WikiSysop: Created page with "== Citation == R. M. Haynes, J. Myers, C. S. López, J. Evans, O. Davulcu, and C. Yoshioka, “A strategic approach for efficient cryo-EM grid optimization using design of experiments,” J. Structural Biology, vol. 217, no. 1, p. 108068, 2025. == Abstract == In recent years, cryo-electron microscopy (cryo-EM) has become a practical and effective method of determining structures at previously unattainable resolutions due to advances in detection, automation, and data..."

2025-04-15T13:53:09Z

Created page with "== Citation == R. M. Haynes, J. Myers, C. S. López, J. Evans, O. Davulcu, and C. Yoshioka, “A strategic approach for efficient cryo-EM grid optimization using design of experiments,” J. Structural Biology, vol. 217, no. 1, p. 108068, 2025. == Abstract == In recent years, cryo-electron microscopy (cryo-EM) has become a practical and effective method of determining structures at previously unattainable resolutions due to advances in detection, automation, and data..."

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== Citation ==

R. M. Haynes, J. Myers, C. S. López, J. Evans, O. Davulcu, and C. Yoshioka, “A strategic approach for efficient cryo-EM grid optimization using design of experiments,” J. Structural Biology, vol. 217, no. 1, p. 108068, 2025.

== Abstract ==

In recent years, cryo-electron microscopy (cryo-EM) has become a practical and effective method of determining
structures at previously unattainable resolutions due to advances in detection, automation, and data processing.
However, sample preparation remains a major bottleneck in the cryo-EM workflow. Even after the arduous
process of biochemical sample optimization, it often takes several iterations of grid vitrification and screening to
determine the optimal grid freezing parameters that yield suitable ice thickness and particle distribution for data
collection. Since a high-quality sample is imperative for high-resolution structure determination, grid optimization
is a vital step. For researchers who rely on cryo-EM facilities for grid screening, each iteration of this
optimization process may delay research progress by a matter of months. Therefore, a more strategic and efficient
approach should be taken to ensure that the grid optimization process can be completed in as few iterations
as possible. Here, we present an implementation of Design of Experiments (DOE) to expedite and strategize the
grid optimization process. A Fractional Factorial Design (FFD) guides the determination of a limited set of
experimental conditions which can model the full parameter space of interest. Grids are frozen with these
conditions and screened for particle distribution and ice thickness. Quantitative scores are assigned to each of
these grid characteristics based on a qualitative rubric. Input conditions and response scores are used to generate
a least-squares regression model of the parameter space in JMP, which is used to determine the conditions which
should, in theory, yield optimal grids. Upon testing this approach on apoferritin and L-glutamate dehydrogenase
on both the Vitrobot Mark IV and the Leica GP2 plunge freezers, the resulting grid conditions reliably yielded
grids with high-quality ice and particle distribution that were suitable for collecting large overnight datasets on a
Krios. We conclude that a DOE-based approach is a cost-effective and time-saving tool for cryo-EM grid
preparation.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S104784772400008X

== Related software ==

== Related methods ==

== Comments ==

2025Haynes OptimalIce - Revision history