Homepage

mmc2021 Abstract Database

Routine Sample Optimization for Single Particle Cryo-EM with chameleon

Routine Sample Optimization for Single Particle Cryo-EM with chameleon

Session

Stream 1: EMAG - Soft and Hybrid Materials

Authors

Development Scientist Michele Darrow (1), Product Manager Paul Thaw (1), Busineess Development Manager Tim Booth (1)

Affiliations

1. SPT Labtech LTD

Keywords

cryoEM, sample preparation, sample optimization, air water interface, inkjet dispensing, nanowire grids, self-wicking, plunge time control, chameleon, thin film formation, ice thickness

Abstract text

The period responsible for the current generation of cryogenic electron microscopy (cryo-EM) instruments and image processing software is often referred to as the “resolution revolution.” The results of these technological advancements have been achievements in higher quality structures and cryo-EM becoming the go-to method for structural biologists. However, sample preparation is widely recognized as a key unresolved step in the high resolution cryo-EM workflow. [1]

With the introduction of next generation sample preparation methods realized through emerging instruments such as chameleon, researchers can drive optimization towards repeatable high-resolution outcomes on a single platform. The chameleon is routinely used to empirically determine the sample dependent behavior for a given concentration, buffer condition and ice thickness for a range of dispense-to-plunge times. With this information subsequent steps can be directed using optimal conditions to mitigate negative effects unique to the biological sample at hand.

The literature demonstrates the clear benefits of chameleon as a platform for obtaining specimen with improved quality using traceable outcome driven decision making. Incorporating the chameleon into the cryoEM workflow and enabling discrete dispense-to-plunge time characterization for the sample at hand represents a paradigm shift in the approach to sample optimization bottlenecks and reduces the knock-on costs (time and money) caused by poor sample quality downstream.

Protein adsorption to the air-water interface during the thin film formation step represents the main obstacle bottlenecking the workflow towards routine structure determination of single particles by high resolution cryo-EM. This behavior can be described by a three-step process. First the diffusion mediated initial adsorption of the protein at the interface, followed by denaturation or unfolding of the adsorbed protein and finally the formation of a ‘film’ with chemical and mechanical properties related to the rate of adsorption and protein stability in solution. [2,3]

We now know air-water interface effects are ubiquitous [5] but due to a large variety of contributing factors, the effects of the denatured film are likely non-uniform. [6,7] The process scales roughly with protein concentration [4] and a combination of specific molecular characteristics influences the rate of adsorption at the interface. [7]

A common effect of conventional slow (> 1 sec) blotting or pin printing techniques is up to a 30-fold increase in sample concentration at the air-water-interface. [8] The denaturation and unfolding, which is pronounced for less stable proteins, leads to low sample quality and results in limited achievable resolutions and the need to collect large data sets with a very low percentage of particles remaining in the resulting reconstructions.

Advancements in automation and self-wicking grid technology allow for controlled and predictable thin film formation resulting in targeted ice thickness at discrete fast plunge times up to 54ms. chameleon has been shown to reproducibly obtain improvements in sample quality using plunge times an order of magnitude faster than the times available using commercially available conventional blotting or pin printing technology. Self-wicking grids are optimized for sample specific thin film formation and are dependent on concentration and viscosity.

Recent data suggests that for a given sample and concentration, faster plunge times lead to a reduction in the concentrating effect at the air water interface and therefore, lower observed particle density correlated to faster plunge times. Although the relationships are non-linear and sample dependent, early use suggests an understanding of this effect for each, and every sample is critical to determining an optimization pathway towards improved sample quality for high resolution.

Rather than eliminate all interactions between protein and the air water interface, since it is impractical to outrun protein adsorption entirely, routine chameleon protocols target a range of discrete dispense-to-plunge times to initially characterize sample behavior with regards to concentration, wicking speed, film thickness and observable particle density and then optimize based on outcomes.

Approaching the chameleon with a new sample requires a first step to determine the fastest plunge time possible for a given sample concentration without particle density dropping too low. Using the sample concentration in hand or 2x when available, the system is directed to prepare a few grids at a range of reducing plunge times. Typically, the plunge time range will start at > 500ms for observable particle densities comparable to conventional methods. The starting plunge time of 500ms, slow by chameleon standards, represents a 2-10x reduction in dwell time compared to other commercially available sample preparation instruments. Grids are accepted at a range of ice thickness from ‘good’ to ‘overwicked.’ Early results point to improvements in specimen quality across a variety of samples for a range of sample-specific plunge times.

After gaining an understanding of the appropriate plunge time range and ice thickness for the sample concentration and characteristics, a second freezing session with a set of much more stringent acceptance criteria corresponding to a smaller range of plunge times and film thickness can produce multiple examples with consistent outcomes.

The conventional or traditional sample preparation methods for cryo-EM result in extreme, sample dependent differences between specimen prepared in a similar manner and due to this a variety of experimental methods have been developed over the last 40 years to deal with the subsequent detrimental behavior. Each method requires its own optimization routine, possibly utilizing multiple instruments.

There are not yet any optimization workflows specific to sample type. Only a consensus that optimization is required for most samples and that approximately ten different methods will be attempted in combination before improvement is seen. Additionally, none of the currently available methods can be determined to work in advance of sample preparation. [1]

The routine optimization protocol available through chameleon represents a paradigm shift by addressing the unique sample dependent air-water interface effects by adjusting the vitrification step to the sample behavior instead of requiring researchers to introduce additional methods to modify sample behavior to accommodate standardized plunge freezing devices.

To be able to capture biochemistry consistently and confidently researchers need the ability to freeze where and when the biochemistry is carried out, placing the requirement for change squarely on the sample preparation devices to modernize, improve ease-of-use, and generate outcomes efficiently to achieve early milestones routinely.  

References

1. B. Carragher, Y. Cheng, A. Frost, R.M. Glaeser, G.C. Lander, E. Nogales, H.-W. Wang, Current outcomes when optimizing ‘standard’ sample preparation for single-particle cryo-EM doi:10.1111/jmi.12834 Published: September 19, 2019
2. Razumovsky L, Damodaran S. Surface Activity−Compressibility Relationship of Proteins at the Air−Water Interface. Langmuir. American Chemical Society; 1999 Feb;15(4):1392–9. 
3. Martin AH, Grolle K, Bos MA, Cohen Stuart MA, van Vliet T. Network forming properties of various proteins adsorbed at the air/water interface in relation to foam stability. J Colloid Interface Sci. 2002 Oct 1;254(1):175–83. 
4. Israelachvili, JN (2011) Intermolecular and Surface Forces, 3rd edn, Elsevier, Amsterdam: Academic Press.
5. A Noble, et al. Nature Methods 15 (2018), p. 793-795.
6. Gunning PA, Mackie AR, Gunning AP, Woodward NC, Wilde PJ, Morris VJ. Effect of Surfactant Type on Surfactant−Protein Interactions at the Air−Water Interface. Biomacromolecules. American Chemical Society; 2004 May;5(3):984–91. 
7. De Jongh HHJ, Kosters HA, Kudryashova E, Meinders MBJ, Trofimova D, Wierenga PA. Protein adsorption at air-water interfaces: A combination of details. Biopolymers. John Wiley & Sons, Ltd; 2004;74(1-2):131–5. 
8. Klebl et al., Need for Speed: Examining Protein Behavior during CryoEM Grid Preparation at Different Timescales, 2020, Structure 28, 1238-1248