# 3-D Imaging Flow Cytometry

> **NIH NIH R21** · UNIVERSITY OF WISCONSIN MILWAUKEE · 2020 · $155,784

## Abstract

PROJECT SUMMARY
This project aims to develop and test two innovative platforms and related software for 3-D imaging flow
cytometry of fluorescent or absorbing (stained) samples. These systems will allow 3-D structural and functional
imaging of many single cells at a subcellular resolution and at a scale that used to be available only in flow
cytometry or recently in 2-D imaging. Thereby, the proposed methods have the potential to fundamentally
change the ways cultured cells, patient-derived samples, and small experimental organisms are studied.
Automated classification based on the 3-D features will enable the diagnosis of hematologic disorders at
single-cell precision.
Existing 3-D microscopy methods can provide the same information at higher resolution; however, by relying
on a scanning mechanism they cannot be applied to suspending cells, especially in a flow configuration, which
is essential for high-speed interrogation. Snapshot 3-D microscopy techniques have been developed to
address this challenge, but they have insufficient spatial resolution for single-cell imaging and suffer from long
data processing time. We overcome these limitations by combining two novel snapshot techniques developed
by the PI with the most rigorous optical imaging theories and cutting-edge component technologies.
We will use an array of lenslets, which simultaneously records many projection images corresponding with
different viewing angles. The use of pupil phase masks, designed using wavefront coding and a theory of 3-D
high-numerical-aperture optical imaging, will increase the resolution of each projection image to the theoretical
limit given by the objective-lens numerical aperture. The target resolution is 0.5 µm, which is comparable to
existing 2-D imaging flow cytometry systems. The target imaging throughputs based on current component
technologies are 120 volumes/sec for fluorescence imaging and 700 volumes/sec for absorption imaging,
which are higher than 100 volumes/sec of cutting-edge 3-D optical microscopy for stationary specimens.
The vast amount of data acquired by these 3-D imaging systems imposes a serious challenge to data
processing. The developed systems record true projection images, which obviate iterative deconvolution
process, thereby allowing much faster tomographic reconstruction than in existing snapshot techniques. Using
general-purpose graphics processing units and optical diffraction tomography, which includes the diffraction of
light by subcellular organelles, our tomographic reconstruction algorithm will be faster yet more accurate than
existing approaches. Further, we will explore the feasibility of applying a deep convolutional neural network to
the images acquired by the developed systems for accurate single-cell classification based on 3-D features.

## Key facts

- **NIH application ID:** 10023268
- **Project number:** 5R21GM135848-02
- **Recipient organization:** UNIVERSITY OF WISCONSIN MILWAUKEE
- **Principal Investigator:** Yongjin Sung
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $155,784
- **Award type:** 5
- **Project period:** 2019-09-24 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10023268

## Citation

> US National Institutes of Health, RePORTER application 10023268, 3-D Imaging Flow Cytometry (5R21GM135848-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10023268. Licensed CC0.

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