Extended methods for spatial cell classification with DBSCAN-CellX.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
01 11 2023
01 11 2023
Historique:
received:
28
03
2023
accepted:
17
10
2023
medline:
3
11
2023
pubmed:
2
11
2023
entrez:
2
11
2023
Statut:
epublish
Résumé
Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.
Identifiants
pubmed: 37914751
doi: 10.1038/s41598-023-45190-4
pii: 10.1038/s41598-023-45190-4
pmc: PMC10620226
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
18868Informations de copyright
© 2023. The Author(s).
Références
Mol Syst Biol. 2010 May 11;6:369
pubmed: 20461076
PLoS Pathog. 2022 Jun 28;18(6):e1010472
pubmed: 35763545
Nat Rev Mol Cell Biol. 2011 Feb;12(2):119-25
pubmed: 21224886
BMC Bioinformatics. 2019 Dec 27;20(Suppl 23):633
pubmed: 31881827
PLoS Comput Biol. 2022 Jun 13;18(6):e1009846
pubmed: 35696439
Mol Syst Biol. 2012 Apr 24;8:579
pubmed: 22531119
Nat Genet. 2017 May;49(5):708-718
pubmed: 28319088
Cytometry A. 2020 Mar;97(3):288-295
pubmed: 31872957
Nat Biotechnol. 2010 Feb;28(2):167-71
pubmed: 20118917
Open Biol. 2014 Jan 22;4:130132
pubmed: 24451547
Cell Rep. 2020 Apr 21;31(3):107523
pubmed: 32320656
Nature. 2021 Aug;596(7871):211-220
pubmed: 34381231
Cell. 2020 Aug 20;182(4):976-991.e19
pubmed: 32702314
Cell Rep. 2021 Oct 12;37(2):109801
pubmed: 34644578
PLoS Comput Biol. 2019 Jan 2;15(1):e1006384
pubmed: 30601802
Cell. 2020 Jul 23;182(2):497-514.e22
pubmed: 32579974
Development. 2019 Jun 27;146(12):
pubmed: 31249009
Nat Rev Microbiol. 2015 Aug;13(8):497-508
pubmed: 26145732
J Virol. 2022 Sep 14;96(17):e0070622
pubmed: 36000839
Mol Syst Biol. 2015 Mar;11(3):790
pubmed: 26148352
Nature. 2015 Jul 2;523(7558):88-91
pubmed: 26009010
Nature. 2019 May;569(7754):66-72
pubmed: 31019299
Data Min Knowl Discov. 2019;33(6):1894-1952
pubmed: 32831623