Spatial Light Interference Microscopy Research Articles

SLIM-assisted automatic cartography of cell death types and rates resulting from localized photodynamic treatment

We report a spatial light interference microscopy (SLIM)-based methodology aimed at automatic monitoring and analysis of changes in cellular morphology within extended fields of view in cytological samples. The experimental validation was performed on HeLa cells in vitro subjected to localized photodynamic treatment. The performed long-term noninvasive monitoring using the SLIM technique allowed us to estimate quantitative parameters characterizing the dynamics of average phase shift in individual cells and to reveal changes in their morphology specific for different mechanisms of cell death. The results obtained evidenced that the proposed SLIM-based methodology provides an opportunity for identification of cell death type and quantification of cell death rate in an automatic mode. The major sources of potential errors that can affect the results obtained are discussed. The developed methodology is promising for automatic monitoring of large ensembles of individual cells and for quantitative characterization of their response to various treatment modalities.

229 Awardee Talk: The quest for an ideal in-field embryo evaluation tool in cattle: Opportunities and challenges

Abstract Accurately evaluating bovine embryos to select the most viable ones for transfer and successful pregnancy has been a challenge since embryo transfer began in cattle over six decades ago. Despite more than 3,100 peer-reviewed studies on bovine embryo evaluation published in PubMed from 1964 to 2023, there is still no consensus or gold standard method for assessing their developmental potential. The evaluation “problem” in assisted reproduction extends beyond embryos to gametes as well. Numerous microscopic techniques (e.g., differential interference contrast, electron, fluorescent, time-lapse, and artificial intelligence-based microscopy) and non-microscopic methodologies (e.g., genomics, transcriptomics, epigenomics, proteomics, metabolomics, and nuclear magnetic resonance) have been tested to find an embryo evaluation technique superior to morphological evaluation. While many of these research tools can accurately determine embryo quality and viability, most are invasive, expensive, labor-intensive, technically complex, and time-consuming, making them impractical for in-field embryo evaluation. Employing complex methods like RNA sequencing, SNP chips, mass spectrometry, and multiphoton microscopy at thousands of embryo production and collection facilities is unrealistic, no matter how accurate they may be. Therefore, future research should focus on innovating field-friendly, simple benchtop tests using existing findings, particularly from omics-based research methodologies. Time-lapse monitoring and artificial intelligence-based automated image analysis also potentially provide accurate embryo evaluation. However, further research is necessary to develop economically feasible options for in-field applications. Our recent work has examined possible applications of nuclear magnetic resonance (NMR) imaging, label-free microscopy, particularly holographic microscopy techniques like gradient light interference microscopy (GLIM), spatial light interference microscopy (SLIM), and multiphoton holographic microscopy in IVF labs. The ideal embryo evaluation tool should be accurate, objective, non-invasive, affordable, and simple enough for widespread adoption by embryo production and collection facilities worldwide. Under current circumstances, it is doubtful that transcriptomics, proteomics, metabolomics, GLIM, SLIM, NMR, multiphoton holographic microscopy, and similar techniques can be directly used for in-field embryo evaluation. However, biomarkers identified by these methodologies could be used for in-field embryo evaluation by devising simple, affordable benchtop techniques. Further investigation on time-lapse microscopy (TLM) and AI-based automated image processing may make these methods more affordable and potentially adoptable by the masses. There is great potential for these methodologies to become widely used, provided they can be made more economically viable. In conclusion, the next generation of microscopy capable of providing objective markers for gamete and embryo quality is on the horizon.

Eliminating the systematic error of spatial light interference microscopy by the wavefront comparison iterative algorithm

Spatial light interference microscopy (SLIM) is a unique quantitative phase imaging technology. Since the phase of direct light is simply assumed to be 0, it has a non-negligible systematic error. Based on the theory of partially coherent SLIM, here we propose a wavefront comparison iterative (WCI) algorithm to eliminate this error for accurate phase imaging. In this paper, we clarified the existence of this systematic error by phase imaging of oil-immersed polystyrene microspheres, and we demonstrated the feasibility of the WCI algorithm by phase imaging of red blood cells. Furthermore, through computer simulation, we have confirmed that for phase imaging of large adherent cells, if the SLIM system compresses the phase of direct light sufficiently small, the WCI algorithm will be feasible too.

Internally focused Phase Optical Transfer Function (iPOTF) for Spatial Light Interference Microscopy (SLIM)

Internally focused Phase Optical Transfer Function (iPOTF) for Spatial Light Interference Microscopy (SLIM)

Single-shot spatial light interference microscopy for dynamic monitoring of membrane fluctuations.

Single-shot spatial light interference microscopy (SS-SLIM) with a pair of non-polarizing beam splitters is proposed for substantially enhancing the speed and efficiency of conventional SLIM systems. Traditional methods are limited by the need for multiple-frame serial modulation and acquisition by spatial light modulators and detectors. Our approach integrates non-polarizing beam splitters to simultaneously capture four phase-shifted intensity images, increasing the imaging speed by at least fourfold while maintaining high quality. This capability is crucial for effectively monitoring the dynamic fluctuations of red blood cell membranes. Furthermore, the potential applications of the SS-SLIM system in biomedical research are demonstrated, particularly in scenarios requiring high temporal resolution and label-free imaging.

Quantification of biophysical markers of inflammation using Spatial Light Interference Microscopy (SLIM) in a repeated social stress disorder murine model

Quantification of biophysical markers of inflammation using Spatial Light Interference Microscopy (SLIM) in a repeated social stress disorder murine model

Spatial light interference microscopy (SLIM) for label-free quantification of neuroinflammation in Alzheimer's disease

Spatial light interference microscopy (SLIM) for label-free quantification of neuroinflammation in Alzheimer's disease

A-144 Three-wavelength Polarization Imaging Method for the Analysis of Individual Human Blood Cells With Dye-Enhanced Dispersion of Dilution Buffer

Abstract Background Performing basic hematology analysis in a near-patient setting enables more rapid clinical decision making in the process of diagnosis. Traditional blood analyzers in central labs use a complex combination of sensing principles and chemical modification of the sample to derive the necessary parameters for a complete blood count (CBC). To reduce cost and maintenance, varying approaches have been employed to determine a CBC with a single-step measurement technique. One common option is the use of optical microscopy to deliver highly reliable results, examples are fluorescence microscopy, digital holographic microscopy, or spatial light interference microscopy. However, most of these technologies lack a proper determination of cellular-level RBC parameters. Therefore, a non-interferometric, quantitative-phase measurement technique was developed to derive relevant CBC parameters from a single measurement, comparable to a standard lab analyzer. Methods The optical setup employed by this method enables the parallel acquisition of brightfield and differential interference contrast (DIC) images by polarization-sensitive cameras. By using three cameras with individual optical filters, quantitative phase and absorption information can be calculated, which enables the determination of mean cellular volume (MCV), mean cellular hemoglobin (MCH), and mean cellular hemoglobin concentration (MCHC) for red blood cells (RBCs). The whole-blood sample is diluted in a buffer containing a non-cell-penetrating dye to enhance optical refraction contrast and therefore precision in the dependent calculated quantities. The sample is then introduced to a glass flow cell, and the cells are allowed to sediment to the bottom for imaging. Machine learning is applied to the derived images to classify unstained native white blood cells (WBCs) to yield a 5-part differential, which distinguishes between neutrophil, monocyte, lymphocyte, eosinophil and basophil. The algorithm is trained by imaging FACS-validated, purified WBC subgroups. All blood samples are measured on an ADVIA® 2120 Hematology System for control. Results A one-step, optical CBC hematology system based on quantitative phase and absorption image analysis was developed. The correlations for MCV and MCHC compared to a standard lab CBC analyzer were 0.77 and 0.84. The accuracy for the 5-part differential WBC classification was approximately 0.8. To enable a visual inspection of the classification results, a pseudo-colored image of each cell was generated and uploaded in a review tool. Conclusion With a more-refined, less-expensive optical setup, a high-quality CBC analyzer suitable for use at the point of care can be derived with the presented method. A possible simplification could be the use of low-cost but high-resolution Fourier ptychography microscopy (FPM) technology. Imaging of blood samples on a single-cell level offers the potential for the analysis of even more morphological parameters, for example, disease-related changes in the cell membrane. Also, the adhesion behavior of the WBCs in the glass flow cell could indicate cell activation status and therefore give information about infection processes. *Trademark disclaimer: “ADVIA, and all associated marks are trademarks of Siemens Healthcare Diagnostics, Inc. or its affiliates. All other trademarks and brands are the property of their respective owners.”

Image reconstruction for low cost spatial light interference microscopy with fixed and arbitrary phase modulation.

During the past decade, spatial light interference microscopy (SLIM) has undergone rapid development, evidenced by its broadening applications in biology and medicine. However, the need for an expensive spatial light modulator (SLM) may limit its adoption, and the requirement for multiple images per plane limits its speed in volumetric imaging. Here we propose to address these issues by replacing the SLM with a mask fabricated from a low cost optical density (OD) filter, and recover high contrast images computationally rather than through phase-shifting. This is done using a specially constructed Wiener filter to recover the object scattering potential. A crucial part of the Wiener filter is estimating the arbitrary phase introduced by the OD filter. Our results demonstrate that not only were we able to estimate the OD filter's phase modulation in situ, but also the contrast of the reconstructed images is greatly improved. Comparisons with other related methods are also performed, with the conclusion that the combination of an inexpensive OD mask and modified Wiener filtering leads to results that are closest to the traditional SLIM setup. Thus, we have demonstrated the feasibility of a low cost, high speed SLIM system utilizing computational phase reconstruction, paving the way for wider adoption of high resolution phase microscopy.

Wavefront Microscopy Using Quadriwave Lateral Shearing Interferometry: From Bioimaging to Nanophotonics

Common cameras are only sensitive to the intensity of light, discarding an essential feature of a light wave: its phase profile or, equivalently, its wavefront profile. This Review focuses on a rising wavefront imaging technique called quadriwave lateral shearing interferometry (QLSI), based on the simple use of a 2-dimensional diffraction grating, aka a cross-grating, in front of a regular camera. We detail the working principle of QLSI and its implementation on an optical microscope. We highlight its microscopy applications in bioimaging and nanophotonics, in particular for the characterization of living cells, nanoparticles, 2D materials, metasurfaces, microscale temperature gradients, and surface topography. Finally, we draw a critical comparison of QLSI with current quantitative phase microscopy techniques, namely, digital holography microscopy (DHM), spatial light interference microscopy (SLIM), and diffraction phase microscopy (DPM).

Single-shot spatial light interference microscopy by demultiplexing based on polarization gratings

Off-axis interferometric modules built on an ordinary bright field microscope make it possible to achieve single-shot quantitative phase imaging (QPI) by adding sufficient spatial carrier into the interferograms. However, compared with its on-axis counterparts, imaging configurations for off-axis interferometric modules have several disadvantages regarding optical aberration, stability, and space-bandwidth utilization of the lenses system. Herein, by demultiplexing technology based on polarization gratings, we propose a single-shot spatial light interference microscopy named as polarization-multiplexing light interference microscopy (PLIM) with on-axis imaging configuration to realize single-shot QPI. Although the imaging system is on-axis, the PLIM system still can generate sufficient spatial carrier, so we can adjust the orientation and absolute value of the spatial carrier independently of the magnification ratio of the imaging system. The experimental results prove that the PLIM system has better temporal phase stability compared with conventional grating-based QPI technologies and is suitable for high resolution QPI.

Research on partially coherent spatial light interference microscopy.

Based on partial coherence theory, this study rigorously deduces the principle of spatial light interference microscopy (SLIM) and improves the calculation method of SLIM. The main problem we found with SLIM is that it simply defaults the phase of the direct light to 0. To address this problem, we propose and experimentally demonstrate a double four-step phase shift method. Simulation results show that this method can reduce the relative error of oil-immersed microsphere reconstruction to about 3.7%, and for red blood cell reconstruction, the relative error can be reduced to about 13%.

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.

The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empirical assessment, a certain amount of subjectivity is unavoidable. Furthermore, the reliance on external contrast agents such as hematoxylin and eosin (H&E), albeit being well-established methods, makes it difficult to standardize color balance, staining strength, and imaging conditions, hindering automated computational analysis. In response to these challenges, we applied spatial light interference microscopy (SLIM), a label-free method that generates contrast based on intrinsic tissue refractive index signatures. Thus, we reduce human bias and make imaging data comparable across instruments and clinics. We applied a mask R-CNN deep learning algorithm to the SLIM data to achieve an automated colorectal cancer screening procedure, i.e., classifying normal vs. cancerous specimens. Our results, obtained on a tissue microarray consisting of specimens from 132 patients, resulted in 91% accuracy for gland detection, 99.71% accuracy in gland-level classification, and 97% accuracy in core-level classification. A SLIM tissue scanner accompanied by an application-specific deep learning algorithm may become a valuable clinical tool, enabling faster and more accurate assessments by pathologists.

Simultaneous 3D deconvolution and halo removal for spatial light interference microscopy through a two-edge apodized Wiener filter.

As one of the most sensitive quantitative phase microscopy techniques, spatial light interference microscopy (SLIM) has undergone rapid development in the past decade and has seen wide application in both basic science and clinical studies. However, as with any other traditional microscope, the axial resolution is the worst among the three dimensions. This leads to lower contrast in the thicker regions of cell samples. Another common foe in the phase contrast image is the halo artifact, which can block underlying structures, in particular when high resolution is desired. Current solutions focus on either halo removal or contrast enhancement alone, and thus need two processing steps to create both high contrast and halo-free phase images. Further, raw images often suffer from artifacts that are both bright and slowly varying, dubbed here as cloud-like artifacts. After deconvolution, these cloud-like artifacts often dominate the image and obscure high-frequency information, which is typically of greatest interest. In this paper, we first analyzed the unique characteristics of the phase transfer function associated with SLIM to find the root of the cloud-like artifacts and halo artifacts. Then we designed a two-edge apodized deconvolution scheme as a counter measure. We show that even with a simple Wiener filter, the two-edge apodization (TEA) can effectively improve the contrast while suppressing the halo and cloud-like artifacts. Our algorithm, named TEA-Weiner, is non-iterative and thus can be implemented in real time. For low-contrast structures inside the cell such as the endoplasmic reticulum (ER), where ringing artifacts are more likely, we show that two-edge apodization can be combined with additional constraints such as total variation so that their contrast can be enhanced simultaneously with other bright structures inside the cell. Comparing our method with other state-of-the-art algorithms, our method has two advantages: First, deconvolution and halo removal are accomplished simultaneously; second, the image quality is highest using TEA-Weiner filtering.

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.

Computational interference microscopy enabled by deep learning.

Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method due to its partially coherent illumination and common path interferometry geometry. However, SLIM’s acquisition rate is limited because of the four-frame phase-shifting scheme. On the other hand, off-axis methods such as diffraction phase microscopy (DPM) allow for single-shot QPI. However, the laser-based DPM system is plagued by spatial noise due to speckles and multiple reflections. In a parallel development, deep learning was proven valuable in the field of bioimaging, especially due to its ability to translate one form of contrast into another. Here, we propose using deep learning to produce synthetic, SLIM-quality, and high-sensitivity phase maps from DPM using single-shot images as the input. We used an inverted microscope with its two ports connected to the DPM and SLIM modules such that we have access to the two types of images on the same field of view. We constructed a deep learning model based on U-net and trained on over 1000 pairs of DPM and SLIM images. The model learned to remove the speckles in laser DPM and overcame the background phase noise in both the test set and new data. The average peak signal-to-noise ratio, Pearson correlation coefficient, and structural similarity index measure were 29.97, 0.79, and 0.82 for the test dataset. Furthermore, we implemented the neural network inference into the live acquisition software, which now allows a DPM user to observe in real-time an extremely low-noise phase image. We demonstrated this principle of computational interference microscopy imaging using blood smears, as they contain both erythrocytes and leukocytes, under static and dynamic conditions.

Fundamental Bounds on the Precision of Classical Phase Microscopes

A wide variety of imaging systems have been designed to measure phase variations, with applications from physics to biology and medicine. In this work, we theoretically compare the precision of phase estimations achievable with classical phase microscopy techniques, operated at the shot-noise limit. We show how the Cram\'{e}r-Rao bound is calculated for any linear optical system, including phase-contrast microscopy, phase-shifting holography, spatial light interference microscopy, and local optimization of wavefronts for phase imaging. Through these examples, we demonstrate how this general framework can be applied for the design and optimization of classical phase microscopes. Our results show that wavefront shaping is required to design phase microscopes with optimal phase precision.

Quantifying myelin content in brain tissue using color Spatial Light Interference Microscopy (cSLIM).

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

High-throughput sperm assay using label-free microscopy: morphometric comparison between different sperm structures of boar and stallion spermatozoa

The capacity for microscopic evaluation of sperm is useful for assisted reproductive technologies (ART), because this can allow for specific selection of sperm cells for in vitro fertilization (IVF). The objective of this study was to analyze the same sperm samples using two high-resolution methods: spatial light interference microscopy (SLIM) and atomic force microscopy (AFM) to determine if with one method there was more timely and different information obtained than the other. To address this objective, there was evaluation of sperm populations from boars and stallions. To the best of our knowledge, this is the first reported comparison when using AFM and high-sensitivity interferometric microscopy (such as SLIM) to evaluate spermatozoa. Results indicate that with the use of SLIM microscopy there is similar nanoscale sensitivity as with use of AFM while there is approximately 1,000 times greater throughput with use of SLIM. With SLIM, there is also allowace for the measurement of the dry mass (non-aqueous content) of spermatozoa, which may be a new label-free marker for sperm viability. In the second part of this study, there was analysis of two sperm populations. There were interesting correlations between the different compartments of the sperm and the dry mass in both boars and stallions. Furthermore, there was a correlation between the dry mass of the sperm head and the length and width of the acrosome in both boars and stallions. This correlation is positive in boars while it is negative in stallions.

Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM).

Current pathology workflow involves staining of thin tissue slices, which otherwise would be transparent, followed by manual investigation under the microscope by a trained pathologist. While the hematoxylin and eosin (H&E) stain is well-established and a cost-effective method for visualizing histology slides, its color variability across preparations and subjectivity across clinicians remain unaddressed challenges. To mitigate these challenges, recently we have demonstrated that spatial light interference microscopy (SLIM) can provide a path to intrinsic, objective markers, that are independent of preparation and human bias. Additionally, the sensitivity of SLIM to collagen fibers yields information relevant to patient outcome, which is not available in H&E. Here, we show that deep learning and SLIM can form a powerful combination for screening applications: training on 1,660 SLIM images of colon glands and validating on 144 glands, we obtained a benign vs. cancer classification accuracy of 99%. We envision that the SLIM whole slide scanner presented here paired with artificial intelligence algorithms may prove valuable as a pre-screening method, economizing the clinician's time and effort.