An open data ecosystem for cell migration research
d The 2014 Cell Migration Workshop
i 3
ai 
Forumously unsuspected feedback mechanisms [4]. Moreover,
results in high content and high throughput microscopy
have established the importance of quantitative analysis
for systems biology and drug discovery [5].
A key remaining challenge is to understand how the
function and signalling of organelles is coordinated and
integrated within cells and tissues. Cell migration is the
product of complex processes operating at different scales,
and could be investigated using a systems microscopy
in more detail.
Data and metadata standardization
Minimum reporting requirements
To be reusable, an experimental data set needs acc-
ompanying metadata, describing both biological and
Glossary
2D: two-dimensional.
2.5D: two-and-a-half-dimensional.
3D: three-dimensional.
CMC: cell migration consortium.
CMG: cell migration gateway.
CV: controlled vocabulary.
OME: open microscopy environment.
0962-8924/
 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tcb.2014.11.005
Corresponding author: Martens, L. (lennart.martens@vib-ugent.be).
Keywords: cell migration; bioinformatics; standardization; meta-analysis
data ecosystem.Paola Masuzzo1,2, Lennart Martens1,2, an
Participants3
1Department of Medical Protein Research, VIB, A. Baertsoenkaa
2Department of Biochemistry, Ghent University, A. Baertsoenka
3See Contributing Authors section at the end of this paper
Cell migration research has recently become both a high
content and a high throughput field thanks to techno-
logical, computational, and methodological advances.
Simultaneously, however, urgent bioinformatics needs
regarding data management, standardization, and dis-
semination have emerged. To address these concerns,
we propose to establish an open data ecosystem for cell
migration research.
Where is the cell migration field migrating to?
Cell migration is crucial in biological processes such as
morphogenesis, immune surveillance, wound healing, and
cancer metastasis [1]. Diverse biological models have been
developed to reflect the range of molecular and physiologi-
cal events involved in cell migration (see Figure S1 in the
supplementary material online). Furthermore, technology
has been an important driver for innovation in cell migra-
tion research. For example, the evolution of light micros-
copy from bright field to confocal, two photon, light sheet,
and superresolution fluorescence microscopy has enabled
the development of complex experimental systems, pro-
gressing from 2D cell migration assays to 2.5D and 3D (see
Glossary) approaches [2] (see Table S1 in the supplemen-
tary material online).
While analyses on 2D substrates have led to essential
insight into the cellular motility machinery, 3D environ-
ments are essential for understanding their physiological
context, and have recently provided novel knowledge regard-
ing invasive behaviour [3]. Although these in vitro assays
are clearly valuable, deeper insight into cell migration can
only be obtained through in vivo approaches. Such assays
have been enabled through live cell microscopy to visualize
moving cells in their native surroundings, revealing previ-, 9000 Ghent, Belgium
3, 9000 Ghent, Belgium
approach [6], combining image analysis at different resolu-
tions with data mining, multivariate statistics, and model-
ling.
These advances in techniques and biological models have
been supported by dedicated efforts in bioinformatics and
computational biology (see Table S1 in the supplementary
material online). Algorithms and tools have been developed
for tracking cells using time lapse images [7], and for proces-
sing and visualizing large sets of complex image data (http://
jcb-dataviewer.rupress.org). The computational approaches
in the field extend to in silico modelling of cell migration and
invasion, especially in tumour development and progression
[8]. Advances in the field have thus been built on a combina-
tion of novel analytical approaches, dedicated software tools
and algorithms, and predictive theoretical models.
Taking on the challenges: an open data ecosystem for
cell migration
Even though the cell migration field has embraced compu-
tational models as a means to integrate and interpret
experiments, a key missing element is the global iterative
connection between experimental data and computational
approaches. This connection requires an open and free data
ecosystem, where standardized and documented results of
cell migration research can be shared and consulted within a
central location, as exemplified in Figure 1. Building such an
ecosystem will require several interdigitated and essential
developments. A public, centralized repository constitutes
the major component; however, it is only viable if supported
by standard formats for the stored data and metadata.
Furthermore, each data set in the repository should conform
to minimum reporting requirements that ensure consistent
annotation (see Table S1 in the supplementary material
online). The following sections describe each of these aspectsTrends in Cell Biology, February 2015, Vol. 25, No. 2 55
Data and metadata generaon
3h0 6h
Data and metadata analysis and interpretaon
Data and metadata standardizaon
(A) 
(B)
(C)
Forummethodological context. Community-wide minimum
reporting requirements have, therefore, been created in
many fields, for example, for proteomics [9] and, of direct
interest to cell migration, for cell perturbation experiments
(http://miaca.sourceforge.net/). The global harmonization
of such field-specific minimum information checklists is
pursued by the BioSharing project (http://biosharing.org/).
The existing requirements can serve as a starting point
to build a specific checklist for in vitro cell migration
experiments. A tentative example of what such a list could
look like is shown in Table 1: example information is
provided about experimental modules and submodules,
from sample preparation over image acquisition and anal-
ysis, to downstream data analysis, and laboratory meta-
data. A second iteration can then extend this to in vivo
studies, which will be more challenging.
Controlled vocabularies
Minimum reporting requirements specify which informa-
tion should be reported, but not yet how this information
should be conveyed. The use of a common terminology
thus becomes important, typically taking the form of a
Controlled
vocabularies
Minimum reporng
requirements
Data and metadata
standard formats
Figure 1. An example of an experimental workflow in the open data ecosystem. (A) Data
analyse and interpret the resulting data and associated metadata. (C) The collected data
reproduction, verification, and exchange: minimum reporting requirements specify the 
(CVs) are used to unambiguously annotate such units of information; and the data are ex
migration data set is ready for (D) submission to, and (E) subsequent dissemination from
use of public cell migration data, including multiscale and meta-scale analyses across 
56x (µm)
y
 (
µm
)
y
 (
µm
)
y
 (
µm
)
x (µm)
X (µm)
Global disseminaon
Re-use of public data(F)
(E)
Trends in Cell Biology February 2015, Vol. 25, No. 2controlled vocabulary (CV). Again, proteomics provides an
example of such a CV for the unique and unambiguous, yet
detailed semantic annotation of (meta-)data [10]. Existing
CVs that can be reused for cell migration experiments
include the Cell Ontology [11] and the Cellular Microscopy
Phenotype Ontology (http://www.ebi.ac.uk/cmpo/).
Standard data and metadata formats
When minimum reporting requirements are coupled to
CVs, data and metadata can be conveyed in an unambigu-
ous and well documented form. However, one more
element is needed for successful standardization: the adop-
tion of standard data formats. As in any data rich field,
software tools are continuously applied in cell migration
research to process and analyse data. However, such
software can only read data presented in known formats,
usually dictated by instrument vendors, and therefore
implying that data can only be read by other researchers
if they have access to the same instrument. Moreover,
such proprietary data formats also suffer from data rot
[12]. These issues can be resolved through community
standard, open data formats, where considerable work
Submission to data repository
of standardized data and
metadata
(D)
TRENDS in Cell Biology 
 and metadata associated with an experiment are generated. (B) Software is used to
 are formatted and reported in the relevant standards to enable data and metadata
core information to be supplied through the software tool; controlled vocabularies
ported using data and metadata standard formats. A fully standards compliant cell
, a global data repository. (F) The open data sharing ecosystem will enable the re-
large scale experiments, ultimately unlocking new knowledge in the field.
uireTable 1. A tentative example of what a minimal reporting req
experimenta
Module Submodule 
Sample Basic condition 
Pretreatment condition 
Assay Assay 
Substrate 
Perturbation 
Image acquisition Time lapse 
Image analysis Software 
Algorithms 
Data analysis Software 
parameters 
Forumhas already been performed by Open Microscopy Environ-
ment (OME) software (http://www.openmicroscopy.org/).
OME has developed widely used bioimage informatics
solutions, including the OME–TIFF format that could be
extended for cell migration data.
Experimental data, however, must always be accompa-
nied by, and interpreted in the context of, overall experi-
mental design. The existing ISA formats offer an
extensible, hierarchical structure for the representation
of such top-level study metadata [13], a concept that can
certainly be re-used in cell migration.
Global dissemination of standardized data and
metadata
Data sharing is central to scientific progress, and is fast
becoming a requirement for funding or publication. Fun-
ders increasingly require grantees to share their data to
maximize their value, while scientific journals require
dissemination to ensure reproducibility of published
results [14]. It is, therefore, logical that the centrepiece
of our proposed data ecosystem should be a public reposi-
tory for cell migration data. The first attempt at creating
such a repository was the Cell Migration Gateway (CMG;
http://www.cellmigration.org), built by the Cell Migration
Consortium. Designed to be a gene-centric collection of
experimental data around proteins and complexes in-
volved in cell migration, it can be used as a starting point
Laboratory Experiment 
aAbbreviations: DMEM, Dulbecco’s modified eagle medium; ECM, extracellular matrix; G
medium.ments checklist might look like for an in vitro cell migration
Information Example
Cell type Dendritic cell
Cell source ATCC
Cell species Human
Cell context GFP reporter
Passages primary cells Passage 4
Medium RPMI 1640
Dimensionality 2D
Medium DMEM
Temperature Room temperature
ECM type Matrigel
ECM concentration 2.5 mg/ml
Post staining YES
Type Compound
Concentration 10 mg
Dimensionality 2D
Imaging modality Bright field
Interval 10 min
Duration 36 h
Software name In-house software
Segmentation Watershed
Tracking Contour
Software name In-house software
Biological replicates 6
Trends in Cell Biology February 2015, Vol. 25, No. 2for the creation of a broader, more comprehensive, and
future-proof cell migration data repository.
This repository should be fully conversant in the com-
munity standard formats and CVs. Furthermore, the re-
pository should assess the adherence of datasets to the
minimum reporting requirements, and perform semantic
validation to check whether CV terms are used out of
context. However, accepting and storing data is only a
small part of the role of a repository: its most relevant
function is the continuous dissemination of information.
The repository thus has to offer multiple modes of access,
as different users will need different types of access (e.g.,
manually versus automatically). It must also provide cross-
references to databases in associated domains. Ideally,
the repository should even serve users outside the field,
enabling integrative analyses across domains in the life
sciences. Over time, the system could be extended to host
free software tools that execute data processing workflows,
perform data analysis, and allow results interpretation.
Re-using public data: the need for novel multiscale and
meta-scale analysis approaches
The data sets generated in cell migration research current-
ly remain isolated due to the lack of a data sharing ecosys-
tem. However, once such an ecosystem is created, it will
become possible to compare and integrate data sets,
and perform multiscale and meta-scale analyses across
Technical replicates 3
Readouts Single cells tracks
Statistics Mann–Whitney U test
User PM
Date 12 September 2014
Purpose Actin KO migration
FP, green fluorescent protein; KO, knockout; RPMI, Roswell Park Memorial Institute
57
experiments. Given the volume and the complexity of these
data, however, conventional data analysis techniques will
no longer be appropriate, necessitating the development of
novel algorithms and approaches. These algorithms could
6 Université Libre de Bruxelles, Brussels, Belgium
7 Medical University of Vienna, Vienna, Austria
8 Radboud University Nijmegen, Nijmegen, The Netherlands
9 The University of Texas, TX, USA
10 University of Nice, Nice, France
Forum Trends in Cell Biology February 2015, Vol. 25, No. 2extract features describing cell migration, to learn migra-
tory patterns that allow classification of data sets into
higher-order classes. Furthermore, such features could
be used to build disease-specific models of pathogen detec-
tion, wound healing or cancer metastasis. Other algo-
rithms could serve as automated data and metadata
quality assessment tools for key data set properties
[15]. Biologists and image processing experts could collab-
orate on small improvements in specific bioassays that can
eliminate the need for novel software (e.g., colour labelling
of cells migrating under high density conditions for im-
proved tracking).
Concluding remarks
We have presented a strategy to create an open data
ecosystem for cell migration research, supported by three
key aspects: (i) standards and minimal reporting require-
ments; (ii) a public, centralized data repository; and (iii)
novel analysis approaches to maximize the utility of the
collected data. This ecosystem will facilitate the manage-
ment, dissemination and exchange of cell migration data,
allowing these data to connect to other data ecosystems in
the life sciences.
Many efforts already exist towards the establishment of
this ecosystem. The crucial step will, therefore, be the high-
level coordination of such efforts from all interested parties
– experimentalists, bioinformaticians, instrument and
software vendors, funding agencies, and journals –
achieved through the creation of a synergistic consortium
composed of all relevant stakeholders.
Acknowledgments
P.M. and L.M. acknowledge funding from Ghent University, Ghent,
Belgium, and from VIB, Ghent, Belgium.
Contributing authors
The full list of authors and affiliations is as follows:
Christophe Ampe2, Kurt I. Anderson4, Joseph Barry5,
Olivier De Wever2, Olivier Debeir6, Christine Decaes-
tecker6, Helmut Dolznig7, Peter Friedl8,9, Cedric Gaggioli10,
Benjamin Geiger11, Ilya G. Goldberg12, Elias Horn13, Rick
Horwitz14, Zvi Kam11, Sylvia E. Le Dévédec15, Danijela
Matic Vignjevic16, Josh Moore17,18, Jean-Christophe
Olivo-Marin19, Erik Sahai20, Susanna A. Sansone21, Victo-
ria Sanz-Moreno22, Staffan Strömblad23, Jason Swedlow18,
Johannes Textor24, Marleen Van Troys2, and Roman Zantl13
4 University of Glasgow, Glasgow, Scotland, UK
5 EMBL, Heidelberg, Germany5811 Weizmann Institute of Science, Rehovot, Israel
12 National Institutes of Health, Bethesda, MD, USA
13 ibidi GmbH, Munich, Germany
14 University of Virginia, VA, USA
15 Leiden Academic Centre for Drug Research, Leiden, The
Netherlands
16 Institut Curie, Paris, France
17 Glencoe Software, Dundee, Scotland, UK
18 University of Dundee, Dundee, Scotland, UK
19 Institut Pasteur, Paris, France
20 London Research Institute, London, UK
21 University of Oxford, Oxford, UK
22 King’s College London, London, UK
23 Karolinska Institutet, Solna, Sweden
24 Utrecht University, Utrecht, The Netherlands.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the online
version, at http://dx.doi.org/10.1016/j.tcb.2014.11.005.
References
1 Friedl, P. and Gilmour, D. (2009) Collective cell migration in
morphogenesis, regeneration and cancer. Nat. Rev. Mol. Cell Biol.
10, 445–457
2 Kramer, N. et al. (2013) In vitro cell migration and invasion assays.
Mutat. Res. 752, 10–24
3 Friedl, P. and Bröcker, E.B. (2000) The biology of cell locomotion within
three-dimensional extracellular matrix. Cell. Mol. Life Sci. 57, 41–64
4 Alexander, S. et al. (2013) Preclinical intravital microscopy of the
tumour-stroma interface: invasion, metastasis, and therapy
response. Curr. Opin. Cell Biol. 25, 659–671
5 Hulkower, K.I. and Herber, R.L. (2011) Cell migration and invasion
assays as tools for drug discovery. Pharmaceutics 3, 107–124
6 Lock, J.G. and Strömblad, S. (2010) Systems microscopy: an emerging
strategy for the life sciences. Exp. Cell Res. 316, 1438–1444
7 Meijering, E. et al. (2012) Methods for cell and particle tracking.
Methods Enzymol. 504, 183–200
8 Edelman, L.B. et al. (2010) In silico models of cancer. Wiley Interdiscip.
Rev. Syst. Biol. Med. 2, 438–459
9 Taylor, C.F. et al. (2007) The minimum information about a proteomics
experiment (MIAPE). Nat. Biotechnol. 25, 887–893
10 Mayer, G. et al. (2013) The HUPO proteomics standards initiative –
mass spectrometry controlled vocabulary. Database (Oxford). 2013,
bat009
11 Bard, J. et al. (2005) An ontology for cell types. Genome Biol. 6, R21
12 Martens, L. et al. (2005) Do we want our data raw? Including binary
mass spectrometry data in public proteomics data repositories.
Proteomics 5, 3501–3505
13 Sansone, S-A. et al. (2012) Toward interoperable bioscience data. Nat.
Genet. 44, 121–126
14 Anon (2007) Democratizing proteomics data. Nat. Biotechnol. 25, 262
15 Foster, J.M. et al. (2011) A posteriori quality control for the curation
and reuse of public proteomics data. Proteomics 11, 2182–2194