DOI: 10.5281/zenodo.6108683
title: Fast, automatized fluorescence nanoscopy resolves rearrangements of SNARE and cargo proteins in platelets co-incubated with cancer cells.

description: <strong>This folder contains all raw data underlying the results presented in a manuscript, submitted to </strong><strong><em>Journal of Nanobiotechnology</em></strong><strong>, and entitled:</strong> <strong>Fast, automatized fluorescence nanoscopy resolves rearrangements of SNARE and cargo proteins in platelets co-incubated with cancer cells.</strong> <strong>Authored by:</strong> Jan Bergstrand<sup>a,+</sup>, Xinyan Miao<sup>a,+</sup>, Chinmaya Venugopal Srambickal<sup>a,+</sup>, Gert Auer<sup>b</sup>, Jerker Widengren<sup>a,*</sup> <sup>a</sup> Royal Institute of Technology (KTH), Department of Applied Physics, Experimental Biomolecular Physics, Albanova Univ Center, SE-106 91 Stockholm, Sweden <sup>b</sup> Karolinska Institutet, Department of Oncology-Pathology, K7, Z1:00, Karolinska University Hospital, 171 76 Stockholm, Sweden <sup>* </sup>To whom correspondence should be addressed. E-mail: jwideng@kth.se, Phone: +46-8-7907813 <sup>+ </sup>These authors contributed equally <strong>The data files are grouped into the different </strong><strong>figures where they are presented</strong><strong>.</strong> <strong>ABSTRACT</strong> Background: Increasing evidence suggests that platelets play a central role in cancer progression, with altered storage and selective release from platelets of specific tumor-promoting proteins as a major mechanism. Fluorescence-based super-resolution microscopy (SRM) can resolve nanoscale spatial distribution patterns of such proteins, and how they are altered in platelets upon different activations. Analysing such alterations by SRM thus represents a promising, minimally invasive strategy for platelet-based diagnosis and monitoring of cancer progression. However, broader applicability beyond specialized research labs will require objective, automated imaging procedures. Moreover, for statistically significant analyses many SRM platelet images are needed, of several different platelet proteins. Such proteins, showing alterations in their distributions upon cancer progression additionally need to be identified. Results: A fast, automated and objective procedure for SRM platelet image acquisition, analysis and classification was developed to overcome these limitations. By stimulated emission depletion SRM we imaged nanoscale patterns of six different platelet proteins; four different SNAREs (soluble N-ethylmaleimide factor attachment protein receptors) mediating protein secretion by membrane fusion of storage granules, and two angiogenesis regulating proteins, representing cargo proteins within these granules coupled to tumor progression. By an automatized procedure, we recorded about 100 SRM images of platelets, for each of these six proteins, and for five different categories of platelets; incubated with cancer cells (MCF-7, MDA-MB-231, EFO-21), non-cancer cells (MCF-10A), or no cells at all. From these images, structural similarity and protein cluster parameters were determined, and probability functions of these parameters were generated for the different platelet categories. By comparing these probability functions between the categories, we could identify nanoscale alterations in the protein distributions, allowing us to classify the platelets into their correct categories, if they were co-incubated with cancer cells, non-cancer cells, or no cells at all. Conclusions: The fast, automatized and objective acquisition and analysis procedure established in this work confirms the role of SNAREs and angiogenesis-regulating proteins in platelet-mediated cancer progression, provides additional fundamental knowledge on the interplay between tumor cells and platelets, and represent an important step towards using tumor-platelet interactions and redistribution of nanoscale protein patterns in platelets as a basis for cancer diagnostics. <strong>Key Words:</strong> STED, super-resolution microscopy, platelet, cancer, tumorigenesis, SNARE protein, dictionary learning