Cytomics and cellular informatics – coping with asymmetry and heterogeneity in biological systems

BH3 helix-derived biophotonic nanoswitches regulate cytochrome c release in permeabilised cells

Dynamic physical interactions between proteins underpin all key cellular processes and are a highly attractive area for the development of research tools and medicines. Protein-protein interactions frequently involve α-helical structures, but peptides matching the sequences of these structures usually do not fold correctly in isolation. Therefore, much research has focused on the creation of small peptides that adopt stable α-helical structures even in the absence of their intended protein targets. We show that short peptides alkylated with azobenzene crosslinkers can be used to photo-stimulate mitochondrial membrane depolarization and cytochrome c release in permeabilised cells, the initial events of the intrinsic apoptosis pathway.

Time-lapse microscopy approaches to track cell cycle and lineage progression at the single-cell level

Time-lapse microscopy can be described as the repeated collection of an image (in n-dimensions; x, y, z, λ) or field of view from a microscope at discrete time intervals. The duration of the time interval defines the temporal resolution, which in turn characterizes the type of event detected. This unit describes the implementation of time-lapse microscopy to link initial cell cycle position during acute exposures to anti-cancer agents with anti-proliferative consequences for individual cells. The approach incorporates fundamental concepts arising from the ability to capture simple video sequences of cells from which it is possible to extract kinetic descriptors that reflect the interplay of mitosis and cell death in the growth of an unsynchronized tumor population. Utilizing a multi-well format enables the user to screen different drug derivatives, multiple dose ranges, or cell cultures with unique genetic backgrounds. The objective of this unit is to present the basic methodology for capturing time-lapse sequences and touch upon subsequent mining of the data for deriving event curves and possible cell lineage maps.

Interoperability of time series cytometric data: a cross platform approach for modeling tumor heterogeneity

The cell cycle, with its highly conserved features, is a fundamental driver for the temporal control of cell proliferation-while abnormal control and modulation of the cell cycle are characteristic of tumor cells. The principal aim in cancer biology is to seek an understanding of the origin and nature of innate and acquired heterogeneity at the cellular level, driven principally by temporal and functional asynchrony. A major bottleneck when mathematically modeling these biological systems is the lack of interlinked structured experimental data. This often results in the in silico models failing to translate the specific hypothesis into parameterized terms that enable robust validation and hence would produce suitable prediction tools rather than just simulation tools. The focus has been on linking data originating from different cytometric platforms and cell-based event analysis to inform and constrain the input parameters of a compartmental cell cycle model, hence partly measuring and deconvolving cell cycle heterogeneity within a tumor population. Our work has addressed the concept that the interoperability of cytometric data, derived from different cytometry platforms, can complement as well as enhance cellular parameters space, thus providing a more broader and in-depth view of the cellular systems. The initial aim was to enable the cell cycle model to deliver an improved integrated simulation of the well-defined and constrained biological system. From a modeling perspective, such a cross platform approach has provided a paradigm shift from conventional cross-validation approaches, and from a bioinformatics perspective, novel computational methodology has been introduced for integrating and mapping continuous data with cross-sectional data. This establishes the foundation for developing predictive models and in silico tracking and prediction of tumor progression