# Machine Learning-Assisted Integrated Optofluidic Nanoplasmonic Biosensing for Precision Immune Profiling and Monitoring

> **NIH NIH R35** · AUBURN UNIVERSITY AT AUBURN · 2024 · $379,956

## Abstract

Machine Learning-Assisted Integrated Optofluidic Nanoplasmonic Biosensing for Precision
 Immune Profiling and Monitoring
Abstract: The intricate and dynamic nature of the immune system demands a comprehensive understanding
of its functional behavior for effective prediction and treatment of immune-related diseases. Cytokines, vital for
intercellular signaling, offer invaluable insights into a range of diseases, including infections, cancer, autoimmune
disorders, and allergy transplantation. Prompt and precise multiparametric cytokine detection at the point of care
is essential for comprehending patients' dynamic immune responses. In our previous MIRA project, we
developed integrated optofluidic nanoplasmonic biosensing platforms for high-throughput, sensitive, and
multiplex cytokine detection from whole blood to single-cell levels. Building on our past accomplishments, we
propose to develop high performance integrated optofluidic nanoplasmonic biosensing technologies for immune
analysis and incorporate machine learning techniques to enable precision Immune profiling and monitoring for
better patient care. The primary objectives of this renewal application are to: 1) advance the next generation of
serum immunoassays by integrating the power of machine learning (ML) to engineer state-of-art plasmonic
nanomaterials and capture probes with significantly enhanced sensing performance for rapid, reliable and
effective diagnosis at point-of-care; 2) Develop ML-enhanced micropillar-based in situ immunoassays for real-
time immune monitoring of on-chip in vitro models for high-resolution, high-throughput immune profiling towards
personalized immunomodulatory therapies; 3) Establish an innovative integrated method by embedding
nanoplasmon rulers within a hydrogel matrix to map the 3D spatiotemporal cytokine secretion profiles of
individual immune cells encapsulated in the hydrogel, providing new insights into immune cell behavior and
communication in a 3D physiologically relevant context. Our vision is to bridge the gap in our fundamental
understanding of the immune system and enhance the diagnostic and predictive power for immune system
diseases. The advanced integrated optofluidic nanoplasmonic biosensing platforms, empowered by machine
learning, will gear the biologists and clinicians with the ability to rapidly and precisely determine patients' immune
statuses. This transformative achievement holds enormous potential for both fundamental research and clinical
applications, ultimately leading to improved patient outcomes and more effective therapies for immune-related
diseases.

## Key facts

- **NIH application ID:** 10842183
- **Project number:** 2R35GM133795-06
- **Recipient organization:** AUBURN UNIVERSITY AT AUBURN
- **Principal Investigator:** Pengyu Chen
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $379,956
- **Award type:** 2
- **Project period:** 2019-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842183

## Citation

> US National Institutes of Health, RePORTER application 10842183, Machine Learning-Assisted Integrated Optofluidic Nanoplasmonic Biosensing for Precision Immune Profiling and Monitoring (2R35GM133795-06). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10842183. Licensed CC0.

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