# Dynamic µOCT for cellular tissue phenotyping

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $587,199

## Abstract

Phenotyping cells and tissue is a critical function that spans basic science to clinical diagnosis. Yet, established
methods for phenotyping cells in tissue are static, are evaluated when the tissue is dead, and typically involve
destruction of the sample. This paradigm misses an entire dimension represented by cellular function and
activity, information that is potentially of great significance in understanding cell/tissue state. Recently, a new
field has emerged that uses coherence-gated imaging to quantify living tissue motion as a proxy of cellular
function and activity. Coherence-based motility imaging is relatively new - much remains to be learned about the
nature of its dynamic signal. In addition, many of the coherence-gated technologies described to date lack the
resolution to investigate individual cells. The ones that are capable of seeing cells do not provide cross-sectional
images and thus miss important architectural patterns associated with tissue maturation.
We have developed a form of coherence-gated imaging called 1-µm optical coherence tomography (µOCT).
µOCT has a resolution of 1 µm axial by 2 µm lateral, enabling cross-sectional visualization of tissue at the cellular
level. Recently, we have discovered that by sequentially acquiring multiple µOCT images and computing the
pixel-per-pixel power spectrum, we observe a dramatic increase in image contrast and new information emerging
from the µOCT datasets. Preliminary studies with this new technology, termed dynamic µOCT (DµOCT), suggest
that it can be used to visualize epithelial maturation, cell death/apoptosis, and cellular activity. In this grant, we
will mature this technology by conducting key validation studies in a variety of clinically relevant human tissues,
animal models, and spheroids to understand the dynamic signal and determine its accuracy for diagnosing
pathology, activity, and response to therapy (apoptosis/necrosis) (Aim 1). We also will advance DµOCT further
by increasing spatial and temporal resolution, creating new data mining analysis pipelines, and developing and
validating technology and probes that enable DµOCT to be implemented in vivo (Aim 2). By expanding our
understanding and implementation of this exciting technology, we hope to provide a powerful new tool that will
have significant and wide-reaching impact in the biological and clinical sciences.

## Key facts

- **NIH application ID:** 10853055
- **Project number:** 5R01CA265742-04
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Oliver Jonas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $587,199
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10853055

## Citation

> US National Institutes of Health, RePORTER application 10853055, Dynamic µOCT for cellular tissue phenotyping (5R01CA265742-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10853055. Licensed CC0.

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