Plasma proteomics and the paediatric patient

Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission

Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

Data harnessing to nurture the human mind for a tailored approach to the child

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

Lessons learned from proteome analysis of perinatal neurovascular pathologies

INTRODUCTION

Perinatal and pediatric diseases related to neurovascular disorders cause significant problems during life, affecting a population with a long life expectancy. Early diagnosis and assessment of the severity of these diseases are crucial to establish an appropriate neuroprotective treatment. Currently, physical examination, neuroimaging and clinical judgment are the main tools for diagnosis, although these tests have certain limitations. There is growing interest in the potential value of noninvasive biomarkers that can be used to monitor child patients at risk of brain damage, allowing accurate, and reproducible measurements.

AREAS COVERED

This review describes potential biomarkers for the diagnosis of perinatal neurovascular diseases and discusses the possibilities they open for the classification and treatment of neonatal neurovascular diseases.

EXPERT OPINION

Although high rates of ischemic and hemorrhagic stroke exist in pediatric populations, most studies have focused on biomarkers of hypoxic-ischemic encephalopathy. Inflammatory and neuronal biomarkers such as S-100B and GFAP, in combination with others yet to be discovered, could be considered as part of multiplex panels to diagnose these diseases and potentially for monitoring response to treatments. Ideally, noninvasive biofluids would be the best source for evaluating these biomarkers in proteomic assays in perinatal patients.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.