LED array reflectance microscopy for scattering-based multi-contrast imaging

Reflectional quantitative differential phase microscopy using polarized wavefront phase modulation

Quantitative phase microscopy (QPM), as a label-free and nondestructive technique, has been playing an indispensable tool in biomedical imaging and industrial inspection. Herein, we introduce a reflectional quantitative differential phase microscopy (termed RQDPM) based on polarized wavefront phase modulation and partially coherent full-aperture illumination, which has high spatial resolution and spatio-temporal phase sensitivity and is applicable to opaque surfaces and turbid biological specimens. RQDPM does not require additional polarized devices and can be easily switched from reflectional mode to transmission mode. In addition, RQDPM inherits the characteristic of high axial resolution of differential interference contrast microscope, thereby providing topography for opaque surfaces. We experimentally demonstrate the reflectional phase imaging ability of RQDPM with several samples: semiconductor wafer, thick biological tissues, red blood cells, and Hela cells. Furthermore, we dynamically monitor the flow state of microspheres in a self-built microfluidic channel by using RQDPM converted into the transmission mode.

Reflectional quantitative phase-contrast microscopy (RQPCM) with annular epi-illumination

Quantitative phase microscopy (QPM) is a label-free microscopic technique that exploits the phase of a wave passing through a sample; hence, it has been applied to many fields, including biomedical research and industrial inspection. However, the high spatiotemporal resolution imaging of reflective samples still challenges conventional transmission QPM. In this paper, we propose reflectional quantitative phase-contrast microscopy based on annular epi-illumination of light-emitting diodes. The unscattered wave from the sample is successively phase-retarded by 0, π/2, π, and 3π/2 through a spatial light modulator, and high-resolution phase-contrast images are obtained, revealing the finer structure or three-dimensional tomography of reflective samples. With this system, we have quantitatively obtained the contour of tissue slices and silicon semiconductor wafers. We believe that the proposed system will be very helpful for the high-resolution imaging of industrial devices and biomedical dynamics.

Epi-illumination dark-field microscopy enables direct visualization of unlabeled small organisms with high spatial and temporal resolution

Dark-field microscopy is known to offer both high resolution and direct visualization of thin samples. However, its performance and optimization on thick samples is under-explored and so far, only meso-scale information from whole organisms has been demonstrated. In this work, we carefully investigate the difference between trans- and epi-illumination configurations. Our findings suggest that the epi-illumination configuration is superior in both contrast and fidelity compared to trans-illumination, while having the added advantage of experimental simplicity and an "open top" for experimental intervention. Guided by the theoretical analysis, we constructed an epi-illumination dark-field microscope with measured lateral and axial resolutions of 260 nm and 520 nm, respectively. Subcellular structures in whole organisms were directly visualized without the need for image reconstruction, and further confirmed via simultaneous fluorescence imaging. With an imaging speed of 20 to 50 fps, we visualize fast dynamic processes such as cell division and pharyngeal pumping in Caenorhabditis elegans.

Single-cell cytometry via multiplexed fluorescence prediction by label-free reflectance microscopy

Traditional imaging cytometry uses fluorescence markers to identify specific structures but is limited in throughput by the labeling process. We develop a label-free technique that alleviates the physical staining and provides multiplexed readouts via a deep learning-augmented digital labeling method. We leverage the rich structural information and superior sensitivity in reflectance microscopy and show that digital labeling predicts accurate subcellular features after training on immunofluorescence images. We demonstrate up to three times improvement in the prediction accuracy over the state of the art. Beyond fluorescence prediction, we demonstrate that single cell-level structural phenotypes of cell cycles are correctly reproduced by the digital multiplexed images, including Golgi twins, Golgi haze during mitosis, and DNA synthesis. We further show that the multiplexed readouts enable accurate multiparametric single-cell profiling across a large cell population. Our method can markedly improve the throughput for imaging cytometry toward applications for phenotyping, pathology, and high-content screening.