Our panel of test screens included several sparse-matrix kits to ensure a good sample of the conditions encountered in crystallization trials. Four 96-well commercial screens were tested (JCSG+, Index, Classics and Wizards I and II, representing 284 unique conditions; Newman et al.
) as well as a 96-condition medium-weight PEG grid. Using a fixed energy, 93% (447/480) of the precipitant solutions transferred successfully and the remaining 7% (33 conditions) failed to dispense. We were curious whether the viscosity of the liquid sample was a predictor of poor transfer capabilities. Fig. 1 shows the viscosities and densities of difficult and well behaved solutions from the Classics and JCSG+ screens, demonstrating that there is no correlation linking viscosity or density alone to inability to dispense. Surface tension is a dominant factor in the mechanism of droplet ejection, but we could not readily characterize this property.
Measurement of viscosity (green circles) and density (red squares) of solutions from the Classics (a) and JCSG+ (b) screens. The larger symbols represent solutions that failed to dispense and show no clear trend toward either extreme.
A full description of the contents of the 33 problematic solutions is shown in Table 1. It was immediately obvious that the majority of the non-transferring conditions (26 out of 33) contained high concentrations of 2-methyl-2,4-pentanediol (MPD). Conditions with MPD at less than 30% concentration could be transferred. Three samples including 30% MPD with observed transfer all contained 0.1 M sodium acetate pH 4.6 and a chloride salt, while other buffered 30% MPD solutions failed, as seen in Table 1. All samples with greater than 30% MPD failed to transfer. Further work has suggested that MPD attenuates acoustic energy and that stronger input enables the delivery of solutions up to ~50% MPD (J. Bramwell, Labcyte, personal communication). Of the seven non-MPD-containing failed wells, three are represented in other screens, where measurable transfer was observed (marked with asterisks in Table 1). A few other unusual constituents such as branched polymers such as pentaerythritol propoxylate and polypropylene glycol are present in two non-transferring wells, but as these are less common our data do not inform whether or not their impact is systematic.
Formulations of 33 crystallization solutions (28 unique) that failed to dispense from a total of 480 from the JCSG+, Classics, Index, Wizards and PEG MW screens
We wished to characterize the successful liquid transfers for reproducibility and accuracy. The standard calibration method of fluorescein-doped deliveries was impractical for the scale of this work. We used the acoustic reflection measurements of liquid heights (included in the instrument run logs) before and after a series of ejections to determine the volume delivered per droplet compared with the calibrated setting (5 nl). While the effect of the distinct solutions on the speed of sound confounds the absolute accuracy of these measurements, the differential nature of the pre- and post-delivery determination provides a relative difference that ought to be robust. Using six repeat experiments, Fig. 2 plots the number of conditions in each screen within CV categories binned by 5% increments. Each screen demonstrates that the majority of conditions have good reproducibility (CV < 10%). In addition, the PEG MW screen, with a moderate diversity of components, had superior precision, with 93 of 96 conditions showing CVs of less than 10% (and many of less than 2%). The overlap of conditions in the commercial screens (Newman et al.
) also provided 84 duplicate solutions from different screens with observed transfer. As an additional measure of reproducibility, the mean difference between the measured volume per droplet for these 42 pairs was only 0.22 ± 0.21 nl.
Figure 2 The distribution of coefficients of variation (CVs) for reproducibility in five crystallization screens calculated from six repeat dispenses. Each block of color shows the count of conditions from that screen within the prescribed range. The brown segment (more ...)
We used the measured residual volume differences (versus a 5 nl target) to calculate semi-empirical adjusted energy parameters for individual wells to gauge potential improvements versus fixed-energy operation. Fig. 3 plots these residuals from four screens, showing a much tighter distribution when well-by-well energy adjustments were applied. This is quantitated in Table 2, where the standard deviation over all wells of the absolute volume (i.e. not the residual) relative to the mean of the distribution improves significantly from 1.28 to 0.76. Naturally, the more chemically diverse sparse-matrix screens have a higher absolute deviation than the limited chemistry of the PEG MW screen, but the salient metric is in the improvement (smaller standard deviation) in each screen upon adjusted-energy dispenses (Table 2). Aside from profiling the absolute volume delivered, we gauged accuracy by calculating the root-mean-square deviation of the residuals (relative to the 5 nl target volume). These r.m.s.d.s showed slightly less improvement than the standard deviations of the absolute volumes (residual r.m.s.d. of 1.30 Å at fixed energy to 1.01 Å with ‘tuned’ energies). It is apparent that our coarsely calculated energy adjustments led to a slightly larger average volume delivered (5.23 nl per droplet mean volume at fixed energy, 5.67 nl mean with tuned energies) and this systematic bias, which is purposefully captured in the residual r.m.s.d., partly offsets the gains of the tighter distribution relative to the mean. Adjusting the constant parameter in our admittedly simplistic first-pass energy-adjustment calculations would presumably improve the accuracy. Furthermore, Fig. 3(b) highlights a segment of the PEG MW screen residual plot in which each set of four points represents increasing concentrations of PEG (5, 15, 20 and 25%). Overall, the energy adjustments yield a much flatter range within each group of four data points. Of note, however, is that those highest PEG concentrations (the fourth point of each set) with the most reduced delivery volumes at fixed energy resulted in over-compensation, a phenomenon that accounts for many of the higher scatter points in Fig. 3(a) and contributes to the upward bias in average volumes. The adjustment energies would be likely to improve with an asymptotic curve instead of our linear extrapolation. These studies investigating the relationship between energy parameters and volume delivery of different chemical series helped to inform more sophisticated developments for the acoustic methodologies that are now forthcoming. However, given the complexity of determining and implementing the energy maps in the current format versus the practical functionality and emerging successes of the fixed-energy operation, we performed all our subsequent crystallization trials using the simpler fixed-energy setting.
Figure 3 Plots of the residual error in measured volume delivered relative to a 5 nl target for experiments at fixed energy throughout (light triangles) versus individually adjusted energies (dark circles). (a) Distribution of residuals across four screens (more ...)
Aggregate and ‘by screen’ statistics of droplet volume distribution
We continued experiments to verify that diverse protein samples would crystallize using this new application of acoustic technology. In addition to a trivial control (lysozyme), we were able to reproduce the crystallization of HCV helicase, HCV polymerase, HSA, HIV-RT and ITK (five in-house targets). Crystals of all six proteins were visible by day 5 and most stopped growing by day 15. Fig. 4 shows typical crystals; various representative volumes were used in these trials but were not a necessary part of optimization.
Figure 4 Images collected on day 15 from crystallization drops of various protein samples. The total drop volume is double the protein value given. The volumes are representative and were not optimized as similar crystals appeared at the various volumes explored. (more ...)
Finally, we experimented with the delivery of a set of oils to look at alternative ways to reduce evaporation from the small crystallization drops and to consider whether acoustic delivery could enable batch-under-oil methods. We used high energy settings in these tests, typically with amplitude at 2.5–3.5 and burst settings greater than 1000. Silicone oil, DMS (polydimethylsiloxane terminated with trimethylsiloxy), FMS [poly(3,3,3-trifluoropropylmethylsiloxane)] and Al’s oil (a 50:50 mixture of silicone and paraffin oil) failed to transfer at these energies. However, we did observe transfer of paraffin oil and mineral oil, but did not quantitate these deliveries. Challenges to using oils in such flying-drop setups remain, as there was noticeable spatter upon aqueous drop and oil surface contact.