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High-throughput microscopy is vital for screening applications, where three-dimensional (3D) cellular models play a key role. However, due to defocus susceptibility, current 3D high-throughput microscopes require axial scanning, which lowers throughput and increases photobleaching and photodamage. Point spread function (PSF) engineering is an optical method that enables various 3D imaging capabilities, yet it has not been implemented in high-throughput microscopy due to the cumbersome optical extension it typically requires. Here we demonstrate compact PSF engineering in the objective lens, which allows us to enhance the imaging depth of field and, combined with deep learning, recover 3D information using single snapshots. Beyond the applications shown here, this work showcases the usefulness of high-throughput microscopy in obtaining training data for deep learning-based algorithms, applicable to a variety of microscopy modalities.
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http://dx.doi.org/10.1038/s41467-024-48502-y | DOI Listing |
J Biomed Opt
September 2025
Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability.
Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach.
Beilstein J Nanotechnol
August 2025
School of Physics and Astronomy, University of Leeds, Leeds LS2 9JT, United Kingdom.
Atomic resolution scanning probe microscopy, and in particular scanning tunnelling microscopy (STM) allows for high-spatial-resolution imaging and also spectroscopic analysis of small organic molecules. However, preparation and characterisation of the probe apex in situ by a human operator is one of the major barriers to high-throughput experimentation and to reproducibility between experiments. Characterisation of the probe apex is usually accomplished via assessment of the imaging quality on the target molecule and also the characteristics of the scanning tunnelling spectra (STS) on clean metal surfaces.
View Article and Find Full Text PDFMethods
September 2025
Gynaecology and Obstetrics, The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, Heilongjiang 150081, PR China. Electronic address:
Single-cell surface-enhanced Raman scattering (SERS) has emerged as a powerful tool for precision medicine owing to its label-free detection, ultrasensitivity, and unique molecular fingerprinting. Unlike conventional bulk analysis, it enables detailed characterization of cellular heterogeneity, with particular promise in circulating tumor cell (CTC) identification, tumor microenvironment (TME) metabolic profiling, subcellular imaging, and drug sensitivity assessment. Coupled with microfluidic droplet systems, SERS supports high-throughput single-cell analysis and multiparametric screening, while integration with complementary modalities such as fluorescence microscopy and mass spectrometry enhances temporal and spatial resolution for monitoring live cells.
View Article and Find Full Text PDFAnal Chem
September 2025
Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.
Infrared (IR) spectroscopic imaging combines the molecular specificity of vibrational spectroscopy with imaging capabilities of microscopy, potentially allowing for simultaneous quantitative observations of drugs and cellular response. However, accurately quantifying drug concentration within changing cells is complicated by the overlap between exogenous molecules' and native cellular spectra. Here, we address this challenge by developing a derivative of the widely used chemotherapeutic doxorubicin as a spectral bioprobe (DOX-IR) using a strongly absorbing metal-carbonyl moiety [(Cp)Fe(CO)].
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America.
Background: When analyzing cells in culture, assessing cell morphology (shape), confluency (density), and growth patterns are necessary for understanding cell health. These parameters are generally obtained by a skilled biologist inspecting light microscope images, but this can become very laborious for high-throughput applications. One way to speed up this process is by automating cell segmentation.
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