For decades, single particle cryo-electron microscopy (cryoEM) grids have commonly been imaged and processed under the assumption that most particles imaged were not adsorbed to the air-water interfaces and were in a single layer as they were plunge-frozen. An ideal grid and sample for single particle collection would have the majority of areas in holes maximally occupied by non-adsorbed, non-interacting particles 10 nm or farther from the air-water interfaces, particles oriented randomly, vitreous ice thin enough to contain the particles plus about 20 nm of additional space, where none of the particles overlap in the beam direction, and where the beam direction is normal to the areas of interest (Figure 1). Collection in such ideal areas of a grid would then be the most efficient use of resources and would result in the highest resolution structure possible for a given number of particles, collection hardware, and collection parameters.

Figure 1

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In practice, during single particle grid preparation and data collection there are many issues that contribute to preventing a sample from following this ideal behavior. As depicted in Figure 2, numerous combinations of air-water interface, particle, and ice behavior are possible for each hole and for regions within each hole, without taking into account surface ice contamination. Each air-water interface might be: (i) free from sample solution constituents (Figure 2, A1), (ii) covered with a layer of primary, secondary, and/or tertiary protein structures (either isolated or forming protein networks) from denatured particles (A2), or (iii) covered with one or more layers of surfactants if present during preparation (A3). It is difficult to distinguish between air-water interfaces that are clean, covered in primary protein structures, or covered in surfactants as they are likely indistinguishable by cryoEM or cryo-electron tomography (cryoET) analysis without a sample-free control for comparison (cryoET may be able to resolve lipid layers at the air-water interface if high tilt angles are collected [Vos et al., 2008]). Bulk particle behavior in regions of holes might include any combination of: (i) non-adsorbed particles without preferred orientation (B1), (ii) particles at an air-water interface without preferred orientation (B2), (iii) particles at an air-water interface with N-preferred orientations (B3), (iv) partially denatured particles at an air-water interface with M-preferred orientations (B4), and/or (v) significantly denatured particles at an air-water interface (B5). Protein degradation in A2 might be considered to be a continuation of the denaturation in B4 and B5. Interactions between neighboring particles at the air-water interface might induce different preferred orientations in B3 and B4, particularly at high concentrations. Ice behavior at the air-water interfaces of each hole might be characterized by any two combinations of: convex ice (C1), flat ice (C2), concave ice where the center is thicker than the particle’s minor axis (C3), and/or concave ice where the center is thinner than the particle’s minor axis (C4). In the case of a convex air-water interface, the particle’s minor axis might be larger than the ice thickness at the edge of the hole.

Figure 2

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The most common technique for preparing cryoEM grids, pioneered in the labs of Robert Glaeser (Jaffe and Glaeser, 1984; Taylor and Glaeser, 1974; Taylor and Glaeser, 1976) and Jacques Dubochet (Adrian et al., 1984; Dubochet et al., 1985; Dubochet et al., 1982), involves applying about 3 microliters of purified protein in solution onto a metal grid covered by a holey substrate that has been glow-

discharged to make hydrophilic, blotting the grid with filter paper, and plunge-freezing the grid with remaining sample into a cryogen to form vitreous ice. Incubation times before and after blotting are on the order of seconds, allowing for the possibility of protein adsorption to the air-water interface due to Brownian motion. Concerns regarding deleterious air-water interface interactions with proteins have been often discussed in the literature. For instance, Jacques Dubochet et al., 1988 observed issues with regards to air-water interface and particle orientation for a small number of samples. In a recent review by Robert Glaeser (Glaeser and Han, 2017), evidence (Trurnit, 1960) using Langmuir-Blodgett (LB) troughs (Langmuir, 1917) was used to propose that upon contact with a clean air-water interface, proteins in solution will denature, forming an insoluble, denatured protein film. This film reduces the surface tension at the air-water interface and might act as a barrier between the remaining particles in solution and the air. Particles in solution might then adsorb to the denatured layer of protein depending on the local particle affinity with the interface, thus creating an ensemble of preferred orientations. Estimates for the amount of time a particle with a mass of 100 kDa to 1 MDa in solution might take to first reach the air-water interface (bulk diffusion) range from 1 ms to 0.1 s (Naydenova and Russo, 2017; Taylor and Glaeser, 2008).

More recent literature, using LB troughs, substantiates that 10–1000 mL volumes of various proteins (commonly 10–1000 kDa and at ≲1 mg/mL) in buffer commonly adsorb to the air-water interface and form <10 nm thick (Gunning et al., 1996; Vliet et al., 2002) denatured viscoelastic protein network films (Birdi, 1972; Damodaran and Song, 1988; de Jongh et al., 2004; Dickinson et al., 1988; Graham and Phillips, 1979; Yano, 2012). The time it takes for adsorption to begin due to bulk diffusion may be on the order of 0.1 to 1 ms, depending on the protein (Kudryashova et al., 2005). For a protein that denatures at the air-water interface (surface diffusion), the surface diffusion time might be on the order of tens of milliseconds (Kudryashova et al., 2005), depending on factors including protein and concentration, surface hydrophobicity, amount of disordered structure, secondary structure, concentration of intramolecular disulfide bonds, buffer, and temperature. Higher bulk protein concentrations have been shown to increase the protein network thickness (Meinders et al., 2001). When several proteins and/or surfactants in solution are exposed to a clean air-water interface, competitive and/or sequential adsorption may occur (Ganzevles et al., 2008; Le Floch-Fouéré et al., 2010; Stanimirova et al., 2014). It has been shown using atomic force microscopy (AFM) imaging of LB protein films that these protein network films may not completely denature down to individual amino acids: adding surfactants to protein solutions in which a protein network film has already formed at the air-water interface will displace the protein layer (desorption [MacRitchie, 1998]) (Gunning and Morris, 2018; Mackie et al., 1999; Wilde et al., 2004) and the resulting protein network segments might partially re-fold in solution (Gunning and Morris, 2018; Mackie et al., 1999; Morris and Gunning, 2008). Time-resolved AFM surfactant-protein displacement experiments for a specific protein, β-lactoglobulin, and different surfactants, Tween 20 and Tween 60, show that displacement of the protein network film by the surfactants occurs at equivalent surface pressures and results in non-uniform surfactant domain growth, implying that the protein network is not uniform (Gunning et al., 2004). Different surfactant displacement behavior and patterns are observed while varying only the proteins, where the degree of protein network displacement isotropy by surfactant decreases for more ordered, globular proteins (Mackie et al., 1999). Non-uniformity of the protein network has also been seen by 3D AFM imaging of β-lactoglobulin LB-protein network films placed on mica (Gunning et al., 1996; Morris and Gunning, 2008). Similar experiments using LB troughs have also shown that proteins with β-sheets partially unfold, with the hydrophobic β-sheets remaining in-tact at the air-water interface and with potentially one or more layers of unstructured, but connected, hydrophilic amino acid strands just below the air-water interface (Yano et al., 2009). This potential for β-sheets to survive bulk protein denaturation is likely due to β-sheets commonly consisting of alternating hydrophobic and hydrophilic (polar or charged) sidechains (Zhang et al., 1993), with the hydrophobic sidechains orienting towards the air. Intermolecular β-sheets may also bind together, strengthening the protein network (Martin et al., 2005; Renault et al., 2002). Moreover, the number of random coils, α-helices, and β-sheets for a protein in bulk solution might each increase or decrease when introduced to a hydrophobic environment (Reddy and Nagara, 1989; Zangi et al., 2002), including the air-water interface (Martin et al., 2003; Yano, 2012), implying that protein conformation when adsorbed to the air-water interface could be different than when in solution (Lad et al., 2006; Vance et al., 2013; Yano, 2012). Measurements of shear stress and compressibility of protein network films versus the internal cohesion of the constituent protein show a correlation: the more stable a protein in bulk solution, the more robust the resulting protein network film at the air-water interface (Martin et al., 2005). At high enough surface concentrations and depending on surface charge distribution, neighboring globular proteins might interact to induce additional preferred orientations as has been shown in surface-protein studies (Billsten et al., 1995; Rabe et al., 2011; Tie et al., 2003). Such nearest neighbor protein-protein interactions may in turn decrease protein affinity to the interface and increase desorption. Similar effects might occur at protein-air-water interfaces.

Given the length of incubation time commonly permitted before plunging a grid for cryoEM analysis, the cross-disciplinary research discussed above suggests that some particles in a thin film on a cryoEM grid will form a viscoelastic protein network film at the air-water interface. The composition and surface profile of the resulting protein network film will vary depending on the structural integrity of the bulk protein and the bulk protein concentration. Bulk protein affinity to the protein network film will then vary depending on the local affinity between the film and the proteins. To better understand the range of particle behaviors with respect to the air-water interfaces in cryoEM grid holes, a representative ensemble of grid and sample preparations needs to be studied in three dimensions.

One method of studying single particle cryoEM grids is using cryoET. CryoET is typically practiced by adding gold fiducials to the sample preparation for tilt-series alignment, which requires additional optimization steps and might not be representative of the same sample prepared without gold fiducials. To avoid the issues imposed by gold fiducials, we have employed the fiducial-less tilt-series alignment method of Appion-Protomo (Noble and Stagg, 2015), allowing for cryoET analysis of all single particle cryoEM grids we have attempted. We used this fiducial-less cryoET method to investigate over 50 single particle cryoEM samples sourced from dozens of users and using grids prepared using either conventional grid preparation techniques or the new Spotiton (Jain et al., 2012) method. Our aim was to determine the locations of particles within the vitreous ice and the overall geometry of the ice in grid holes (related to the possible combinations in Figure 2).

We have also found that the usefulness of performing cryoET on a single particle cryoEM grid extends beyond the goal of understanding the arrangements of particles in the ice. CryoET allows for the determination of optimal collection locations and strategies, single particle post-processing recommendations, understanding particle structural heterogeneity, understanding pathological particles, and de novo model building. We contend that cryoET should be routinely performed on single particle cryoEM grids in order to fully understand the nature of the sample on the grid and to assist with the entire single particle collection and processing workflow. We have made available a standalone Docker version of the Appion-Protomo fiducial-less tilt-series alignment suite used in these investigations at https://github.com/nysbc/appion-protomo.

The fiducial-less tomography pipeline at the New York Structural Biology Center (NYSBC) consisting of Leginon (Suloway et al., 2005; Suloway et al., 2009) or SerialEM (Mastronarde, 2003) for tilt-series collection and Appion-Protomo (Noble and Stagg, 2015; Winkler and Taylor, 2006) for tilt-series alignment allows for the routine study of grids and samples prepared for single particle cryoEM in three dimensions. The resulting analysis sheds light on long standing questions regarding how single particle samples prepared using traditional methods (manual, Vitrobot, and CP3 plunging), or with new automated plunging with Spotiton (Jain et al., 2012), behave with respect to the air-water interfaces. In the following sections, we report and discuss how tomography collection areas were determined and analyzed, the observation that the vast majority of particles are local to the air-water interfaces and the implications with regards to potential denaturation, the prevalence of overlapping particles in the direction orthogonal to the grid, the observation that most cryoEM imaging areas and particles are tilted several degrees with respect to the electron beam, the value of cryoET to determine optimal collection locations and strategies, the benefits of using cryoET to understand pathological particle behavior, and the use of fiducial-less cryoET for isotropic de novo model generation.

Analysis of single particle tomograms

Single particle tomograms of samples described in Table 1 have each been analyzed visually using 3dmod from the IMOD package (Kremer et al., 1996). After orienting a tomogram such that one of the air-water interfaces is approximately parallel to the visual plane, traversing through the slices of the tomogram allows for the determination of relative particle locations, orientations, ice thickness variations in holes, and measurement of the minimum particle distance from the air-water interfaces. For many of the samples shown here and made available in the data depositions, particle orientations can be explicitly determined by direct visualization. Contamination on the surface of the air-water interface is used to determine the approximate location of the interface and to measure the ice thicknesses. After analyzing hundreds of single particle tomograms, we have concluded that sequestered layers of proteins in holes always correspond to an air-water interface, thus providing a second method for determining the location of the interface.

Table 1

Sample #	Sample name	Grid type	Ice thickness (center, edge, substrate) in nm ± a few nm			# of Layers (center, edge, substrate)			Apparent preferred orientation in layer?	Min. particle/layer distance from air- water interface (nm ± a few nm)
1*	32 kDa Kinase	Carbon Spotiton	65	45	--	0	0	0	Unknown	<5
2	32 kDa Kinase	Gold Spotiton	30	--	--	0	--	--	Unknown	<5
3	Insulin Receptor	Gold Spotiton	55	--	--	1–2	--	--	No	5
4*†	Hemagglutinin	Carbon Spotiton	25–95	100–210	--	0 or 2	2	--	Some	5
5*	HIV-1 Trimer Complex 1	Carbon Spotiton	75–210	--	--	2	--	--	Yes	5–10
6*	HIV-1 Trimer Complex 1	Gold Spotiton	20	--	--	1	--	--	Some	5
7*	HIV-1 Trimer Complex 2	Carbon Spotiton	190	265	--	2	2	2	Yes	5
8	147 kDa Kinase	Gold Spotiton	15	--	--	1	--	--	Unknown	<5
9	150 kDa Protein	Holey Carbon Spotiton	35	70	--	2	2	2	Some	<5
10*	Stick-like Protein 1‡	Carbon Spotiton	80	--	--	1	--	--	No	<5
11	Stick-like Protein 2 (150 kDa)‡	Carbon CFlat	100	100	--	1	1	--	Unknown	5
12*	Stick-like Protein 2‡	Gold Spotiton	135–190	--	--	1	--	--	Some	5
13*	Neural Receptor‡	Carbon Spotiton	60–90	--	--	1	--	--	Yes	5
14*	Neural Receptor‡	Carbon Spotiton	80–90	100–140	135	1	1	1	Yes	5
15	200 kDa Protein	CFlat Carbon + Gold mesh	40–60	95	110	1	1	2	No	5
16	Small, Popular Protein	Carbon Spotiton	30	70	--	1	2	2	No	5
17*	Glycoprotein with Bound Lipids (deglycosylated)	Carbon Spotiton	15	90	130	1	2	2	Yes	<5
18	Glycoprotein with Bound Lipids (deglycosylated)‡	Gold Spotiton	155	--	--	2	--	--	Some	<5
19*	Lipo-protein	Holey Carbon	0–95	85–100	--	Uniformly distributed in ice			Unknown	5
20*	GPCR	Carbon Spotiton	25	--	--	1	2	--	No	5
21*†	Rabbit Muscle Aldolase (1 mg/mL)	Gold Spotiton	15	50	--	1	2	--	No	<5
22*†	Rabbit Muscle Aldolase (6 mg/mL)	Carbon Spotiton	60–110	75–130	85	2	2	2	Some	5
23	Un-named Protein	Holey Carbon	35	--	60	1	--	2	Yes	5
25*	Protein in Nanodisc (0.58 mg/mL)	Gold Spotiton	30	65	--	1–2	2	--	No	5–10
26	IDE	Carbon Spotiton	25	60	95	1	2	2	Unknown	5
27*	IDE	Gold Spotiton	40	--	--	1	--	--	No	5–10
28	Small, Helical Protein	Gold Spotiton	50	75	--	1	2	--	Some	5
29	300 kDa Protein	Carbon Spotiton	30	100	--	1	2	2	No	5
30*†	GDH	Holey Carbon	30	85	100	1	1	3	Some	5
31*†	GDH	Holey Carbon	60	120	140	1	2	3	Yes	5
32*†	GDH (2.5 mg/mL)+0.001% DDM	Carbon Spotiton	50	180	190	1	2	--	Yes	<5
33*†	DnaB Helicase-helicase Loader	Gold Quantifoil	50–55	80–100	--	1	2	--	No	5
34*†	Apoferritin	Gold Spotiton	25–30	--	--	1	--	--	No	5
35*†	Apoferritin	Gold Spotiton	25	--	--	1	--	--	No	5
36*†	Apoferritin	Holey Carbon Spotiton	30	125	135	1	2	2	No	5
37*†	Apoferritin (1.25 mg/mL)	Holey Carbon Spotiton	30–50	100	105	1	2	2	No	5
38*†	Apoferritin (0.5 mg/mL)	Holey Gold Spotiton	25–30	55	--	1	2	--	No	<5
39*†	Apoferritin with 0.5 mM TCEP	Carbon Spotiton	40–90	145–175	--	1–2	2	1	No	5
40	Protein with Carbon Over Holes	Carbon Quantifoil	110	70–100	--	1	1	--	Some	5–10
41	Protein and DNA Strands with Carbon Over Holes	Carbon Quantifoil	60	--	--	1	--	--	Some	5–10
42*†	T20S Proteasome	Holey Carbon	35	115	120	1	2	3	Some	<5
43*†	T20S Proteasome	Holey Carbon	125	140–160	150	2	2	2	Some	5
44*†	T20S Proteasome	Gold Quantifoil	50–75	--	--	1	--	--	Some	5
45*†	Mtb 20S Proteasome	Carbon Spotiton	35	80	115	0	1	1	No	5–10
46	Protein on Streptavidin	Holey Carbon	20–100	80–120	--	0–2	1–2	--	No	10

1. *A video is included for this sample.

   †A dataset is deposited for this sample.

2. ‡Intentionally thick ice.

Table 1 is organized with the single particle sample mass in roughly descending order. Over 1000 single particle tomograms of over 50 different sample preparations have been collected over a 1-year period. Most of these samples are reported on here. These samples include widely studied specimen such as glutamate dehydrogenase (GDH), apoferritin, and T20S proteasome (samples #30–32, #34–39, and #42–44, respectively), along with various unique specimens such as a neural receptors, lipo-protein, and particles on affinity grids (samples #13,14, #19, and #40, 41, 46, respectively). Samples that are not specifically named have yet to be published. Over half of the samples were prepared on gold or carbon nanowire grids, while the remaining were prepared on a variety of carbon and gold holey grids using common cryo-plunging machines and techniques. Samples showing regions of ice in grid holes with near-ideal conditions – less than 100 nm ice thickness, no overlapping particles, and little or no preferred orientation – are highlighted in blue (21 of 46 samples; 46%) in Tables 1 and 2. Samples showing regions of ice in grid holes with ideal conditions – near-ideal conditions plus no particle-air-water interface interaction – are highlighted in green (2 of 46 samples; 4%). Over half of the samples only contained areas that are not ideal for collection due to ice thickness being greater than 100 nm, overlapping particles, and/or preferred orientation.

Table 2

Sample #	Sample name	Air-water interface, particle behavior, and layer/ice angle (bottom, center)	Air-water interface, particle behavior, and layer/ice angle (bottom, edge)	Ice behavior (bottom)	Air-water interface, particle behavior, and layer/ice angle (top, center)	Air-water interface, particle behavior, and layer/ice angle (top, edge)	Ice behavior (top)	Notes
1*	32 kDa Kinase	A, B1 or B2 or B3 (50%), 8°	A, B1 or B2 or B3 (50%), 10°	C2	A, B1 or B2 or B3‡ (50%), 8°	A, B1 or B2 or B3‡ (50%), 10°	C2	Particles aggregate into clouds.
2	32 kDa Kinase	A, B1 or B2 or B3 (50%), 4–8°	--	C1 or C2	A, B1 or B2 or B3‡ (50%), 4–8°	--	C1 or C2	Gold beads are glow discharge contamination.
3	Insulin Receptor	A, B1 or B2 or B3 (100%), 3–5°	--	C2 or C3	A, B1 or B2 or B3‡ (100%), 3–5°	--	C2 or C3	Gold beads are glow discharge contamination.
4*†	Hemagglutinin	A2, No particles, 3–7°	A, B3 (40%), 5° or A, B3 (40%), 3°	C3 or C4	A2‡, No particles, 3–7° or A, B3 (50%), 7°	A, B3 (50%), 5–7°	C3 or C4	Where very thin ice in the center of holes excludes particles, protein fragments remain.
5*	HIV-1 Trimer Complex 1	A2, B1, B3 (30%), 1–5°	--	C1, C2, or C3	A2, B1, B3 (30%), 1–5°	--	C1, C2, or C3	Trimer domains and/or unbound receptors are adsorbed to air-water interfaces.
6*	HIV-1 Trimer Complex 1	A2, B3 (80%), 6°	--	C2	A2, B3‡ (80%), 6°	--	C2	Trimer domains and/or unbound receptors are adsorbed to air-water interfaces.
7*	HIV-1 Trimer Complex 2	A, B2 or B3 (50%), 1°	A, B2 or B3 (50%), 3°	C1 or C2	A, B2 or B3 (70%), 1°	A, B2 or B3 (70%), 3°	C1 or C2
8	147 kDa Kinase	A, B2 or B3 (50%), 0°	--	C2 or C3	A, B2 or B3‡ (50%), 0°	--	C2 or C3	Gold beads are glow discharge contamination.
9	150 kDa Protein	A, B2 or B3 (60%), 7–10°	A, B2 or B3 (60%), 8°	C2 or C3	A, B2 or B3‡ (60%), 7°	A, B2 or B3 (40%), 9°	C2 or C3
10*	Stick-like Protein 1	A and A2, B4 and B5 (1%), 10°	--	C2	A2, B4 and B5 (50%), 10°	--	C2
11	Stick-like Protein 2 (150 kDa)	A2, B3 and B4 and B5 (70%), 7°	A2, B3 and B4 and B5 (70%), 7°	--	A2, B3 and B4 and B5‡ (70%), 7°	A2, B3 and B4 and B5‡ (70%), 7°	--	Determinations are not accurate due to over focusing and minimal tilt angles.
12*	Stick-like Protein 2	A2, B3 (80%), 0°	--	C2 or C3	A2, B3 (1%), 0°	--	C2 or C3	Note 1. Note 2.
13*	Neural Receptor	A2, B3 (80%), 3–10°	--	C2 or C3	A2, No particles, 3–10°	--	C2 or C3	Note 1. Note 2.
14*	Neural Receptor	--	A2, No particles, 2–7° or A2, B3 (70%), 5°	C3	--	A2, B3 (70%), 7° or A2, No particles, 7°	C3	Note 1. Note 2. Two tomograms have one orientation, one has the opposite.
15	200 kDa Protein	A, B2 or B3 (60%), 2°	A, B2 or B3 (50%), 4°	C3	No particles or A, B2 or B3‡ (60%), 2°	A, No particles, 11°	C3
16	Small, Popular Protein	A, B2 or B3 (90%), 6°	A, B2 or B3 (90%), 9°	C2	A, B2 or B3‡ (90%), 6°	A, B2 or B3 (90%), 1°	C3
17*	Glycoprotein with Bound Lipids (deglycosylated)	A, B3 (70%), 4°	A, B3 (80%), 10°	C3	A, B3‡ (70%), 4°	A, B3 (80%), 11°	C3	Lipid membrane dissociates from protein in center.
18	Glycoprotein with Bound Lipids (glycosylated)	A, B3 (50%), 10°	--	C2 or C3	A, B3 (60%), 4°	--	C2 or C3
19*	Lipo-protein	No particles or A, B2, 3°	A, B3, 11°	C3, C4	No particles or A, B2‡, 5°	A, B3, 11°	C3, C4	Particles are uniformly distributed in the ice.
20*	GPCR	A, B2 or B3 (70%), 3°	A, B2 or B3 (60%), --	C3	A, B2 or B3‡ (70%), 3°	A, B2 or B3 (60%), --	C3
21*†	Rabbit Muscle Aldolase (1 mg/mL)	A, B2 or B3 (90%), 3–9°	A, B2 or B3 (80%), 6°	C3	A, B2 or B3‡ (90%), 3–9°	A, B2 or B3 (80%), 10°	C3
22*†	Rabbit Muscle Aldolase (6 mg/mL)	A, B1, B2 or B3 (90%), 5°	A, B1, B2 or B3 (90%), 5°	C2 or C3	A, B1, B2 or B3 (90%), 5°	A, B1, B2 or B3 (90%), 5°	C2 or C3
23	Un-named Protein	A, B3 (40%), 0–3°	--	C2 or C3	A, B3‡ (40%), 0–3°	--	C2 or C3
24	Un-named Protein	A, B3 (80%), 2°	A, B3 (60%), 4–6°	C3	A, B3‡ (80%), 2°	A, B3 (60%), 4–9°	C3
25*	Protein in Nanodisc (0.58 mg/mL)	A, B2 (80%), 8–10°	A, B2 (80%), 8–10°	C2 or C3	A, B2‡ (80%), 8–10°	A, B2 (80%), 8–10°	C2 or C3
26	IDE	A2, B2 or B3 and B4 and B5 (50%), 0°	A2, B1, B2 or B3 and B4 and B5 (50%), 5°	C3	A2, B2 or B3 and B4 and B5‡ (50%), 0°	A2, B1, B2 or B3 and B4 and B5 (50%), 2°	C3	Note 1.
27*	IDE	A, B2 or B3 (95%), 0–4°	--	C2	A, B2 or B3 (95%), 0–4°	--	C2
28	Small, Helical Protein	A, B2 or B3 (80%), 5°	A, B2 or B3 (70%), 3°	C3	A, B2 or B3‡ (80%), 5°	A, B2 or B3 (70%), 7°	C3
29	300 kDa Protein	A or A2, B2 or B3 (70%), 7°	A or A2, B2 or B3 (50%), 13°	C3	A or A2, B2 or B3‡ (70%), 7°	A or A2, B2 or B3 (50%), 9°	C3
30*†	GDH	A, B3 (70%), 10°	A, B1, B3 (50%), 1°	C2	A, B3‡ (70%), 10°	A, B1, B3 (50%), 16°	C3	Note 2. Some non-adsorbed particles stack between layers.
31*†	GDH	A, B3 (40%), --	A, B1, B3 (40%), 10°	C3	A, B3‡ (40%), --	A, B1, B3 (40%), 2°	C2
32*†	GDH (2.5 mg/mL)+0.001% DDM	A, B3 (40%), 4°	A, B1, B3 (40%), 7°	C2	A, B3‡ (30%), 4°	A, B1, B3 (30%), 6°	C3	Some non-adsorbed particles stack between layers.
33*†	DnaB Helicase-helicase Loader	A, B2 or B3 (90%), 1°	A, B2 or B3 (90%), 4°	C3	A, B2 or B3 (<5%), 1°	A, B2 or B3 (<5%), 1°	C2	Gold flakes from Quantifoil are on the top.
34*†	Apoferritin	A2, B2 or B3 (50%), 4–6°	--	C2 or C3	A2, B2 or B3‡ (50%), 4–6°	--	C2 or C3	Note 1. Note 2.
35*†	Apoferritin	A2, B2 or B3 (60%), 4–12°	--	C2 or C3	A2, B2 or B3‡ (60%), 4–12°	--	C2 or C3	Note 1. Note 2.
36*†	Apoferritin	A2, B3 (50%), 5°	A2, B1, B3 (50%), 10°	C3	A2, B3‡ (70%), 5°	A2, B1, B3 (60%), 3°	C3	Note 1. Note 2.
37*†	Apoferritin (1.25 mg/mL)	A2, B2 or B3 (50%), 4–7°	A2, B1, B2 or B3 (50%), 6°	C3	A2, B2 or B3‡ (40%), 4°	A2, B1, B2 or B3 (30%), 4°	C3	Note 1. Note 2.
38*†	Apoferritin (0.5 mg/mL)	A2, B2 or B3 (20%), 5°	--	C2 or C3	A2, B2 or B3‡ (20%), 1°	--	C2 or C3	Note 1. Note 2.
39*†	Apoferritin with 0.5 mM TCEP	A, B2 or B3 (40%), -- or A, B2 or B3 (50%), 3°	A, B1, B2 or B3 (40%), 5–9°	C3	A, B2 or B3 (40%), -- or A, B2 or B3‡ (50%), 3°	A, B1, B2 or B3 (40%), 2–8°	C3	Note 1. Note 2.
40	Protein with Carbon Over Holes	Carbon, B1 (30%), B3 (60%), 5°	Carbon, B1 (30%), B3 (60%), 5–9°	C2	A, B3 (5%), 5°	A, B3 (5%), 5°	C1 or C2	Note 3.
41	Protein and DNA Strands with Carbon Over Holes	A, No particles, 2–3°	--	C2 or C3	Carbon, B1 (20%), B3 (60%), 2–3°	--	C2	Some non-adsorbed particles make contact with particle layer. Most non-adsorbed particles are attached to DNA strands.
42*†	T20S Proteasome	A, B3 (80%), 3°	A, B1 (5%), B3 (80%), 14°	C3	A, B3‡ (80%), 3°	A, B1 (5%), B3 (20%), 3°	C2	Note 2. Note 3.
43*†	T20S Proteasome	A, B3 (10%), 2–5°	A, B3 (10%), 2–5°	C2	A, B1 (20%), B3 (90%), 5–7°	A, B1 (20%), B3 (95%), 5–7°	C3	Note 3.
44*†	T20S Proteasome	A, B1 (10%), B3 (80%), 11°	--	C3	A, B3 (2%), 11°	--	C2	Note 2. Note 3.
45*†	Mtb 20S Proteasome	--	A, B1, B2 or B3 (30%), 6°	C3	--	A, B1, B2 or B3 (30%), 11°	C3	Heavy contamination.
46	Protein on Streptavidin	Streptavidin, B2 (10–30%), 0° or Streptavidin, No particles, 12°	Streptavidin or A2, 2 (10–30%), 12°	C1, C2, or C3	Streptavidin, B2 (10–30%), 0° or Streptavidin‡, No particles, 12°	Streptavidin, 2 (10–30%), 13–14°	C1, C2, or C3	Note 1. Some holes have a layer of streptavidin only on top, some have a layer on top and bottom. Particles are attached to streptavidin and sometimes the apposed air-water interface.

1. *A video is included for sample.

   †A dataset is deposited for sample.

2. Note 1: Apparent protein fragments/domains are adsorbed to the air-water interfaces.

   Note 2: Partial particles exist.

3. Note 3: Non-adsorbed particles make contact with particle layer.

Ice thickness

Averages ± (one standard deviation and measurement error) of the minimum ice thickness at the center and near the edge of grid holes was calculated. At the center, the ice thickness is about 30 ± 13 nm for gold nanowire grids prepared with Spotiton (N = 11), 47 ± 40 nm for carbon nanowire grids prepared with Spotiton (N = 17), and 56 ± 35 nm for carbon holey grids prepared using conventional methods (N = 10) (Figure 3A). Ice thickness about 100 nm from the edge of grid holes is about 61 ± 11 nm for gold nanowire grids prepared with Spotiton (N = 4), 107 ± 54 nm for carbon nanowire grids prepared with Spotiton (N = 16), and 99 ± 24 nm for carbon holey grids prepared using conventional methods (N = 8) (Figure 3B).

Figure 3

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Figure 3—source data 1

Ice thickness and angle measurements for Figure 3.

https://doi.org/10.7554/eLife.34257.007

Download elife-34257-fig3-data1-v2.xlsx

Table 2 categorizes each sample in terms of Figure 2. Categorizations into A, B, and C, where possible, have been judged by visual inspection. Air-water interfaces that are visually clean are denoted with ‘A’ from Figure 2 due to A1, A2 (primary structure), and A3 being indistinguishable by cryoET without collecting high tilt angles, which was not done in this study. For particles smaller than about 100 kDa, distinguishing between A1/A3 and A2 was not possible by cryoET.

If a region in grid holes contains layers of particles relative to the air-water interface (possibly B1 – B4), then the particle saturation of the corresponding layer is recorded in Table 2 as an approximate percentage in parentheses where 100% means that no additional particles could be fit into the layer. The angle of particle layer with respect to the electron beam is recorded for each region if applicable. The average tilt ± (one standard deviation and measurement error) of layers at the centers of holes is 4.7 ± 3.0° and at the edges of holes is 6.9 ± 3.5° (Figure 3). There is no apparent correlation between microscope and tilt direction or magnitude. About 83% of the samples contained single particle layers (N = 30) in the centers of holes while about 22% contained double particle layers (N = 8; several samples have different holes with single and double layers of particles in their centers). Near the edges of holes, about 7% contain single particle layers (N = 2), while about 75% contained double particle layers (N = 21). Finally, in Table 2 the ice curvature of each air-water interface is specified using the options in Figure 2C. For these measurements, the bottom of each tomogram is defined as having a lower z-slice value than the top as viewed in 3dmod, yet the relative orientation of each recorded sample is not known due to unknown sample application orientation on the grid relative to the EM stage. Thus, correlations between air-water interface behavior and sample application direction on the grids cannot be made from this study.

Cross-sectional depictions

Several schematic diagrams of cross-sections of particle and ice behavior in holes as determined by cryoET are shown in Figure 4 for selected samples and tomograms. Ice thickness measurements and particle sizes are approximately to scale. Each cross-section is tilted corresponding to the tilt of the tomogram from which it was derived relative to the electron beam. The preferred orientation distributions are reflected in the cross-sectional depictions. The cross-sectional characteristics depicted are not necessarily representative of the average because only one of several collected tomograms are depicted.

Figure 4

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Several tomographic slice-through videos from representative imaging areas of samples are shown in the included Videos. Most of the Videos include the corresponding hole magnification image, which is an order of magnitude lower magnification than exposure magnification, with the location of the targeted area specified. Tilt-series collection range, grid type, and collection equipment are also specified. Tomography may also be performed at hole magnification, allowing for particle location determination across multiple sized holes, ice thickness determination, and local grid tilt (Video 1 - sample #20). For sample #20, a GPCR with a particle extent of about 5 nm, a tomographic analysis at hole magnification (about 20 Å pixelsize) is sufficient to localize ice contamination, particle layers, and to measure ice thickness with an accuracy of about 10 nm. To orient the reader to this single particle tomography data, Figure 5 shows tomogram slice-throughs of adsorbed and non-adsorbed particles for a selection of samples with thicker ice.

Figure 5

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Video 1

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The vast majority of particles are localized to the air-water interfaces

The primary result gleaned from over 1000 single particle tomograms of over 50 different grid/sample preparations is that the vast majority of all particles (approximately 90%) are local to an air-water interface. As shown in Table 1, Table 2, Figure 4, and the Videos, most particles

prepared with sample incubation times on the order of 1 s on the grid are within 5–10 nm of an air-water interface (ie. are characterized by B2, B3, or B4 in Table 2). This observation implies that most particles, not only in this study but in cryoEM single particle studies as a whole, are adsorbed to an air-water interface.

Particle adsorption sometimes implies preferred orientation

A sequestered particle that is adsorbed to a clean air-water interface and that has had time to equilibrate will likely be oriented relative to that air-water interface such that the local surface hydrophobicity of the particle is maximally exposed, assuming that the particle is not prone to denaturation at the interface. If a particle is prone to denaturation at the interface and if the interface is already coated with a denatured layer of protein, then the preferred orientations of the same sequestered particle on the protein film-air-water interface might change. If the particle is not sequestered, but is in a protein-concentrated environment, then neighboring particle-particle interactions might change the possible preferred orientations of the particles. For each of these cases, an ensemble of particles at air-water interfaces arrived at by diffusion, as is the case with most single particle cryoEM datasets, will exhibit all possible particle orientations. The percentage of particles in each preferred orientation might be then mapped back onto all possible relative local particle-air-water interface affinities. Particles that have had less time to equilibrate before observation (e.g. before plunge-freezing) might have more realized orientations in the ensemble than if they had more time to equilibrate at the air-water interfaces. Several example tomogram slice-throughs of samples with varying amounts of apparent preferred orientations at and away from air-water interfaces are shown in Figures 5 and 6.

Figure 6

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Protein adsorption to an air-water interface has potential consequences with regard to protein denaturation, data collection, and image processing. In the remainder of this section, we will discuss the implications of protein adsorption on protein denaturation and present possible evidence of air-water interface denaturation from cryoET.

Observed denatured proteins by cryoET

Several samples show clear protein fragments at air-water interfaces (samples #4–6, 10–14, 26, 30, 34–38, and 46; Figure 6A–E, blue arrows). The neural receptor, hemagglutinin, HIV-1 trimer complex 1, apoferritin, and GDH samples in particular (samples #13, #35, #4, #5, and #30, respectively) show protein fragments and domains on the air-water interfaces (Figure 6A–E and corresponding Videos, blue arrows). For the neural receptors (sample #13), densities on the air-water interface show a clear relationship in size to the 13 kDa Ig-like domains that constitute the proteins. Several apoferritin samples (samples #34–38) also show apparent protein fragments at the air-water interfaces (Figure 6B and Videos 2–7 corresponding to samples #34–38). One hemagglutinin sample contained holes where the ice became too thin for whole particles to reside and is instead occupied exclusively by protein fragments (Figure 6C and Video 7 corresponding to sample #4). An HIV-1 trimer sample also shows clear protein fragments on each air-water interface, although these are likely receptors intentionally introduced to solution before plunge-freezing (Figure 6D and Video 8 corresponding to sample #5). GDH similarly shows sequestered protein fragments in open areas near particles at the air-water interface (Figure 6E and Video 9 corresponding to sample #30).

Video 2

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Video 5

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Video 7

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Video 8

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Video 9

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Several samples show clear partial particles at air-water interfaces (samples 10–14, 34–39, and 46; Figure 6A,B,F, green arrows). Neural receptor (sample #13) particle fragments can be seen adsorbed to the air-water interface (Figure 6A, green arrow). Sample #13 consists of two distinct air-water interfaces, as can be seen in Figure 6A, where the bottom interface is covered with particles and protein fragments while the top interface is covered with protein fragments and a small number of partial particles (see also Video 11 corresponding to sample #13). The partial T20S proteasome particles shown in Figure 6F and the Video 10 (sample #42) might be an example of protein denaturation at the air-water interface. In this sample, the observed partial particles are oriented as rare top-views rather than abundant side-views of the particle and exist adjacent to areas of the air-water interface that do not harbor adsorbed particles. Also of note is that all of the domains of the neural receptor and some of the domains of apoferritin, hemagglutinin, HIV-1 trimer complex 1, and GDH are composed of series of β-sheets, which have the potential to not denature at the air-water interface. This observation might correlate with the cross-disciplinary literature presented in the introduction showing that β-sheets may potentially survive air-water interface interaction (Martin et al., 2005; Renault et al., 2002; Yano et al., 2009). It is unclear, however, whether these unclean air-water interfaces are due to unclean preparation conditions (Glaeser et al., 2016), protein degradation in solution, unfolding at the air-water interfaces, or a combination of these factors.

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Video 11

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While the observations described above might correlate with the research from the food science and surface physics literature as outlined in the introduction, it is not clear from this study whether particles are adsorbed to films of denatured protein at the air-water interface or if some particles are adsorbed directly to the air-water interface. From the cross-disciplinary literature presented in the introduction, we speculate that adsorption rates for proteins that first denature at the air-water interface will differ from those that adsorb directly to the air-water interface. For a protein that does denature at the air-water interface, there is an additional amount of diffusion time, possibly on the order of tens of milliseconds, for surface diffusion to take place. Proteins that adsorb directly to the air-water interface are only time-limited by the bulk diffusion time of that sample preparation. The bulk diffusion time may be orders of magnitudes less than the surface diffusion time. The rate at which proteins adsorb to a protein network film depends on the affinity between that protein film and the bulk particles. The additional surface diffusion time along with the additional bulk protein adsorption time to the denatured protein film may allow for speed advances in sample application and plunging to outrun bulk protein adsorption to the denatured proteins on the air-water interfaces, depending on the grid preparation and particle behavior. Secondary effects, such as bulk particle flow – in conventional grid preparation when blotting paper is applied and in nanowire grid preparation with Spotiton when the protein solution reaches the nanowires on the grid bars and wicks away – and flow due to thermal convection – potentially due to contact with tweezers and the blotting process – may change the effective concentration of bulk particles near the air-water interfaces.

Protein network films may not be particle-friendly

Evidence from the literature in the introduction shows that proteins do denature at air-water interfaces, with an apparent dependency on protein concentration and structural rigidity. Evidence from this study showing that some air-water interfaces do harbor protein fragments and/or partial particles might be additional examples of denaturation due to the air-water interface. Evidence from LB trough studies of the small, disordered protein β-casein additionally show that increasing the concentration of bulk proteins in solution from 0.1 to 100 mg/mL results in an increased thickness of the denatured protein film at the air-water interface from 5 to 50 nm (Meinders et al., 2001). This observation implies that bulk proteins may denature not only at the air-water interface, but also at the subsequently formed protein network film interface depending on the bulk protein concentration. This in turn implies that proteins adsorbed to the protein film undergo conformational change, at least at higher concentrations. Thus, if an increase in the thickness of a protein network film of a given protein at high concentration is observed, concern that bulk proteins adsorbed to the protein network film are undergoing conformational change might be warranted. We speculate that if particles are undergoing conformational change at either the protein-air-water interface or at the protein-protein network interface, then anomalous structures might be present after 2D and 3D classification that are practically indistinguishable from the nominal structures. These anomalous structures might contribute toward artefactual 3D reconstructions, towards lower resolutions, and/or toward lower density contributions on the peripheries of resulting 3D reconstructions. In the last two cases, lower resolutions on the peripheries of the reconstruction might also be a result of radial inaccuracies in alignment, and thus these two resolution-degrading factors would need to be decoupled on a per-sample basis before drawing conclusions. Apoferritin, as shown in Figure 6B and the Videos 2–6 (samples #34–38), might be an explicit example of observed conformational change due to the air-water interface if the observed particle degradation is indeed caused by air-water interface denaturation.

Air-water interface symmetries and asymmetries

Several samples show an asymmetry between particle saturation at the top and bottom air-water interfaces. For example, samples #10, 12–15, 33, and 44 have particles covering one air-water interface with the other interface showing no particles, samples #4, 7, 9, 18, 32, 36, 39, 42, and 43 have more particles covering one air-water interface than the other, and samples #1, 8, 9, 16, 17, 20–22, 24–31, 39, and 45 have a roughly equal number of particles on each air-water interface (Figure 6). Particles that layer only on one air-water interface suggest that they are either sticking to the first available air-water interface (the interface on the back of the grid prior to blotting for conventional grid preparation techniques or the interface in the direction of application momentum for Spotiton), or to the first-formed protein network film. This first-formed protein network film might form nearly instantaneously after the first air-water interface is created with the sample dispenser. For a particle that denatures at the air-water interface, since the bulk diffusion time is one or more orders of magnitude less than the surface diffusion time, if the second available air-water interface is formed before the first air-water interface is saturated with bulk particles and if the protein concentration is high enough, then one might expect denaturation to occur at the second air-water interface. This would allow for a layer of particles to adsorb to each air-water interface. Further study into such sample behavior using cryoET while taking into account sample application directionality might lead to a clearer model for why particles adsorb preferentially to one air-water interface over the other.

Ideal samples are a rarity

Only two samples, #25: protein in nanodisc and #46: protein on streptavidin, exhibit ideal characteristics – less than 100 nm ice thickness, no overlapping particles, little or no preferred orientation, and areas with no particle-air-water interface interaction. Sample #25 contains regions of single layers of particles in nanodiscs without preferred orientation in 30 nm ice (see corresponding Video 12). While the particle layers are on the air-water interfaces in thicker areas near the edges of holes, the lack of preferred orientation implies that some fraction of the particles contain protein that is not in contact with the air-water interface, thus satisfying the ideal condition. Sample #46 contains particles dispersed on streptavidin, which is used to both randomly orient the particles and to avoid at least one air-water interface (Figure 4). The majority of areas with particles consists of ice thin enough to satisfy the ideal condition.

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A single particle dataset consisting primarily of adsorbed particles to air-water interfaces not only opens up the possibility of protein and degradation conformational change as described in this section, but additionally has implications on data collection and image processing as described in the next three sections.

A significant fraction of areas in holes have overlapping particles in the electron beam direction

A large fraction of the samples studied here contain imaging areas in holes, often limited to near the edges of holes, contain a single layer of particles at an air-water interface with additional non-adsorbed particles or two layers of particles with or without additional non-adsorbed particles (denoted in Table 1 as having 1+, 2, or 2+ layers in holes) (Figure 3). When this occurs, it is often the case that projection images collected in these areas will contain overlapping particles (Figure 7A, middle and right). These overlapping particles may cause several issues. First, overlapping particles picked as one particle will need to be discarded during post-processing (particles not circled in Figure 7). If these particles are not discarded, then anomalous results might be expected in any 3D refinement containing these particles – particularly in refinement models that use maximum likelihood methods such as Relion (Scheres, 2012), cryoSPARC (Punjani et al., 2017), and Xmipp (Scheres et al., 2007; Scheres et al., 2008) – thus reducing the reliability and accuracy of the refinement results. Second, overlapping particles reduce the accuracy of whole-image defocus estimation (as depicted by particle color in Figure 7). For instance, an exposure area perpendicular to the electron beam containing two parallel layers of particles with identical concentrations will result in a whole-image defocus estimation located halfway-between the two layers, thus limiting the resolution of each particle depending on their distance from the midway point. For such an image collected with a defocus range of 1 to 2 microns and with a 10 nm deviation from the midway point, the particles will have a resolution limit of about 2.5 Å. A 50 nm deviation from the midway point will result in a resolution limit of about 6 Å. Third, overlapping particles might reduce the accuracy of per-particle or local defocus estimation. If the concentrations of overlapping particles are too high, then local and potentially per-particle defocus estimation might contain fragments of particles at different heights than the particle of interest. Fourth, overlapping particles reduce the efficiency of data processing and thus data collection. The second and the third issues posed above might be partially resolved if the ice thickness is known by duplicating each particle, CTF correcting one

Figure 7

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with (midway defocus + thickness/2) and the other with (midway defocus – thickness/2), then discarding the particle with the lower high-frequency cross correlation value partway through single particle alignment. The issues posed above may be a primary source of discarded particles during mean filtering, CTF confidence filtering, 2D classification, and 3D classification.

Fiducial-less cryoET may be used to determine optimal single particle collection areas and strategies

As shown in Table 1 and Figure 3, ice thickness in holes is commonly greater at the edges than in the centers. Most samples that have this ice behavior have a single layer of particles on one air-water interface, with either a second layer on the apposed air-water interface or additional non-adsorbed particles, or both (Figure 3). At a certain distance from the edge of the holes (usually between 100 to 500 nm from the edge), the ice commonly becomes thin enough for only one layer of particles to fit between – usually the particle’s minor axis plus 10 to 20 nm of space between the particles and the air-water interfaces. Provided that particle concentration is high enough for accurate CTF estimation, specimen drift is low enough for sufficient correction, and the particles have little or no apparent preferred orientation, then collection a certain distance away from the edges of these holes would be the most efficient use of resources. Collection in these areas would be less likely to result in anomalous structures compared with collecting in thicker areas with overlapping particles in projections (Figure 8, left). If in the same case the

Figure 8

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particles show preferred orientations in tomography, then the second most efficient and accurate collection method would be collecting while intentionally tilting the stage (Tan et al., 2017), provided that the sample drift is sufficiently low and the concentration is not so high that neighboring particles begin to overlap in the tilted projections (Figure 8, middle).

However, if the ice is consistently thick across the holes and across the grid, and/or there is a significant number of overlapping particles in the direction of the electron beam, then it might be determined from cryoET that the sample is not fit for high resolution collection (Figure 8, right). If the type of grid used is lacey, then tomography at hole magnification where the imaging area includes several hole sizes may be used to determine hole sizes with thinner ice and to determine if there are one or two particle layers in these areas (Video 1 for sample #20, deposition data for sample #36). Routinely performing cryoET on cryoEM grids allows for sample owners to determine where and how to collect optimal data most efficiently, or to determine whether or not the grid is collectible to the desired resolution. It takes about 30 to 45 min to collect, process, and analyze a single tomogram. Thus, routine single particle grid and sample characterization by cryoET may not only provide information for optimizing grid preparation of a particular sample, but may also increase microscope efficiency.

Fiducial-less cryoET may be used to understand critical protein behavior

During the course of this study, cryoET of single particle cryoEM grids has been valuable and even critical for understanding particle stoichiometry and anomalous behavior. For example, cryoET has been used on several HIV-1 trimer preparations with receptors to understand the stoichiometry of the bound receptors by direct visualization of individual particles in 3D (samples #5–7 corresponding to Videos 8, 13 and 14). In another example, sample #17, the size of the ‘glycoprotein with bound lipids’ particles varied discretely with the radial distance from the edge of holes (Figure 4 and Video 15). In single particle cryoEM micrographs, this observation was not immediately explicable and would have required a single particle data collection followed by alignment and classification before reliable conclusions could be made. Instead, a single tomogram of the sample was collected and it was observed that near the edges of the hole the particles with lipids existed in two layers at the air-water interfaces. Beyond a radial distance from the edge of about 300 nm where the ice became about 15 nm thin the particles and lipids dissociated, with the particles remaining in a single layer (see Video 1 for sample #20). A solution to this issue was found where glycosylated particles were prepared using Spotiton with conditions that intentionally created thick ice (Figure 4, sample #18). A further example highlighting the importance of using cryoET to understand the behavior of samples on grids is sample #40 (Figure 4). This sample consisted of a very low concentration of particles in solution prepared with a carbon layer over holes to increase the concentration in holes. CryoET showed that the particles were forming two layers on the carbon: a layer directly on the carbon with about 60% saturation and a layer scattered on top of the first layer with about 30% saturation. This observation made clear that particle overlap would be an issue in single particle processing and introduced the possibility that since the particle layers were directly touching that this might induce conformational change in some of the particles. Similarly for sample #41 (Figure 4), cryoET on particles and DNA strands prepared with carbon over holes revealed that a considerable fraction of projection areas consisted of overlapping particles due to some non-adsorbed particles attached to DNA strands. In this situation, it was determined that single particle cryoEM on this sample would be highly inefficient for studying the complex of interest. In the cases described here, cryoET was an expedient and sometimes indispensable method for determining particle behavior.

Video 13

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Video 14

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Video 15

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Fiducial-less SPT can generate de novo initial models with no additional preparation

A useful and sometimes critical benefit of being able to perform fiducial-less cryoET on a single particle grid is that the resulting tomograms can be processed through single particle tomography alignment and classification in order to generate de novo templates for single particle micrograph picking and for use as initial models in single particle alignment (Cong and Ludtke, 2010). Inconsistencies in ab initio reconstructions can lead to structural uncertainties during refinement, as shown in the literature (Ludtke et al., 2011). In one example reported here (sample #33 and the corresponding Video 16), Gaussian particle picking and 2D classification of DnaB helicase-helicase loader particles from single particle micrographs showed one predominant orientation with apparent C6 symmetry and very few different orientations (Figure 9A). Efforts to generate an ab initio reconstruction with common-lines approaches (Elmlund and Elmlund, 2012; Ludtke et al., 1999) failed (Figure 9A). We suspected that a reliable template could not be generated due to missing many low contrast side-views and more complete particle picking could not be performed without a reliable template – a classic catch-22.

Figure 9

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Video 16

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To ameliorate this problem, five tilt-series in representative areas were collected at the end of a single particle collection session, aligned in Appion-Protomo (Noble and Stagg, 2015; Winkler and Taylor, 2006), and about 1000 particles were processed through sub-tomogram alignment, classification, and multireference alignment using Dynamo (Castaño-Díez et al., 2012; Castaño-Díez et al., 2017). This resulted in three de novo initial models, each showing an asymmetric cracked ring (Figure 9B), contradicting the C6-symmetric reconstruction determined by 2D classification and common-lines approaches. The most populated class from single particle tomography (SPT) was then used to both template pick the single particle micrographs in Relion (Scheres, 2012) and as initial models for single particle alignment, resulting in a 4.1 Å structure of the DnaB helicase-helicase loader (manuscript in preparation) (Figure 9B). In this example, cryoET revealed that the apparent symmetry in the prevalent top view particles as seen in the Gaussian picked 2D class averages was in fact a projection of the globally asymmetric particle. There are two key benefits to performing fiducial-less cryoET to generate de novo initial models as opposed to fiducial-based cryoET: (1) No additional gold bead + sample preparation and optimization is involved as with conventional fiducial-based tilt-series alignment and (2) the exact sample from which single particle micrographs are collected is used, thus removing the possibility of sample variation across grid preparations.

Several representative tilt-series from the datasets have been deposited to the Electron Microscopy Data Bank (EMDB) in the form of binned by 4 or 8 tomograms and to the Electron Microscopy Pilot Image Archive (EMPIAR) in the form of unaligned tilt-series images (one including super-resolution frames), Appion-Protomo tilt-series alignment runs, and aligned tilt-series stacks. Protomo estimations for the orientation of the local ice normal based on the tilt-series alignment of the particles in the ice, which includes potential systematic stage and beam axis error, are available in all deposited EMPIAR datasets as a plot located: protomo_alignments/tiltseries####/media/angle_refinement/series####_orientation.gif A Docker-based version of Appion-Protomo fiducial-less tilt-series alignment is available at http://github.com/nysbc/appion-protomo.

1. 1. Alex J Noble
   2. Venkata P Dandey
   3. Hui Wei
   4. Julia Brasch
   5. Jillian Chase
   6. Priyamvada Acharya
   7. Yong Zi Tan
   8. Zhening Zhang
   9. Laura Y Kim
   10. Giovanna Scapin
   11. Micah Rapp
   12. Edward T Eng
   13. William J Rice
   14. Anchi Cheng
   15. Carl J Negro
   16. Lawrence Shapiro
   17. Peter D Kwong
   18. David Jeruzalmi
   19. Amedee des Georges
   20. Clinton S Potter
   21. Bridget Carragher

   (2018) Rabbit muscle aldolase single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10187).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10187
2. 1. Alex J Noble
   2. Venkata P Dandey
   3. Hui Wei
   4. Julia Brasch
   5. Jillian Chase
   6. Priyamvada Acharya
   7. Yong Zi Tan
   8. Zhening Zhang
   9. Laura Y Kim
   10. Giovanna Scapin
   11. Micah Rapp
   12. Edward T Eng
   13. William J Rice
   14. Anchi Cheng
   15. Carl J Negro
   16. Lawrence Shapiro
   17. Peter D Kwong
   18. David Jeruzalmi
   19. Amedee des Georges
   20. Clinton S Potter
   21. Bridget Carragher

   (2017) Mtb 20S proteasome on a carbon nanowire grid plunged with Spotiton

   Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7154).

   https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7154
3. 1. Alex J Noble
   2. Venkata P Dandey
   3. Hui Wei
   4. Julia Brasch
   5. Jillian Chase
   6. Priyamvada Acharya
   7. Yong Zi Tan
   8. Zhening Zhang
   9. Laura Y Kim
   10. Giovanna Scapin
   11. Micah Rapp
   12. Edward T Eng
   13. William J Rice
   14. Anchi Cheng
   15. Carl J Negro
   16. Lawrence Shapiro
   17. Peter D Kwong
   18. David Jeruzalmi
   19. Amedee des Georges
   20. Clinton S Potter
   21. Bridget Carragher

   (2018) T20S proteasome single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10188).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10188
4. 1. Noble AJ
   2. Dandey VP
   3. Wei H
   4. Brasch J
   5. Chase J
   6. Acharya P
   7. Tan YZ
   8. Zhang Z
   9. Kim LY
   10. Scapin G
   11. Rapp M
   12. Eng ET
   13. Rice WJ
   14. Cheng A
   15. Negro CJ
   16. Shapiro L
   17. Kwong PD
   18. Jeruzalmi D
   19. des Georges A
   20. Potter CS
   21. Carragher B

   (2017) CryoET of hemagglutinin single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10129).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10129
5. 1. Noble AJ
   2. Dandey VP
   3. Wei H
   4. Brasch J
   5. Chase J
   6. Acharya P
   7. Tan YZ
   8. Zhang Z
   9. Kim LY
   10. Scapin G
   11. Rapp M
   12. Eng ET
   13. Rice WJ
   14. Cheng A
   15. Negro CJ
   16. Shapiro L
   17. Kwong PD
   18. Jeruzalmi D
   19. des Georges A
   20. Potter CS
   21. Carragher B

   (2017) CryoET of rabbit muscle aldolase single particle super-res

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10130).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10130
6. 1. Noble AJ
   2. Dandey VP
   3. Wei H
   4. Brasch J
   5. Chase J
   6. Acharya P
   7. Tan YZ
   8. Zhang Z
   9. Kim LY
   10. Scapin G
   11. Rapp M
   12. Eng ET
   13. Rice WJ
   14. Cheng A
   15. Negro CJ
   16. Shapiro L
   17. Kwong PD
   18. Jeruzalmi D
   19. des Georges A
   20. Potter CS
   21. Carragher B

   (2017) CryoET of rabbit muscle aldolase single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10131).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10131
7. 1. Noble AJ
   2. Dandey VP
   3. Wei H
   4. Brasch J
   5. Chase J
   6. Acharya P
   7. Tan YZ
   8. Zhang Z
   9. Kim LY
   10. Scapin G
   11. Rapp M
   12. Eng ET
   13. Rice WJ
   14. Cheng A
   15. Negro CJ
   16. Shapiro L
   17. Kwong PD
   18. Jeruzalmi D
   19. des Georges A
   20. Potter CS
   21. Carragher B

   (2017) CryoET of glutamate dehydrogenase single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10132).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10132
8. 1. Noble AJ
   2. Dandey VP
   3. Wei H
   4. Brasch J
   5. Chase J
   6. Acharya P
   7. Tan YZ
   8. Zhang Z
   9. Kim LY
   10. Scapin G
   11. Rapp M
   12. Eng ET
   13. Rice WJ
   14. Cheng A
   15. Negro CJ
   16. Shapiro L
   17. Kwong PD
   18. Jeruzalmi D
   19. des Georges A
   20. Potter CS
   21. Carragher B

   (2017) CryoET of glutamate dehydrogenase single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10133).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10133
9. 1. Noble AJ
   2. Dandey VP
   3. Wei H
   4. Brasch J
   5. Chase J
   6. Acharya P
   7. Tan YZ
   8. Zhang Z
   9. Kim LY
   10. Scapin G
   11. Rapp M
   12. Eng ET
   13. Rice WJ
   14. Cheng A
   15. Negro CJ
   16. Shapiro L
   17. Kwong PD
   18. Jeruzalmi D
   19. des Georges A
   20. Potter CS
   21. Carragher B

   (2017) CryoET of glutamate dehydrogenase + 0.001% DDM single particle

   Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10134).

   https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10134
10. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of DNAB helicase-helicase loader single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10135).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10135
11. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of apoferritin single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10136).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10136
12. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of apoferritin single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10137).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10137
13. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of apoferritin single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10138).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10138
14. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of apoferritin single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10139).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10139
15. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of apoferritin single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10140).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10140
16. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of apoferritin with 0.5 mM TCEP single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10141).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10141
17. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Phase plate cryoET of T20S proteasome single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10142).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10142
18. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of T20S proteasome single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10143).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10143
19. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of T20S proteasome single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10144).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10144
20. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) CryoET of Mtb 20S proteasome single particle

    Publicly available at the Electron Microscopy Data Bank (accession no. EMPIAR-10145).

    https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10145
21. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Hemagglutinin on a carbon nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7135).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7135
22. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Rabbit muscle aldolase on a gold nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7138).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7138
23. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Rabbit muscle aldolase on a carbon nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7139).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7139
24. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Protein in nanodisc on a gold nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7140).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7140
25. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Glutamate dehydrogenase on a holey carbon grid

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7141).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7141
26. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Glutamate dehydrogenase on a holey carbon grid

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7142).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7142
27. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) GDH + 0.001% DDM on a carbon nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7143).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7143
28. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) DNAB helicase-helicase loader on a gold Quantifoil grid

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7144).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7144
29. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Apoferritin on a gold nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7145).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7145
30. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Apoferritin on a gold nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7146).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7146
31. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Apoferritin on a holey carbon nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7147).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7147
32. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Apoferritin on a holey carbon nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7148).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7148
33. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Apoferritin on a holey gold nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7149).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7149
34. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) Apoferritin with 0.5 mM TCEP on a carbon nanowire grid plunged with Spotiton

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7150).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7150
35. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) T20S proteasome on a holey carbon grid

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7151).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7151
36. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) T20S proteasome on a holey carbon grid

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7152).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7152
37. 1. Noble AJ
    2. Dandey VP
    3. Wei H
    4. Brasch J
    5. Chase J
    6. Acharya P
    7. Tan YZ
    8. Zhang Z
    9. Kim LY
    10. Scapin G
    11. Rapp M
    12. Eng ET
    13. Rice WJ
    14. Cheng A
    15. Negro CJ
    16. Shapiro L
    17. Kwong PD
    18. Jeruzalmi D
    19. des Georges A
    20. Potter CS
    21. Carragher B

    (2017) T20S proteasome on a gold Quantifoil grid

    Publicly available at the Electron Microscopy Data Bank (accession no. EMD-7153).

    https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-7153

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Author details

1. Alex J Noble

   National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8634-2279

2. Venkata P Dandey

   National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

   Contribution

   Investigation

   Contributed equally with

   Hui Wei

   Competing interests

   No competing interests declared

3. Hui Wei

   National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

   Contribution

   Investigation

   Contributed equally with

   Venkata P Dandey

   Competing interests

   No competing interests declared

4. Julia Brasch

   1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
   2. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Jillian Chase

   1. Department of Chemistry and Biochemistry, City College of New York, New York, United States
   2. Program in Biochemistry, The Graduate Center of the City University of New York, New York, United States

   Contribution

   Formal analysis, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

6. Priyamvada Acharya

   1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
   2. Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Maryland, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Yong Zi Tan

   1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
   2. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

   Contribution

   Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6656-6320

8. Zhening Zhang

   National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Laura Y Kim

   National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Giovanna Scapin

    1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
    2. Department of Structural Chemistry and Chemical Biotechnology, Merck & Co., Inc, New Jersey, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Micah Rapp

    1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
    2. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Edward T Eng

    National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

    Contribution

    Investigation, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8014-7269

13. William J Rice

    National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

14. Anchi Cheng

    National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

    Contribution

    Software, Investigation

    Competing interests

    No competing interests declared

15. Carl J Negro

    National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States

    Contribution

    Software

    Competing interests

    No competing interests declared

16. Lawrence Shapiro

    Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

17. Peter D Kwong

    Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Maryland, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

18. David Jeruzalmi

    1. Department of Chemistry and Biochemistry, City College of New York, New York, United States
    2. Program in Biochemistry, The Graduate Center of the City University of New York, New York, United States
    3. Program in Biology, The Graduate Center of the City University of New York, New York, United States
    4. Program in Chemistry, The Graduate Center of the City University of New York, New York, United States

    Contribution

    Resources, Supervision, Funding acquisition

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5886-1370

19. Amedee des Georges

    1. Department of Chemistry and Biochemistry, City College of New York, New York, United States
    2. Program in Biochemistry, The Graduate Center of the City University of New York, New York, United States
    3. Program in Chemistry, The Graduate Center of the City University of New York, New York, United States
    4. Advanced Science Research Center, The Graduate Center of the City University of New York, New York, United States

    Contribution

    Resources, Supervision, Funding acquisition, Writing—review and editing

    Competing interests

    No competing interests declared

20. Clinton S Potter

    1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
    2. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2394-0831

21. Bridget Carragher

    1. National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, New York, United States
    2. Department of Biochemistry and Molecular Biophysics, Columbia University, New York, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing—review and editing

    For correspondence

    bcarr@nysbc.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0624-5020

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