Live Cell Imaging Software

Description:

Introduction

Recent technological developments in fluorescent microscopy provide new opportunities for multi parameter imaging in living cells. However, as experiments may run for hours or days, manpower restrictions apply when controlling and evaluating the experiments. Likewise, the cell’s sensitivity to phototoxicity creates the necessity to use laser resources efficiently. This poses a challenge whenever key events happen spontaneously after hours and then proceed rapidly. Here, overly frequent temporal sampling (usually chosen at experimental set-up) might lead to premature photoxicity while too infrequent sampling might result in a poor temporal resolution of the events under study.

Single cell experiments often follow certain temporal pathways like biochemical signal transduction with a defined sequence of hallmarks. These hallmarks are visualized by changes of intensities or distribution of fluorescent dyes. Different hallmarks may require different set-ups of the experiment. These set-ups including selected dyes, selected lasers, sampling speed, spatial magnification are influenced by the biologist’s view on the underlying process.

Image analysis and automation have been recently used to facilitate data evaluation in live cell microscopy. High content screening solutions study large amounts of living cells, use image analysis to detect cell shape (segmentation) and hence generate data series. Other software packages (Image J, Cell Tracker) provide cell segmentation and tracking for offline image stacks. However, we are not aware of any system that allows online evaluation of temporal intra cellular signals combined with a threshold-based decision system that adaptively changes the measurement parameter based on a-priori biological knowledge and is applicable for a wide range of experimental settings.

Technology

Figure 1: (Left) The graphical description language for single cell time course measurement. Top to right presents sequential steps. Lines represent measured time series data (cells, channels per cell, data series per channel). Boxes represent image analysis activities, data filtering or parameter setting, threshold detection or microscope automation. (Center) Cell sample for light imaging (DIC) and one fluorescent channel (TMRM) for which a threshold is detected. (Right) Switch to additional fluorescence channels for the labeled cell at three time points subsequent to threshold detection.

The invention described here uses image analysis to extract time series of fluorescent signals from cells under microscopy, compare signal changes within each cell against given thresholds and subsequently adapt image modalities (sampling rates, laser excitation, magnification) during single cell measurements by microscope automation. This thresholding can also be applied to a large range of single cell experiments by using a graphical language to describe the time course of a single measurement by defining thresholds and subsequent control actions (see Fig. 1) based on a-priori biological models of the measurement processes.

Our software system can be integrated into standard fluorescence microscopes as an analysis and decision logic for performing time lapse single cell microscopy. Because of the generality of the graphical framework, it can be applied to a large class of settings ranging from studies in cancer research (e.g. programmed cell death), cellular physiology, microbiology up to drug toxicity studies on a high throughput scale.

Applications

The applications of fluorescent imaging are centered on biological research, such as neuroscience and cancer biology. The technique offers the ability to image and analyze living cells at higher resolution, which can provide novel biological insight. Stem cell research is a particularly hot area of growth.

Advantages

This invention allows an adaptive investigation of single cells tailored to the studied process (stimulus). It helps to mitigate phototoxicity since sampling frequency and/or laser resources are increased/switched on whenever needed, pending on the actual state of the experiment. It reduces necessary manual intervention like configuration changes in experiments that may last for several days by microscope automation. As a by-product, this architecture is aimed to include legacy equipment by decoupling the analysis process of legacy equipment by separating the microscope specific driver from the automation and analysis logic

Feature	Benefit
Automated detection of biological events	Event resolution at higher sampling rate leading to new biological insight
Adaptation of image modalities in response to biological events	Reduction of photo-toxicity and photo-bleaching through economic use of resources
Online automated image analysis	Less effort than manual analyses