Inverse scattering for reflection intensity phase microscopy

Perspective on quantitative phase imaging to improve precision cancer medicine

Significance

Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states.

Aim

This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection.

Approach

We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging.

Results

QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research.

Conclusions

QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.

Phase contrast reflectance confocal brain imaging at 1650 nm

Significance

The imaging depth of microscopy techniques is limited by the ability of light to penetrate biological tissue. Recent research has addressed this limitation by combining a reflectance confocal microscope with the NIR-II (or shortwave infrared) spectrum. This approach offers significant imaging depth, is straightforward in design, and remains cost-effective. However, the imaging system, which relies on intrinsic signals, could benefit from adjustments in its optical design and post-processing methods to differentiate cortical cells, such as neurons and small blood vessels.

Aim

We implemented a phase contrast detection scheme to a reflectance confocal microscope using NIR-II spectral range as illumination.

Approach

We analyzed the features retrieved in the images while testing the imaging depth. Moreover, we introduce an acquisition method for distinguishing dynamic signals from the background, allowing the creation of vascular maps similar to those produced by optical coherence tomography.

Results

The phase contrast implementation is successful to retrieve deep images in the cortex up to 800    μ m using a cranial window. Vascular maps were retrieved at similar cortical depth and the possibility of combining multiple images can provide a vessel network.

Conclusions

Phase contrast reflectance confocal microscopy can improve the outlining of cortical cell bodies. With the presented framework, angiograms can be retrieved from the dynamic signal in the biological tissue. Our work presents an optical implementation and analysis techniques from a former microscope design.

Label-free superior contrast with c-band ultra-violet extinction microscopy

In 1934, Frits Zernike demonstrated that it is possible to exploit the sample's refractive index to obtain superior contrast images of biological cells. The refractive index contrast of a cell surrounded by media yields a change in the phase and intensity of the transmitted light wave. This change can be due to either scattering or absorption caused by the sample. Most cells are transparent at visible wavelengths, which means the imaginary component of their complex refractive index, also known as extinction coefficient k, is close to zero. Here, we explore the use of c-band ultra-violet (UVC) light for high-contrast high-resolution label-free microscopy, as k is naturally substantially higher in the UVC than at visible wavelengths. Using differential phase contrast illumination and associated processing, we achieve a 7- to 300-fold improvement in contrast compared to visible-wavelength and UVA differential interference contrast microscopy or holotomography, and quantify the extinction coefficient distribution within liver sinusoidal endothelial cells. With a resolution down to 215 nm, we are, for the first time in a far-field label-free method, able to image individual fenestrations within their sieve plates which normally requires electron or fluorescence superresolution microscopy. UVC illumination also matches the excitation peak of intrinsically fluorescent proteins and amino acids and thus allows us to utilize autofluorescence as an independent imaging modality on the same setup.

Optical Diffraction Tomography Using Nearly In-Line Holography with a Broadband LED Source

We present optical tomography methods for a 3D refractive index reconstruction of weakly scattering objects using LED light sources. We are able to record holograms by minimizing the optical path difference between the signal and reference beams while separating the scattered field from its twin image. We recorded multiple holograms by illuminating the LEDs sequentially and reconstructed the 3D refractive index reconstruction of the sample. The reconstructions show high signal-to-noise ratio in which the effect of speckle artifacts is highly minimized due to the partially incoherent illumination of the LEDs. Results from combining different illumination wavelengths are also described demonstrating higher acquisition speed.

Review of bio-optical imaging systems with a high space-bandwidth product

Optical imaging has served as a primary method to collect information about biosystems across scales-from functionalities of tissues to morphological structures of cells and even at biomolecular levels. However, to adequately characterize a complex biosystem, an imaging system with a number of resolvable points, referred to as a space-bandwidth product (SBP), in excess of one billion is typically needed. Since a gigapixel-scale far exceeds the capacity of current optical imagers, compromises must be made to obtain either a low spatial resolution or a narrow field-of-view (FOV). The problem originates from constituent refractive optics-the larger the aperture, the more challenging the correction of lens aberrations. Therefore, it is impractical for a conventional optical imaging system to achieve an SBP over hundreds of millions. To address this unmet need, a variety of high-SBP imagers have emerged over the past decade, enabling an unprecedented resolution and FOV beyond the limit of conventional optics. We provide a comprehensive survey of high-SBP imaging techniques, exploring their underlying principles and applications in bioimaging.

Spatial light interference microscopy: principle and applications to biomedicine

In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.

Single cell capture, isolation, and long-term in-situ imaging using quantitative self-interference spectroscopy

Single cell research with microfluidic chip is of vital importance in biomedical studies and clinical medicine. Simultaneous microfluidic cell manipulations and long-term cell monitoring needs further investigations due to the lack of label-free quantitative imaging techniques and systems. In this work, single cell capture, isolation and long-term in-situ monitoring was realized with a microfluidic cell chip, compact cell incubator and quantitative self-interference spectroscopy. The proposed imaging method could obtain quantitative and dynamic refractive index distribution in living cells. And the designed microfluidic chip could capture and isolate single cells. The customized incubator could support cell growth conditions when single cell was captured in microfluidic chip. According to the results, single cells could be trapped, transferred and pushed into the culture chamber with the microfluidic chip. The incubator could culture single cells in the chip for 120 h. The refractive index sensitivity of the proposed quantitative imaging method was 0.0282 and the relative error was merely 0.04%.

3D reconstruction of weakly scattering objects from 2D intensity-only measurements using the Wolf transform

A new approach to optical diffraction tomography (ODT) based on intensity measurements is presented. By applying the Wolf transform directly to intensity measurements, we observed unexpected behavior in the 3D reconstruction of the sample. Such a reconstruction does not explicitly represent a quantitative measure of the refractive index of the sample; however, it contains interesting qualitative information. This 3D reconstruction exhibits edge enhancement and contrast enhancement for nanostructures compared with the conventional 3D refractive index reconstruction and thus could be used to localize nanoparticles such as lipids inside a biological sample.

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.

Computational nanosensing from defocus in single particle interferometric reflectance microscopy

Single particle interferometric reflectance (SPIR) microscopy has been studied as a powerful imaging platform for label-free and highly sensitive biological nanoparticle detection and characterization. SPIR's interferometric nature yields a unique 3D defocus intensity profile of the nanoparticles over a large field of view. Here, we utilize this defocus information to recover high signal-to-noise ratio nanoparticle images with a computationally and memory efficient reconstruction framework. Our direct inversion approach recovers this image from a 3D defocus intensity stack using the vectorial-optics-based forward model developed for sub-diffraction-limited dielectric nanoparticles captured on a layered substrate. We demonstrate proof-of-concept experiments on silica beads with a 50 nm nominal diameter.

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

LED array microscopy is an emerging platform for computational imaging with significant utility for biological imaging. Existing LED array systems often exploit transmission imaging geometries of standard brightfield microscopes that leave the rich backscattered field undetected. This backscattered signal contains high-resolution sample information with superb sensitivity to subtle structural features that make it ideal for biological sensing and detection. Here, we develop an LED array reflectance microscope capturing the sample's backscattered signal. In particular, we demonstrate multimodal brightfield, darkfield, and differential phase contrast imaging on fixed and living biological specimens including Caenorhabditis elegans (C. elegans), zebrafish embryos, and live cell cultures. Video-rate multimodal imaging at 20 Hz records real time features of freely moving C. elegans and the fast beating heart of zebrafish embryos. Our new reflectance mode is a valuable addition to the LED array microscopic toolbox.