Approaches to automated protein crystal harvesting

Abstract

The harvesting of protein crystals is almost always a necessary step in the determination of a protein structure using X-ray crystallographic techniques. However, protein crystals are usually fragile and susceptible to damage during the harvesting process. For this reason, protein crystal harvesting is the single step that remains entirely dependent on skilled human intervention. Automation has been implemented in the majority of other stages of the structure-determination pipeline, including cloning, expression, purification, crystallization and data collection. The gap in automation between crystallization and data collection results in a bottleneck in throughput and presents unfortunate opportunities for crystal damage. Several automated protein crystal harvesting systems have been developed, including systems utilizing microcapillaries, microtools, microgrippers, acoustic droplet ejection and optical traps. However, these systems have yet to be commonly deployed in the majority of crystallography laboratories owing to a variety of technical and cost-related issues. Automation of protein crystal harvesting remains essential for harnessing the full benefits of fourth-generation synchrotrons, free-electron lasers and microfocus beamlines. Furthermore, automation of protein crystal harvesting offers several benefits when compared with traditional manual approaches, including the ability to harvest microcrystals, improved flash-cooling procedures and increased throughput.

The Low-Cost, Semi-Automated Shifter Microscope Stage Transforms Speed and Robustness of Manual Protein Crystal Harvesting

“…One strategy to eliminate the mounting bottleneck has been to avoid the need for transfer entirely, by developing in situ diffraction techniques (Bingel-Erlenmeyer et al, 2011;Michalska et al, 2015;Soliman, Warkentin, Apker, & Thorne, 2011). Other approaches to ex situ screening have tried to design human out of the process, via novel harvesting techniques, or by reproducing the human mounting technique with advanced robotics (Cipriani et al, 2012;Deller & Rupp, 2014;Viola et al, 2011;Viola, Carman, Walsh, Frankel, & Rupp, 2007). Nevertheless, no affordable and scalable solution to the overall bottleneck of crystal transfer has emerged, and the systematic inefficiency of the harvesting step remains.…”

“…These are all complex and costly engineering problems to solve. Fully-automated crystal mounting, compatible with existing experimental practice, and accessible to the wider community may therefore, be some years off (Deller & Rupp, 2014).…”

Automated Particle Collection for Protein Crystal Harvesting

“…1. This slow and laborious process can take an expert operator on average 2.4 minutes per crystal [2]. Further compounding this task, the size range of the crystals is on the order of hand tremor (50 µm [3]).…”

“…This tremor can cause the operator to inadvertently damage a crystal while harvesting and render it unusable. Together, the slow harvesting rate and the high loss rate make crystal harvesting the major bottleneck of highthroughput crystallography [2]. Many attempts to automate or B. Zeydan, L. Somm, R. Pieters, Y. Fang, A. J. Petruska, D. Sargent, and B. J. Nelson are with ETH Zurich, Switzerland.…”

Fully automatic macromolecular crystallography: the impact of MASSIF-1 on the optimum acquisition and quality of data

“…Coupling the extremely bright and small beams that can be produced at modern third-generation synchrotrons with automatic sample changers and on-line data analysis [10,[12][13][14][15][16][17][18][19][20] has been one of the great advances, allowing not only large numbers of structures to be solved [21] but also incredibly challenging systems to be characterized structurally. [22][23][24][25] As the field of macromolecular crystallography (MX) reaches maturity, efforts are now focussing on automating the steps that still require considerable human involvement: crystal mounting, [26] data collection, [7,11,27] processing [16,[28][29][30][31] and validation. [32] The full automation of MX data collection, where human involvement is limited to sending samples and accessing data, has been discussed within the field for many years.…”