Identification, Measurement, and Assessment of the Clinical Utility of Human Pancreatic Polypeptide by Liquid Chromatography–Tandem Mass Spectrometry

Human pancreatic polypeptide (HPP) is a 36 amino acid peptide hormone that plays a role in the bidirectional communication between the digestive system and the brain. HPP measurements are used to assess vagal nerve function following sham feeding and to detect gastroenteropancreatic-neuroendocrine tumors. These tests have historically been conducted by radioimmunoassays, but liquid chromatography–tandem mass spectrometry (LC–MS/MS) has several advantages such as improved specificity and elimination of radioactive molecules. Here, we present our LC–MS/MS method. Initially, samples were immunopurified and subjected to LC-high resolution accurate mass tandem mass spectrometry (HRAM-MS/MS) to identify circulating forms of the peptide in human plasma. We identified 23 forms of HPP, including several glycosylated forms. The most abundant peptides then were used for targeted LC–MS/MS measurements. LC–MS/MS performance for precision, accuracy, linearity, recovery, limit of detection, and carryover met our acceptance criteria based on CLIA regulations. Additionally, we observed the expected physiological rise in HPP in response to sham feeding. Our results indicate that HPP measurement by LC–MS/MS produces clinically equivalent results to our established immunoassay when several peptides are monitored, making it a suitable replacement. The measurement of peptide fragments, including modified species, might have additional clinical value

Supplementary data: Serum steroid profiling in the diagnosis of adrenocortical carcinoma: a prospective cohort study

Serum steroid profiling in the diagnosis of adrenocortical carcinoma: a prospective cohort study</p

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods

Additional file 1 of A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples

Additional file 1: Figure S1. Figure showing the correlation of peak areas across the entire range of synthetic peptide amounts spiked in phosphate buffered saline followed by enrichment and targeted analysis

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods

Supplementary Figure 2 from Reexpression of Tumor Suppressor, sFRP1, Leads to Antitumor Synergy of Combined HDAC and Methyltransferase Inhibitors in Chemoresistant Cancers

PDF file, 3984K, Methylation pattern sequencing analysis of the sFRP1 promoter.</p

Targeted Detection of SARS-CoV‑2 Nucleocapsid Sequence Variants by Mass Spectrometric Analysis of Tryptic Peptides

COVID-19 vaccines are becoming more widely available, but accurate and rapid testing remains a crucial tool for slowing the spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Although the quantitative reverse transcription-polymerase chain reaction (qRT-PCR) remains the most prevalent testing methodology, numerous tests have been developed that are predicated on detection of the SARS-CoV-2 nucleocapsid protein, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and immunoassay-based approaches. The continuing emergence of SARS-CoV-2 variants has complicated these approaches, as both qRT-PCR and antigen detection methods can be prone to missing viral variants. In this study, we describe several COVID-19 cases where we were unable to detect the expected peptide targets from clinical nasopharyngeal swabs. Whole genome sequencing revealed that single nucleotide polymorphisms in the gene encoding the viral nucleocapsid protein led to sequence variants that were not monitored in the targeted assay. Minor modifications to the LC-MS/MS method ensured detection of the variants of the target peptide. Additional nucleocapsid variants could be detected by performing the bottom-up proteomic analysis of whole viral genome-sequenced samples. This study demonstrates the importance of considering variants of SARS-CoV-2 in the assay design and highlights the flexibility of mass spectrometry-based approaches to detect variants as they evolve