Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model

Sandra Mayr, Fabian Hauser, Sujitha Puthukodan, Markus Axmann, Janett Göhring, Jaroslaw Jacak


We present the software platform 2CALM that allows for a comparative analysis of 3D localisation microscopy data representing protein distributions in two biological samples. The in-depth statistical analysis reveals differences between samples at the nanoscopic level using parameters such as cluster-density and -curvature. An automatic classification system combines multiplex and multi-level statistical approaches into one comprehensive parameter for similarity testing of the compared samples. We demonstrated the biological importance of 2CALM, comparing the protein distributions of CD41 and CD62p on activated platelets in a 3D artificial clot. Additionally, using 2CALM, we quantified the impact of the inflammatory cytokine interleukin-1β on platelet activation in clots. The platform is applicable to any other cell type and biological system and can provide new insights into biological and medical applications.


LM has progressed immensely over the last decade [1–5], however only a few of the approaches towards a comparative analysis of the resulting data have been achieved [6–9]. Primarily, these studies utilised comparative analyses of single molecules in the context of co-localisation in cells [10–14], with the majority visualising the 2D and 3D arrangement of proteins [10, 12, 13, 15, 16].


Equalization and bootstrap resampling

We use inferential statistics to examine the relationships between the features of two samples based on a series of smaller samples in order to generalize how those features will relate to the larger sample [67, 36, 68, 69, 98–103]. For analysis, we chose representative subsets of two samples, which will be referred to as the bootstrap-resamples. The bootstrap procedure involves choosing random samples (with replacement) from a large dataset and analysing each bootstrap sample in a similar way. Sampling with replacement means that each point is selected randomly from the original dataset. Thus, a particular point from the original dataset may appear multiple times in a given bootstrap sample. The number of total points included in a bootstrap sample (including data from both compared samples) is equal. Let N1, N2 be the number of points in both samples, respectively. We perform M times random resampling of each sample with the same number of points N<min(N1,N2). Default values for M = 100 and N is ~2000–10 000 points. These bootstrap-samples (bs1(k),bs2(k))(k = 1,…,M) are pairwise stored for further analysis.


In this study, we demonstrated that 3D LM (dSTORM) with subsequent advanced statistical analyses can be used as a tool to determine and classify differences in protein distributions between two datasets. We have successfully used 2CALM for a comparison of CD41/CD62p distributions in platelets within a clot and for determination of the effect of IL-1β-treated platelets on CD62p membrane distribution.


We would like to thank Ilse Kammerhofer as well as Carolin Steffenhagen and Heinz Redl for technical support and Anja Peterbauer from the Red Cross Transfusion Service for supplying human platelet samples.

Citation: Mayr S, Hauser F, Puthukodan S, Axmann M, Göhring J, Jacak J (2020) Statistical analysis of 3D localisation microscopy images for quantification of membrane protein distributions in a platelet clot model. PLoS Comput Biol 16(6): e1007902.

Editor: Alessandro Esposito, University of Cambridge, UNITED KINGDOM

Received: July 18, 2019; Accepted: April 22, 2020; Published: June 30, 2020

Copyright: © 2020 Mayr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Software is avalibale on as well as the data to generate the figures and to use the software.

Funding: This work was supported by the Interreg Project ATCZ14 Czech-Austrian Center for Supracellular Medical Research (CAC-SuMeR), the Fonds zur Förderung der Wissenschaftlichen Forschung (FWF) (Grant number: W 1250, Funder Id:, under the Doctorate College program “Nano-Analytics of Cellular Systems(NanoCell)” and stad-Alone Projct '' 3D Lithographical Scaffolds for Stem Cell Differentiation (LiSSCeD) '' Grant number: P 31827. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.