APDC: An accessible, open-source package for smart automation of single-cell fluorescence microscopy experiments

ORAL

Abstract

Single-cell fluorescence microscopy experiments provide highly quantitative information about spatiotemporal dynamics and heterogeneity of gene expression, but two major practical challenges limit their utility. One is their high tunability, with vastly many potential designs, e.g. combinations of labeled genes, induction levels, and expression measurement times. Another is the considerable hands-on experimenter time often required for data collection, mostly spent finding cells of interest and suitable imaging conditions. Our open-source software package APDC (Acquire-Process-Decide-Control) automates design and execution of such experiments, extending the current approach of microscopy event specification across “multi-dimensional axes” to express experiment (re)designs in a control-theoretic framework, interconnecting operations to acquire and process images, make decisions, and control experimental conditions while integrating sequential model-based design of experiments (MBDoE) to accelerate parameter estimation by selecting optimal numbers of cells to image at potential experimental conditions. APDC implements these functionalities for simulated as well as physical microscopes, allowing for orthogonal method prototyping and testing independently of the particulars of any real-world apparatus. We validate these innovations by studying the glucocorticoid receptor, whose intracellular localization regulates the expression of dual-specificity phosphatase 1, part of signaling cascades involved in diverse cellular processes and whose dysregulation is therefore involved in the onset and evolution of numerous diseases. Investigations are conducted across multiple cell lines, with further acceleration via MBDoE, utilizing kinetic parameters fit to data from one line to serve as a Bayesian prior for another. We demonstrate dramatically faster and cheaper experiments without loss of accuracy, reducing hands-on experimenter time by an order of magnitude.

* This work was supported by NSF award 1941870 and NIH award R35GM124747.

Publication: Cook, Joshua et al. "Sequential Design of Single-Cell Experiments to Identify Discrete Stochastic Models for Gene Expression." Proceedings of the IEEE Conference on Decision & Control 2024 (2024): 7778–7785. Web.

Presenters

  • Dmitri Svetlov

    Colorado State University

Authors

  • Dmitri Svetlov

    Colorado State University

  • Jack Forman

    Colorado State University

  • Eric Ron

    Colorado State University

  • Tatsuya Morisaki

    Colorado State University

  • Timothy J Stasevich

    Colorado State University

  • Brian E Munsky

    Colorado State University