This project will develop a framework to represent, analyze and interpret shapes extracted from images, supporting a wide range of biological investigations. The primary objectives are: (1) to develop a mathematical framework and computational tools for the quantification and analysis of shapes; (2) to integrate these computational models with machine learning and statistical inference methods to enable new discoveries, transforming imaging data into biological knowledge; (3) to deliver novel quantitative methodologies for shape analysis that start from a biological premise, rather than a purely geometric one. The aim is thus not only to quantitatively describe shape, but to develop methods for linking morphological variation to its underlying biological causes. To ensure that the project focuses on methods that are most promising to biology with significant breadth of application, model and tool development will be guided and supported by a set of diverse case studies, ranging from the sub-cellular to organismal scales.
Shape represents a complex and rich source of biological information that is fundamentally linked to underlying mechanisms and function. However, shape is still often examined on a qualitative basis in many disciplines in biology, an approach that is time consuming and prone to human subjectivity. While ad hoc quantitative methods do exist, they are often inaccessible to non-experts and do not easily generalize to a wide variety of problems. The inability of biologists to systematically link shape to genetics, development, environment, function and evolution often precludes advances in biological research spanning diverse spatial and temporal scales, from the movement of molecules within a cell to adaptive changes in organismal morphology. The primary goal of this project is to develop a new suite of widely applicable quantitative methods and tools into the study of biological shape to address the significant need for consistent and repeatable analysis of shape data.
A. Overview Mitochondria are the energy generator and distributor of cells. They also play important roles in controlling many basic cellular processes such as signal transduction. A remarkable property of mitochondria is that they undergo frequent shape change as well as active movement, presumably to meet the changing needs at different locations inside cells. Indeed, abnormal shape and movement of mitochondria often indicate dysfunction. There is extensive research evidence indicating that mitochondrial dysfunction plays an important role in the onset and progression of many human neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease. Currently, however, biological studies on mitochondria are limited by the lack of computational techniques and software tools for characterizing their shapes and shape changes. This limitation hinders the progress researchers can make towards understanding the biology of mitochondria as well as related human diseases. The major outcome of this research project is that, in collaboration with other participating investigators of this collaborative research award, we have developed the computational techniques and software tools that are needed to overcome this limitation. We have made these techniques and tools freely available to the research community through publishing our computational techniques in research papers and through distributing our software tools via the internet. To test and validate the techniques and software tools we developed, we have also used them to study a basic question in mitochondrial biology, namely whether shape change of mitochondria is connected with their movement. Although the focus of this research project is on technology development and on understanding the basic biology of mitochondria, the computational techniques and software tools we developed can be used to study mitochondrial dysfunction in related human diseases. B. Summary of research outcomes B.1 Major technological contributions - We have developed computational techniques and software tools for automated characterization and classification of mitochondrial shapes and shape changes (See image 1). - We have developed super-resolution imaging and image analysis techniques that enable us to study the dynamic behavior of mitochondria at nanometer resolution. This represents a ten-fold improvement in resolution compared to conventional techniques (See images 2 & 3). B.2 Major research findings - We found that OPA1 (optic atrophy 1) gene plays important roles in controlling shape changes as well as spatial distributions of mitochondria. This finding provides insights into why mutation of OPA1gene causes vision loss in humans. B.3 Research publications Research results related to this project have been published in 6 journal papers, 2 book chapters, and 10 peer-reviewed conference papers. C. Summary of education and outreach outcomes This award provided research training and supplies to 4 PhD students, 2 MS students, and 2 undergraduate students from biomedical engineering and chemical engineering programs at Carnegie Mellon University. Some of them went on to pursue doctoral training in biomedical engineering at leading research universities, including MIT and UC Berkeley. Others went on to work in the pharmaceutical or biotechnology industry. Funding of this award has also supported a variety of outreach activities to promote science and engineering education. D. List of publications D.1 Journal papers H.-C. Lee, Y. Yu, J. Kovaceciv, and G. Yang (2014). Computational analysis and modeling of mitochondrial transport and morphological dynamics in the axon, under review. Yiyi Yu, Hao-Chih Lee, Kuan-Chieh Jackie Chen, Joseph Suhan, Minhua Qiu, Ge Yang (2014). Inner membrane fusion mediates spatial organization of axonal mitochondria, under review. S. Gunawardena S., G. Yang G., and L. S. B. Goldstein L. S. B. (2013) Presenilin controls kinesin-1 and dynein function during AP vesicle transport in vivo, Human Molecular Genetics, vol. 22, pp. 3838-3843. G. Yang (2013) Bioimage informatics for understanding spatiotemporal dynamics of cellular processes (invited review), Wiley Interdisciplinary Reviews Systems Biology and Medicine, vol. 5, pp. 367-380. E. A. Booth-Gauthier, T. A. Alcoser T. A., G. Yang, and K. N. Dahl (2012) Force-induced changes in subnuclear movement and rheology, Biophysical Journal, vol. 103, pp. 2423-2431. S. Roy S., G. Yang G., Y. Tang, and D. Scott (2012) A simple photoactivation and image analysis module for visualizing and analyzing axonal transport with high temporal resolution, Nature Protocols, vol. 7, pp. 62-68, 2012. D.2 Book chapters G. Yang (2014). Image-based computational tracking and analysis of spindle protein dynamics. Mitosis: Methods and Protocols, in Methods in Molecular Biology David Sharp ed. Springer. G. Yang and H.-C. Lee (2015). Computational image analysis techniques for cell mechanobiology. Micro and nano techniques in cell mechanobiology Yu Sun, Craig Simmons, Deok-Ho Kim, eds. Cambridge University Press. D.3 Conference papers (total number = 10; omitted due to space limitations)
