Community standards for open cell migration data

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.

Liquid Biopsy and Primary Brain Tumors

“…Detection limits vary and sensitivity appears to be better from CSF, but the risk for lumbar puncture, especially with a large tumor mass in the brain, is higher than for drawing blood. Data from very different clinical studies and approaches using liquid biopsy may be shared in fair conditions for reanalysis by others, similar to cell migration studies in publicly available data repositories [ 91 ]. It is likely that direct tumor sampling by removal of a brain tumor or stereotactic biopsy of tumor tissue will remain the method of choice to allow for reliable diagnosis by both histology and genetic profiling.…”

Sharing biological data: why, when, and how

“…Individual research communities have augmented these general standards. For example, communities have produced specialized guidelines for reporting cell migration data [94], time-lapse data [95], 3D microscopy images of whole brains (https://www.dorywork space.org/), and fluorescence microscopy [96].…”

Developing open-source software for bioimage analysis: opportunities and challenges

“…These efforts are also resulting in community efforts towards new open formats. 36,37 On a higher level, internal interoperability can be achieved by plugin mechanisms to ensure that within a given software application, modules from various origins can communicate with each other. Software such as ImageJ 17 /Fiji, 18 CellProfiler, 20 KNIME, 38 or Icy 19 are known in the image data science field as platforms that are extensible using plugin architectures.…”