Refractive index measurements of single, spherical cells using digital holographic microscopy

Patient's dermal fibroblasts as disease markers for visceral myopathy

Visceral myopathy (VSCM) is a rare genetic disease, orphan of pharmacological therapy. VSCM diagnosis is not always straightforward due to symptomatology similarities with mitochondrial or neuronal forms of intestinal pseudo-obstruction. The most prevalent form of VSCM is associates with variants in the gene ACTG2, encoding the protein gamma-2 actin. Overall, VSCM is a mechano-biological disorder, in which different genetic variants lead to similar alterations to the contractile phenotype of enteric smooth muscles, resulting in the emergence of life-threatening symptoms. In this work we analyzed the morpho-mechanical phenotype of human dermal fibroblasts from patients affected with VSCM, demonstrating that they retain a clear signature of the disease when compared with different controls. We evaluated several biophysical traits of fibroblasts, and we show that a measure of cellular traction forces can be used as a non-specific biomarker of the disease. We propose that a simple assay based on traction forces could be designed to provide a valuable support for clinical decision or pre-clinical research.

Algal cell viability assessment: The role of environmental factors in phytoplankton population dynamics

The viability of algal cells is one of the most fundamental issues in marine ecological research. In this work, a method was designed to identify algal cell viability based on digital holography and deep learning, which divided algal cells into three categories: active, weak, and dead cells. This method was applied to measure algal cells in surface waters of the East China Sea in spring, revealing about 4.34 %-23.29 % weak cells and 3.98 %-19.47 % dead cells. Levels of nitrate and chlorophyll a were the main factors affecting the viability of algal cells. Furthermore, algal viability changes during the heating and cooling were observed in laboratory experiments: high temperatures led to an increase in weak algal cells. This may provide an explanation for why most harmful algal blooms occur in warming months. This study provided a novel insight into how to identify the viability of algal cells and understand their significance in the ocean.

The Impact of Experimental Conditions on Cell Mechanics as Measured with Nanoindentation

The evaluation of cell elasticity is becoming increasingly significant, since it is now known that it impacts physiological mechanisms, such as stem cell differentiation and embryogenesis, as well as pathological processes, such as cancer invasiveness and endothelial senescence. However, the results of single-cell mechanical measurements vary considerably, not only due to systematic instrumental errors but also due to the dynamic and non-homogenous nature of the sample. In this work, relying on Chiaro nanoindenter (Optics11Life), we characterized in depth the nanoindentation experimental procedure, in order to highlight whether and how experimental conditions could affect measurements of living cell stiffness. We demonstrated that the procedure can be quite insensitive to technical replicates and that several biological conditions, such as cell confluency, starvation and passage, significantly impact the results. Experiments should be designed to maximally avoid inhomogeneous scenarios to avoid divergences in the measured phenotype.

Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma

Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.

Identification of living and dead microalgae cells with digital holography and verified in the East China Sea

The death of microalgae plays an important role in ocean energy flow and material circulation. The existing methods for the identification of dead and living microalgae cells all have defects such as the need for staining and pre-treatment. In this work, a label-free method to identify living and dead algae cells based on digital holography microscopy and machine learning was designed. At the stage of model training, ten feature vectors were extracted from the holograms, and twelve classification models of machine learning algorithm were trained. Compared with the staining method results, the accuracy of this method can reach 94.8%. At the stage of field verification, the death rate calculated by this method was also consistent with staining method. The method proposed in this paper provides a new method for the study of marine microalgae death which has the advantages of label-free, non-invasive, high accuracy and potential for in-situ application.

Cell and nucleus refractive-index mapping by interferometric phase microscopy and rapid confocal fluorescence microscopy

We present a multimodal technique for measuring the integral refractive index and the thickness of biological cells and their organelles by integrating interferometric phase microscopy (IPM) and rapid confocal fluorescence microscopy. First, the actual thickness maps of the cellular compartments are reconstructed using the confocal fluorescent sections, and then the optical path difference (OPD) map of the same cell is reconstructed using IPM. Based on the co-registered data, the integral refractive index maps of the cell and its organelles are calculated. This technique enables rapidly measuring refractive index of live, dynamic cells, where IPM provides quantitative imaging capabilities and confocal fluorescence microscopy provides molecular specificity of the cell organelles. We acquire human colorectal adenocarcinoma cells and show that the integral refractive index values are similar for the whole cell, the cytoplasm and the nucleus on the population level, but significantly different on the single cell level.

Whole phase curvature-based particle positioning and size determination by digital holography

We demonstrate the positioning and characterization of a transparent particle with a diameter of 60 µm in sparse particle fields. Particles appear elongated in optical setups with small numerical apertures that are used in digital holography; thus, an accurate method to position them is required. We propose a new optimization method using the whole phase curvature of a reconstructed wave along the optical axis to obtain not only the precise axial position but also the radius and refractive index of a particle. Experimental results show that the axial positions of particles can be detected with a standard deviation of 38.6 µm, corresponding to 66% of the average particle diameter. A radius of 29.3±0.4µm and a refractive index of 1.5910±0.0017 agree well with the manufacturer specifications of particles.

Deep-learning projector for optical diffraction tomography

Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been derived by the minimization of an objective function in which the data-fidelity term encourages measurement consistency while the regularization term enforces prior constraints. In this work, we propose to train a convolutional neural network (CNN) as the projector in a projected-gradient-descent method. We iteratively produce high-quality estimates and ensure measurement consistency, thus keeping the best of CNN-based and regularization-based worlds. Our experiments on two-dimensional-simulated and real data show an improvement over other conventional or deep-learning-based methods. Furthermore, our trained CNN projector is general enough to accommodate various forward models for the handling of multiple-scattering events.

How a phase image of a cell with nucleus refractive index smaller than that of the cytoplasm should look like?: A Comment on two papers by Steelman et al. and Schürmann et al. Read the Responses to this Comment: e201800091 and e201800095

In recent papers Steelman et al. ("Is the nuclear refractive index lower than cytoplasm? Validation of phase measurements and implications for light scattering technologies") and Schürmann et al. ("Cell nuclei have lower refractive index and mass density than cytoplasm") obtained quantitative phase images of whole cells of various types and corresponding isolated nuclei and concluded that the refractive index (RI) of the nucleus is significantly smaller than that of the cytoplasm. The comment shows that this conclusion and assumptions used in retrieving the RI necessarily imply a characteristic dip in the center of the whole-cell phase images. This dip is not present in any of the phase images in the discussed papers, which is a strong argument against the conclusion of smaller nucleus RI. It is also discussed whether a different processing of the phase images can help to clarify this issue.

Accurate evaluation of size and refractive index for spherical objects in quantitative phase imaging

Measuring the average refractive index (RI) of spherical objects, such as suspended cells, in quantitative phase imaging (QPI) requires a decoupling of RI and size from the QPI data. This has been commonly achieved by determining the object's radius with geometrical approaches, neglecting light-scattering. Here, we present a novel QPI fitting algorithm that reliably uncouples the RI using Mie theory and a semi-analytical, corrected Rytov approach. We assess the range of validity of this algorithm in silico and experimentally investigate various objects (oil and protein droplets, microgel beads, cells) and noise conditions. In addition, we provide important practical cues for the analysis of spherical objects in QPI.

Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography

Cells alter the path of light, a fact that leads to well-known aberrations in single cell or tissue imaging. Optical diffraction tomography (ODT) measures the biophysical property that causes these aberrations, the refractive index (RI). ODT is complementary to fluorescence imaging and does not require any markers. The present study introduces RI and fluorescence tomography with optofluidic rotation (RAFTOR) of suspended cells, facilitating the segmentation of the 3D-correlated RI and fluorescence data for a quantitative interpretation of the nuclear RI. The technique is validated with cell phantoms and used to confirm a lower nuclear RI for HL60 cells. Furthermore, the nuclear inversion of adult mouse photoreceptor cells is observed in the RI distribution. The applications shown confirm predictions of previous studies and illustrate the potential of RAFTOR to improve our understanding of cells and tissues.

Integral refractive index imaging of flowing cell nuclei using quantitative phase microscopy combined with fluorescence microscopy

We suggest a new multimodal imaging technique for quantitatively measuring the integral (thickness-average) refractive index of the nuclei of live biological cells in suspension. For this aim, we combined quantitative phase microscopy with simultaneous 2-D fluorescence microscopy. We used 2-D fluorescence microscopy to localize the nucleus inside the quantitative phase map of the cell, as well as for measuring the nucleus radii. As verified offline by both 3-D confocal fluorescence microscopy and 2-D fluorescence microscopy while rotating the cells during flow, the nucleus of cells in suspension that are not during division can be assumed to be an ellipsoid. The entire shape of a cell in suspension can be assumed to be a sphere. Then, the cell and nucleus 3-D shapes can be evaluated based on their in-plain radii available from the 2-D phase and fluorescent measurements, respectively. Finally, the nucleus integral refractive index profile is calculated. We demonstrate the new technique on cancer cells, obtaining nucleus refractive index values that are lower than those of the cytoplasm, coinciding with recent findings. We believe that the proposed technique has the potential to be used for flow cytometry, where full 3-D refractive index tomography is too slow to be implemented during flow.

Mechanical mismatch between Ras transformed and untransformed epithelial cells

The organization of the actin cytoskeleton plays a key role in regulating cell mechanics. It is fundamentally altered during transformation, affecting how cells interact with their environment. We investigated mechanical properties of cells expressing constitutively active, oncogenic Ras (Ras^V12) in adherent and suspended states. To do this, we utilized atomic force microscopy and a microfluidic optical stretcher. We found that adherent cells stiffen and suspended cells soften with the expression of constitutively active Ras. The effect on adherent cells was reversed when contractility was inhibited with the ROCK inhibitor Y-27632, resulting in softer Ras^V12 cells. Our findings suggest that increased ROCK activity as a result of Ras has opposite effects on suspended and adhered cells. Our results also establish the importance of the activation of ROCK by Ras and its effect on cell mechanics.

Optical influence of oil droplets on cone photoreceptor sensitivity

Oil droplets are spherical organelles found in the cone photoreceptors of vertebrates. They are generally assumed to focus incident light into the outer segment, and thereby improve light catch because of the droplets' spherical lens-like shape. However, using full-wave optical simulations of physiologically realistic cone photoreceptors from birds, frogs and turtles, we find that pigmented oil droplets actually drastically reduce the transmission of light into the outer segment integrated across the full visible wavelength range of each species. Only transparent oil droplets improve light catch into the outer segments, and any enhancement is critically dependent on the refractive index, diameter of the oil droplet, and diameter and length of the outer segment. Furthermore, oil droplets are not the only optical elements found in cone inner segments. The ellipsoid, a dense aggregation of mitochondria situated immediately prior to the oil droplet, mitigates the loss of light at the oil droplet surface. We describe a framework for integrating these optical phenomena into simple models of receptor sensitivity, and the relevance of these observations to evolutionary appearance and loss of oil droplets is discussed.

Investigation on dynamics of red blood cells through their behavior as biophotonic lenses

The possibility to adopt biological matter as photonic optical elements can open scenarios in biophotonics research. Recently, it has been demonstrated that a red blood cell (RBC) can be seen as an optofluidic microlens by showing its imaging capability as well as its focal tunability. Moreover, correlation between an RBC’s morphology and its behavior as a refractive optical element has been established and its exploitation for biomedical diagnostic purposes has been foreseen. In fact, any deviation from the healthy RBC morphology can be seen as additional aberration in the optical wavefront passing through the cell. By this concept, accurate localization of focal spots of RBCs can become very useful in the blood disorders identification. We investigate the three-dimensional positioning of such focal spots over time for samples with two different osmolarity conditions, i.e., when they assume discocyte and spherical shapes, respectively. We also demonstrate that a temporal variation of an RBC’s focal points along the optical axis is correlated to the temporal fluctuations in the RBC’s thickness maps. Furthermore, we show a sort of synchronization of the whole erythrocytes ensemble.

Cell nuclei have lower refractive index and mass density than cytoplasm

Common perception regards the nucleus as a densely packed object with higher refractive index (RI) and mass density than the surrounding cytoplasm. Here, the volume of isolated nuclei is systematically varied by electrostatic and osmotic conditions as well as drug treatments that modify chromatin conformation. The refractive index and dry mass of isolated nuclei is derived from quantitative phase measurements using digital holographic microscopy (DHM). Surprisingly, the cell nucleus is found to have a lower RI and mass density than the cytoplasm in four different cell lines and throughout the cell cycle. This result has important implications for conceptualizing light tissue interactions as well as biological processes in cells.

ODTbrain: a Python library for full-view, dense diffraction tomography

BACKGROUND

Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far.

RESULTS

We present a Python library that implements the backpropagation algorithm for diffraction tomography in 3D. By establishing benchmarks based on finite-difference time-domain (FDTD) simulations, we showcase the superiority of the backpropagation algorithm over the backprojection algorithm. Furthermore, we discuss how measurment parameters influence the reconstructed refractive index distribution and we also give insights into the applicability of diffraction tomography to biological cells.

CONCLUSION

The present software library contains a robust implementation of the backpropagation algorithm. The algorithm is ideally suited for the application to biological cells. Furthermore, the implementation is a drop-in replacement for the classical backprojection algorithm and is made available to the large user community of the Python programming language.