Biology subjects

Bruce, G. D.

Publications and source records attributed to Bruce, G. D..

3 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↗

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↗