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[[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 |pages= |isbn=978-0-19-518218-7 |url=https://books.google.com/?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]], and so hard to interpret. 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>
==Techniques==
Single particle analysis can be done on both [[negative stain|negatively stained]] and vitreous ice-embedded [[Cryogenic transmission electron microscopy|CryoTEM]] samples. Single particle analysis methods are, in general, reliant on the sample being homogeneous, although techniques for dealing with [https://pubmed.ncbi.nlm.nih.gov/24089719/ conformational heterogeneity] are being developed.
Images (micrographs), in the past, were collected on film are digitized using high-quality scanners or using built-in [[charge-coupled device|CCD]] detectors coupled to a phosphorescent layer. Now it is common to use direct electron detectors to collect images. The image processing is carried out using specialized software [[Software tools for molecular microscopy|programs]] (for instance<ref>{{Cite web | url=http://www.femtoscanonline.com/wiki/en/processing/%D0%B0%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7_%D0%B2%D1%8B%D0%B4%D0%B5%D0%BB%D0%B5%D0%BD%D0%BD%D1%8B%D1%85_%D0%BE%D0%B1%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D0%B9 |title = 送彩金38满100提现_无需申请自动送彩金【官网】}}</ref>), often run on multi-processor [[Computer cluster|computer clusters]]. Depending on the sample or the desired results, various steps of two- or three-dimensional processing can be done.
===Alignment and classification===
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 [https://www.leica-microsystems.com/science-lab/brief-introduction-to-contrasting-for-em-sample-preparation/ metal stain] used (if used) 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]].
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.
===Image filtering===
Image filtering ([[band pass filter]]ing) is often used to reduce the influence of high and/or low [[spatial frequency]] information in the images, which can affect the results of the alignment and classification procedures. This is particularly useful in [[negative stain]] images. The algorithms make use of fast Fourier transforms ([[Fast Fourier transform|FFT]]), often employing [[Normal distribution|gaussian shaped]] soft-edged masks in [[Reciprocal lattice#Reciprocal space|reciprocal space]] to suppress certain frequency ranges. High-pass filters remove low spatial frequencies (such as ramp or gradient effects), leaving the higher frequencies intact. Low-pass filters remove high spatial frequency features and have a blurring effect on fine details.
===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]] 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===
Transmission electron microscopy images are projections of the object showing the distribution of density through the object, similar to medical X-rays. By making use of the [[projection-slice theorem]] a three-dimensional reconstruction of the object can be generated by combining many images (2D projections) of the object taken from a range of viewing angles. Proteins in vitreous ice ideally adopt a random distribution of orientations (or viewing angles), allowing a fairly [[isotropic]] reconstruction if a large number of particle images are used. This contrasts with [[electron tomography]], where the viewing angles are limited due to the geometry of the sample/imaging set up, giving an [[Anisotropy|anisotropic]] reconstruction. [[Filtered back projection]] is a commonly used method of generating 3D reconstructions in single particle analysis, although many alternative algorithms exist.<ref name="Dynamics" />
Before a reconstruction can be made, the orientation of the object in each image needs to be estimated. Several methods have been developed to work out the relative [[Euler angles]] of each image. Some are based on common lines (common 1D projections and [[radon transform|sinograms]]), others use iterative projection matching algorithms. The latter works by beginning with a simple, low resolution 3D starting model and compares the experimental images to projections of the model and creates a new 3D to bootstrap towards a solution.
Methods are also available for making 3D reconstructions of [[Helix|helical]] samples (such as [[tobacco mosaic virus]]), taking advantage of the inherent [[Rotational symmetry|helical symmetry]]. Both real space methods (treating sections of the helix as single particles) and reciprocal space methods (using diffraction patterns) can be used for these samples.
===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_Microscopy/Initial_model#Random_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}}</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 [https://pubmed.ncbi.nlm.nih.gov/20888964/ orthogonal tilt reconstruction], +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 [https://www.c-cina.org/research/algorithms/missing-cone/ the missing cone] (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 [https://www.nature.com/articles/nmeth.2472 beam-induced movement] makes collecting high-tilt images of samples in vitreous ice challenging.
===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.
==Examples==
*
* ''[[Methanococcus maripaludis]]'' chaperonin,<ref>Japanese Protein databank http://www.pdbj.org/emnavi/emnavi_movie.php?id=5137</ref> reconstructed to 0.43 nanometer resolution.<ref name="Zhang J">{{Cite journal |vauthors=Zhang J, Baker ML, Schröder GF, etal |title=Mechanism of folding chamber closure in a group II chaperonin |journal=Nature |volume=463 |issue=7279 |pages=379–83 |date=January 2010 |pmid=20090755 |pmc=2834796 |doi=10.1038/nature08701|bibcode=2010Natur.463..379Z }}</ref> This bacterial protein complex is a machine for folding other proteins, which get trapped within the shell.
* [[Fatty acid synthase]]<ref>Japanese Protein databank http://www.pdbj.org/emnavi/emnavi_movie.php?id=1623</ref> from yeast at 0.59 nanometer resolution.<ref name="Gipson">{{Cite journal|vauthors=Gipson P, Mills DJ, Wouts R, Grininger M, Vonck J, Kühlbrandt W |title=Direct structural insight into the substrate-shuttling mechanism of yeast fatty acid synthase by electron cryomicroscopy |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=107 |issue=20 |pages=9164–9 |date=May 2010 |pmid=20231485 |pmc=2889056 |doi=10.1073/pnas.0913547107|bibcode=2010PNAS..107.9164G }}</ref> This huge enzyme complex is responsible for building the long chain fatty acids essential for cellular life.
* A 0.33 nanometer reconstruction of [[Golden shiner virus|Aquareovirus]].<ref>Japanese Protein databank http://www.pdbj.org/emnavi/emnavi_movie.php?id=5160</ref><ref name="Zhang X">{{Cite journal|vauthors=Zhang X, Jin L, Fang Q, Hui WH, Zhou ZH |title=3.3 A cryo-EM structure of a nonenveloped virus reveals a priming mechanism for cell entry |journal=Cell |volume=141 |issue=3 |pages=472–82 |date=April 2010 |pmid=20398923 |doi=10.1016/j.cell.2010.03.041 |pmc=3422562}}</ref> These viruses infect fish and other aquatic animals. The reconstruction has high enough resolution to have amino acid side chain densities easily visible.
==Primary database==
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