# Image Tools for Computational Cellular Barcoding and Automated Annotation

> **NIH NIH R01** · J. DAVID GLADSTONE INSTITUTES · 2024 · $398,349

## Abstract

PROJECT SUMMARY
With technological breakthroughs in high-throughput single-cell imaging and screening, we can precisely monitor
native cell behavior in response to diverse stimuli. Improvements in resolution and new detection capacity further
enrich the recording from each cell. Many image-processing steps that help to extract the full breadth of the
recording can be automated to a throughput comparable to the imaging itself. However, because biological
samples can be complex and nonhomogeneous, it is valuable to specify subsets of cells when addressing
downstream biological questions. With the amount of data that can now be generated from high-throughput
measurements, this subset-selection step is a significant bottleneck to obtaining a quantitative result about the
biological sample. Currently, the gold standard to reliably filter through cell data is manual annotation by a
technician. This approach is costly and time-consuming, creating a significant bottleneck to answering important
biological questions. To overcome this bottleneck, we will develop tools to automate annotation with three unique
approaches: chemical annotation, annotation amplification, and cellular barcoding. Chemical annotation will
deliver a computer-readable cell label via an additional biomarker. Annotation amplification will use small,
curated datasets to generate large ones. Cellular barcoding will identify pixel-based signatures to uniquely
identify individual cells. Once annotation is addressed computationally, relevant cells can be classified in-line
with the acquisition. We can then produce a large annotated dataset. Both the computational tools and data
repository will be shared with the scientific community as a validation set for new models and as a foundation for
algorithms that could be developed across research groups studying cells with fluorescence imaging. The goal
of this work is to generate the technology and define the experimental-computational methods that automate the
highly manual steps of cell curation through a strong interplay between wet-lab and machine-learning techniques.
The technology we propose is relevant to a broad scope of high-throughput measurement applications, because
it enables curating samples computationally rather than experimentally.

## Key facts

- **NIH application ID:** 10752706
- **Project number:** 5R01LM013617-03
- **Recipient organization:** J. DAVID GLADSTONE INSTITUTES
- **Principal Investigator:** STEVEN M FINKBEINER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $398,349
- **Award type:** 5
- **Project period:** 2022-01-19 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10752706

## Citation

> US National Institutes of Health, RePORTER application 10752706, Image Tools for Computational Cellular Barcoding and Automated Annotation (5R01LM013617-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10752706. Licensed CC0.

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