@article {69880, title = {DeepPD: Joint phase and object estimation from phase diversity with neural calibration of a deformable mirror}, journal = {arXiv}, year = {2025}, month = {2025 Apr 19}, abstract = {

Sample-induced aberrations and optical imperfections limit the resolution of fluorescence microscopy. Phase diversity is a powerful technique that leverages complementary phase information in sequentially acquired images with deliberately introduced aberrations--the phase diversities--to enable phase and object reconstruction and restore diffraction-limited resolution. These phase diversities are typically introduced into the optical path via a deformable mirror. Existing phase-diversity-based methods are limited to Zernike modes, require large numbers of diversity images, or depend on accurate mirror calibration--which are all suboptimal. We present DeepPD, a deep learning-based framework that combines neural representations of the object and wavefront with a learned model of the deformable mirror to jointly estimate both object and phase from only five images. DeepPD improves robustness and reconstruction quality over previous approaches, even under severe aberrations. We demonstrate its performance on calibration targets and biological samples, including immunolabeled myosin in fixed PtK2 cells.

}, doi = {10.48550/arxiv.2504.14157}, url = {https://doi.org/10.48550/arxiv.2504.14157}, author = {Magdalena C. Schneider and Courtney Johnson and C{\'e}dric Allier and Larissa Heinrich and Diane Adjavon and Joren Husic and Patrick La Riviere and Stephan Saalfeld and Hari Shroff} } @article {69423, title = {Live-cell single-molecule fluorescence microscopy for protruding organelles reveals regulatory mechanisms of MYO7A-driven cargo transport in stereocilia of inner ear hair cells}, journal = {Nat Commun}, year = {2025}, month = {2025 Sep 01}, abstract = {

Stereocilia are F-actin-based cylindrical protrusions on the apical surface of inner ear hair cells that function as biological mechanosensors of sound and acceleration. During stereocilia development, specific unconventional myosins transport proteins and phospholipids as cargo and mediate elongation, differentiation and acquisition of the mechanoelectrical transduction (MET). How unconventional myosins localize themselves and cargo in stereocilia using energy from ATP hydrolysis is only partially understood. Here, we developed STELLA-SPIM microscopy to visualize movement of single myosin molecules in live hair cell stereocilia. STELLA-SPIM demonstrated that MYO7A, a component of MET machinery, shows processive movement toward stereocilia tips when chemically dimerized or constitutively activated by missense mutations disabling tail-mediated autoinhibition. Conversely, MYO7A shows step-wise but not processive movement in stereocilia when its tail is tethered to the plasma membrane or F-actin in the presence of MYO7A interacting partners. We posit that MYO7A dimerizes and moves processively in stereocilia when unleashed from autoinhibition.

}, doi = {10.1038/s41467-025-63102-0}, url = {https://doi.org/10.1038/s41467-025-63102-0}, author = {Takushi Miyoshi and Harshad D Vishwasrao and Inna A Belyantseva and Mrudhula Sajeevadathan and Yasuko Ishibashi and Samuel M Adadey and Narinobu Harada and Hari Shroff and Thomas B Friedman} } @article {69315, title = {Altruistic feeding and cell-cell signaling during bacterial differentiation actively enhance phenotypic heterogeneity}, journal = {Sci Adv}, volume = {10}, year = {2024}, month = {2024 Oct 18}, pages = {eadq0791}, abstract = {

Starvation triggers bacterial spore formation, a committed differentiation program that transforms a vegetative cell into a dormant spore. Cells in a population enter sporulation nonuniformly to secure against the possibility that favorable growth conditions, which put sporulation-committed cells at a disadvantage, may resume. This heterogeneous behavior is initiated by a passive mechanism: stochastic activation of a master transcriptional regulator. Here, we identify a cell-cell communication pathway containing the proteins ShfA (YabQ) and ShfP (YvnB) that actively promotes phenotypic heterogeneity, wherein Bacillus subtilis cells that start sporulating early use a calcineurin-like phosphoesterase to release glycerol, which simultaneously acts as a signaling molecule and a nutrient to delay nonsporulating cells from entering sporulation. This produced a more diverse population that was better poised to exploit a sudden influx of nutrients compared to those generating heterogeneity via stochastic gene expression alone. Although conflict systems are prevalent among microbes, genetically encoded cooperative behavior in unicellular organisms can evidently also boost inclusive fitness.

}, doi = {10.1126/sciadv.adq0791}, url = {https://doi.org/10.1126/sciadv.adq0791}, author = {Taylor B. Updegrove and Thomas Delerue and V. Anantharaman and Hyomoon Cho and Carissa Chan and Thomas Nipper and Hyoyoung Choo-Wosoba and Lisa Jenkins and Lixia Zhang and Yijun Su and Hari Shroff and Jiji Chen and Carole Bewley and L. Aravind and Kumaran S Ramamurthi} } @article {69348, title = {Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation}, journal = {arXiv}, year = {2024}, month = {2024 Apr 6}, abstract = {

Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based backbone model from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5 by 5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.

}, author = {Azaan Rehman and Alexander Zhovmer and Ryo Sato and Yosuke Mukoyama and Jiji Chen and Alberto Rissone and Rosa Puertollano and Harshad Vishwasrao and Hari Shroff and Christian A. Combs and Hui Xue} } @article {69149, title = {Prediction of Cellular Identities from Trajectory and Cell Fate Information}, journal = {arXiv}, year = {2024}, month = {2024 Jan 11}, abstract = {

Determining cell identities in imaging sequences is an important yet challenging task. The conventional method for cell identification is via cell tracking, which is complex and can be time-consuming. In this study, we propose an innovative approach to cell identification during early C. elegans embryogenesis using machine learning. We employed random forest, MLP, and LSTM models, and tested cell classification accuracy on 3D time-lapse confocal datasets spanning the first 4 hours of embryogenesis. By leveraging a small number of spatial-temporal features of individual cells, including cell trajectory and cell fate information, our models achieve an accuracy of over 90\%, even with limited data. We also determine the most important feature contributions and can interpret these features in the context of biological knowledge. Our research demonstrates the success of predicting cell identities in 4D imaging sequences directly from simple spatio-temporal features.

}, doi = {10.48550/arXiv.2401.06182}, url = {https://doi.org/10.48550/arXiv.2401.06182}, author = {Baiyang Dai and Jiamin Yang and Hari Shroff and Patrick La Riviere} } @article {69282, title = {Three-dimensional spatio-angular fluorescence microscopy with a polarized dual-view inverted selective-plane illumination microscope (pol-diSPIM)}, journal = {bioRxiv}, year = {2024}, month = {2024 Mar 12}, abstract = {

Polarized fluorescence microscopy is a valuable tool for measuring molecular orientations, but techniques for recovering three-dimensional orientations and positions of fluorescent ensembles are limited. We report a polarized dual-view light-sheet system for determining the three-dimensional orientations and diffraction-limited positions of ensembles of fluorescent dipoles that label biological structures, and we share a set of visualization, histogram, and profiling tools for interpreting these positions and orientations. We model our samples, their excitation, and their detection using coarse-grained representations we call orientation distribution functions (ODFs). We apply ODFs to create physics-informed models of image formation with spatio-angular point-spread and transfer functions. We use theory and experiment to conclude that light-sheet tilting is a necessary part of our design for recovering all three-dimensional orientations. We use our system to extend known two-dimensional results to three dimensions in FM1-43-labelled giant unilamellar vesicles, fast-scarlet-labelled cellulose in xylem cells, and phalloidin-labelled actin in U2OS cells. Additionally, we observe phalloidin-labelled actin in mouse fibroblasts grown on grids of labelled nanowires and identify correlations between local actin alignment and global cell-scale orientation, indicating cellular coordination across length scales.Competing Interest StatementH.S., A.K., S.M., P.L.R., R.O., Y.W., and T.C. hold US Patent $\#$11428632.

}, doi = {10.1101/2024.03.09.584243}, url = {https://www.biorxiv.org/content/early/2024/03/12/2024.03.09.584243}, author = {Talon Chandler and Min Guo and Yijun Su and Jiji Chen and Yicong Wu and Junyu Liu and Atharva Agashe and Robert S. Fischer and Shalin B. Mehta and Abhishek Kumar and Tobias I. Baskin and Valentin Jamouille and Huafeng Liu and Vinay Swaminathan and Amrinder Nain and Rudolf Oldenbourg and Patrick La Riviere and Hari Shroff} }