Automated Microscopy: An Integrated System for Molecular Microscopy
Sponsor: NIH 1 RO1 GM61939-01 6/1/00-5/31/04
PIs: Bridget Carragher, Clinton S. Potter, David Kriegman, Zhi-Pei Liang,
Ron Millgan
Summary:
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Molecular microscopy has become an increasingly important
tool for structural biology but the methodology is very labor intensive and very
slow. It is generally recognized that the development of improved capabilities for
three-dimensional electron microscopy are critical for progress in emerging
integrative research in molecular cell biology. We aim to develop a system for
rapid routine structure determination of macromolecular assemblies. Our ultimate
goal is to develop an integrated system that can produce a three-dimensional
electron density map of a structure within a few hours of inserting a specimen
in the electron microscope. The motivation for this work is to provide answers
to interesting biological questions. We will initially use our work on motor-microtubule
complexes and actomyosin as the driver for the development of the integrated system.
By tightly coupling the development of the new system with its implementation in a
laboratory whose primary goal is answering fundamental questions in cell biology,
we will obtain immediate and invaluable feedback as to how the system is used in
practice.
Developing this system will involve devising new
approaches and integrating the results of several ongoing research projects. The
primary specific aims are: (1) To remove the requirement for using film to acquire
the high magnification electron micrographs. This will require the development of
feature recognition algorithms and new imaging strategies that take into account
the characteristics of currently available digital cameras. (2) Improve and automate
our existing software for helical image analysis. We will incorporate new methods
for determining the helical parametersof an unknown specimen, methods for improving
the resolution, andmethods for analyzing non-helical specimens. (3) Integration of the
acquisition and the analysis steps. This will require incorporation of machine
learning techniques to produce a system that is highly efficient in terms of throughput
and data quality.
The general framework for integrated acquisition and analysis to
be developed will be readily extendible to other specimens (helical tubes,
single particles, two-dimensional crystals). Thus, once the system has been
successfully implemented it will be made generally available to the scientific
community. The system we plan to develop has the potential to revolutionize the
field of three-dimensional electron microscopy and make this approach accessible
to a wide community.
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