Biology subjects

Wijesinghe, P.

Publications and source records attributed to Wijesinghe, P..

8 recordsLinked to original sources

bioRxiv2024

Sidelobe suppressed Bessel beams for one-photon light-sheet microscopy

Bessel beams (BB) have found widespread adoption in various forms of light-sheet microscopy. However, for one-photon fluorescence, the transverse profile of the beam poses challenges due to the detrimental effect of the sidelobes. Here, we mitigate this issue by using a computer generated phase element for generating a sidelobe suppressed Bessel beam (SSBB). We then progress to perform a comparison of biological imaging using SSBB to standard BB in a light-sheet geometry. The SSBB peak intensity is more than an order of magnitude higher than the first sidelobe. In contrast to a standard BB light-sheet, SSBB does not need deconvolution and propagates to depths exceeding 400m in phantom samples maintaining a transverse size of 5 m. Finally, we demonstrate the advantage of using a SSBB light-sheet for biological applications by imaging fixed early-stage zebrafish larvae. In comparison to the standard BB, we observe a two-fold increase in contrast-to-noise ratio (CNR) when imaging the labelled cellular eye structures and the notochords. Our results provide an effective approach to generating and using SSBB light-sheets to enhance contrast for one-photon light-sheet microscopy.

biophysics↗

bioRxiv2024

Estimating full-field displacement in biological images using deep learning

The estimation of full-field displacement between biological image frames or in videos is important for quantitative analyses of motion, dynamics and biophysics. However, the often weak signals, poor biological contrast and a multitude of noise processes typical to microscopy make this a formidable challenge for many contemporary methods. Here, we present a deep-learning method, termed Displacement Estimation FOR Microscopy (DEFORM-Net), that outperforms traditional digital image correlation and optical flow methods, as well as recent learned approaches, offering simultaneous high accuracy, spatial sampling and speed. DEFORM-Net is experimentally unsupervised, relying on displacement simulation based on a random fractal Perlin-noise process and optimised training loss functions, without the need for experimental ground truth. We demonstrate its performance on real biological videos of beating neonatal mouse cardiomyocytes and pulsed contractions in Drosophila pupae, and in various microscopy modalities. We provide DEFORM-Net as open source, including inference in the ImageJ/FIJI platform, for rapid evaluation, which will empower new quantitative applications in biology and medicine.

biophysics↗

bioRxiv2024

Multimodal mechano-microscopy reveals mechanical phenotypes of breast cancer spheroids in three dimensions

Cancer cell invasion relies on an equilibrium between cell deformability and the biophysical constraints imposed by the extracellular matrix (ECM). However, there is little consensus on the nature of the local biomechanical alterations in cancer cell dissemination in the context of three-dimensional (3D) tumor microenvironments (TME). While the shortcomings of two-dimensional (2D) models in replicating in situ cell behavior are well known, 3D TME models remain underutilized because contemporary mechanical quantification tools are limited to surface measurements. Here, we overcome this major challenge by quantifying local mechanics of cancer cell spheroids in 3D TMEs. We achieve this using multimodal mechano-microscopy, integrating optical coherence microscopy-based elasticity imaging with confocal fluorescence microscopy. We observe that non-metastatic cancer spheroids show no invasion while showing increased peripheral cell elasticity in both stiff and soft environments. Metastatic cancer spheroids, however, show ECM-mediated softening in a stiff microenvironment and, in a soft environment, initiate cell invasion with peripheral softening associated with early metastatic dissemination. This exemplar of live-cell 3D mechanotyping supports that invasion increases cell deformability in a 3D context, illustrating the power of multimodal mechano-microscopy for quantitative mechanobiology in situ.

bioengineering↗

bioRxiv2023

UVA hyperspectral light-sheet microscopy for volumetric metabolic imaging: application to pre-implantation embryo development

Cellular metabolism is a key regulator of energetics, cell growth, regeneration and homeostasis. Spatially mapping the heterogeneity of cellular metabolic activity is of great importance for unraveling the overall cell and tissue health. In this regard, imaging the endogenous metabolic co-factors NAD(P)H and FAD with sub-cellular resolution and in a non-invasive manner would be useful to determine tissue and cell viability in a clinical environment, but practical use is limited by current imaging techniques. In this article, we demonstrate the use of phasor-based hyperspectral light-sheet (HS-LS) microscopy using a single UVA excitation wavelength as a route to mapping metabolism in three dimensions. We show that excitation solely at a UVA wavelength of 375 nm can simultaneously excite NAD(P)H and FAD autofluorescence, while their relative contributions can be readily quantified using a hardware-based spectral phasor analysis. We demonstrate the potential of our HS-LS system by capturing dynamic changes in metabolic activity during pre-implantation embryo development. To validate our approach, we delineate metabolic changes during pre-implantation embryo development from volumetric maps of metabolic activity. Importantly, our approach overcomes the need for multiple excitation wavelengths, two-photon imaging or significant post-processing of data, paving the way towards clinical translation, such as in situ, non-invasive assessment of embryo viability.

biophysics↗

bioRxiv2021

Learned deconvolution using physics priors for structured light-sheet microscopy

Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserves perceptual content. Our method rapidly and robustly deconvolves and superresolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1,000-10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams, including in calibration beads, oocytes, preimplantation embryos, and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.

biophysics↗

bioRxiv2021

Adult porcine (Sus scrofa) derived inner ear cells possessing multipotent stem/progenitor cell characteristics in in vitro cultures

The human inner ear compared with that of other mammalian species is very complex. Although the mouses cochlea is frequently studied the mouses inner ear continues to develop postnatally whilst the human inner ear is fully developed by the third month of gestation which leads one to question the applicability of findings based on research on mice to human regenerative therapies. Here, we report a novel in vitro culture of adult porcine (Sus scrofa) inner ear cells developed from post-mortem labyrinth specimens. Anatomical findings based on maximal transverse and vertical axial diameters and the length of the cochlear duct suggest that the pigs cochlea is similar to the human cochlea. In vitro cultures of porcine cochlear and vestibular cells showed the persistence of both inner ear hair cell (HC), supporting cell (SC) and stem/progenitor cell characteristics across passages up to 6 based on scanning electron microscopy, fluorescence immunocytochemistry and quantitative reverse transcription polymerase chain reaction (RT-qPCR). Our findings showed that porcine cochlear and vestibular epithelia maintained multipotent stem/progenitor cell populations into adulthood although their regenerative capacities differed across the passages. The development of a viable and reproducible method to culture porcine inner ear cells provides an important investigative tool that can be utilized to study and evaluate the pathophysiological causes and cellular consequences of human inner ear disorders.

cell biology↗

bioRxiv2020

Widefield light sheet microscopy using an Airy beam combined with deep-learning super-resolution

Imaging across length scales and in depth has been an important pursuit of widefield optical imaging. This promises to reveal fine cellular detail within a widefield snapshot of a tissue sample. Current advances often sacrifice resolution through selective sub-sampling to provide a wide field of view in a reasonable time scale. We demonstrate a new avenue for recovering high-resolution images from sub-sampled data in light-sheet microscopy using deep-learning super-resolution. We combine this with the use of a widefield Airy beam to achieve high-resolution imaging over extended fields of view and depths. We characterise our method on fluorescent beads as test targets. We then demonstrate improvements in imaging amyloid plaques in a cleared brain from a mouse model of Alzheimers disease, and in excised healthy and cancerous colon and breast tissues. This development can be widely applied in all forms of light sheet microscopy to provide a two-fold increase in the dynamic range of the imaged length scale. It has the potential to provide further insight into neuroscience, developmental biology and histopathology.

biophysics↗

bioRxiv2019

Molecular phylogeny of wood decay fungi of hardwood and their ability to produce laccase that correlates with triphenylmethane dye decolorization

Though Sri Lanka belongs to one of the 34 biodiversity hotspots of the world, its microfolora specially fungi are not well studied and underrepresented in the global literature. Here we report the fungal species diversity of decaying hardwood of a Sri Lankan dry zone forest for the first time. Decaying hardwoods were collected from historically important Dimbulagala forest reserve, Sri Lanka and fungi associated with these woods were isolated. Out of 35 fungal species identified using morphological and molecular methods, 11 species were first records in Sri Lanka. All the tested isolates were able to utilize wood as the sole carbon source and produced varying degrees of laccase. Isolates of Perenniporia tephropora, Coriolopsis caperata, Gymnopilus dilepis, Fusarium solani and Vanderbylia fraxinea were among the top six laccase producers. Except Fusarium solani, the rest of the isolates showed more than 70% decolorization of the of triphenylmethane dye and there was a significant positive correlation between laccase production and dye decolorization. To the best of our knowledge laccase production and dye decolorization ability of Vanderbylia fraxinea and Gymnopilus dilepis have never been reported in the fungal kingdom before. Perenniporia tephropora was isolated from one of the strongest decay resistant hardwood species, Ebony (Diospyros ebenum) also known as dark wood and V. fraxinea was isolated from another medicinally important hardwood Neem (Azadirachta indica). Findings of this study confirms that decaying hardwood of Sri Lanka provide unexplode a unique niche for discovering fungal species with biotechnological applications such as high laccase producers and dye decolorizers.

microbiology↗