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.

Isotopic Distribution Calibration for Mass Spectrometry.

Mass spectrometry (MS) is widely used in science and industry. It allows accurate, specific, sensitive, and reproducible detection and quantification of a huge range of analytes. Across MS applications, quantification by MS has grown most dramatically, with >50 million experiments/year in the USA alone. However, quantification performance varies between instruments, compounds, different samples, and within- and across runs, necessitating normalization with analyte-similar internal standards (IS) and use of IS-corrected multipoint external calibration curves for each analyte, a complicated and resource-intensive approach, which is particularly ill-suited for multi-analyte measurements. We have developed an internal calibration method that utilizes the natural isotope distribution of an IS for a given analyte to provide internal multipoint calibration. Multiple isotope distribution calibrators for different targets in the same sample facilitate multiplex quantification, while the emerging random-access automated MS platforms should also greatly benefit from this approach. Finally, isotope distribution calibration allows mathematical correction for suboptimal experimental conditions. This might also enable quantification of hitherto difficult, or impossible to quantify, targets, if the distribution is adjusted in silico to mimic the analyte. The approach works well for high resolution, accurate mass MS for analytes with at least a modest-sized isotopic envelope. As shown herein, the approach can also be applied to lower molecular weight analytes, but the reduction in calibration points does reduce quantification performance.

Development of a pregnancy-specific reference material for thyroid biomarkers, vitamin D, and nutritional trace elements in serum.

OBJECTIVES: Matrix differences among serum samples from non-pregnant and pregnant patients could bias measurements. Standard Reference Material 1949, Frozen Human Prenatal Serum, was developed to provide a quality assurance material for the measurement of hormones and nutritional elements throughout pregnancy. METHODS: Serum from non-pregnant women and women in each trimester were bottled into four levels based on pregnancy status and trimester. Liquid chromatography tandem mass spectrometry (LC-MS/MS) methods were developed and applied to the measurement of thyroid hormones, vitamin D metabolites, and vitamin D-binding protein (VDBP). Copper, selenium, and zinc measurements were conducted by inductively coupled plasma dynamic reaction cell MS. Thyroid stimulating hormone (TSH), thyroglobulin (Tg), and thyroglobulin antibody concentrations were analyzed using immunoassays and LC-MS/MS (Tg only). RESULTS: Certified values for thyroxine and triiodothyronine, reference values for vitamin D metabolites, VDBP, selenium, copper, and zinc, and information values for reverse triiodothyronine, TSH, Tg, and Tg antibodies were assigned. Significant differences in serum concentrations were evident for all analytes across the four levels (p≤0.003). TSH measurements were significantly different (p<0.0001) among research-only immunoassays. Tg concentrations were elevated in research-only immunoassays vs. Federal Drug Administration-approved automated immunoassay and LC-MS/MS. Presence of Tg antibodies increased differences between automated immunoassay and LC-MS/MS. CONCLUSIONS: The analyte concentrations' changes consistent with the literature and the demonstration of matrix interferences in immunoassay Tg measurements indicate the functionality of this material by providing a relevant matrix-matched reference material for the different stages of pregnancy.

Examining Associations Between Women's Mental Health and Obesity.

Obesity is a common comorbidity associated with mental illness. It is important to understand the many ways weight gain and obesity can impact the cause and course of mental illness in women, with a special focus on vulnerable life stages. Women seem disproportionally impacted by the weight gain side effects of medications, and issues such as weight gain are more likely to impact symptoms of mental illness, impacting self-esteem. This article summarizes the existing literature on the associations between women's mental health and obesity. Understanding this association will lead to better health outcomes.

Associations Between Parental Mood and Anxiety Psychopathology and Offspring Brain Structure: A Scoping Review.

A family history of mood and anxiety disorders is one of the most well-established risk factors for these disorders in offspring. A family history of these disorders has also been linked to alterations in brain regions involved in cognitive-affective processes broadly, and mood and anxiety disorders specifically. Results from studies of brain structure of children of parents with a history of mood or anxiety disorders (high-risk offspring) have been inconsistent. We followed the PRISMA protocol to conduct a scoping review of the literature linking parental mood and anxiety disorders to offspring brain structure to examine which structures in offspring brains are linked to parental major depressive disorder (MDD), anxiety, or bipolar disorder (BD). Studies included were published in peer-reviewed journals between January 2000 and July 2021. Thirty-nine studies were included. Significant associations between parental BD and offspring caudate volume, inferior frontal gyrus thickness, and anterior cingulate cortex thickness were found. Associations were also identified between parental MDD and offspring amygdala and hippocampal volumes, fusiform thickness, and thickness in temporoparietal regions. Few studies have examined associations between parental anxiety and high-risk offspring brain structure; however, one study found associations between parental anxiety symptoms and offspring amygdala structure, and another found similar associations with the hippocampus. The direction of grey matter change across studies was inconsistent, potentially due to the large age ranges for each study and the non-linear development of the brain. Children of parents with MDD and bipolar disorders, or elevated anxiety symptoms, show alterations in a range of brain regions. Results may further efforts to identify children at high risk for affective disorders and may elucidate whether alterations in specific brain regions represent premorbid markers of risk for mood and anxiety disorders.

A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens.

BACKGROUND: The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time. METHODS: Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data. FINDINGS: The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922-0.997) (86/88) sensitivity and 100% (95% CI = 0.958-1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method. INTERPRETATION: Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures. FUNDING: This study was supported by DBT/Wellcome Trust India Alliance Margdarshi Fellowship grant IA/M/15/1/502023 awarded to AP and the generosity of Eric and Wendy Schmidt.