# 3D diagnosis of Hirschsprung disease using open-top light-sheet microscopy

> **NIH NIH R43** · ALPENGLOW BIOSCIENCES, INC. · 2022 · $252,522

## Abstract

Abstract
Hirschsprung disease is a congenital defect that affects an estimated 1:5000 liveborn infants. It
is characterized by absent ganglion cells (neurons) in the distal rectum and a variable length of
contiguous proximal bowel. The diagnosis of Hirschsprung disease relies upon subjective
pathologist interpretation of up to 100 tissue sections from suction rectal biopsies to prove the
absence of ganglion cells and the presence of hypertrophic nerves. Due to the labor-intensive
and time-consuming nature of the current diagnostic process, there is a need to more efficiently
and comprehensively diagnose Hirschsprung disease. Our team has pioneered the use of 3D
open-top light-sheet (OTLS) microscopy, which enables rapid, high-throughput imaging of large
clinical samples. In combination with cutting-edge machine learning techniques, we hypothesize
that 3D OTLS microscopy can provide more accurate and consistent assessment of ganglion
cells and hypertrophic nerves in suction rectal biopsies from patients with suspected
Hirschsprung disease. We will test this hypothesis by developing a multiplex staining protocol
and machine learning analysis pipeline, which will be piloted on 100 rectal biopsy specimens
from fresh resection specimens.

## Key facts

- **NIH application ID:** 10484508
- **Project number:** 1R43DK133083-01
- **Recipient organization:** ALPENGLOW BIOSCIENCES, INC.
- **Principal Investigator:** Nicholas Reder
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $252,522
- **Award type:** 1
- **Project period:** 2022-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10484508

## Citation

> US National Institutes of Health, RePORTER application 10484508, 3D diagnosis of Hirschsprung disease using open-top light-sheet microscopy (1R43DK133083-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10484508. Licensed CC0.

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