Biology subjects

Vishwasrao, H.

Publications and source records attributed to Vishwasrao, H..

4 recordsLinked to original sources

bioRxiv2026

Intravital single-molecule imaging reveals cytoskeletal turnover as a driver of membrane remodeling in live animals

Understanding how cells regulate plasma membrane architecture inside intact living organs in a live animal has been limited by the inability to directly measure molecular dynamics in vivo. Here we introduce intravital single-molecule microscopy (iSiMM), an imaging approach that enables tracking of individual, endogenously expressed cytoskeletal components at the plasma membrane in live mice. Applying iSiMM to murine acinar secretory cells, we identify discrete basolateral membrane domains built on deeply folded membrane infolds that function as a pre-existing membrane reservoir. Single-molecule measurements reveal continuous, regulated molecular turnover within these domains. Physiological stimulation accelerates cytoskeletal exchange promoting rapid membrane unfolding and cell expansion. Together, these findings establish iSiMM as a general strategy for probing molecular kinetics underlying dynamic cellular behaviors in intact organs. One-sentence summaryIntravital single-molecule microscopy enables direct measurement of molecular kinetics underlying dynamic cellular behaviors in intact living organs.

cell biology↗

bioRxiv2022

Incorporating the image formation process into deep learning improves network performance in deconvolution applications

We present Richardson-Lucy Network (RLN), a fast and lightweight deep learning method for 3D fluorescence microscopy deconvolution. RLN combines the traditional Richardson-Lucy iteration with a fully convolutional network structure, improving network interpretability and robustness. Containing only [~]16 thousand parameters, RLN enables 4- to 50-fold faster processing than purely data-driven networks with many more parameters. By visual and quantitative analysis, we show that RLN provides better deconvolution, better generalizability, and fewer artifacts than other networks, especially along the axial dimension. RLN outperforms Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides 4- to 6-fold faster reconstructions of large, cleared tissue datasets than classic multi-view pipelines. We demonstrate RLNs performance on cells, tissues, and embryos imaged with widefield-, light sheet-, and structured illumination microscopy.

biophysics↗

bioRxiv2019

Accelerating iterative deconvolution and multiview fusion by orders of magnitude

We describe theoretical and practical advances in algorithm and software design, resulting in ten to several thousand-fold faster deconvolution and multiview fusion than previous methods. First, we adapt methods from medical imaging, showing that an unmatched back projector accelerates Richardson-Lucy deconvolution by at least 10-fold, in most cases requiring only a single iteration. Second, we show that improvements in 3D image-based registration with GPU processing result in speedups of 10-100-fold over CPU processing. Third, we show that deep learning can provide further accelerations, particularly for deconvolution with a spatially varying point spread function. We illustrate the power of our methods from the subcellular to millimeter spatial scale, on diverse samples including single cells, nematode and zebrafish embryos, and cleared mouse tissue. Finally, we show that our methods facilitate the use of new microscopes that improve spatial resolution, including dual-view cleared tissue light-sheet microscopy and reflective lattice light-sheet microscopy.

bioengineering↗

bioRxiv2017

Reflective imaging improves resolution, speed, andcollection efficiency in light sheet microscopy

Light-sheet fluorescence microscopy (LSFM) enables high-speed, high-resolution, gentle imaging of live biological specimens over extended periods. Here we describe a technique that improves the spatiotemporal resolution and collection efficiency of LSFM without modifying the underlying microscope. By imaging samples on reflective coverslips, we enable simultaneous collection of multiple views, obtaining 4 complementary views in 250 ms, half the period it would otherwise take to collect only two views in symmetric dual-view selective plane illumination microscopy (diSPIM). We also report a modified deconvolution algorithm that removes the associated epifluorescence contamination and fuses all views for resolution recovery. Furthermore, we enhance spatial resolution (to < 300 nm in all three dimensions) by applying our method to a new asymmetric diSPIM, permitting simultaneous acquisition of two high-resolution views otherwise difficult to obtain due to steric constraints at high numerical aperture (NA). We demonstrate the broad applicability of our method in a variety of samples of moderate (< 50 m) thickness, studying mitochondrial, membrane, Golgi, and microtubule dynamics in single cells and calcium activity in nematode embryos.

bioengineering↗