Biena Mathew

Formation of three-dimensional spheroids

In vivo experiments are a relevant aspect in molecular life science for observing effects on tissues and living specimens. However, due to the time required to introduce fluorescent markers, financial resources and last but not least ethical issues their application is in many cases not feasible. On the other hand, many powerful technologies of molecular life sciences are available through relatively simple modifications to cells cultivated as monolayers, i.e. in two dimensions. In order to bridge the gap between cell cultures and real tissues, multicellular cell aggregates such as spheroids have been introduced. Assays based on spheroids have become increasingly popular in the fields of cell adhesion, tumor biology and drug testing. Compared to two-dimensional cell monolayer cultures, spheroids closely resemble the physiology of in vivo tissues in terms of three-dimensional morphology, development and function.

The major focus of recent studies has been the behavior and the properties of spheroids once the formation process has been completed. However, the initial dynamic processes that drive the aggregation of isolated cells forming a spheroid are unclear. This is a serious issue, since the processes underlying spheroid self-assembly can be related to the early stages of cancer metastasis. Spheroid formation could represent an important intermediate survival mechanism for cancer dissemination. Hence, processes underlying the organization of cells within a spheroid need to be well characterized and better understood.

This project intends to investigate the key physical parameters that affect the initial spheroid formation. Live imaging of cellular and subcellular processes with two- and three-dimensional time lapse-microscopy techniques and image analysis will be performed to gain direct insight into the physiological behavior of three-dimensional spheroids. A mathematical model will be founded on the analysis of the resulting image data.

Three-dimensional image analysis

Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other.

In this project we developed a novel and fully automated three-dimensional cell nuclei segmentation algorithm that is accurate and robust (Mathew et al., 2015). Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity.

Preimplantation mouse development

Preimplantation development is key to mammalian pregnancy success. About 70% of pregnancies fail during the first few days before the embryo is ready for implantation. Understanding the underlying processes will help to identify approaches that increase the success rates of pregnancies. During the pre-implantation phase, the fertilized oocyte divides several times to separate into three distinct populations of cells that will give rise to the embryo proper, the placenta and the yolk sac cells.

In this project, we investigate the complex and highly dynamic process of cell decision and cell pattering in the preimplantation embryo. We use an interdisciplinary approach combining cell biology, developmental biology, advanced microscopy, three-dimensional image analysis and three-dimensional mathematical modelling.