CryoEM Workflow Acceleration with Feret Signatures

Abstract

Common challenges in cryogenic electron microscopy, such as orientation bias, conformational diversity, and 3D misclassification, complicate single particle analysis and lead to significant resource expenditure. We previously introduced an in silico method using the maximum Feret diameter distribution, the Feret signature, to characterize sample heterogeneity of disc-shaped samples. Here, we expanded the Feret signature methodology to identify preferred orientations of samples containing arbitrary shapes with only about 1000 particles required. This method enables real-time adjustments of data acquisition parameters for optimizing data collection strategies or aiding in decisions to discontinue ineffective imaging sessions. Beyond detecting preferred orientations, the Feret signature approach can serve as an early-warning system for inconsistencies in classification during initial image processing steps, a capability that allows for strategic adjustments in data processing. These features establish the Feret signature as a valuable auxiliary tool in the context of single particle analysis, significantly accelerating the structure determination process.

Keywords: Feret signature; analytical tool; cryogenic electron microscopy; data processing; single particle analysis; structural biology; structure determination; workflow optimization.

Figure A1

Characterization of preferred orientations using simulated Feret signatures. F_max distributions from 2D projections of influenza hemagglutinin trimer reconstruction at 4.2 Å resolution (EMD:8731; scale bar = 40 Å).

Figure A2

Robustness of Feret signatures to random sampling. (a) Schematic of random split into two equivalent subsets. Initial dataset is beta-galactosidase extract from Relion tutorial. (b) F_max distributions from particles randomly selected from first subset (left panel) and second subset (right panel).

Figure A3

Feret diameters. A Feret diameter (yellow) is the distance between two parallel tangents (black lines) of an object (gray); here, the side-view projection of Influenza Hemagglutinin. The projection can have many different Feret diameters, three are shown here, but only the maximum (center) is kept for the analysis.

Figure 1

An assessment of preferred orientation using Feret signatures. The F_max distributions of particles randomly picked from particle extracts of 0° tilt and a tilted dataset of the Influenza hemagglutinin trimer from Tan et al. (2017) [8]. (a) F_max distributions from particles picked from the 0° tilt dataset. (b) F_max distributions from particles picked from the 40° tilt dataset. (c) Influenza hemagglutinin trimer reconstruction at 4.2 Å resolution (EMD:8731; scale bar = 40 Å). For all panels, black dots indicate the F_max values for the preferred orientation, and red dots indicate the F_max values for the missing orientation. The red line in (a,b) corresponds to the view marked in red in (c).

Figure 2

Assessment of limited preferred orientations using Feret signatures. F_max distributions of particles randomly picked from 0° tilt dataset with limited preferred orientation, and tilted dataset of Rabbit Muscle Aldolase from Noble et al. (2018) [10]. (a) Extraction of micrographs and particle picking from tilt series at 0° tilt and 40° tilt. (b) F_max distributions from particles selected from 0° tilt micrographs. (c) F_max distributions from particles selected from 40° tilt micrographs.

Figure 3

Feret signature robustness during preferred orientation assessment. The F_max distributions of particles randomly picked from a 0° tilt dataset with preferred orientation (left panels) and the respective tilted dataset (right panels) from Tan et al. (2017) [7], with the same dataset used for Figure 1. (a) F_max distributions from 10,000 randomly picked particles per tilt. (b) F_max distributions from 5000 randomly picked particles per tilt. (c) F_max distributions from 3000 randomly picked particles per tilt. (d) F_max distributions from 1000 randomly picked particles per tilt; the minimum number of particles required to detect a Feret signature. (e) F_max distributions from 500 randomly picked particles per tilt. A detailed analysis of the influence of noise using simulation studies of differently sized and shaped particles [12] shows that the distinction between different Feret signatures is robust even if the signal-to-noise ratio is low. However, if the particles under investigation are nearly globular, the expected differences between the tilted and untilted distributions will be more subtle and a higher signal-to-noise ratio (i.e., more particles) may be necessary to make a robust conclusion about preferred orientations. Nevertheless, the number of required images will still be small compared to high-resolution datasets.

Figure 4

Feret signatures identify inconsistency in classification during the early stages of image processing. F_max distributions of particles randomly picked using low-resolution templates of the four conformations determined by Xu et al. (2016) [15] (scale bar = 60 Å). (a) The procedure to generate the simulated Feret signature from synthetic 2D projections of a cryoEM template and (b) F_max distributions of the simulated Feret signatures from the four integrin conformations: bent, intermediate 1, intermediate 2, and upright (from left to right, represented in dark blue). These are superimposed with the corresponding experimental F_max distributions (light blue) derived by template-based particle picking. (c) Simulated F_max distributions from the four integrin conformations (dark blue), superimposed with the corresponding experimental F_max distributions derived from particles classified by 3D refinement.