STPDL. These results suggest that the newly established STPDLDS cell line may be a useful tool for study of periodontal disease in Down's syndrome patients.

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.

Diagnostic significance of plasma lipid markers and machine learning-based algorithm for gastric cancer.

Biomarkers may be of value for the early detection of gastric cancer (GC) and the preoperative identification of tumor characteristics to guide treatment strategies. The present study analyzed the expression levels of phospholipids in plasma from patients with GC using liquid chromatography/electrospray ionization-mass spectrometry (LC/ESI-MS) to detect reliable biomarkers for GC. Furthermore, combining the results with a machine learning strategy, the present study attempted to establish a diagnostic system for GC. A total of 20 plasma samples from preoperative patients with GC and 16 plasma samples from tumor-free patients (controls) were selected from our biobank named 'SHINGEN (Yamanashi Biobank of Gastroenterological Cancers)', which includes a total of 1,592 plasma samples, and were analyzed by LC/ESI-MS. The obtained data were discriminated using a machine learning-based diagnostic algorithm, whose discriminant ability was confirmed through leave-one-out cross-validation. Using LC/ESI-MS, the levels of 236 lipid molecules were determined. Biomarker analysis revealed that a few lipids that were downregulated in the GC group could discriminate between the GC and control groups. Whole lipid composition analysis using partial least squares regression revealed good discrimination ability between the GC and control groups. Integrative analysis of all molecules using the aforementioned machine learning method exhibited a diagnostic accuracy of 94.4% (specificity, 93.8%; sensitivity, 95.0%). In conclusion, the outcomes of the present study suggested the potential future application of the aforementioned system in clinical settings. By accumulating more reliable data, the present system will be able to detect early-stage cancer and will be capable of predicting the efficacy of each therapeutic strategy.

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.

Miniaturized String Sampling Probe and Electrospray Extraction/Ionization within the Ion Inlet Tube for Mass Spectrometric Endoscopy.

A moving string sampling probe and a new ESI based ionization source that can be readily incorporated into the existing endoscopes are developed for performing in vivo mass spectrometry during the endoscopic procedure. The medical-grade silk suture driven by a stepping motor is used to perform the sampling on the region of interest when the probe head is brought gently into contact with the surface of the gastrointestinal tissue. The tissues and the compounds adhered to the sampling string are transported to an ionization region inside the ion inlet tube in which they are extracted and ionized by the charging droplets generated from an electrospray outside the ion inlet. Since the extraction/ionization and sampling processes are isolated, organic solvents, high voltage (HV), and heating can be used for the optimization of ionization without compromising the biocompatibility of the sampling probe. The demonstration of the in vivo analysis of the gastric mucosa of a mouse is performed using a 2 m long gastrointestinal endoscope.

High-performance Collective Biomarker from Liquid Biopsy for Diagnosis of Pancreatic Cancer Based on Mass Spectrometry and Machine Learning.

Background: Most pancreatic cancers are found at progressive stages when they cannot be surgically removed. Therefore, a highly accurate early detection method is urgently needed. Methods: This study analyzed serum from Japanese patients who suffered from pancreatic ductal adenocarcinoma (PDAC) and aimed to establish a PDAC-diagnostic system with metabolites in serum. Two groups of metabolites, primary metabolites (PM) and phospholipids (PL), were analyzed using liquid chromatography/electrospray ionization mass spectrometry. A support vector machine was employed to establish a machine learning-based diagnostic algorithm. Results: Integrating PM and PL databases improved cancer diagnostic accuracy and the area under the receiver operating characteristic curve. It was more effective than the algorithm based on either PM or PL database, or single metabolites as a biomarker. Subsequently, 36 statistically significant metabolites were fed into the algorithm as a collective biomarker, which improved results by accomplishing 97.4% and was further validated by additional serum. Interestingly, specific clusters of metabolites from patients with preoperative neoadjuvant chemotherapy (NAC) showed different patterns from those without NAC and were somewhat comparable to those of the control. Conclusion: We propose an efficient screening system for PDAC with high accuracy by liquid biopsy and potential biomarkers useful for assessing NAC performance.

Zoledronate promotes inflammatory cytokine expression in human CD14-positive monocytes among peripheral mononuclear cells in the presence of γδ T cells.

Bisphosphonates distributed to bone exert toxic effects specifically towards osteoclasts. On the other hand, intravenous administration of a nitrogen-containing bisphosphonate (N-BP) such as zoledronate induces acute-phase reactions (APRs), including influenza-like fever 1 day later, indicating an interaction with immunocompetent cells circulating blood. Although it has been reported that activation of γδ T cells is pivotal to induce an APR following treatment with zoledronate, downstream events, including the production of inflammatory cytokines after activation of γδ T cells, remain obscure. We investigated the effects of zoledronate on inflammatory cytokine expression in human peripheral blood mononuclear cells (PBMCs) in vitro. While zoledronate induced mRNA expressions of tumour necrosis factor-α (TNF-α), interleukin (IL)-1ß, IL-6 and interferon-γ (IFN-γ) in PBMC, depletion of γδ T cells abolished that zoledronate-induced expression of those cytokines, indicating the necessity of γδ T cells for expression induction by zoledronate. However, which types of cells were responsible for the production of those cytokines in blood remained unclear. As it is generally accepted that monocytes and macrophages are primary sources of inflammatory cytokines, CD14+ cells from PBMC were exposed to zoledronate in the presence of PBMC, which resulted in induced expression of mRNAs for IL-1ß, IL-6 and IFN-γ, but not for TNF-α. These results indicate that CD14+ cells are responsible, at least in part, for the production of IL-1ß, IL-6 and IFN-γ in blood exposed to zoledronate. This suggests that CD14+ cells play an essential role in the occurrence of APRs following N-BP administration.

Probe Electrospray Ionization (PESI) and Its Modified Versions: Dipping PESI (dPESI), Sheath-Flow PESI (sfPESI) and Adjustable sfPESI (ad-sfPESI).

In 2007, probe electrospray ionization/mass spectrometry (PESI/MS) was developed. In this technique, the needle is moved down along a vertical axis and the tip of the needle touched to the sample. After capturing the sample at the needle tip, the needle is then moved up and a high voltage is applied to the needle at the highest position to generate electrospray. Due to the discontinuous sampling followed by the generation of spontaneous electrospray, sequential and exhaustive electrospray takes place depending on the surface activity of the analytes. As modified versions of PESI, dipping PESI (dPESI), sheath-flow PESI (sfPESI) and adjustable sfPESI (ad-sfPESI) have been developed. These methods are complementary to each other and they can be applicable to surface and bulk analysis of various biological samples. In this article, the characteristics of these methods and their applications to real samples will be reviewed.

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.

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

Using probe electrospray ionization mass spectrometry and machine learning for detecting pancreatic cancer with high performance.

A rapid blood-based diagnostic modality to detect pancreatic ductal adenocarcinoma (PDAC) with high accuracy is an unmet medical need. The study aimed to validate a unique diagnosis system using Probe Electrospray Ionization Mass Spectrometry (PESI-MS) and Machine Learning to the diagnosis of PDAC. Peripheral blood samples were collected from a total of 322 consecutive PDAC patients and 265 controls with a family history of PDAC. Five µl of serum samples were analyzed using PESI-MS system. The mass spectra from each specimen were then fed into machine learning algorithms to discriminate between control and cancer cases. A total of 587 serum samples were analyzed. The sensitivity of the machine learning algorithm using PESI-MS profiles to identify PDAC is 90.8% with specificity of 91.7% (95% CI 83.9%-97.4% and 82.8%-97.7% respectively). Combined PESI-MS profiles with age and CA19-9 as predictors, the accuracy for stage 1 or 2 of PDAC is 92.9% and for stage 3 or 4 is 93% (95% CI 86.3-98.2; 87.9-97.4 respectively). The accuracy and simplicity of the PESI-MS profiles combined with machine learning provide an opportunity to detect PDAC at an early stage and must be applicable to the examination of at-risk populations.