Computational hyperspectral light-sheet microscopy

Light sheet illumination in single-molecule localization microscopy for imaging of cellular architectures and molecular dynamics

Single-molecule localization microscopy has revealed cellular architectures and molecular dynamics beyond the diffraction limit of light. However, imaging thick samples presents challenges from increased fluorescence background. Light sheet illumination, which utilizes a plane of light for optical sectioning, is effective in reducing fluorescence background, photobleaching, and photodamage. Here, we present the principles of single-molecule localization microscopy and light sheet illumination, followed by an introduction to light sheet microscopy geometries and their imaging applications. Finally, we discuss light sheet illumination approaches for high- and super-resolution imaging of biological structures and dynamics.

Hyperspectral oblique plane microscopy enables spontaneous, label-free imaging of biological dynamic processes in live animals

Spontaneous Raman imaging has emerged as powerful label-free technique for investigating the molecular composition of medicines and biological specimens. Although Raman imaging can facilitate understanding of complex biological phenomena in vivo, current imaging modalities are limited in speed and sample compatibility. Here, we introduce a single-objective line-scanning light-sheet microscope, named [Formula: see text]-OPM, which records Raman images on a timescale of minutes to seconds. To demonstrate its function, we use [Formula: see text]-OPM to map and identify microplastic particles based on their Raman spectral characteristics. In live zebrafish embryos, we show that [Formula: see text]-OPM can capture wound dynamics at five-minute intervals, revealing rapid changes in cellular and extracellular matrix composition in the wounded region. Finally, we use [Formula: see text]-OPM to synchronize and average 36,800 individual frames to obtain hyperspectral videos of a zebrafish embryo's beating heart at an effective 28 frames per second, recording compositional changes throughout the cardiac cycle.

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.

New approaches for low phototoxicity imaging of living cells and tissues

Fluorescence microscopy is a powerful tool used in scientific and medical research, but it is inextricably linked to phototoxicity. Neglecting phototoxicity can lead to erroneous or inconclusive results. Recently, several reports have addressed this issue, but it is still underestimated by many researchers, even though it can lead to cell death. Phototoxicity can be reduced by appropriate microscopic techniques and carefully designed experiments. This review focuses on recent strategies to reduce phototoxicity in microscopic imaging of living cells and tissues. We describe digital image processing and new hardware solutions. We point out new modifications of microscopy methods and hope that this review will interest microscopy hardware engineers. Our aim is to underscore the challenges and potential solutions integral to the design of microscopy systems. Simultaneously, we intend to engage biologists, offering insight into the latest technological advancements in imaging that can enhance their understanding and practice.

Efficient compressed sensing reconstruction for 3D fluorescence microscopy using OptoMechanical Modulation Tomography (OMMT) with a 1+2D regularization

OptoMechanical Modulation Tomography (OMMT) exploits compressed sensing to reconstruct high resolution microscopy volumes from fewer measurement images compared to exhaustive section sampling in conventional light sheet microscopy. Nevertheless, the volumetric reconstruction process is computationally expensive, making it impractically slow to use on large-size images, and prone to generating visual artefacts. Here, we propose a reconstruction approach that uses a 1+2D Total Variation (TV1+2) regularization that does not generate such artefacts and is amenable to efficient implementation using parallel computing. We evaluate our method for accuracy and scaleability on simulated and experimental data. Using a high quality, but computationally expensive, Plug-and-Play (PnP) method that uses the BM4D denoiser as a benchmark, we observe that our approach offers an advantageous trade-off between speed and accuracy.

In Situ Visualization of Membrane Fouling Evolution during Ultrafiltration Using Label-Free Hyperspectral Light Sheet Fluorescence Imaging

Profound understanding of fouling behaviors and underlying mechanisms is fundamentally important for fouling control in membrane-based environmental applications. Therefore, it entails novel noninvasive analytical approaches for in situ characterizing the formation and development of membrane fouling processes. This work presents a characterization approach based on hyperspectral light sheet fluorescence microscopy (HSPEC-LSFM), which is capable of discriminating various foulants and providing their 2-dimensional/3-dimensional spatial distributions on/in membranes in a label-free manner. A fast, highly sensitive and noninvasive imaging platform was established by developing a HSPEC-LSFM system and further extending it to incorporate a laboratory-scale pressure-driven membrane filtration system. Hyperspectral data sets with a spectral resolution of ∼1.1 nm and spatial resolution of ∼3 μm as well as the temporal resolution of ∼8 s/plane were obtained, and the fouling formation and development process of foulants onto membrane surfaces, within the pores and on the pore walls were clearly observed during the ultrafiltration of protein and humic substances solutions. Pore blocking/constriction at short times while cake growth/concentration polarization at longer times was found to have coupled effects for the flux decline in these filtration tests, and yet the contribution of each effect as well as the transition of the governing mechanisms was found distinct. These results demonstrate in situ label-free characterization of membrane fouling evolution with the recognition of foulant species during filtration and provide new insights into membrane fouling. This work offers a powerful tool to investigate dynamic processes for a wide range of membrane-based explorations.

Computational optical sensing and imaging 2021: feature issue introduction

This Feature Issue includes 2 reviews and 34 research articles that highlight recent works in the field of Computational Optical Sensing and Imaging. Many of the works were presented at the 2021 OSA Topical Meeting on Computational Optical Sensing and Imaging, held virtually from July 19 to July 23, 2021. Articles in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.

Computational Optical Sensing and Imaging 2021: introduction to the feature issue

This feature issue includes two reviews and 34 research papers that highlight recent works in the field of computational optical sensing and imaging. Many of the works were presented at the 2021 Optica (formerly OSA) Topical Meeting on Computational Optical Sensing and Imaging, held virtually from 19 July to 23 July 2021. Papers in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.