Community standards for open cell migration data.

Jaime Prilusky,Staffan Strömblad,Assaf Zaritsky,Josh Moore,Alejandra González-Beltrán ,Peter Friedl,Philippe Roudot,Gert-Jan Bakker,Simone Leo,Mark Kittisopikul,Gwendolien Sergeant,Sébastien Besson,Lennart Martens,Paola Masuzzo,Matthias Gunzer,Yael Paran,Philippe Rocca-Serra,Marc Schuster,Marleen Van Troys ,Jason R Swedlow ,Robert H Eibl ,Sylvia E Le Dévédec ,Christophe Ampè ,Merijn Van Erp ,Susanna Assunta Sansone

doi:10.1093/gigascience/giaa041

Abstract

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration.

Highlights

Towards FAIR and open cell migration dataDue to advances in molecular biology, microscopy technologies and automated image analysis, cell migration research currently produces spatially and temporally resolved, complex and large datasets
The cell migration-specific part of Minimum Information About a Cell Migration Experiment (MIACME) is partitioned into three conceptual domains (Figure 2): (1) the experimental setup: the assay, cell model, environmental conditions and perturbations; (2) the imaging condition: the microscopy settings; and (3) the data: the raw images, summary information about the data, processed images and the derived quantitative analysis outputs
Proteomics and structural biology have greatly benefited from well-developed data standards[30] that contributed to rapid progress in these fields

Summary

Introduction

Towards FAIR and open cell migration dataDue to advances in molecular biology, microscopy technologies and automated image analysis, cell migration research currently produces spatially and temporally resolved, complex and large datasets. Experimental imaging techniques have de facto entered the “big data” era[1,2] This creates, on the one hand, challenges[3] for standardising and maintaining data-driven cell migration research in public repositories while, on the other hand, offers unprecedented opportunities for data integration, data mining, and meta-analyses. The aim was to overcome the current fragmentation of cell migration research and facilitate data exchange, dissemination, verification, interoperability and reuse, as well as encourage data sharing[7] This should increase the reproducibility of experiments, enable data mining and meta-analyses, and satisfy the FAIR principles for Findable, Accessible, Interoperable and Reusable data[8]. It will attract computational scientists to the field, producing in silico models allowing numerical hypotheses to be tested experimentally[9,10]

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Results

Conclusion