# EXODUS-enabled High-throughput Multi-omics Profiling of Extracellular Vesicles for Diagnosis of Preclinical Alzheimer's Disease

> **NIH NIH R41** · WELLSIM BIOMEDICAL TECHNOLOGIES, INC. · 2021 · $251,359

## Abstract

PROJECT SUMMARY / ABSTRACT
Alzheimer's disease (AD) is the most widespread neurodegenerative disorder and has caused a major global
health concern with the aging population. Early diagnosis of AD before irreversible brain damage or mental
decline is critical for timely intervention, symptomatic treatment, and improved patient function. Accumulating
studies indicate that neuron-derived extracellular vesicles (EVs) are important biomarkers for AD. However,
researchers face significant challenges in the efficient isolation and accurate analysis of EVs, limiting the broad
study and application of EVs in early diagnosis or targeted therapy of AD. WellSIM proposes to develop and
validate a high-throughput platform and workflow based on our revolutionary EXODUS technique for reliable and
reproducible isolation and analysis of EVs from plasma and CSF with unparalleled throughput, purity, yield, and
sensitivity. Based on hi-EXODUS-NGS and hi-EXODUS-MS integrative analysis, transcriptomic and proteomic
profiling of EVs will be developed to discover and detect EV-derived multi-class biomarkers for AD diagnosis.

## Key facts

- **NIH application ID:** 10383586
- **Project number:** 1R41AG076098-01
- **Recipient organization:** WELLSIM BIOMEDICAL TECHNOLOGIES, INC.
- **Principal Investigator:** Yuchao Chen
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $251,359
- **Award type:** 1
- **Project period:** 2021-09-30 → 2023-09-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10383586

## Citation

> US National Institutes of Health, RePORTER application 10383586, EXODUS-enabled High-throughput Multi-omics Profiling of Extracellular Vesicles for Diagnosis of Preclinical Alzheimer's Disease (1R41AG076098-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10383586. Licensed CC0.

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