Lucien Hinderling

@lhinderling.bsky.social

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tries to make microscopes smarter · bioimage analysis, optogenetics, ml, 3d printing, open science · postdoc in cellular signalling dynamics @PertzLab · https://hinderling.github.io

Screenshot of the abstract: Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.
screenshots of some of the sample implementations
Figure legend: Categories and capabilities of smart microscopy systems integrating real-time image analysis and feedback control. Top: Smart microscopy workflows can be classified based on the driving logic behind decision-making: Event-driven (reacting to rare biological events), Outcome-driven (using feedback-control to steer biological systems toward a desired state), Quality-driven (optimizing signal quality or imaging metrics), and Information-driven (guided by models that predict which measurements/perturbations will yield the most informative data). Middle: Central feedback loop between the microscope and an image analysis system, which continuously exchanges images and commands to guide acquisition dynamically. Bottom: Key control actions enabled by smart microscopy: adjusting imaging modality (e.g. switching from brightfield to fluorescence, adjusting sampling rate), repositioning the field of view (e.g. tracking, drift correction), optimizing acquisition settings (e.g. adaptive optics), and performing photomanipulation (e.g. FRAP, ablation, optogenetics).
screenshot of split computer screen - left shows small, unexpanded cells; right side shows another sample that was expanded, where the parasites are much larger. scalebar (20 micrometer) illustrates the difference: left the scale bar is a bit larger than a whole cell, where as on the right the cells are 3-5 times longer than the scalebar. blows my mind that this works so well hahaha
screen recording of napari, showing top and side view of some sparsly seeded MCF10A cells. nuclei look round from top but super flat from the side
Screenshot of figure showing exposure calibration experiments for two different spin-coating speeds.
Screenshot of table showing reagents required for protocol.
Screenshot of bioicons.org, showing an ilustration of a mouse.
two side by side videos, showing starling murmurations and collective cell movement of human breast cells
Screenshot of a figure from the paper that shows cells confined on 2D patterns and 3D micro channels with different geometries.
flyer for the seminar, Aug 31 2023, 15:30 GMT webpage: signalingdynamics.org
Fibroblast cells growing in a circular shape, with a fluorescent cytosolic and nuclear marker.