Defining 3D Phenotypic Cell States of Polymorphonuclear Neutrophils via Novel Computational Pipeline

Overview

Reliable characterization of cell states, which encompass a cell’s function, environment, and structure, is fundamental to comprehending healthy cell function and molecular pathways driving transition into diseased states. Development in microscopy and sequencing has created a massive influx of data; however, scalable pipelines to convert raw data into human-interpretable, biologically relevant results remain absent. Neutrophils are a particular system of interest as their nuclei undergo a marked morphological change in maturation: differentiation into multilobed structures, driven by chromatin reorganization and gene expression profiles. In these systems, 3D nuclear morphology serves as a robust phenotypic proxy for underlying molecular cell state identity. Hence, neutrophils present a good candidate for developing and testing such a pipeline. I hypothesize that cell states in neutrophils can be defined using morphological and spatial information extracted from 3D imaging data, creating a bridge to their molecular signatures. The methodology integrated preprocessing, robust segmentation, a custom extracted feature set, and unsupervised clustering to define cell states. Within the feature set, a novel recursive erosion-based algorithm was created to automate 3D lobe counting with 80% accuracy. Adaptive quantization was utilized to optimize textural information features, reducing memory usage by 99% while preserving critical features. Unsupervised clustering of the resulting feature matrix successfully defined discrete neutrophil states. This project established a high-throughput computational pipeline to extract meaningful 3D morphological information from polymorphonuclear neutrophil imaging data. Ultimately, this pipeline bridges the gap between raw 3D microscopy and biologically interpretable cell states, providing the computational infrastructure to complement time-series and genetic sequencing data. By linking physical cell states with genomic drivers, this enables precise investigation of molecular disease mechanisms and targeted drug development.

Competition history

• CSEF 2026 Biochemistry/ Molecular Biology (Senior Division) · Entry S-04-34

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