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.