Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates
Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution l...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Bioinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2023.1237551/full |
| _version_ | 1797465038636711936 |
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| author | Luca Panconi Dylan M. Owen Juliette Griffié |
| author_facet | Luca Panconi Dylan M. Owen Juliette Griffié |
| author_sort | Luca Panconi |
| collection | DOAJ |
| description | Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern—a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed. |
| first_indexed | 2024-03-09T18:15:51Z |
| format | Article |
| id | doaj.art-8e94e4cce66c469c805e09d2b64fa556 |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-03-09T18:15:51Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-8e94e4cce66c469c805e09d2b64fa5562023-11-24T08:43:23ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472023-11-01310.3389/fbinf.2023.12375511237551Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregatesLuca Panconi0Dylan M. Owen1Juliette Griffié2School of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United KingdomSchool of Mathematics, Centre of Membrane Proteins and Receptors (COMPARE), Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United KingdomDepartment of Biochemistry and Biophysics, Stockholm University, Stockholm, SwedenMany proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern—a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.https://www.frontiersin.org/articles/10.3389/fbinf.2023.1237551/fullcluster analysissingle molecule localisation microscopy (SMLM)protein aggregatesimage quantificationbioinformacticsspatial point pattern (SPP) |
| spellingShingle | Luca Panconi Dylan M. Owen Juliette Griffié Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates Frontiers in Bioinformatics cluster analysis single molecule localisation microscopy (SMLM) protein aggregates image quantification bioinformactics spatial point pattern (SPP) |
| title | Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates |
| title_full | Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates |
| title_fullStr | Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates |
| title_full_unstemmed | Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates |
| title_short | Cluster analysis for localisation-based data sets: dos and don’ts when quantifying protein aggregates |
| title_sort | cluster analysis for localisation based data sets dos and don ts when quantifying protein aggregates |
| topic | cluster analysis single molecule localisation microscopy (SMLM) protein aggregates image quantification bioinformactics spatial point pattern (SPP) |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2023.1237551/full |
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