Microfluidic sorting of protein nanocrystals by size for X-ray free-electron laser diffraction

Abstract

The advent and application of the X-ray free-electron laser (XFEL) has uncovered the structures of proteins that could not previously be solved using traditional crystallography. While this new technology is powerful, optimization of the process is still needed to improve data quality and analysis efficiency. One area is sample heterogeneity, where variations in crystal size (among other factors) lead to the requirement of large data sets (and thus 10-100 mg of protein) for determining accurate structure factors. To decrease sample dispersity, we developed a high-throughput microfluidic sorter operating on the principle of dielectrophoresis, whereby polydisperse particles can be transported into various fluid streams for size fractionation. Using this microsorter, we isolated several milliliters of photosystem I nanocrystal fractions ranging from 200 to 600 nm in size as characterized by dynamic light scattering, nanoparticle tracking, and electron microscopy. Sorted nanocrystals were delivered in a liquid jet via the gas dynamic virtual nozzle into the path of the XFEL at the Linac Coherent Light Source. We obtained diffraction to ∼4 Å resolution, indicating that the small crystals were not damaged by the sorting process. We also observed the shape transforms of photosystem I nanocrystals, demonstrating that our device can optimize data collection for the shape transform-based phasing method. Using simulations, we show that narrow crystal size distributions can significantly improve merged data quality in serial crystallography. From this proof-of-concept work, we expect that the automated size-sorting of protein crystals will become an important step for sample production by reducing the amount of protein needed for a high quality final structure and the development of novel phasing methods that exploit inter-Bragg reflection intensities or use variations in beam intensity for radiation damage-induced phasing. This method will also permit an analysis of the dependence of crystal quality on crystal size.

FIG. 1.

(a) Microfluidic sorting device schematic with labeled channels ([I] = inlet, [S] = side outlet, and [C] = center outlet). (b) Close up of the central region showing simulated $\nabla {\mathit{E}}^2$ values and differential particle deflection based on size. The DEP-active region is indicated within the constriction channel where large $\nabla {\mathit{E}}^2$ values are apparent. (c) Fluorescence microscopy imaging showing PSI crystals flowing through the device. Large crystals are seen focused into [C] and small crystals deflect into [S]. Particles flow from left to right.

FIG. 2.

DLS heat map of (a) the PSI crystal suspension prior to fractionation, indicating a wide size distribution (∼200 nm to ∼20 μm), and (b) a PSI crystal fraction collected from the [S] channels indicating a narrowed submicron size distribution (∼200 nm to ∼600 nm). Signal increases from blue (lowest) to red (highest). (c) and (d) show DLS histograms corresponding to (a) and (b), respectively. (e) and (f) show DLS histograms of the PSI suspension prior to sorting (∼200 nm to ∼10 μm) and an [S] channel fraction (∼150 nm to ∼550 nm), respectively, measured using a cuvette-based instrument for comparison with (a)–(d), confirming the differences in the size distribution.

FIG. 3.

NanoSight NTA data of the [S] channel fraction. (a) Averaged particle size distribution for three measurements indicating a submicron size range between ∼100 nm and ∼650 nm with the majority being <300 nm. (b) Image taken of the suspended particles diffracting as they are measured by the instrument illustrating low size dispersity. Not appreciable in the image is the small particle size, which is evident in the distance traversed per frame as measured in (a).

FIG. 4.

PSI crystal characterization post-fractionation. (a) Brightfield image of a droplet of the [S] channel fraction. (b) SONICC image of the same droplet, indicating that crystallinity is maintained.

FIG. 5.

(a) TEM image of PSI crystals from the bulk sample. (b) High magnification TEM image of a portion of this group, showing an intact, well-ordered lattice structure. (c) FFT from the crystal marked by the arrow in (a) and (b) illustrating a well-ordered lattice. (d) TEM image of a submicron PSI crystal from the [S] fraction. (e) Enlargement of (d), showing the well-ordered crystalline lattice is maintained. (f) Corresponding FFT with Bragg spots from the ordered lattice.

FIG. 6.

(a) Diffraction pattern on the front CSPAD (at a distance of ∼88 mm) from a sorted PSI crystal showing sharp spots and low mosaicity, with (b) corresponding shape transforms on the inner part of the back CSPAD (at a distance of ∼2 m), indicating a small crystal.

FIG. 7.

Comparison of SFX data quality by simulated diffraction from PSI crystals with size distributions representing the unsorted and sorted crystal fractions. (a) Crystal volumes from the simulated narrow submicron crystal dataset. (b) CC* of merged reflections from 5000 and 10 000 crystals with side lengths of 0.1–10 μm (blue and green, respectively), and of 5000 merged, indexable patterns from ∼10 000 crystals with side lengths of 0.15–0.55 μm (orange). (c) Average multiplicity and (d) SNR of reflections in each resolution shell. A significantly smaller amount of protein is required for high quality reflection lists if the crystal size distribution is narrowed. The drop in quality at high resolution for the 0.15–0.55 μm crystal dataset is due to low signal strength at that resolution.