Journal Article Ppublish
PubMed: 39785704 DOI: 10.1091/mbc.E24-10-0461
Behind the scenes of cellular organization: Quantifying spatial phenotypes of puncta structures with statistical models including random fields.
Mol Biol Cell 36(3):ar22
Authors: Kyriacos Nicolaou, Josiah B Passmore, Lukas C Kapitein, Bela M Mulder, Florian Berger
Affiliations
- Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, 3584 CH Utrecht, The Netherlands. Centre for Complex Systems Studies, Utrecht University, 3584 CE Utrecht, The Netherlands.
- Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, 3584 CH Utrecht, The Netherlands.
- Institute AMOLF, 1098 XG Amsterdam, The Netherlands.
- Centre for Complex Systems Studies, Utrecht University, 3584 CE Utrecht, The Netherlands.
Abstract
The cellular interior is a spatially complex environment shaped by nontrivial stochastic and biophysical processes. Within this complexity, spatial organizational principles-also called spatial phenotypes-often emerge with functional implications. However, identifying and quantifying these phenotypes in the stochastic intracellular environment is challenging. To overcome this challenge for puncta, we discuss the use of inference of point-process models that link the density of points to other imaged structures and a random field that captures hidden processes. We apply these methods to simulated data and multiplexed immunofluorescence images of Vero E6 cells. Our analysis suggests that peroxisomes are likely to be found near the perinuclear region, overlapping with the endoplasmic reticulum, and located within a distance of 1 µm to mitochondria. Moreover, the random field captures a hidden variation of the mean density in the order of 15 µm. This length scale could provide critical information for further developing mechanistic hypotheses and models. By using spatial statistical models including random fields, we add a valuable perspective to cell biology.
MeSH
Cell Physiological Phenomena, Cells, Molecular Biology, Phenotype, Environment, Models, Statistical