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Maintaining the momentum in cryoEM forbiological discovery†
Yehuda Halfon,a Louie Aspinall,a Joshua White,a Isobel Jackson Hirst,bYiheng Wang,b Michele C. Darrow,cd Stephen P. Muench b
and Rebecca F. Thompson *a

Received 17th June 2022, Accepted 11th July 2022
DOI: 10.1039/d2fd00129b

Cryo-electron microscopy (cryoEM) has been transformed over the last decade, with
continual new hardware and software tools coming online, pushing the boundaries ofwhat is possible and the nature and complexity of projects that can be undertaken.
Here we discuss some recent trends and new tools which are creating opportunities tomake more eﬀective use of the resources available within facilities (both staﬀ andequipment). We present approaches for the stratiﬁcation of projects based on risk andknown information about the projects, and the impacts this might have on the
allocation of microscope time. We show that allocating diﬀerent resources (microscopetime) based on this information can lead to a signiﬁcant increase in ‘successful’ use of
the microscope, and reduce lead time by enabling projects to ‘fail faster’. This model
results in more eﬃcient and sustainable cryoEM facility operation.

Introduction
Cryo-electron microscopy (cryoEM) and in particular single particle analysis has
gained huge traction in the past decade as a tool for structure determination.1,2This technique has transformed researchers' ability to gain insight into
membrane proteins, heterogeneous complexes and other samples not amenable
to other structural techniques such as X-ray crystallography. This surge in activity
has been underpinned by wide-spread investment in electron microscopy infrastructure, from instruments based within individual laboratories or institutional
research facilities, to large national and international electron microscopy
centres.3,4 For the purposes of this manuscript, we will consider a ‘facility’ to beaSchool of Molecular and Cellular Biology, Faculty of Biological Sciences & Astbury Centre for Structural andMolecular Biology, University of Leeds, Leeds, LS2 9JT, UK. E-mail: r.f.thompson@leeds.ac.ukbSchool of Biomedical Sciences, Faculty of Biological Sciences & Astbury Centre for Structural and Molecular
Biology, University of Leeds, Leeds, LS2 9JT, UKc

The Rosalind Franklin Institute, Harwell Campus, Didcot, OX11 0QS, UKd

SPT Labtech Ltd, Melbourn Science Park, Melbourn, SG8 6HB, UK† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2fd00129b

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a location with a set of cryoEM hardware, dedicated staﬀ and a remit to support
the research of more than one research group. The investment in cryoEM facilities
across the globe spans beyond the microscopes and associated hardware (e.g.,
direct electron detectors), into ancillary laboratory space including equipment for
sample preparation, computing hardware to process and store the data, specialised rooms and buildings to house the equipment, and, of course, expert staﬀ tomanage, maintain and operate equipment. To support end-to-end structure
determination and model/map interpretation, the full pipeline of infrastructure isrequired, although this does not necessarily need to be co-located in one facility
or even country.
There are a diverse set of cryoEM facilities globally, with diﬀering microscope
hardware, funding models, project mixes, user communities and core aims. All ofthese factors inuence how a facility infrastructure should be resourced andstructured to support its user communities. The operational model, data collection strategies employed and wider resources including staﬀ and computing
should be structured to match the aims of the projects within the facility.
The single particle structure determination pathway is oen presented asa linear, stepwise process, although those with cryoEM experience will likely
recognise the need for multiple retrograde steps in order to progress through thestages (Fig. 1). At the facility level, the eﬃciency of and support available for eachstep in the process should be considered and optimised to maximise the output
from the most expensive (per day) of these steps: data acquisition.
Due to the cost per day, much emphasis is placed (including in this manuscript) on eﬃciency and throughput at the cryoEM data acquisition stage, conducted on the microscope. While this is of relevance (whether the cost is directly
incurred by the user or taken on through core/centralised funding) whenaccessing high-end cryoEM instrumentation, time is now generally not the mostcommon ‘bottleneck’ in our view – although eﬃcient use of microscope resources
is still vital given the overall demand for high-end cryoEM instrument access.

Fig. 1 (A) Example of a single particle cryoEM pipeline showing a linear progression
through the stages in blue arrows, with common retrograde steps shown in red. (B)Average researcher time investment needed at each stage of the process. (C) Direct costs
associated with each stage (cost of machine time, consumables). Time and cost are bothintertwined and the actual costs borne by a project also vary based on the ﬁnancial modelof a facility, for example some centres receive core funding so instrument access is free atpoint of access.
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Sample preparation remains a major challenge for many cryoEM projects. For
the majority of single particle cryoEM researchers, the main approach used forcryoEM specimen preparation is lter paper-based blotting followed by plunging
into a cryogen such as liquid ethane, as rst pioneered by Jaques Dubochet andcolleagues.5 Whilst countless high-resolution structures have and still are being
obtained from grids prepared using blotting-based techniques,6,7 for many
samples the production and/or reproduction of good quality grids using thisapproach is challenging. Research into the causes of variation in grid quality
indicates that issues such as protein aggregation, denaturation, preferred particle
orientation, subunit dissociation and particle concentration are being caused or
exacerbated by interactions between the sample and the air–water interface or the
sample and the lter paper.8–11 This has led to the development of alternatives toblotting-based approaches that aim to minimise uncontrolled sample interactions and improve reproducibility.12–15 Most of these technologies do this bygenerating small droplets (using a variety of methods) which are deposited on thegrid en route to the cryogen. By removing the blotting step, decreasing the time
between sample deposition and vitrication, and automating more of the process(including sample deposition), some of the aforementioned issues can be
reduced and even completely avoided for otherwise diﬃcult samples.16–18
Although this new generation of technology increases the range of grid preparation tools available, we still have a poor understanding of many of the fundamental processes that occur during grid preparation which make some proteins
more amenable than others to downstream structural studies. The process ofmaking cryoEM grids is very quick in the context of the structure determination
pipeline, however screening and working iteratively through the process to nda grid with suitable characteristics for data collection can be a major hurdle.
Grid screening to assess suitability for data acquisition typically involves
a manual inspection of the particle distribution and ice thickness, and/or theacquisition and processing of a small test dataset. The process of screening can betime consuming and subjective, especially for those new to the eld, but evenhighly experienced individuals are not always able to accurately predict thesubsequent outcome of a data collection. Once a suitable grid is obtained, a single
particle data acquisition session can be scheduled (or take place immediately).
The microscope, detector, collection parameters (dose rate, total dose, magnication) and length of the collection can be chosen to try and match the needs ofthe project.
Aer data acquisition, single particle image processing approaches are used toreconstruct a three-dimensional (3D) EM density map of the specimen which canthen be subject to model building and further interpretation. Leading soware
packages for single particle reconstruction such as RELION and cryoSPARC19,20
oﬀer a pipelined approach to this workow. Graphical user interfaces present
users with a list of jobs ordered according to their respective position in thepipeline, with default parameters enabling non-expert users to complete the basic
workow and achieve informative results. However, most projects presently
require an iterative approach in which sections of the pipeline, such as 3D classication, are revisited several times, with diﬀerent parameters and intentions, togain a better understanding of a dataset and its heterogeneity. Thus, processing of
single particle datasets demands a signicant amount of computational

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resources and hands-on computational time invested by the user, both of which
are potential bottlenecks in progressing from sample to structure.
Notably, the steps at the beginning of this workow – pre-processing of images
including motion correction and CTF estimation – are routinely performed onthe-y,21 enabling quality assessment of the data coming from the microscope inreal-time. More recently, the application of machine learning soware has facilitated the automation of steps that traditionally required extensive user input.
Soware packages such as crYOLO22 and TOPAZ23 permit accurate, unsupervised
particle picking through the use of pre-trained, convolutional neural-network(CNN) models. As such, the identication of particles in micrographs can beincorporated into a fully automated processing pipeline. Extraction of theseparticles is then followed by 2D classication aer which 2D class averages
containing recognisable protein features must be identied and carried forwards,
to select ‘good’ particles and discard those that are sub-optimal. This selection is
traditionally subjective and carried out manually by the user. However, recent
work has demonstrated the utility of a CNN model for unsupervised 2D classselection, overcoming subjectivity and expanding the section of the single particle
workow that is amenable to automation.24 For example, the Schemes framework
within RELION 4.0 permits robust unsupervised processing up to, and including,the generation of a series of initial models, signicantly reducing the time
between image acquisition and 3D reconstruction.
In this article we will discuss past trends in single particle data acquisition andtheir impact on the present and future operations of cryoEM research facilities,
with a focus on how these might inuence the eﬃciency and throughput ofstructure determination by cryoEM. We will discuss the challenges and opportunities that these emerging technologies present and propose guiding principles
for eﬃcient facility operation going forward.

Results and discussion
CryoEM screening and data acquisition
It is well documented that there has been a substantial growth in the quantity ofcryoEM structures being solved, and a trend of improving resolution over the past
decade. Here we looked to interpret these trends in the context of microscope
hardware and direct electron detectors used. The rapid growth in cryoEM structure determination has been driven by the use of Titan Krios microscopes; since2016 the total number of structures deposited using Titan Krios microscopes
manufactured by FEI (now Thermo Fisher Scientic (TFS)) has outnumbered
every other microscope type (Fig. 2A). Historically the predominantly used directelectron detector was the K2, only surpassed now by use of the K3, both manufactured by Gatan (Fig. 2B and C). As EMDB submissions tend to coincide with thenal stages of a project (usually upon publication), it is likely that these statistics
lag 1–2 years behind data being collected today. We see long ‘tails’ as use ofa particular detector drops oﬀ, as data sometimes collected several years ago isfurther analysed or reanalysed and published, the Falcon 2 being a good exampleof this.
The rst electron microscopy data bank (EMDB) deposition for a single
particle cryoEM structure from a Titan Krios microscope with a direct electron
detector was in 2013 (EMD-2238), where data was collected on a Falcon I. In the >9This journal is © The Royal Society of Chemistry 2022

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Fig. 2 Analysis of single particle EMDB submissions to reveal trends in microscope anddetector usage. 2022 data has been adjusted to predicted full year values on a pro rata
basis projecting a year end total, to assist identiﬁcation of trends. (A) Single particle EMDB
submissions using speciﬁc microscope technology over time. (B) Total single particle
EMDB submissions making use of speciﬁc direct electron detectors. (C) Use of directelectron detector models for single particle data acquisition over time.

years since, direct electron detector technology has evolved considerably. All threeof the major direct electron detector manufacturers (Direct Electron, Gatan, andTFS) have released multiple iterations of detector in this time. Looking across all
submissions to the EMDB, we see the average resolution reported for single
particle structures improving as new iterations of detector technology are released(Fig. 3). This trend is particularly linear for the Thermo Fisher Scientic Falconseries of direct electron detectors (Fig. 3A). As new detector technology comes
online, these typically come with an upli in the throughput of image acquisition
as well as an improvement in the quality of the images obtained (sometimes

Fig. 3 Average resolution for single particle EMDB submissions related to the directelectron detector used, taking no account of sample, microscope or other variables. (A)TFS cameras Falcon 1-4. (B) Gatan cameras K2, K3 ﬁltered and unﬁlterd. (C) Direct electron
cameras DE-12, DE-16, DE-20, DE-64.
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presented through DQE measurements) (ESI Table S1†).31–40 This improvement in
detector quality has, alongside improvements in protein biochemistry, cryoEM
sample preparation, and image processing methods, led to an average improvement in the resolution of cryoEM structures. However, we and others have notedthat single particle data collected on the same sample and sometimes the samegrid using diﬀerent camera technology results in signicant diﬀerences in resolution and so detectors are likely to be a key factor.
Alongside improvements in detector speed, fringe free illumination (FFI) andaberration free image shi collection (AFIS) have been introduced and implemented to further increase the speed of collection. AFIS enables the use of beamimage shi for collection of single particle data, whilst preventing consequential
coma and astigmatism that would otherwise reduce data quality. This is achievedby compensatory adjustments to the deection coils and objective stigmatorwhen performing beam-image shi at diﬀerent distances. Collecting with AFIS istypically performed within 6–12 mm (ref. 25) of the stage location and increases
the throughput substantially by reducing the number of stage movements
required. Each stage move takes time, alongside the associated stage settling wait
time, and so reducing the number of stage movements increases the speed ofa typical acquisition.
The diameter of illuminated area determines how many images can becollected per hole while ensuring each acquisition area is not double exposed.
Typically, when the condenser 2 aperture is imaged out-of-focus (while the sample
is in focus), wave interference at the edge of the condenser beam appears as
Fresnel fringes. As a result a larger illuminated area must be used to exclude thesefringes from the image. FFI involves an adjustment to the microscope to minimise the presence of Fresnel fringes in the recorded image at a specic magnication. With FFI implemented, both the C2 aperture and the sample will be infocus and no, or very few, Fresnel fringes will be visible in the recorded image.
This allows for a reduction of the beam size and more images acquired froma single hole, againallowing more images to be acquired per stage move (Fig. 4).
These advances mean that on average, the number of images collected per hour
in a single particle data acquisition session is far higher than in previous years.
However, disentangling trends in average dataset size is challenging because
EMDB submissions can contain a mixture of micrographs and particle stacks,
amongst other data and each EMDB entry can be associated with multiple EMPIAR
entries and vice versa. These complications make it diﬃcult to assess the relationship between the amount of data collected to yield each EMDB submission.
Implications for ‘standard’ single particle pipelines
To achieve a high level of condence that a cryoEM grid is suitable for highresolution imaging, the screening step is essential. Two main factors areassessed during screening: (1) sample biochemistry and suitability; (2) grid andice quality. While a relatively well-dened process currently exists for assessing
sample biochemistry and suitability (purication techniques, activity assays,
negative stain EM), this aspect of grid screening is oen not optimised. Sample
homogeneity, stability during the freezing process, and interaction with thesubstrate or the air–water-interface can all create issues which are only foundduring cryoEM grid screening.
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Fig. 4 Impact of acquisition schemes of AFIS and FFI. (A) A typical collection scheme isshown with and without AFIS. Without AFIS, the stage moves to each hole individually.
Black arrows are shown to indicate this stage movement. With AFIS, the stage centres on
one hole, and beam-image shift is used to acquire images in adjacent holes without
moving the stage. The diﬀerent AFIS groups are coloured uniquely, with the central holenumbered. (B) A typical hole template is shown without (left) and with (right) fringe-freeillumination. The absence of Fresnel fringes means that a greater number of acquisitions is
possible within the same space.

Both the specimen preparation and microscope hardware can inuence thetime taken and success of the screening step. Newer specimen preparation
technologies provide a view of the grid from the freezing process which can beused to judge the quality of the grid and ice without loading into a microscope.15,27–30 Screening for appropriate ice thickness, and particle concentrationand distribution can be done manually. However this is dependent on microscope
operator experience, and is always a subjective judgement. Additionally someissues, such as preferred orientation or partial denaturation, will only become
obvious aer processing at least a small dataset.
In the majority of facilities, the ‘working day’ (for the purposes of this paper weconsider this an 8 h window) is 9am–5pm, where the majority of staﬀ are on site.
Automated data collection (which can run 24 h per day) is then used to collectdata when staﬀ are oﬀsite. Due to these standard working patterns, the majorityof facilities scheduling occurs in 24 hour time blocks. Historically this would havemeant 24 h collection on a single project. However, recently developed tools inprograms such as EPU and SerialEM have opened possibilities to collect multiple
datasets on diﬀerent grids while the microscope is unattended. This enables
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users to collect datasets more tailored to the needs of the project and based on
their target resolution or aim of the project, whilst also utilising the imaging
resources of the facility better, especially over weekends or public holidays.
Multi-grid imaging also enables a more eﬃcient collection of small ‘proofof-concept’ datasets, typically 0.25–4 hours, for the purpose of providing better
understanding of the condition of the grid and indication of whether a longer
data acquisition is required and/or warranted. This style of data collection isnecessary because it is diﬃcult to predict through manual assessment of the
micrographs alone if the complete complex is present, if factors that may
impact structure determination such as preferred orientation are present, andif the desired resolution will be obtainable from the grids. Once a suitablesmall-scale data collection has been completed and processed, the relationshipbetween the resolution of a 3D reconstruction and the number of contributing
particles can be assessed. By running a series of 3D renements on random
subsets of particles from the particle stack, where the subset size is doubledaer each renement, a B-factor plot (or Rosenthal–Henderson plot)26 can begenerated.
With tools that remove the current connement to 24 h ‘blocks’ and anincreasing acknowledgement that manual screening is unlikely to condently
predict success at the data acquisition stage, there is an opportunity to change thestandard pipelines for cryoEM data acquisition. A workow based on manual
assessment of micrographs (Fig. 5A) with a single ‘gate’ determining if the project
should proceed to the next stage is likely to be replaced by a multi-step process(Fig. 5B) that makes use of short collections and more robust metrics of quality tomore condently predict success of data acquisition sessions.

Allocation of microscope resources based on project risk proleWe have conducted an analysis to show the theoretical gains that can be made byallocating diﬀerent projects varying microscope resources, based on theFig. 5 Manual vs. automated approaches to cryoEM screening and data analysis. (A)‘Standard’ pipeline from cryoEM screening relying on manual inspection of micrographs
and particle distributions. (B) Using automated or semi-automated tools to collect andprocess a small dataset before going on to a larger data collection session if warranted.
The pathway in (B) identiﬁes failure more quickly without incurring as many resource oropportunity costs.
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Examples of project categorisation and downstream suggested workﬂow withchance of success expressed as a % (data shown in ESI Table S2†). Retrograde steps areshown in red arrows. (A) Fixed pipeline, all projects receive 8 h of screening time followed
by 24 h of data collection if manual inspection identiﬁes a good grid. (B–E) Stratiﬁed
approach to resources applied to each project. (B) Low chance of failure (e.g., wellcharacterised icosohedral virus), project is immediately allocated 24 h collection. (C)Medium chance of failure, 4 h screening is allocated with a collection session scheduled if
the small processed dataset conﬁrms grid is suitable. (D) High chance of failure, 8 hscreening is allocated with a collection session scheduled if the small processed dataset
conﬁrms grid is suitable. (E) Variable chance of failure or where there are other time
restrictions applied, 24 h is allocated in the ﬁrst instance with further collection scheduled
where initial processing conﬁrms suitability.

Fig. 6

properties of the project. In this analysis we have compared a ‘xed’ pipeline(Fig. 6A) against ‘probability based’ pipelines (Fig. 6B–E). For each pipeline, wehave assigned a global chance of probability for each attempt at the pipeline, andfor the variable pipelines a percentage of projects that fall into each category.
These numbers are based on qualitative data from our cryoEM facility, but inreality the nature and mix of projects between facilities and even in each facility
over time will always change. We see that the situations presented reect a realistic mix of projects and an example framework which can be altered to suitdiﬀerent project and hardware mixes within other facilities.
The denition of ‘success’ varies largely on a project by project basis, butessentially a ‘successful’ progression through the pipeline means no requirement
for a retrograde step (i.e. the quality of data obtained was suﬃcient to answer thequestion in hand). For example, for ‘successful’ screening; in the xed pipeline,
success might look like monodisperse particles at a good concentration withvitreous ice. For a ‘medium’ risk project, success would be dened as 2D classes
from a pilot collection showing secondary structure detail, a complete complex
and a range of views.

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In the xed pipeline, all projects are assigned the same microscope resources
initially – an 8 h manual screening session. Aer this screening, if manual
inspection of the micrographs looks promising (vitreous ice, good particle
distribution as judged by eye) a 24 h collection is scheduled. For variable pipelines, we have provided examples of ‘low’, ‘medium’, ‘high’, and ‘variable’ risk
projects. A ‘low’ risk project would involve a well dened, homogeneous, specimen in the hands of an experienced researcher. Examples might include icosohedral viruses. We assigned these projects an 80% chance of success and estimatethat 15% of projects fall into this category. ‘Medium’ risk projects form the
majority of projects seen in our facility, at 60% of projects. These are projects
where there is a 50/50 chance the grids will be optimal during the screening
session, either because the sample requires optimisation (biochemistry or cryoEM grid preparation) or because the user is developing their cryoEM skills, orboth. Samples may include those not previously imaged by cryoEM but where
preliminary data from negative stain looks promising. ‘High’ risk projects arethose where there are obvious challenges with the sample (for example, a medium
risk project that has had 3+ imaging sessions would move into the high-risk
category).
During standard workows, there is usually a time gap between when grids are
made and then when they are imaged. For the majority of projects this is
acceptable or workable, but there are specic cases where microscope time mayneed to be scheduled to allow immediate feedback about the grids to allow moregrids to be made. An example might include a challenging protein purication
where the protein cannot be frozen or stored. We have termed these ‘variable’
probability.
Here we have proposed that each categorised ‘risk’ of project is assigned
diﬀerent up-front microscope time based on the risk prole of the project. For
example, low risk projects are directed immediately to a 24 h collection session, as
the grids are likely to be suitable for collection. Medium risk projects are initiallyassigned 4 h screening, high risk projects 8 h of screening and variable risk
projects 48 h microscope time. Within this initial session, users would be expected to collect a small subset of data (even 20 minutes can be highly informative) on their most promising grid(s) and then process this data to yield at least
2D class averages. Only if these show evidence of a promising structure (secondary
structure detail, range of views, whole complex present) will a 24 h data collection
session be allocated.
The full data calculations are shown in ESI Table S2,† and are based on theworkows and % chance of success shown in Fig. 6. This shows that if a xed
pipeline is used for 100 projects, 2000 hours of microscope time is required, of
which 1200 h is within ‘working hours’ while 800 h is out of hours. If we compare
this to the alternative approach of allocating resources based on project risk, forthe mix of projects shown here, for 100 projects, 1656 h of microscope time isrequired, of which 570 hours are ‘working hours’ and 1086 are ‘out of hours’.
Overall, taking a risk based approach to projects means that for 100 projects
with the same overall probability of success (0.5 for xed pipeline, 0.51 for variable pipeline), less microscope time is used overall, reducing demand for
microscope time and decreasing wait times between sessions. Signicantly, theamount of ‘working hours’ required is also reduced in the risk-based approach.
Given that scheduling of microscope time is typically based around when humanThis journal is © The Royal Society of Chemistry 2022

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operators are present to input into decision making, this also helps to reduce thewait time for microscopes. In the risk-based approach, more microscope hours
lead to a ‘successful’ outcome and fewer microscope hours are wasted (or ‘failed’)compared with the xed pipeline.
While the theoretical argument for the risk based approach is clearly
compelling, there are barriers to its eﬃcient implementation. The project mix andresources available, along with the culture of the workplace relating to out ofhours and weekend working will all vary how the framework is stratied and theresulting potential benets. Splitting projects into risk bins may be extremely
challenging and require the active and willing participation and cooperation of
the user community. For the system to work optimally, facility staﬀ should havegood working knowledge of projects coming through the facility and a user
community who understand the population level benets of engaging with thesystem (e.g. reduced time between requested microscope sessions) and providing
information to enable the best possible characterisation of the projects. With thisin place, project categorisation should always be considered dynamically. Even
with these considerations in mind, we feel the majority of facilities will likely
benet from project and resource allocation based on risk, compared with a ‘onesize ts all’ approach.
Another barrier to implementation of this model is facility staﬀ time. Shorter
microscope sessions may require loading of the microscope more frequently, anda larger number of shorter collections increases the amount of staﬀ support
required. One mechanism of tackling this is increasing users’ independence on
the microscope; a second is increasing staﬃng within the facility. The model andnancial constraints of each facility will impact which routes are taken to deliver
facility operations.
In our experience, long waits for microscope time (especially screening time)
are one of the biggest concerns for a cryoEM researcher, as it can be diﬃcult tomake any progress on the project when the outcome from initial screening is not
yet known. Generally, microscope time ‘little and oen’ is considered to be more
useful than longer sessions with large wait times in between. Focusing facility
operations to keep the time users wait between sessions to a minimum allows us
to meet this need of the cryoEM community. Another benet of this system is it
allows a larger amount of microscope time to be allocated up front to the projects
that will really stand to benet, maximising chances of successful structure
determination for these more challenging systems.
For the purposes of this analysis we have allocated 24 h to each ‘data collection’ session. In reality, as discussed above 24 h may not be the optimal amount oftime to collect data; for some projects it may be far too much and for someprojects not enough. It is highly likely that the model presented here for a risk
based approach to allocation of resources can and should be extended out toinclude a variable amount of data collection time based on the known propertiesof the macromolecule and the desired outcome for the project. This would likely
further improve the eﬃciency of the framework.
Many institutions worldwide are looking to improve their environmental
sustainability, and oen have ‘net zero’ emission targets. Operating hardware
such as electron microscopes has a carbon cost, but the data generated may havea signicant and longer lasting carbon footprint. Data generated must be processed and then stored; many funders then stipulate that these data must be
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stored past the end of the grant, for up to 10 years. Collecting more data thanrequired has not only a microscope time ‘cost’ associated with it but also a carbon
cost, which scales according to the amount of data. Larger datasets are morecomputationally expensive to process, and store. A nal benet of the risk-based
framework proposed here is that overall less ‘bad’ data will be produced, which
will not require processing and storage.

Conclusion
To industrial researchers, the framework presented may sound familiar as there
are well-studied systems in place to identify priorities and risks, and optimisebusiness resources to meet these needs. When these systems are well-dened andwell-used, they can create optimal workows with clear ‘stages’ and ‘stage-gates’
to help both users and facility staﬀ to understand what criteria to assess theirproject against, when to follow a certain workow or when to stop collecting data.
These systems, if thoughtfully applied to the cryoEM facility, could substantiallyshi the way we implement single particle cryoEM data collection beyond thestandard 24 hour allocation block and to improve the overall eﬃciency of
microscope time, maximising biological discovery.
A vision for a future cryoEM facility would integrate data from across the pipeline, from biochemical and biophysical analysis, cryoEM specimen preparation andon-the-y analysis of micrographs into 2D and 3D data, as data are collected. These
data could then be used to make dynamic, on-the-y decisions about progressing or
halting data acquisition, automatically moving onto the next sample.
To work towards an idealised vision and maximum output for imaging biological specimens, much work is still outstanding both on the specimen preparation and image processing pipeline elements to support new approaches in
microscope scheduling. Many variables impacting the success of specimen
preparation are still not understood requiring cryoEM grids to be empiricallytested which can take valuable time and resources. Work to better understand thefactors inuencing specimen preparation and next generation specimen preparation devices may transform this portion of the pipeline. During microscope datacollection sessions, machine learning approaches may help users to identify
optimal areas for data acquisition with minimal or no human intervention. On
the image processing side, on-the-y image processing, automation, and theimplementation of deep learning mean that assessing the quality of a sample andits amenability to high-resolution structure determination has never been fasterand will improve further in the future. Coupled with new automated, optimised
acquisition capabilities across grids, this oﬀers new opportunities to the user,
such as investigating the eﬀect of buﬀer conditions on the compositional
heterogeneity of a sample, or assessing the inuence of additives (such as
surfactants and detergents) in improving particle behaviours (e.g. preferredorientation) all within a single session.
Only when information is integrated across the cryoEM pipeline, supported by
fast and accurate image processing, will we be able to most eﬃciently distributetime on high-end cryoEM infrastructure. While this future is not quite here,
recent exciting advances have and will continue to challenge facility managers toconsider how best to organise the distribution of resources within their facility tomaximise biological discovery.
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Paper

Experimental
EMDB analysis
Data was downloaded from the EMDB and EMPIAR on 22nd May 2022. Only
singleparticledatalteredaccording
to
the
eld
structure_determination_method ¼ single particle was collected. The eld
microscope_name was altered to ensure all Titan Krios microscopes were
included (both FEI and TFS manufacturer titles). In lm_detector_model again
similar categories were collated. Any entries that used multiple detectors associated with 1 EMDB entry were excluded.

Author contributions
RT, SPM and MD conceptualised and supervised the project. YH, LA, JW, IH and
YW curated and analysed data. All authors contributed to writing and editing themanuscript.

Conﬂicts of interest
MCD is employed by SPT Labtech and works on the chameleon specimen preparation system.

Acknowledgements
We thank Neil Fonseca and Andrii Ludin from the EMDB for their support with
EMDB and EMPIAR data acquisition.

References
1 W. Kühlbrandt, The Resolution Revolution, Science, 2014, 343(6178), 1443–
1444.
2 M. A. Cianfrocco and E. H. Kellogg, What Could Go Wrong? A Practical Guide
to Single-Particle Cryo-EM: From Biochemistry to Atomic Models, J. Chem. Inf.
Model., 2020, 60(5), 2458–2469.
3 B. Alewijnse, et al., Best practices for managing large CryoEM facilities, J.
Struct. Biol., 2017, 199(3), 225–236.
4 K. Sader, et al., Industrial cryo-EM facility setup and management, Acta
Crystallogr., Sect. D: Struct. Biol., 2020, 76(4), 313–325.
5 J. Dubochet, et al., Cryo-electron microscopy of vitried specimens, Q. Rev.
Biophys., 1988, 21(2), 129–228.
6 Y. Cheng, Single-Particle Cryo-EM at Crystallographic Resolution, Cell, 2015,
161(3), 450–457.
7 T. Nakane, et al., Single-particle cryo-EM at atomic resolution, Nature, 2020,
587(7832), 152–156.
8 A. J. Noble, et al., Routine single particle CryoEM sample and gridcharacterization by tomography, Elife, 2018, 7, e34257.
9 D. Lyumkis, Challenges and opportunities in cryo-EM single-particle analysis,
J. Biol. Chem., 2019, 294(13), 5181–5197.
30 | Faraday Discuss., 2022, 240, 18–32

This journal is © The Royal Society of Chemistry 2022

View Article Online

Open Access Article. Published on 15 July 2022. Downloaded on 9/25/2024 12:52:53 PM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Paper

Faraday Discussions

10 R. M. Glaeser and B.-G. Han, Opinion: hazards faced by macromolecules whenconned to thin aqueous lms, Biophys. Rep., 2017, 3(1–3), 1–7.
11 E. D'Imprima, et al., Protein denaturation at the air–water interface and how toprevent it, eLife, 2019, 8, e42747.
12 Y. Z. Tan and J. L. Rubinstein, Through-grid wicking enables high-speed
cryoEM specimen preparation, Acta Crystallogr., Sect. D: Struct. Biol., 2020,76(11), 1092–1103.
13 D. Kontziampasis, et al., A cryo-EM grid preparation device for time-resolved
structural studies, IUCrJ, 2019, 6(6), 1024–1031.
14 H. D. White, et al., A second generation apparatus for time-resolved electron
cryo-microscopy using stepper motors and electrospray, J. Struct. Biol., 2003,
144(1–2), 246–252.
15 M. C. Darrow, et al., Chameleon: Next Generation Sample Preparation for
CryoEM based on Spotiton, Microsc. Microanal., 2019, 25(S2), 994–995.
16 T. S. Levitz, et al., Eﬀects of chameleon dispense-to-plunge speed on particle
concentration, complex formation, and nal resolution: A case study using
the Neisseria gonorrhoeae ribonucleotide reductase inactive complex, J.
Struct. Biol., 2022, 214(1), 107825.
17 A. J. Noble, et al., Reducing eﬀects of particle adsorption to the air–waterinterface in cryo-EM, Nat. Methods, 2018, 15(10), 793–795.
18 G. Weissenberger, R. J. M. Henderikx and P. J. Peters, Understanding theinvisible hands of sample preparation for cryo-EM, Nat. Methods, 2021,
18(5), 463–471.
19 J. Zivanov, et al., New tools for automated high-resolution cryo-EM structure
determination in RELION-3, eLife, 2018, 7, e42166.
20 A. Punjani, et al., cryoSPARC: algorithms for rapid unsupervised cryo-EM
structure determination, Nat. Methods, 2017, 14(3), 290–296.
21 R. F. Thompson, et al., Collection, pre-processing and on-the-y analysis ofdata for high-resolution, single-particle cryo-electron microscopy, Nat.
Protoc., 2019, 14(1), 100–118.
22 T. Wagner, et al., SPHIRE-crYOLO is a fast and accurate fully automatedparticle picker for cryo-EM, Commun. Biol., 2019, 2(1), 218.
23 T. Bepler, et al., Positive-unlabeled convolutional neural networks for particle
picking in cryo-electron micrographs, Nat. Methods, 2019, 16(11), 1153–1160.
24 D. Kimanius, et al., New tools for automated cryo-EM single-particle analysis
in RELION-4.0, Biochem. J., 2021, 478(24), 4169–4185.
25 S. Konings, M. Kuijper, J. Keizer, F. Grollios, T. Spanjer and P. Tiemeijer,
Advances in Single Particle Analysis Data Acquisition, Microsc. Microanal.,
2019, 25, 1012–1013.
26 P. B. Rosenthal and R. Henderson, Optimal determination of particle
orientation, absolute hand, and contrast loss in single-particle electron
cryomicroscopy, J. Mol. Biol., 2003, 333(4), 721–745.
27 T. Jain, et al., Spotiton: a prototype for an integrated inkjet dispense andvitrication system for cryo-TEM, J. Struct. Biol., 2012, 179(1), 68–75.
28 V. P. Dandey, et al., Spotiton: New features and applications, J. Struct. Biol.,2018, 202(2), 161–169.
29 R. B. G. Ravelli, et al., Cryo-EM structures from sub-nl volumes using pinprinting and jet vitrication, Nat. Commun., 2020, 11(1), 2563.

This journal is © The Royal Society of Chemistry 2022

Faraday Discuss., 2022, 240, 18–32 | 31

View Article Online

Open Access Article. Published on 15 July 2022. Downloaded on 9/25/2024 12:52:53 PM.
This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Faraday Discussions

Paper

30 R. I. Koning, et al., Automated vitrication of cryo-EM samples with
controllable sample thickness using suction and real-time opticalinspection, Nat. Commun., 2022, 13(1), 2985.
31 FEI, Falcon II 16 Megapixel TEM Direct Electron Detector with Back-Thinned
Sensor Technology, 2012.
32 Scientic, T.F., Falcon 3EC Direct Electron Detector, 2018.
33 Scientic, T.F., Imaging Beam-Sensitive and Low-Contrast So Materials, 2021.
34 Scientic, T.F., Falcon 4i Direct Electron Detector, 2022.
35 Gatan, K2 Direct Detection Camera, 2015.
36 Gatan, K3 Direct Detection Cameras, 2020.
37 Electron, D., DE-12 Camera, 2014.
38 Electron, D., DE-16 Camera System, 2019.
39 Electron, D., DE-20 Camera System, 2017.
40 Electron, D., DE-64 Camera System, 2019.

32 | Faraday Discuss., 2022, 240, 18–32

This journal is © The Royal Society of Chemistry 2022