Digital holographic microscopy evaluation of dynamic cell response to electroporation

An experimental and theoretical study on cell swelling for osmotic imbalance induced by electroporation

The cellular membrane serves as a pivotal barrier in regulating intra- and extracellular matter exchange. Disruption of this barrier through pulsed electric fields (PEFs) induces the transmembrane transport of ions and molecules, creating a concentration gradient that subsequently results in the imbalance of cellular osmolality. In this study, a multiphysics model was developed to simulate the electromechanical response of cells exposed to microsecond pulsed electric fields (μsPEFs). Within the proposed model, the diffusion coefficient of the cellular membrane for various ions was adjusted based on electropore density. Cellular osmolality was governed and described using Van't Hoff theory, subsequently converted to loop stress to dynamically represent the cell swelling process. Validation of the model was conducted through a hypotonic experiment and simulation at 200 mOsm/kg, revealing a 14.2% increase in the cell's equivalent radius, thereby confirming the feasibility of the cell mechanical model. With the transmembrane transport of ions induced by the applied μsPEF, the hoop stress acting on the cellular membrane reached 179.95 Pa, and the cell equivalent radius increased by 11.0% when the extra-cellular medium was supplied with normal saline. The multiphysics model established in this study accurately predicts the dynamic changes in cell volume resulting from osmotic imbalance induced by PEF action. This model holds theoretical significance, offering valuable references for research on drug delivery and tumor microenvironment modulation.

Grading of glioma tumors using digital holographic microscopy

Gliomas are the most common type of cerebral tumors; they occur with increasing incidence in the last decade and have a high rate of mortality. For efficient treatment, fast accurate diagnostic and grading of tumors are imperative. Presently, the grading of tumors is established by histopathological evaluation, which is a time-consuming procedure and relies on the pathologists' experience. Here we propose a supervised machine learning procedure for tumor grading which uses quantitative phase images of unstained tissue samples acquired by digital holographic microscopy. The algorithm is using an extensive set of statistical and texture parameters computed from these images. The procedure has been able to classify six classes of images (normal tissue and five glioma subtypes) and to distinguish between gliomas types from grades II to IV (with the highest sensitivity and specificity for grade II astrocytoma and grade III oligodendroglioma and very good scores in recognizing grade III anaplastic astrocytoma and grade IV glioblastoma). The procedure bolsters clinical diagnostic accuracy, offering a swift and reliable means of tumor characterization and grading, ultimately the enhancing treatment decision-making process.

Rapid and precise tracking of water influx and efflux across cell membranes induced by a pulsed electric field

Quantitative measurements of water content within a single cell are notoriously difficult. In this work, we introduce a single-shot optical method for tracking the intracellular water content, by mass and volume, of a single cell at video rate. We utilize quantitative phase imaging and a priori knowledge of a spherical cellular geometry, leveraging a two-component mixture model to compute the intracellular water content. We apply this technique to study CHO-K1 cells responding to a pulsed electric field, which induces membrane permeabilization and rapid water influx or efflux depending upon the osmotic environment. The effects of mercury and gadolinium on water uptake in Jurkat cells following electropermeabilization are also examined.

Quantitative phase microscopy monitors subcellular dynamics in single cells exposed to nanosecond pulsed electric fields

A substantial body of literature exists to study the dynamics of single cells exposed to short duration (<1 μs), high peak power (~1 MV/m) transient electric fields. Much of this research is limited to traditional fluorescence-based microscopy techniques, which introduce exogenous agents to the culture and are only sensitive to a single molecular target. Quantitative phase imaging (QPI) is a coherent imaging modality which uses optical path length as a label-free contrast mechanism, and has proven highly effective for the study of single-cell dynamics. In this work, we introduce QPI as a useful imaging tool for the study of cells undergoing cytoskeletal remodeling after nanosecond pulsed electric field (nsPEF) exposure. In particular, we use cell swelling, dry mass and disorder strength measurements derived from QPI phase images to monitor the cellular response to nsPEFs. We hope this demonstration of QPI's utility will lead to a further adoption of the technique for the study of directed energy bioeffects.

Self-supervised pretraining for transferable quantitative phase image cell segmentation

In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.