@article {69227, title = {A series of spontaneously blinking dyes for super-resolution microscopy}, journal = {Nat Methods}, year = {2026}, month = {2024 Apr 16}, abstract = {

Spontaneously blinking fluorophores toggle between nonfluorescent and fluorescent forms without caging groups or redox buffers, enabling super-resolution imaging. The intrinsic blinking of such dyes is governed by molecular structure and modulated by environment; there is no one-size-fits-all fluorophore suitable for every imaging context. We report dyes with tuned on:off ratios that enable single-molecule localization microscopy and super-resolution optical fluctuation imaging of biomolecular structures in vitro and in cells.

}, doi = {10.1038/s41592-026-03062-5}, url = {https://doi.org/10.1038/s41592-026-03062-5}, author = {Katie L. Holland and Sarah E. Plutkis and Timothy A. Daugird and Abhishek Sau and Jonathan B. Grimm and Brian P. English and Qinsi Zheng and Sandeep Dave and Fariha Rahman and Liangqi Xie and Peng Dong and Ariana N. Tkachuk and Timothy A. Brown and Robert H. Singer and Zhe Liu and Catherine G. Galbraith and Siegfried M. Musser and Wesley R. Legant and Luke D. Lavis} } @article {68655, title = {Smart Lattice Light Sheet Microscopy for imaging rare and complex cellular events}, journal = {Nature Methods}, volume = {21}, year = {2024}, month = {2024 Feb 01}, pages = {301-310}, abstract = {

Light sheet microscopes enable rapid, high-resolution imaging of biological specimens; however, biological processes span a variety of spatiotemporal scales. Moreover, long-term phenotypes are often instigated by rare or fleeting biological events that are difficult to capture with a single imaging modality and constant imaging parameters. To overcome this limitation, we present smartLLSM, a microscope that incorporates AI-based instrument control to autonomously switch between epifluorescent inverted imaging and lattice light sheet microscopy. We apply this technology to two major scenarios. First, we demonstrate that the instrument provides population-level statistics of cell cycle states across thousands of cells on a coverslip. Second, we show that by using real-time image feedback to switch between imaging modes, the instrument autonomously captures multicolor 3D datasets or 4D time-lapse movies of dividing cells at rates that dramatically exceed human capabilities. Quantitative image analysis on high-content + high-throughput datasets reveal kinetochore and chromosome dynamics in dividing cells and determine the effects of drug perturbation on cells in specific mitotic stages. This new methodology enables efficient detection of rare events within a heterogeneous cell population and records these processes with high spatiotemporal 4D imaging over statistically significant replicates.

}, doi = {10.1038/s41592-023-02126-0}, url = {https://doi.org/10.1038/s41592-023-02126-0}, author = {Yu Shi and Jimmy S. Tabet and Daniel E. Milkie and Timothy A. Daugird and Chelsea Q. Yang and Andrea Giovannucci and Wesley R. Legant} } @booklet {67224, title = {Teaching deep neural networks to localize single molecules for super-resolution microscopy}, year = {2020}, abstract = {

Single-molecule localization fluorescence microscopy constructs super-resolution images by sequential imaging and computational localization of sparsely activated fluorophores. Accurate and efficient fluorophore localization algorithms are key to the success of this computational microscopy method. We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art. Our contributions are a novel network architecture for simultaneous detection and localization, and new loss function which phrases detection and localization as a Bayesian inference problem, and thus allows the network to provide uncertainty-estimates. In contrast to standard methods which independently process imaging frames, our network architecture uses temporal context from multiple sequentially imaged frames to detect and localize molecules. We demonstrate the power of our method across a variety of datasets, imaging modalities, signal to noise ratios, and fluorophore densities. While existing localization algorithms can achieve optimal localization accuracy at low fluorophore densities, they are confounded by high densities. Our method is the first deep-learning based approach which achieves state-of-the-art on the SMLM2016 challenge. It achieves the best scores on 12 out of 12 data-sets when comparing both detection accuracy and precision, and excels at high densities. Finally, we investigate how unsupervised learning can be used to make the network robust against mismatch between simulated and real data. The lessons learned here are more generally relevant for the training of deep networks to solve challenging Bayesian inverse problems on spatially extended domains in biology and physics.

}, url = {https://arxiv.org/abs/1907.00770}, author = {Artur Speiser and Lucas-Raphael M{\"u}ller and Ulf Matti and Christopher J. Obara and Wesley R. Legant and Jonas Ries and Jakob H. Macke and Srinivas C. Turaga} }