High-throughput Analysis Software Research Articles

Automated desorption electrospray ionization two-dimensional tandem mass spectrometry: Instrumentation and software for high throughput analysis

Two-dimensional tandem mass spectrometry (2D-MS/MS) provides rapid unsupervised access to all product ions of all ionized precursor ions. In this study, desorption electrospray ionization is used to create ion populations from complex mixtures prior to the use of 2D MS/MS to characterize each sample in an array. Automated high-throughput examination of each sample in the array is achieved by integrating movement of the sample array with automated acquisition of data from each sample through a serial handshake procedure. Position control is achieved using an Arduino microprocessor, a RAMPS control board, two stepper motors, and a custom MATLAB graphical user interface. Data processing improvements to the 2D-MS/MS workflow included optimizing the contrast of the intensity distribution of individual precursor to product ion transitions in local regions of the 2D spectrum to enhance visual discrimination of peaks from the background. This study demonstrates the combination of several advanced capabilities for practical mass spectrometry including high throughput experiments on unmodified samples with unconstrained sample acquisition and fully automated instrumental control and data manipulation. These capabilities are demonstrated with several model mixtures spotted on a PTFE-coated substrate, where the total analysis time was 64 s. Details of the system and discussion of the system performance are provided.

Abstract 2799: Peripheral blood glycoproteomic biomarkers as a powerful new tool for the detection of nasopharyngeal carcinoma

Abstract Introduction: Nasopharyngeal carcinoma (NPC) is a malignancy characterized by distinct geographic, ethnic, and genetic differences. It presents a major threat to human health, particularly in East Asia and Southeast Asia. NPC commonly invades adjacent tissues and organs, resulting in complex and difficult to treat clinical presentations. NPC commonly remains largely asymptomatic during early disease stages due to its small primary foci. Hence, NPC patients are diagnosed mostly at advanced stage. Currently, no accurate and effective methods are available for the early diagnosis, evaluation, or prognosis for NPC. While Epstein-Barr virus (EBV) DNA and EBV serology tests have been advocated for early diagnosis and screening for NPC, the sensitivities of these two tests vary. due to the fact that only a small proportion of individuals infected with EBV ultimately develop NPC. The ability to detect neoplasms at early stages provides useful information for early intervention. We have recently demonstrated that interrogating the serum glycoproteome, using a proprietary platform that couples artificial intelligence (AI) to targeted liquid chromatography-massspectrometry (LC-MS) yields highly informative biomarkers in a range of neoplastic diseases. Experimental procedures in this proof-of-principle study, a total of 106 plasma samples (1:1 ratio of histologically confirmed cases of NPC and cancer-free controls), which were matched for age (average 49 years) were used. All subjects were male, from a multi-ethnic patient population in Malaysia, including three ethnic minority groups (Bidayuh, Iban and Kadaza). To identify diagnostic glycoprotein profiles for NPC we applied an in-house developed targeted glycoproteomics workflow, whereby plasma proteins were first digested with trypsin/LysC, followed by targeted analysis using LC-MS. The LC-MS generated chromatographic peaks were integrated using an in-house AI-based software for high throughput analysis and further subjected to multivariate statistical analysis. Summary of new data among the 579 glycopeptides interrogated in this study we found 62 glycopeptides statistically significantly abundant in NPC cases compared to healthy controls (at FDR <0.1). From these, we built a predictive algorithm that differentiates NPC cases from controls with high sensitivity and selectivity (AUC of 0.955). Six glycopeptide markers were found as associated with ethnicity. Conclusions: In summary, our results indicate that LC-MS-based glycopeptide profiling provides a highly accurate means of for differentiating NPC cases from healthy controls. Follow-up studies with larger sample size are currently in progress. Citation Format: Klaus Lindpaintner, Thi Thin Aye, Gege Xu, Rachel Rice, Alan Mtchell, Daniel Serie, A Malek, LH Low, Lp Tan, Ct Low, Cy Wong, H Sawali, Yy Yapp, As Khoo. Peripheral blood glycoproteomic biomarkers as a powerful new tool for the detection of nasopharyngeal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2799.

PYQUAN: A rapid workflow around the AMDIS deconvolution software for high throughput analysis of pyrolysis GC/MS data

Pyrolysis-GC/MS is a powerful technique to characterize soil organic matter. Most samples produce a high number of peaks in chromatograms, many of them overlapping, which complicates data analysis. In order to facilitate the speed and ease of analysis, we have developed an automated workflow that enables us to rapidly identify and quantify peaks from (pyrolysis-)GC/MS data. It is based on identification by the software program AMDIS, followed by peak deconvolution and quantification using our own developed, user-friendly, Python script. Visual inspection of each peak and the possibility to re-quantify individual peaks with adjusted parameters are included in the Python script as well. We applied this workflow to a series of dissolved organic matter samples with increasing glucose concentrations. More than 97% of the peaks were automatically correctly identified and quantified, including compounds with nearly similar spectra and RTs less than 10s apart.