Label-free, non-invasive, and repeatable cell viability bioassay using dynamic full-field optical coherence microscopy and supervised machine learning

Stain-Free Approach to Determine and Monitor Cell Heath Using Supervised and Unsupervised Image-Based Deep Learning

Cell-based medicinal products (CBMPs) are a growing class of therapeutics that promise new treatments for complex and rare diseases. Given the inherent complexity of the whole human cells comprising CBMPs, there is a need for robust and fast analytical methods for characterization, process monitoring, and quality control (QC) testing during their manufacture. Existing techniques to evaluate and monitor cell quality typically constitute labor-intensive, expensive, and highly specific staining assays. In this work, we combine image-based deep learning with flow imaging microscopy (FIM) to predict cell health metrics using cellular morphology "fingerprints" extracted from images of unstained Jurkat cells (immortalized human T-lymphocyte cells). A supervised (i.e., algorithm trained with human-generated labels for images) fingerprinting algorithm, trained on images of unstained healthy and dead cells, provides a robust stain-free, non-invasive, and non-destructive method for determining cell viability. Results from the stain-free method are in good agreement with traditional stain-based cytometric viability measurements. Additionally, when trained with images of healthy cells, dead cells and cells undergoing chemically induced apoptosis, the supervised fingerprinting algorithm is able to distinguish between the three cell states, and the results are independent of specific treatments or signaling pathways. We then show that an unsupervised variational autoencoder (VAE) algorithm trained on the same images, but without human-generated labels, is able to distinguish between samples of healthy, dead and apoptotic cells along with cellular debris based on learned morphological features and without human input. With this, we demonstrate that VAEs are a powerful exploratory technique that can be used as a process monitoring analytical tool.

Dynamic contrast optical coherence tomography (DyC-OCT) for label-free live cell imaging

Dynamic contrast optical coherence tomography (DyC-OCT), an emerging imaging method, utilizes fluctuation patterns in OCT signals to enhance contrast, thereby enabling non-invasive label-free volumetric live cell imaging. In this mini review, we explain the core concepts behind DyC-OCT image formation and its system configurations, serving as practical guidance for future DyC-OCT users. Subsequently, we explore its applications in delivering high-quality, contrast-enhanced images of cellular morphology, as well as in monitoring changes in cellular activity/viability assay experiments.

Comparison of cell viability methods for human mesenchymal/stromal stem cells and human A549 lung carcinoma cells after freeze-thaw stress

For the safety and efficacy of frozen cell therapy products, determination of cellular viability is key. However, results of cell viability measurements do not only depend on the cell line or on the inflicted stress, but also on the assay used, making inter-experimental comparisons difficult. The aim of this study was thus to assess commonly used viability assays in clinically relevant human mesenchymal/stromal stem cells and human A549 lung carcinoma cells. Post freeze-thaw stress viability and proliferation were evaluated under different conditions using trypan blue, acridine orange/DAPI stain, alamarBlue, ATP, and neutral red assays. Significant differences in cell viability between metabolic assays were observed, likely due to their distinct intrinsic detection mechanisms. Membrane-integrity based assays generally overestimated cell viabilities in this study. Furthermore, noticeable differences in inter-assay sensitivities were observed. These differences highlight that cell viability methods should be meticulously selected and their associated results carefully interpreted in a relevant context to ensure reliable conclusions. Indeed, although cell membrane integrity based assays are a popular choice to determine cellular quality attributes after freezing and thawing, we demonstrate that metabolic assays may be more suitable in this context.

Dynamic full-field optical coherence tomography module adapted to commercial microscopes allows longitudinal in vitro cell culture study

Dynamic full-field optical coherence tomography (D-FFOCT) has recently emerged as a label-free imaging tool, capable of resolving cell types and organelles within 3D live samples, whilst monitoring their activity at tens of milliseconds resolution. Here, a D-FFOCT module design is presented which can be coupled to a commercial microscope with a stage top incubator, allowing non-invasive label-free longitudinal imaging over periods of minutes to weeks on the same sample. Long term volumetric imaging on human induced pluripotent stem cell-derived retinal organoids is demonstrated, highlighting tissue and cell organization processes such as rosette formation and mitosis as well as cell shape and motility. Imaging on retinal explants highlights single 3D cone and rod structures. An optimal workflow for data acquisition, postprocessing and saving is demonstrated, resulting in a time gain factor of 10 compared to prior state of the art. Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated, allowing rapid organoid screening.

Dynamic optical coherence tomography for cell analysis [Invited]

Label-free live optical imaging of dynamic cellular and subcellular features has been made possible in recent years thanks to the advances made in optical imaging techniques, including dynamic optical coherence tomography (D-OCT) methods. These techniques analyze the temporal fluctuations of an optical signal associated with the active movements of intracellular organelles to obtain an ensemble metric recapitulating the motility and metabolic state of cells. They hence enable visualization of cells within compact, static environments and evaluate their physiology. These emerging microscopies show promise, in particular for the three-dimensional evaluation of live tissue samples such as freshly excised biopsies and 3D cell cultures. In this review, we compare the various techniques used for dynamic OCT. We give an overview of the range of applications currently being explored and discuss the future outlook and opportunities for the field.

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures

Background

In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays.

Methods

We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15).

Results

Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time.

Conclusions

Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Biological Cells as Natural Biophotonic Devices: Fundamental and Applications-introduction to the feature issue

This feature issue of Biomedical Optics Express presents a cross-section of interesting and emerging work of relevance to the use of biological cells or microorganisms in optics and photonics. The technologies demonstrated here aim to address challenges to meeting the optical imaging, sensing, manipulating and therapy needs in a natural or even endogenous manner. This collection of 15 papers includes the novel results on designs of optical systems or photonic devices, image-assisted diagnosis and treatment, and manipulation or sensing methods, with applications for both ex vivo and in vivo use. These works portray the opportunities for exploring the field crossing biology and photonics in which a natural element can be functionalized for biomedical applications.