Content deleted Content added
Artoria2e5 (talk | contribs) →Map visualization and fitting: actually, fitting rigid bodies and building new chains are different. Also validation. |
No edit summary |
||
| (21 intermediate revisions by 12 users not shown) | |||
Line 1:
{{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]],
==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
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 [[
In addition, single particle analysis can also be performed in an individual particle mode using an ICP-MS unit.
Line 13 ⟶ 14:
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]].{{
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.
Line 23 ⟶ 24:
===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
===Three-dimensional reconstruction===
Line 35 ⟶ 36:
===Tilt methods===
The specimen stage of the microscope can be tilted (typically along a single axis), allowing the single particle technique known as [[wikibooks:
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 |
===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
For higher-resolution structures, it is possible to build the macromolecule directly, without prior
As high-resolution
===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>
==Examples==
Line 60 ⟶ 59:
==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==
| |||