Single particle analysis: Difference between revisions

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{{Short description|Method of analyzing transmission electron microscopy imagery}}

[[File:SingleParticleAnalysis.png|thumb|right|Single particle analysis segments and averages many particles from a sample, allowing for computer algorithms to process the individual images into a combined "representative" image. This allows for improvements in signal to noise, and can be combined with [[deconvolution]] to provide limited improvements to spatial resolution in the image.]]

'''Single particle analysis''' is a group of related computerized image processing techniques used to analyze images from [[Transmission electron microscope|transmission electron microscopy]] (TEM).<ref name="Frank">{{Cite book|first=Joachim |last=Frank |title=Three-dimensional electron microscopy of macromolecular assemblies: visualization of biological molecules in their native state |publisher=Oxford University Press |___location=Oxford |year=2006 |isbn=978-0-19-518218-7 |url=https://books.google.com/books?id=vWaSRUjicbgC}}{{Page needed|date=August 2010}}</ref> These methods were developed to improve and extend the information obtainable from TEM images of particulate samples, typically [[proteins]] or other large biological entities such as [[virus]]es. Individual images of stained or unstained particles are very [[Signal noise|noisy]], andmaking so hard tointerpretation interpretdifficult. Combining several digitized images of similar particles together gives an image with stronger and more easily interpretable features. An extension of this technique uses single particle methods to build up a [[Transmission electron microscopy#Three-dimensional imaging|three-dimensional reconstruction]] of the particle. Using [[cryogenic transmission electron microscopy|cryo-electron microscopy]] it has become possible to generate reconstructions with sub-[[Nanometre|nanometer]] [[Resolution (electron density)|resolution]] and near-atomic resolution<ref name="Zhou">{{Cite journal|author=Zhou ZH |title=Towards atomic resolution structural determination by single-particle cryo-electron microscopy |journal=Current Opinion in Structural Biology |volume=18 |issue=2 |pages=218–28 |date=April 2008 |pmid=18403197 |pmc=2714865 |doi=10.1016/j.sbi.2008.03.004}}</ref><ref name="Dynamics">{{Cite journal|vauthors=Wang Q, Matsui T, Domitrovic T, Zheng Y, Doerschuk PC, Johnson JE |title=Dynamics in cryo EM reconstructions visualized with maximum-likelihood derived variance maps |journal=Journal of Structural Biology|volume=181|issue=3 |pages=195–206 |date=March 2013 |doi=10.1016/j.jsb.2012.11.005|pmid=23246781 |pmc=3870017}}</ref> first in the case of highly symmetric viruses, and now in smaller, asymmetric proteins as well.<ref name="Bartesaghi">{{Cite journal| doi = 10.1126/science.aab1576| issn = 1095-9203| volume = 348| issue = 6239| pages = 1147–1151| last1 = Bartesaghi| first1 = Alberto| last2 = Merk| first2 = Alan| last3 = Banerjee| first3 = Soojay| last4 = Matthies| first4 = Doreen| last5 = Wu| first5 = Xiongwu| last6 = Milne| first6 = Jacqueline L. S.| last7 = Subramaniam| first7 = Sriram| title = 2.2 Å resolution CryoTEM structure of β-galactosidase in complex with a cell-permeant inhibitor| journal = Science| date = 2015-06-05| pmid = 25953817| pmc = 6512338| bibcode = 2015Sci...348.1147B}}</ref> Single particle analysis can also be performed by [[inducedinductively coupled plasma mass spectroscopyspectrometry]] (ICP-MS).

==Techniques==

Single particle analysis can be done on both [[negative stain|negatively stained]] and vitreous ice-embedded [[transmission electron cryomicroscopy]] (CryoTEM) samples. Single particle analysis methods are, in general, reliant on the sample being homogeneous, although techniques for dealing with conformational heterogeneity<ref>{{cite journal |last1=Lyle |first1=N |last2=Das |first2=RK |last3=Pappu |first3=RV |title=A quantitative measure for protein conformational heterogeneity. |journal=The Journal of Chemical Physics |date=28 September 2013 |volume=139 |issue=12 |pages=121907 |doi=10.1063/1.4812791 |pmid=24089719|pmc=3724800 |bibcode=2013JChPh.139l1907L }}</ref> are being developed.

Images (micrographs) are taken with an electron microscope using [[Charge-coupled device|charged-coupled device]] (CCD) detectors coupled to a phosphorescent layer (in the past, they were instead collected on film and digitized using high-quality scanners). The image processing is carried out using specialized software [[Software tools for molecular microscopy|programs]], often run on multi-processor [[Computercomputer cluster|computer clusters]]s. Depending on the sample or the desired results, various steps of two- or three-dimensional processing can be done.

In addition, single particle analysis can also be performed in an individual particle mode using an ICP-MS unit.

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Biological samples, and especially samples embedded in thin [[vitreous ice]], are highly radiation sensitive, thus only low electron doses can be used to image the sample. This low dose, as well as variations in the metal stain used (if used)<ref>{{cite web |last1=Pandithage |first1=Ruwin |title=Brief Introduction to Contrasting for EM Sample Preparation |url=https://www.leica-microsystems.com/science-lab/brief-introduction-to-contrasting-for-em-sample-preparation/ |language=en |date=2 October 2013}}</ref> means images have high noise relative to the signal given by the particle being observed. By aligning several similar images to each other so they are in register and then averaging them, an image with higher [[signal-to-noise ratio]] can be obtained. As the noise is mostly randomly distributed and the underlying image features constant, by averaging the intensity of each pixel over several images only the constant features are reinforced. Typically, the optimal alignment (a [[Translation (geometry)|translation]] and an in-plane rotation) to map one image onto another is calculated by [[cross-correlation]].

However, a micrograph often contains particles in multiple different orientations and/or conformations, and so to get more representative image averages, a method is required to group similar particle images together into multiple sets. This is normally carried out using one of several data analysis and image classification algorithms, such as [[Multivariate statistics|multi-variate statistical analysis]] and hierarchical ascendant classification, or [[k-means clustering|''k''-means clustering]].{{cncitation needed|date=January 2023}}

Often data sets of tens of thousands of particle images are used, and to reach an optimal solution an [[Iteration|iterative]] procedure of alignment and classification is used, whereby strong image averages produced by classification are used as reference images for a subsequent alignment of the whole data set.

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===Contrast transfer function===

Due to the nature of image formation in the electron microscope, [[Bright-field microscopy|bright-field]] TEM images are obtained using significant [[Focus (optics)|underfocus]]. This, along with features inherent in the microscope's lens system, creates blurring of the collected images visible as a [[point spread function]]. The combined effects of the imaging conditions are known as the [[contrast transfer function]] (CTF), and can be approximated mathematically as a function in reciprocal space. Specialized image processing techniques such as phase flipping and amplitude correction / [[Wiener filter|Wiener filtering]]ing can (at least partially)<ref name="Downing">{{Cite journal|vauthors=Downing KH, Glaeser RM |title=Restoration of weak phase-contrast images recorded with a high degree of defocus: the "twin image" problem associated with CTF correction |journal=Ultramicroscopy |volume=108 |issue=9 |pages=921–8 |date=August 2008 |pmid=18508199 |pmc=2694513 |doi=10.1016/j.ultramic.2008.03.004}}</ref> correct for the CTF, and allow high resolution reconstructions.

===Three-dimensional reconstruction===

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===Tilt methods===

The specimen stage of the microscope can be tilted (typically along a single axis), allowing the single particle technique known as [[wikibooks:Three_Dimensional_Electron_MicroscopyThree Dimensional Electron Microscopy/Initial_modelInitial model#Random_Conical_TiltRandom Conical Tilt|random conical tilt.]]<ref name="RCT">{{Cite journal|vauthors=Radermacher M, Wagenknecht T, Verschoor A, Frank J |title=Three-dimensional reconstruction from a single-exposure, random conical tilt series applied to the 50S ribosomal subunit of Escherichia coli |journal=Journal of Microscopy |volume=146 |issue=Pt 2 |pages=113–36 |date=May 1987 |pmid=3302267 |doi=10.1111/j.1365-2818.1987.tb01333.x|doi-access=free }}</ref> An area of the specimen is imaged at both zero and at high angle (~60-70 degrees) tilts, or in the case of the related method of orthogonal tilt reconstruction,<ref>{{cite book |last1=Leschziner |first1=A |chapter=The Orthogonal Tilt Reconstruction Method |title=Cryo-EM, Part B: 3-D Reconstruction |series=Methods in Enzymology |date=2010 |volume=482 |pages=237–62 |doi=10.1016/S0076-6879(10)82010-5 |pmid=20888964|isbn=9780123849915 }}</ref> +45 and −45 degrees. Pairs of particles corresponding to the same object at two different tilts (tilt pairs) are selected, and by following the parameters used in subsequent alignment and classification steps a three-dimensional reconstruction can be generated relatively easily. This is because the viewing angle (defined as three [[Euler angles]]) of each particle is known from the tilt geometry.

3D reconstructions from random conical tilt suffer from missing information resulting from a restricted range of orientations. Known as the missing cone<ref>{{cite web |url=https://www.c-cina.org/research/algorithms/missing-cone/}}</ref> (due to the shape in reciprocal space), this causes distortions in the 3D maps. However, the missing cone problem can often be overcome by combining several tilt reconstructions. Tilt methods are best suited to [[Negative stain|negatively stained]] samples, and can be used for particles that adsorb to the carbon support film in preferred orientations. The phenomenon known as charging or beam-induced movement<ref>{{cite journal |last1=Li |first1=Xueming |last2=Mooney |first2=Paul |last3=Zheng |first3=Shawn |last4=Booth |first4=Christopher R. |last5=Braunfeld |first5=Michael B. |last6=Gubbens |first6=Sander |last7=Agard |first7=David A. |last8=Cheng |first8=Yifan |title=Electron counting and beam-induced motion correction enable near-atomic-resolution single-particle cryo-EM |journal=Nature Methods |date=June 2013 |volume=10 |issue=6 |pages=584–590 |doi=10.1038/nmeth.2472 |pmid=23644547 |language=en |issn=1548-7105|pmc=3684049 }}</ref> makes collecting high-tilt images of samples in vitreous ice challenging.

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===Map visualization and fitting===

Various software [[Software tools for molecular microscopy|programs]] are available that allow viewing the 3D maps. These often enable the user to manually dock in protein coordinates (structures from [[X-ray crystallography]] or NMR) of subunits into the electron density. Several programs can also fit subunits computationally.<ref>{{cite web |title=Cryo-EM structure solution with Phenix |url=https://phenix-online.org/documentation/overviews/cryo-em_index.html |website=phenix-online.org}}</ref><ref>{{cite journal |last1=Nicholls |first1=RA |last2=Tykac |first2=M |last3=Kovalevskiy |first3=O |last4=Murshudov |first4=GN |title=Current approaches for the fitting and refinement of atomic models into cryo-EM maps using CCP-EM. |journal=Acta Crystallographica. Section D, Structural Biology |date=1 June 2018 |volume=74 |issue=Pt 6 |pages=492–505 |doi=10.1107/S2059798318007313 |pmid=29872001|pmc=6096485 |bibcode=2018AcCrD..74..492N |doi-access=free }}</ref>

For higher-resolution structures, it is possible to build the macromolecule directly, without prior structualstructural knowledge from other methods. Computer algorithms have also been developed for this task.<ref>{{citationcite journal |doi=10.1038/s41586-024-07215-4 |biorxiv=10.1101/2023.05.16.541002 |title=Automated model building and protein identification in cryo-EM maps |date=2024 |last1=Jamali |first1=Kiarash |last2=Käll |first2=Lukas |last3=Zhang |first3=Rui |last4=Brown |first4=Alan |last5=Kimanius |first5=Dari |last6=Scheres |first6=Sjors H. W. |titlejournal=Automated model building and protein identification in cryo-EM mapsNature |journalvolume=bioRxiv : The Preprint Server for Biology628 |dateissue=16 May 20238007 |pages=2023.05.16.541002450–457 |doi=10.1101/2023.05.16.541002|pmid=3729268138408488 |pmc=10245678 |bibcode=2024Natur.628..450J }}</ref>

As high-resolution cyrocryo-EM models are relative new, quality control tools are not as plentiful as it is for X-ray models. Nevertheless, cyrocryo-EM ("real space") versions of the [[difference density map]],<ref>{{cite journal |last1=Yamashita |first1=Keitaro |last2=Palmer |first2=Colin M. |last3=Burnley |first3=Tom |last4=Murshudov |first4=Garib N. |title=Cryo-EM single-particle structure refinement and map calculation using Servalcat |journal=Acta Crystallographica Section D Structural Biology |date=1 October 2021 |volume=77 |issue=10 |pages=1282–1291 |doi=10.1107/S2059798321009475 |pmid=34605431 |pmc=8489229 |bibcode=2021AcCrD..77.1282Y |doi-access=free |quote=}}</ref> cross-validation using a "free" map (comparable to the use of a free [[R-factor]]),<ref>{{cite journal |last1=Falkner |first1=B |last2=Schröder |first2=GF |title=Cross-validation in cryo-EM-based structural modeling. |journal=Proceedings of the National Academy of Sciences of the United States of America |date=28 May 2013 |volume=110 |issue=22 |pages=8930–5 |doi=10.1073/pnas.1119041110 |pmid=23674685|pmc=3670386 |bibcode=2013PNAS..110.8930F |doi-access=free }}</ref><ref>{{cite journal |last1=Beckers |first1=Maximilian |last2=Mann |first2=Daniel |last3=Sachse |first3=Carsten |title=Structural interpretation of cryo-EM image reconstructions |journal=Progress in Biophysics and Molecular Biology |date=March 2021 |volume=160 |pages=26–36 |doi=10.1016/j.pbiomolbio.2020.07.004 |pmid=32735944 |doi-access=free}}</ref> and various [[structure validation]] tools have beganbegun to appear.

===Single particle ICP-MS===

Single particle-induced coupled plasma-mass spectroscopy (SP-ICP-MS) is used in several areas where there is the possibility of detecting and quantifying suspended particles in samples of environmental fluids, assessing their migration, assessing the size of particles and their distribution, and also determining their stability in a given environment. SP-ICP-MS was designed for particle suspensions in 2000 by Claude Degueldre. He first tested this new methodology at the Forel Institute of the University of Geneva and presented this new analytical approach at the 'Colloid 2oo2' symposium during the spring 2002 meeting of the EMRS, and in the proceedings in 2003.<ref>C.{{cite Degueldrejournal &| Pdoi=10. 1016/S0927-Y. Favarger,7757(02)00568-X «| title=Colloid analysis by single particle inductively coupled plasma-mass spectroscopy: aA feasibility study »,| Colloidsdate=2003 and| Surfaceslast1=Degueldre A:| Physicochemicalfirst1=C. and| Engineeringlast2=Favarger Aspects,| symposiumfirst2=P.-Y. C| ofjournal=Colloids theand E-MRSSurfaces 2002A: SpringPhysicochemical Meetingand inEngineering Strasbourg,Aspects France, vol.| volume=217, no| 1,issue=1–3 28 avril 2003, p.| pages=137–142 (ISSN 0927-7757, DOI 10.1016/S0927-7757(02)00568-X)}}</ref> This study presents the theory of SP ICP-MS and the results of tests carried out on clay particles (montmorillonite) as well as other suspensions of colloids. This method was then tested on thorium dioxide nanoparticles by Degueldre & Favarger (2004),<ref>C{{cite Degueldrejournal et| Pdoi=10.1016/j.talanta.2003.10.016 -Y Favarger, «| title=Thorium colloid analysis by single particle inductively coupled plasma-mass spectrometry »,| Talanta,date=2004 vol| last1=Degueldre | first1=C. 62,| nojournal=Talanta 5,| 19volume=62 avril| 2004,issue=5 p.| pages=1051–1054 (ISSN| 0039-9140,pmid=18969397 DOI 10.1016/j.talanta.2003.10.016}}</ref> zirconium dioxide by Degueldre et al (2004)<ref>C.{{cite Degueldre,journal P.| -Ydoi=10. Favarger et C1016/j.aca.2004.04.015 Bitea, «| title=Zirconia colloid analysis by single particle inductively coupled plasma–mass spectrometry »,| Analyticadate=2004 Chimica| Acta,last1=Degueldre vol| first1=C. 518,| nolast2=Favarger 1,| 2first2=P.-Y. août| 2004,last3=Bitea p| first3=C. 137–142| (ISSNjournal=Analytica 0003-2670,Chimica DOIActa 10| volume=518 | issue=1–2 | pages=137–142 | bibcode=2004AcAC.1016/j.aca518.2004.04.015)137D }}</ref> and gold nanoparticles, which are used as a substrate in nanopharmacy, and published by Degueldre et al (2006).<ref>C.{{cite Degueldre,journal P.| -Ydoi=10. Favarger et S1016/j.aca.2005.09.021 Wold, «| title=Gold colloid analysis by inductively coupled plasma-mass spectrometry in a single particle mode »,| Analyticadate=2006 Chimica| Acta,last1=Degueldre vol| first1=C. 555,| nolast2=Favarger 2,| 12first2=P.-Y. janvier| 2006,last3=Wold p| first3=S. 263–268| (ISSNjournal=Analytica 0003-2670,Chimica Acta | volume=555 | issue=2 DOI| 10pages=263–268 | bibcode=2006AcAC.1016/j.aca555.2005.09.021)263D }}</ref> Subsequently, the study of uranium dioxide nano- and micro-particles gave rise to a detailed publication, Ref. Degueldre et al (2006).<ref>C.{{cite Degueldre,journal P.| -Ydoi=10. Favarger, R1016/j. Rossé et Stalanta.2005.05.006 Wold, «| title=Uranium colloid analysis by single particle inductively coupled plasma-mass spectrometry »,| Talanta,date=2006 vol| last1=Degueldre | first1=C. 68,| nolast2=Favarger 3,| 15first2=P.-Y. janvier| 2006,last3=Rossé p| first3=R. | last4=Wold | first4=S. | journal=Talanta | volume=68 | issue=3 | pages=623–628 (ISSN| 0039-9140,pmid=18970366 DOI 10.1016/j.talanta.2005.05.006}}</ref> Since 2010 the interest for SP ICP-MS has exploded.

==Examples==

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==Primary database==

* [http://www.emdatabank.org/index.html EM Data Bank] {{Webarchive|url=https://web.archive.org/web/20190205053534/http://www.emdatabank.org/index.html |date=2019-02-05 }} ([[EM Data Bank]])

==References==