IMART software for correction of motion artifacts in images collected in intravital microscopy
Kenneth W Dunn, Kevin S Lorenz, Paul Salama and Edward J Delp
Intravital microscopy is a uniquely powerful tool, providing the ability to characterize cell and organ physiology in the natural context of the intact, living animal. With the recent development of high-resolution microscopy techniques such as confocal and multiphoton microscopy, intravital microscopy can now characterize structures at subcellular resolution and capture events at sub-second temporal resolution. However, realizing the potential for high resolution requires remarkable stability in the tissue. Whereas the rigid structure of the skull facilitates high-resolution imaging of the brain, organs of the viscera are free to move with respiration and heartbeat, requiring additional apparatus for immobilization. In our experience, these methods are variably effective, so that many studies are compromised by residual motion artifacts. Here we demonstrate the use of IMART, a software tool for removing motion artifacts from intravital microscopy images collected in time series or in three dimensions.
Automated analysis of clonal cancer cells by intravital imaging
Sarah Earley Coffey, Randy J Giedt and Ralph Weissleder
Longitudinal analyses of single cell lineages over prolonged periods have been challenging particularly in processes characterized by high cell turn-over such as inflammation, proliferation, or cancer. RGB marking has emerged as an elegant approach for enabling such investigations. However, methods for automated image analysis continue to be lacking. Here, to address this, we created a number of different multicolored poly- and monoclonal cancer cell lines for in vitro and in vivo use. To classify these cells in large scale data sets, we subsequently developed and tested an automated algorithm based on hue selection. Our results showed that this method allows accurate analyses at a fraction of the computational time required by more complex color classification methods. Moreover, the methodology should be broadly applicable to both in vitro and in vivo analyses.
Optimization of the dorsal skinfold window chamber model and multi-parametric characterization of tumor-associated vasculature
Azusa Maeda and Ralph S DaCosta
The dorsal skinfold window chamber (DSWC) model is a unique tool that enables analysis of various aspects of tumor biology and therapeutic response. Although the protocol for the murine DSWC model is standardized, certain tumors fail to grow or require a particular environment to promote growth. Given such limitations, we optimized the DSWC model for a slow-growing tumor that regresses spontaneously in the standard protocol. We further characterized the vascular network in the tumor model compared with that of non-tumor-bearing mice and observed significant differences in multiple parameters related to vascular structure and function.