Quantitative Phase Imaging Data Research Articles

Antiproliferative and Morphological Effects of Fenretinide Lipid Nanosystems in Colon Adenocarcinoma Cells

Objective: Colon adenocarcinoma is characterized by the downregulation of the retinoic acid receptor, making natural retinoids such as all-trans retinoic acid, 9-cis retinoic acid and 13-cis retinoic acid effective in treatment and chemoprevention due to their ability to increase RARβ expression. However, major limitations to their use include tolerability and acquired resistance. In this study, we evaluated fenretinide, a semisynthetic derivative of all-trans retinoic acid, in an HT-29 cell line. Fenretinide was evaluated both as a free drug and encapsulated in self-assembling phosphatidylcholine nanosystems with the aim of increasing the aqueous solubility and cell availability of the drug. Methods: Fenretinide was encapsulated in lipid nanosystems obtained in water by the dispersion of an amphiphilic mixture of phospholipids, glyceryl tributyrate and polysorbate 80. The physico-chemical characterization of the nanosystems was carried out by dynamic light scattering and spectrophotometry. The biological activity was evaluated by quantitative phase imaging microscopy, MTT assay, flow cytometry and confocal laser-scanning fluorescence microscopy. Results: Fenretinide in phosphatidylcholine nanosystems was more active than free fenretinide in inhibiting HT-29 cells’ proliferation, as indicated by quantitative phase imaging data. Indeed, encapsulated fenretinide increased duplication time, decreased dry mass and decreased the rate of cell growth more efficiently than fenretinide. Moreover, encapsulated fenretinide effectively decreased the motility of the cells that survived the treatment. Conclusions: The results indicate that the proposed nanosystems can be considered a valuable alternative to natural retinoids in the chemoprevention and treatment of colorectal cancer. This is due to the favorable pharmacologic characteristics of fenretinide in colorectal cancer and the improved drug activity provided by nanoencapsulation.

Comparison of automated and manual intracellular particle tracking using quantitative phase imaging

Transport within cells is commonly studied using particle tracking methods. However, these typically require either labeling or identification of specific organelles that can be identified and tracked from label-free imaging modalities, limiting application of this approach. Quantitative phase imaging (QPI) provides dynamic data on the redistribution of mass within live cells, potentially enabling broader application of particle tracking methods. In previous work, we developed quantitative phase velocimetry (QPV) to automatically track the motion of subcellular control volumes from QPI data. However, the relationship of QPV to traditional particle tracking methods has not been established. Here, we directly compare QPV to manual particle tracking across multiple drug treatment conditions. We find that QPV effective diffusivity is correlated with diffusivity measured from manual particle tracking. The differences between QPV and manual tracking are explained by the difference in effective size of particles tracked by QPV. Overall, these data indicate that automated tracking of the motion of cellular mass from QPI data can effectively be used to characterize effective diffusivity within living cells.

Label-Free Digital Holographic Microscopy for In Vitro Cytotoxic Effect Quantification of Organic Nanoparticles.

Cytotoxicity quantification of nanoparticles is commonly performed by biochemical assays to evaluate their biocompatibility and safety. We explored quantitative phase imaging (QPI) with digital holographic microscopy (DHM) as a time-resolved in vitro assay to quantify effects caused by three different types of organic nanoparticles in development for medical use. Label-free proliferation quantification of native cell populations facilitates cytotoxicity testing in biomedical nanotechnology. Therefore, DHM quantitative phase images from measurements on nanomaterial and control agent incubated cells were acquired over 24 h, from which the temporal course of the cellular dry mass was calculated within the observed field of view. The impact of LipImage™ 815 lipidots® nanoparticles, as well as empty and cabazitaxel-loaded poly(alkyl cyanoacrylate) nanoparticles on the dry mass development of four different cell lines (RAW 264.7, NIH-3T3, NRK-52E, and RLE-6TN), was observed vs. digitonin as cytotoxicity control and cells in culture medium. The acquired QPI data were compared to a colorimetric cell viability assay (WST-8) to explore the use of the DHM assay with standard biochemical analysis methods downstream. Our results show that QPI with DHM is highly suitable to identify harmful or low-toxic nanomaterials. The presented DHM assay can be implemented with commercial microscopes. The capability for imaging of native cells and the compatibility with common 96-well plates allows high-throughput systems and future embedding into existing experimental routines for in vitro cytotoxicity assessment.

Quantifying Subcellular Growth Dynamics using Quantitative Phase Imaging

Unregulated growth has a drastic effect on the health of organisms. Unbalanced biomass production and degradation within cells can lead to diseases including diabetes, cancer, and organ hypertrophy. Understanding such growth dysregulations and the development of treatments requires understanding growth. Average growth of cell populations can be measured based on proliferation, while more advanced growth measurement approaches study single cells and can extract cell-to-cell heterogeneity information. We have developed a technique that extends further to measure localization of growth within single cells, using quantitative phase imaging (QPI) microscopy technique.QPI measures the phase shift of light as it passes through a sample, which is linearly proportional to cell biomass in the light path. As a result, QPI data show the changing biomass distribution within cells. Cell growth is commonly measured by QPI as the rate of change of biomass in whole cells over time. This approach has previously been shown to rapidly measure single cell growth and growth inhibition by drugs or therapies. Our work expands the scope of QPI further with an algorithm that utilizes the biomass distribution data to output a discretized subcellular growth map.Our approach is based on solution of a mass balance that considers biomass advection in subcellular regions. Observed changes in mass within each region of the cell can be caused by either advection of mass or biosynthesis/degradation. We can directly measure the change in biomass in each subcellular volume using QPI. We then measure the velocity of mass movement using particle image velocimetry on QPI images. Combining these two allows us to measure the amount of biosynthesis in each subcellular region. Our approach visualizes subcellular growth dynamics in real-time and quantify intracellular growth inhibition in response to drug treatments targeting cell growth mechanisms.

Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data

Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.

The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death

Cell viability and cytotoxicity assays are highly important for drug screening and cytotoxicity tests of antineoplastic or other therapeutic drugs. Even though biochemical-based tests are very helpful to obtain preliminary preview, their results should be confirmed by methods based on direct cell death assessment. In this study, time-dependent changes in quantitative phase-based parameters during cell death were determined and methodology useable for rapid and label-free assessment of direct cell death was introduced. The goal of our study was distinction between apoptosis and primary lytic cell death based on morphologic features. We have distinguished the lytic and non-lytic type of cell death according to their end-point features (Dance of Death typical for apoptosis versus swelling and membrane rupture typical for all kinds of necrosis common for necroptosis, pyroptosis, ferroptosis and accidental cell death). Our method utilizes Quantitative Phase Imaging (QPI) which enables the time-lapse observation of subtle changes in cell mass distribution. According to our results, morphological and dynamical features extracted from QPI micrographs are suitable for cell death detection (76% accuracy in comparison with manual annotation). Furthermore, based on QPI data alone and machine learning, we were able to classify typical dynamical changes of cell morphology during both caspase 3,7-dependent and -independent cell death subroutines. The main parameters used for label-free detection of these cell death modalities were cell density (pg/pixel) and average intensity change of cell pixels further designated as Cell Dynamic Score (CDS). To the best of our knowledge, this is the first study introducing CDS and cell density as a parameter typical for individual cell death subroutines with prediction accuracy 75.4% for caspase 3,7-dependent and -independent cell death.

Shear Modulus Measurement by Quantitative Phase Imaging and Correlation with Atomic Force Microscopy

Many approaches have been developed to characterize cell elasticity. Among these, atomic force microscopy (AFM) combined with modeling has been widely used to characterize cellular compliance. However, such approaches are often limited by the difficulties associated with using a specific instrument and by the complexity of analyzing the measured data. More recently, quantitative phase imaging (QPI) has been applied to characterize cellular stiffness by using an effective spring constant. This metric was further correlated to mass distribution (disorder strength) within the cell. However, these measurements are difficult to compare to AFM-derived measurements of Young’s modulus. Here, we describe, to our knowledge, a new way of analyzing QPI data to directly retrieve the shear modulus. Our approach enables label-free measurement of cellular mechanical properties that can be directly compared to values obtained from other rheological methods. To demonstrate the technique, we measured shear modulus and phase disorder strength using QPI, as well as Young’s modulus using AFM, across two breast cancer cell-line populations dosed with three different concentrations of cytochalasin D, an actin-depolymerizing toxin. Comparison of QPI-derived and AFM moduli shows good agreement between the two measures and further agrees with theory. Our results suggest that QPI is a powerful tool for cellular biophysics because it allows for optical quantitative measurements of cell mechanical properties.

Quantitative Phase Imaging and Artificial Intelligence: A Review

Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have opened up the possibility of an exciting frontier. The fast and label-free nature of QPI enables the rapid generation of large-scale and uniform-quality imaging data in two, three, and four dimensions. Subsequently, the AI-assisted interrogation of QPI data using data-driven machine learning techniques results in a variety of biomedical applications. Also, machine learning enhances QPI itself. Herein, we review the synergy between QPI and machine learning with a particular focus on deep learning. Further, we provide practical guidelines and perspectives for further development.

Molecular and biophysical analysis of apoptosis using a combined quantitative phase imaging and fluorescence resonance energy transfer microscope.

Apoptotic mechanisms are often dysregulated in cancerous phenotypes. Additionally, many anticancer treatments induce apoptosis and necrosis, and the monitoring of this apoptotic activity can allow researchers to identify therapeutic efficiency. Here, we introduce a microscope which combines quantitative phase imaging (QPI) with the ability to detect molecular events via fluorescence (or Förster) resonance energy transfer (FRET). The system was applied to study cells undergoing apoptosis to correlate the onset of apoptotic enzyme activity as observed using a FRET-based apoptosis sensor with whole cell morphological changes analyzed via QPI. The QPI data showed changes in cell disorder strength during the initiation of apoptotic enzymatic activity.

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.

Hybrid Random Walk-Linear Discriminant Analysis Method for Unwrapping Quantitative Phase Images of Biological Samples

Quantitative phase imaging (QPI) of biological specimens is an emerging approach that uses the phase shift of light as it passes through living cells to extract biophysical properties, such as cell dry mass and the intracellular distribution of mass. Previous QPI applications include measurement of red blood cell mechanical properties and drug resistance of a heterogeneous cancer cell population. An essential part of processing any QPI dataset is phase unwrapping, the removal of phase ambiguities inherent to QPI. Classical phase unwrapping algorithms, such as the widely-used Goldstein algorithm, are often inadequate for biological samples, due to cells’ varying morphology and because cell optical pathlength often exceeds one wavelength of visible light. We present a new phase unwrapping algorithm, the hybrid random walk-linear discriminant analysis (HRL) method, for correcting phase ambiguities where standard algorithms have failed. HRL makes two key assumptions about QPI biological data based on previous observations: cell optical path length is never less than zero; and uncorrected regions are predominantly one wavelength lower than the correct level. These constraints are applied to Goldstein-unwrapped QPI data using a biased random walk based on a linear discriminant analysis (LDA)-generated image, which emphasizes the edges of cells and phase-wrapped regions. A genetic algorithm is then applied to fine-tune the LDA coefficients. The HRL method results in highly accurate (area under curve = 0.999) phase unwrapping of biological QPI data. Our ongoing work measures mass changes in cytotoxic T cells and target cancer cells using accurate phase unwrapping to identify T cells capable of tumor-specific killing for use in cancer immunotherapy. The general procedure of image segmentation based on HRL enables precise identification of cell edges and may have applications for many other imaging approaches beyond QPI techniques.

Hybrid random walk-linear discriminant analysis method for unwrapping quantitative phase microscopy images of biological samples.

Standard algorithms for phase unwrapping often fail for interferometric quantitative phase imaging (QPI) of biological samples due to the variable morphology of these samples and the requirement to image at low light intensities to avoid phototoxicity. We describe a new algorithm combining random walk-based image segmentation with linear discriminant analysis (LDA)-based feature detection, using assumptions about the morphology of biological samples to account for phase ambiguities when standard methods have failed. We present three versions of our method: first, a method for LDA image segmentation based on a manually compiled training dataset; second, a method using a random walker (RW) algorithm informed by the assumed properties of a biological phase image; and third, an algorithm which combines LDA-based edge detection with an efficient RW algorithm. We show that the combination of LDA plus the RW algorithm gives the best overall performance with little speed penalty compared to LDA alone, and that this algorithm can be further optimized using a genetic algorithm to yield superior performance for phase unwrapping of QPI data from biological samples.