DryMass: handling and analyzing quantitative phase microscopy images of spherical, cell-sized objects

The molecular basis of tumor metastasis and current approaches to decode targeted migration-promoting events in pediatric neuroblastoma

Cell motility is a crucial biological process that plays a critical role in the development of multicellular organisms and is essential for tissue formation and regeneration. However, uncontrolled cell motility can lead to the development of various diseases, including neoplasms. In this review, we discuss recent advances in the discovery of regulatory mechanisms underlying the metastatic spread of neuroblastoma, a solid pediatric tumor that originates in the embryonic migratory cells of the neural crest. The highly motile phenotype of metastatic neuroblastoma cells requires targeting of intracellular and extracellular processes, that, if affected, would be helpful for the treatment of high-risk patients with neuroblastoma, for whom current therapies remain inadequate. Development of new potentially migration-inhibiting compounds and standardized preclinical approaches for the selection of anti-metastatic drugs in neuroblastoma will also be discussed.

Fabrication and validation of an LED array microscope for multimodal, quantitative imaging

The combination of multiple imaging modalities in a single microscopy system can enable new insights into biological processes. In this work, we describe the construction and rigorous characterization of a custom microscope with multimodal imaging in a single, cost-effective system. Our design utilizes advances in LED technology, robotics, and open-source software, along with existing optical components and precision optomechanical parts to offer a modular and versatile design. This microscope is operated using software written in Arduino and Python and has the ability to run multi-day automated imaging experiments when placed inside of a cell culture incubator. Additionally, we provide and demonstrate methods to validate images taken in brightfield and darkfield, along with validation and optimization for differential phase contrast (DPC) quantitative phase imaging.

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine

Quantitative phase imaging (QPI) is a label-free, wide-field microscopy approach with significant opportunities for biomedical applications. QPI uses the natural phase shift of light as it passes through a transparent object, such as a mammalian cell, to quantify biomass distribution and spatial and temporal changes in biomass. Reported in cell studies more than 60 years ago, ongoing advances in QPI hardware and software are leading to numerous applications in biology, with a dramatic expansion in utility over the past two decades. Today, investigations of cell size, morphology, behavior, cellular viscoelasticity, drug efficacy, biomass accumulation and turnover, and transport mechanics are supporting studies of development, physiology, neural activity, cancer, and additional physiological processes and diseases. Here, we review the field of QPI in biology starting with underlying principles, followed by a discussion of technical approaches currently available or being developed, and end with an examination of the breadth of applications in use or under development. We comment on strengths and shortcomings for the deployment of QPI in key biomedical contexts and conclude with emerging challenges and opportunities based on combining QPI with other methodologies that expand the scope and utility of QPI even further.

Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer

Quantitative phase imaging (QPI) measures the growth rate of individual cells by quantifying changes in mass versus time. Here, we use the breast cancer cell lines MCF-7, BT-474, and MDA-MB-231 to validate QPI as a multiparametric approach for determining response to single-agent therapies. Our method allows for rapid determination of drug sensitivity, cytotoxicity, heterogeneity, and time of response for up to 100,000 individual cells or small clusters in a single experiment. We find that QPI EC₅₀ values are concordant with CellTiter-Glo (CTG), a gold standard metabolic endpoint assay. In addition, we apply multiparametric QPI to characterize cytostatic/cytotoxic and rapid/slow responses and track the emergence of resistant subpopulations. Thus, QPI reveals dynamic changes in response heterogeneity in addition to average population responses, a key advantage over endpoint viability or metabolic assays. Overall, multiparametric QPI reveals a rich picture of cell growth by capturing the dynamics of single-cell responses to candidate therapies.

PNIPAAm microgels with defined network architecture as temperature sensors in optical stretchers

Stretching individual living cells with light is a standard method to assess their mechanical properties. Yet, heat introduced by the laser light of optical stretchers may unwittingly change the mechanical properties of cells therein. To estimate the temperature induced by an optical trap, we introduce cell-sized, elastic poly(N-isopropylacrylamide) (PNIPAAm) microgels that relate temperature changes to hydrogel swelling. For their usage as a standardized calibration tool, we analyze the effect of free-radical chain-growth gelation (FCG) and polymer-analogous photogelation (PAG) on hydrogel network heterogeneity, micromechanics, and temperature response by Brillouin microscopy and optical diffraction tomography. Using a combination of tailor-made PNIPAAm macromers, PAG, and microfluidic processing, we obtain microgels with homogeneous network architecture. With that, we expand the capability of standardized microgels in calibrating and validating cell mechanics analysis, not only considering cell and microgel elasticity but also providing stimuli-responsiveness to consider dynamic changes that cells may undergo during characterization.

HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model

Quantitative phase imaging with off-axis digital holography in a microscopic configuration provides insight into the cells' intracellular content and morphology. This imaging is conventionally achieved by numerical reconstruction of the recorded hologram, which requires the precise setting of the reconstruction parameters, including reconstruction distance, a proper phase unwrapping algorithm, and component of wave vectors. This paper shows that deep learning can perform the complex light propagation task independent of the reconstruction parameters. We also show that the super-imposed twin-image elimination technique is not required to retrieve the quantitative phase image. The hologram at the single-cell level is fed into a trained image generator (part of a conditional generative adversarial network model), which produces the phase image. Also, the model's generalization is demonstrated by training it with holograms of size 512×512 pixels, and the resulting quantitative analysis is shown.

Spatio-temporal performance in an incoherent holography lattice light-sheet microscope (IHLLS)

We propose an Incoherent holography detection technique for lattice light-sheet (IHLLS) systems for 3D imaging without moving either the sample stage or the detection microscope objective, providing intrinsic instrumental simplicity and high accuracy when compared to the original LLS schemes. The approach is based on a modified dual-lens Fresnel Incoherent Correlation Holography technique to produce a complex hologram and to provide the focal distance needed for the hologram reconstruction. We report such an IHLLS microscope, including characterization of the sensor performance, and demonstrate a significant contrast improvement on beads and neuronal structures within a biological test sample as well as quantitative phase imaging. The IHLLS has similar or better transverse performances when compared to the LLS technique. In addition, the IHLLS allows for volume reconstruction from fewer z-galvo displacements, thus facilitating faster volume acquisition.