SignificanceImaging changes in subcellular structure is critical to understanding cell behavior but labeling can be impractical for some specimens and may induce artifacts. Although darkfield microscopy can reveal internal cell structures, it often produces strong signals at cell edges that obscure intracellular details. By optically eliminating the edge signal from darkfield images, we can resolve and quantify changes to cell structure without labeling.AimWe introduce a computational darkfield imaging approach named quadrant darkfield (QDF) to separate smaller cellular features from large structures, enabling label-free imaging of cell organelles and structures in living cells.ApproachUsing a programmable LED array as the illumination source, we vary the direction of illumination to encode additional information about the feature size within cells. This is possible due to the varying levels of directional scattering produced by features based on their sizes relative to the wavelength of light used.ResultsQDF successfully resolved small cellular features without interference from larger structures. QDF signal is more consistent during cell shape changes than traditional darkfield. QDF signals correlate with flow cytometry side scatter measurements, effectively differentiating cells by organelle content.ConclusionsQDF imaging enhances the study of subcellular structures in living cells, offering improved quantification of organelle content compared with darkfield without labels. This method can be simultaneously performed with other techniques such as quantitative phase imaging to generate a multidimensional picture of living cells in real-time.
Functional precision medicine aims to identify effective treatments for patients without targetable mutations. Current research in oncology screens patient-derived models of cancer using endpoint assays. However, these assays fail to capture dynamic responses and tumor heterogeneity. We developed multiparametric quantitative phase imaging (mQPI), a labelfree technique, to screen single cells in real-time. Our work shows mQPI correlates with endpoint assays in measuring drug response in cells from patient derived xenograft organoids (PDxOs), while also capturing time of response and singlecell heterogeneity. Additionally, we demonstrate that mQPI can differentiate organoids that come from the same patient, but which originated from different sites.
Measuring cancer cell response to therapy is an essential component of developing new therapies or determining the optimal treatment for a particular patient. In this talk I will discuss the use of quantitative phase imaging (QPI) to measure the distribution of mass within single cells, and how this distribution changes over time. Based on this approach, my lab has developed new approaches to quantify the time-dependence and intracellular spatial distribution of cell response to small-molecule inhibitors. These data can be used to study basic cell physiology, as well as how cancer cells respond to therapies.
The field of microfluidics provides a robust toolkit for biomedical applications such as disease diagnosis and drug discovery, especially when combined with advanced microscopy techniques. An important challenge facing the combination of microfluidic devices with quantitative microscopy techniques, such as quantitative phase imaging (QPI), is the mismatch in refractive index between channel structures and aqueous media. This mismatch can introduce artifacts at material interfaces due to scattering and, in the case of QPI, phase unwrapping. We will show that these issues can be addressed through the use of MY133-V2000, a UV-curable, fluorinated polymer with a low refractive index similar to water (n = 1.33). MY133-V2000 can be fabricated into microfluidic devices using standard soft lithography techniques based on an SU-8 or polydimethylsiloxane (PDMS) mold. The addition of fluorine reduces the overall polarizability of the material, lowering refractive index. However, this introduces a new challenge due to the typically low adhesion of fluorinated polymers. We will discuss device integration and packaging strategies to overcome this limitation. Using QPI, we will demonstrate measurement of the distribution of cell biomass in live, adherent cells, both in the center of the channel and at the interface with microchannel structures, to demonstrate the dramatic reduction in artifacts due to the matching indices of refraction. We will also discuss applications to other microscopy techniques, including fluorescence. MY133-V2000 therefore provides QPI researchers with the opportunity to leverage the advantages of microfluidics for a diverse range of biomedical applications.
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.
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