Juliette Griffie | EPFL Graph Search

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Event-driven acquisition for content-enriched microscopy

Juliette Griffie, Dora Mahecic, Suliana Manley, Willi Leopold Stepp, Martin Weigert, Chen Zhang

Event-driven acquisition uses neural-network-based recognition of specific biological events to trigger switching between slow and fast super-resolution imaging, enriching the capture of interesting events with high spatiotemporal resolution. A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.

NATURE PORTFOLIO2022

Fluorescent D-Amino Acids for Super-resolution Microscopy of the Bacterial Cell Wall

Juliette Griffie, Suliana Manley, Jiangtao Qiao, Luc Reymond, Ophélie Rutschmann, Faustine Ambroisine Charline Ryckebusch, Willi Leopold Stepp, Chen Zhang

Fluorescent D-amino acids (FDAAs) have previ-ously been developed to enable in situ highlighting of locations of bacterial cell wall growth. Most bacterial cells lie at the edge of the diffraction limit of visible light; thus, resolving the precise details of peptidoglycan (PG) biosynthesis requires super-resolution micros-copy after probe incorporation. Single molecule localization microscopy (SMLM) has stringent requirements on the fluorophore photophysical properties and therefore has remained challenging in this context. Here, we report the synthesis and characterization of new FDAAs compatible with one-step labeling and SMLM imaging. We demonstrate the incorporation of our probes and their utility for visualizing PG at the nanoscale in Gram-negative, Gram-positive, and mycobacteria species. This improved FDAA toolkit will endow researchers with a nanoscale perspective on the spatial distribution of PG biosynthesis for a broad range of bacterial species.

AMER CHEMICAL SOC2022

Single-molecule localization microscopy

Juliette Griffie, Suliana Manley

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM.

SPRINGERNATURE2021