Browsing by Author "St-Pierre, Francois"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Advancing life science with adaptive intelligent microscopy
    (2022-04-22) Safaei, Seyed Mojtaba; St-Pierre, Francois; Aazhang, Behnaam
    Biology is dynamic: the location, shape, and function of molecules and cells change with time. When studying biological systems, it is critical to adapt data collection and analysis to fit their current states. However, the lack of real-time interactions in traditional microscopy makes it impossible to guide the experiments and adapt to the biological events. In this thesis, we introduce closed-loop microscopy (CLM) approaches that address the current shortcomings by providing real-time interactions between acquisition and analysis. CLM is implemented in an event-driven way; acquisition events notify the downstream analysis, resulting in feedback that triggers real-time actions. CLM is particularly suited for long experiments that study rare biological events; experiments in which adapting to the real-time changes increases the probability of success. We demonstrated examples in which CLM reduced sample size variation across trials, achieved five times higher throughput in fluorescent protein characterization, and enabled the study of rotavirus at low multiplicities of infection.
  • Thumbnail Image
    Item
    Automating multi-parameter engineering of protein-based sensors of neural electrical activity
    (2020-08-31) Liu, Zhuohe; Kemere, Caleb T.; St-Pierre, Francois
    Engineered voltage-sensitive fluorescent proteins, termed genetically encoded voltage indicators (GEVIs), are emerging sensors for noninvasive microscopy of neural activity. However, the sensitivity and kinetics of existing GEVIs are often not sufficient for accurately reporting fast voltage dynamics in vivo, and they are not optimal for long-term recording due to low brightness and lack of photostability. A system for rapidly evaluating new variants across all performance characteristics is critically needed to accelerate GEVI development progress. This work reports an automated platform that screens libraries of GEVIs in a high-throughput 96-well plate format. This platform quantifies sensitivity, kinetics, brightness, and photostability to identify promising GEVI candidates. Using this platform, a faster and more sensitive indicator, JEDI-1P, was identified and validated in vitro and in behaving zebrafish. The platform is anticipated to optimize versatile biosensors that excel in deep-tissue imaging and promote understanding of neural computation and neurological diseases.