# Antisense Oligonucleotides targeting APP to prevent neurodegeneration in models of Down Syndrome and Alzheimer's disease

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2022 · $270,276

## Abstract

Project Summary/Abstract: Critical to ensuring rigor in investigation of neurodegenerative
disorders is the use of methods by which to accurately, and in an unbiased way to, assess
degenerative phenotypes, including the number of neurons in vulnerable populations. Unbiased
stereological assessment is the standard in the field. However, these studies are technically
demanding and are costly in terms of the equipment required to conduct them. Thus, limitation of
access to existing, specialized and prohibitively expensive systems has been a hindrance to
progress, especially during the Covid-19 pandemic. To overcome these limitations we propose to
develop a novel scalable open-source stereological software system with drastically reduced
system requirements and at the fraction of the cost of commercial stereology systems. The
proposed system will be capable of running on standard windows systems and standard
microscopy hardware that is widely available in laboratories. Increasing the availability of
microscope systems capable of stereological analysis promises to dramatically improve access
to stereological data collection, speed analyses and enhance rigor as well as transparency. As
such, our goal is to achieve stereological protocol requirements with a standard lab microscope
without the need for expensive specialized hardware.
We will acquire statistically equivalent stereological data using innovative analytical components
using well-established computer vision methods like “visual odometry” and “depth from focus” that
will make use of camera images in combination with open-loop stage positioning data to replace
the functionality of expensive feedback-based high precision 3D positioning sensors. An
additional benefit to developing an open-source system architecture that de-couples hardware
from software protocols is that it will more naturally allow integration of AI/machine learning based
components to automate and speed manual stereology tasks like feature/cell detection and
counting. To demonstrate reproducibility and statistical equivalence with currently in use
commercial systems, we will use samples previously analyzed using existing systems. Future
directions would include automated cell detection to improve the reproducibility and effeciency of
performing stereology experiments.

## Key facts

- **NIH application ID:** 10543710
- **Project number:** 3R01AG061151-04S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** William C Mobley
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $270,276
- **Award type:** 3
- **Project period:** 2019-04-15 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10543710

## Citation

> US National Institutes of Health, RePORTER application 10543710, Antisense Oligonucleotides targeting APP to prevent neurodegeneration in models of Down Syndrome and Alzheimer's disease (3R01AG061151-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10543710. Licensed CC0.

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