@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 {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} }