Digital holographic microscopy: a noninvasive method to analyze the formation of spheroids

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.

Simple and automatic monitoring of cancer cell invasion into an epithelial monolayer using label-free holographic microscopy

The invasiveness of cancer cells describes the metastasizing capability of a primary tumor. The straightforward detection and quantification of cancer cell invasion are important to predict the survival rate of a cancer patient and to test how anti-cancer compounds influence cancer progression. Digital holographic microscopy based M4 Holomonitor (HM) is a technique that allows the label-free monitoring of cell morphological and kinetical parameters in real-time. Here, a fully confluent epithelial monolayer derived from the African green monkey kidney (Vero) on a gelatin-coated surface was established, then HeLa cells were seeded on top of the monolayer, and their behavior was monitored for 24 h using HM. Several cancer cells showing invasiveness were detected during this period, while other HeLa cells did not show any signs of aggressivity. It was demonstrated that the invasion of single cancer cells is soundly observable and also quantifiable through monitoring parameters such as phase shift, optical volume, area, and motility, which parameters can easily be obtained and processed automatically. Based on the experimental data, the invasion speed of cancer cells entering the epithelial layer can be defined as the shrinking of detected single-cell volume per unit time. The invasion speed and its correlation with cell migration parameters were analyzed in depth. A clear linear relationship between migration and invasion speed was found, cancer cells with stronger migration have slower invasion speed. These results not only describe the effect of how cancer cells invade the underlying monolayer in contrast to non-invasive HeLa cells, but could help in future research to optimize drugs affecting cell invasibility in a fully automated, label-free and high-throughput manner.