# Multiplex gene sequencing and metabolomics analysis from newborn dried blood spots to improve screening and diagnosis of metabolic disorders.

> **NIH NIH R01** · YALE UNIVERSITY · 2023 · $497,184

## Abstract

Project summary: This application responds to PA-20-272 Administrative Supplements to Existing NIH Grants
and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional). It will significantly contribute to our
ability to accurately identify and provide early, lifesaving treatment to newborns with inborn errors of
metabolism. While newborn screening (NBS) using tandem mass spectrometry (MS/MS) identifies most
affected babies, it is accompanied by frequent false-positive results that require collecting blood and urine
samples for additional confirmatory testing. There is an urgent need for a more efficient second-tier NBS
approach for confirming all screen-positive cases directly from the newborn dried blood spot (DBS) cards
collected at birth. This is especially critical for infants at risk for metabolic disease in their first weeks of life. The
overall objective of our proposal is to combine novel DNA sequencing and metabolomics technology to
diagnose inborn metabolic disorders from DBS, and to demonstrate the clinical feasibility of this approach for
second-tier screening. To achieve this objective, we have developed multiplex gene sequencing (RUSPseq)
for rapid genetic testing (Aim 1); and liquid chromatography tandem mass spectrometry (LC-MS/MS) and data
mining (AI/ML) to identify novel metabolic markers that have been integrated in a novel second-tier screening
panel to separate true and false-positive cases (Aim 2). The gene panel missed genetic variants in several
confirmed metabolic cases, while the effectiveness for reducing false-positives using the metabolomics-AI/ML
approach varied between the four metabolic disorders studied (range 51-100%). This supplement's goal is to
perform genome sequencing of DBS samples from screen-positive cases to extend and strengthen the existing
research described in Aim 3; and to enhance and refine the metabolomic-AI/ML algorithms to further improve
the separation of true and false-positive cases. We will work with the public NBS program and NBSTRN to
translate this combined approach into second-tier NBS. These outcomes will have significant impact by
reducing diagnostic delays and uncertainties, and by reducing iterative testing rounds and the cost associated
with them, thereby reducing the burden on the healthcare system as well as patients and their families.

## Key facts

- **NIH application ID:** 10881231
- **Project number:** 3R01HD102537-04S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Curt Scharfe
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $497,184
- **Award type:** 3
- **Project period:** 2020-09-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10881231

## Citation

> US National Institutes of Health, RePORTER application 10881231, Multiplex gene sequencing and metabolomics analysis from newborn dried blood spots to improve screening and diagnosis of metabolic disorders. (3R01HD102537-04S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10881231. Licensed CC0.

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