This document discusses the development of an open data exchange ecosystem for cell migration research. It describes CellMissy, an open-source software tool for managing and analyzing cell migration data. CellMissy allows researchers to capture experimental metadata, import diverse data types into a structured database, and perform both collective and single-cell analysis. The document proposes extending CellMissy's data sharing capabilities into a larger repository called MULTIMOT to facilitate global exchange of cell migration data as part of the broader open science movement.

1. AN OPEN DATA EXCHANGE
FOR CELL MIGRATION RESEARCH
paola masuzzo
@pcmasuzzo
paola.masuzzo@vib-ugent.be
computational omics and systems biology group
VIB / Ghent University, Ghent, Belgium

2. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it

3. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it

4. CC BY-SA 4.0
Cell migration is necessary for many
physiological conditions
Masuzzo, PhD thesis, 2016

5. CC BY-SA 4.0
Unfortunately, it is also implicated in many
diseases, such as metastatic cancer
Masuzzo, PhD thesis, 2016

6. CC BY-SA 4.0
A typical experimental workflow for a cell
migration study is composed of diverse steps
Servier Medical Art, CC-BY 3.0; Cell Image Library, CC-BY 3.0
sample
preparation
image
acquisition
image
processing
data
analysis

7. CC BY-SA 4.0
High-throughput experiments
produce complex and rich data sets
sample
preparation
image
acquisition
image
processing
data
analysis
• paper laboratory
notebooks
• electronic
laboratory
notebooks
• spreadsheets
• text files
• protocols
• papers...
• raw files
• XML files
• proprietary
microscope or
acquisition
software files 
ND2 for Nikon, LIF
for Leica, OIB or
OIF for Olympus,
LSM or ZVI for
Zeiss
• image files with
pixel values and
metadata
• png, jpeg, tiff, avi
• text files
describing
processing
algorithms
• text files
describing
extracted features
• graphs, plots
• analysis pipelines
• text files
describing
computational
algorithms...
Servier Medical Art, CC-BY 3.0; Cell Image Library, CC-BY 3.0

8. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013

9. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013

10. CC BY-SA 4.0
CellMissy guides and captures the
experimental setup

11. CC BY-SA 4.0
This experimental setup encloses detailed
metadata annotation

12. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013

13. CC BY-SA 4.0
CellMissy can automatically import all the
data and metadata

14. CC BY-SA 4.0
All the data are then stored in a structured
way in a relational database

15. CC BY-SA 4.0
CellMissy is our open-source tool for cell
migration data management and analysis
https://github.com/compomics/cellmissy
Masuzzo, Bioinformatics, 2013

16. CC BY-SA 4.0
Cell migration can occur in both collective
and individual fashion
Collective
migration
Multicellular
streaming
Mesenchymal
Amoeboid
(blebs)
Amoeboid
(pseudopodia,
filopodia)
INDIVIDUAL
MIGRATION
COLLECTIVE
MIGRATION
Adapted from Friedl, J. Exp. Med., 2010

17. CC BY-SA 4.0
CellMissy enables efficient collective cell
migration data exploration and analysis
time (min)
Area(μm2
)
wound area
cell-covered area

18. CC BY-SA 4.0
A primary focus of data analysis is statistical
comparison of samples
analysis report with graphs,
tables and results
cell sheet velocity (µm²/min)

19. CC BY-SA 4.0
Many informative parameters can be derived
from single-cell trajectories
x
y
single cell
Masuzzo, 2017, under revision

20. CC BY-SA 4.0
Many informative parameters can be derived
from single-cell trajectories
Euclidean
distance
Cumulative
distance
x
y
single cell
Masuzzo, 2017, under revision

21. CC BY-SA 4.0
Many informative parameters can be derived
from single-cell trajectories
Euclidean
distance
Cumulative
distance
x
y
single cell parameter mathematical description
di: instantaneous
displacement of the
cell centroid between
adjacent time points
𝑑𝑖 = 𝑥𝑖+1 − 𝑥𝑖
2 + 𝑦𝑖+1 − 𝑦𝑖
2
si: instantaneous speed
between adjacent time
points
𝑠𝑖 = 𝑑(𝑝𝑖, 𝑝𝑖+1) ∆𝑡
αi: turning angle
between consecutive
steps
𝛼𝑖 = 𝑡𝑎𝑛−1 𝑦𝑖+1 − 𝑦𝑖 𝑥𝑖+1 − 𝑥𝑖
dtot: cumulative
distance, total distance
travelled
𝑑 𝑡𝑜𝑡 =
𝑖=1
𝑁−1
𝑑 𝑝𝑖, 𝑝𝑖+1
dnet: Euclidean distance,
net distance travelled
𝑑 𝑛𝑒𝑡 = 𝑑 𝑝1, 𝑝 𝑁
ep_dr: end-point
directionality ratio
(confinement ratio,
meandering index)
𝑒𝑝_𝑑𝑟 = 𝑑 𝑛𝑒𝑡 𝑑 𝑡𝑜𝑡
MD: median
displacement
𝑀𝐷 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝑑𝑖
MS: median speed 𝑀𝑆 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝑠𝑖
MTA: median turning
angle
𝑀𝑇𝐴 = 𝑚𝑒𝑑𝑖𝑎𝑛 𝛼𝑖
Masuzzo, 2017, under revision

22. CC BY-SA 4.0
Step-centric parameters are aggregated
values of all migration steps

23. CC BY-SA 4.0
Trajectory-centric parameters are instead
computed on each cell and then averaged for the
cell population
...
trajectory 1
trajectory 2
trajectory 3
trajectory 4
trajectory 5

24. CC BY-SA 4.0
The new single-cell module allows for both
these computations to take place
...
trajectory 1
trajectory 2
trajectory 3
trajectory 4
trajectory 5
trajectory-centric parameters
trajectory displacement (µm)
density
step displacement (µm)
density
pool of migration steps
step-centric parameters
Masuzzo, 2017, under revision

25. CC BY-SA 4.0
A flexible two-step filtering criterion is
implemented for data quality control
Masuzzo, 2017, under revision

26. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it

27. CC BY-SA 4.0
Data and metadata exchange options are
already available in CellMissy
lab A

28. CC BY-SA 4.0
Data and metadata exchange options are
already available in CellMissy
lab A lab B

29. CC BY-SA 4.0
Data and metadata exchange options are
already available in CellMissy
lab B
This is one file in CellMissy! (≈10 MB)
lab A

30. CC BY-SA 4.0
But we can easily extend this concept
to a bigger scale
Data
Repository
Local Software

31. CC BY-SA 4.0
MULTIMOT is creating an open cell migration
data exchange ecosystem
https://multimot.org/
Masuzzo, Trends in Cell Biology, 2016

32. CC BY-SA 4.0
MULTIMOT is creating an open cell migration
data exchange ecosystem
https://cmso.science/
Masuzzo, Trends in Cell Biology, 2016

33. CC BY-SA 4.0
MULTIMOT: towards global exchange
of cell migration data
CellMissy: a ‘little but fierce’ software
for cell migration data
Open science: why you should speak it
and how to learn it

34. CC BY-SA 4.0
This open data ecosystem falls into the
broader context of open science
Knoth and Pontika, Open Science Taxonomy, figshare, 2015

35. CC BY-SA 4.0
There are four widely recognized thematic
pillars of open science
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1

36. CC BY-SA 4.0
There are four widely recognized thematic
pillars of open science
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1
OPEN
SCIENCE
D
A
T
A
C
O
D
E
P
A
P
E
R
S
R
E
V
I
E
W
> OPEN DATA

37. CC BY-SA 4.0
Open data means freedom to access, use
and re-use for any purpose
http://opendefinition.org/od/

38. CC BY-SA 4.0
Open data means freedom to access, use
and re-use for any purpose
There are many open knowledge definition conformant licenses
CC0 waiver
https://creativecommons.org/publicdomain/zero/1.0/
CC BY (Attribution only)
https://creativecommons.org/licenses/by/4.0/
CC BY-SA (Attribution ShareAlike)
https://creativecommons.org/licenses/by-sa/4.0/
http://opendefinition.org/od/, http://opendefinition.org/licenses

39. CC BY-SA 4.0
A lot of repositories are available to upload
research materials and data

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A lot of repositories are available to upload
research materials and data

41. CC BY-SA 4.0
You certainly don’t need to know more than
1,500 repositories by heart
https://biosharing.org/databases/

42. CC BY-SA 4.0
There are four widely recognized thematic
pillars of open science
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1
OPEN
SCIENCE
D
A
T
A
C
O
D
E
P
A
P
E
R
S
R
E
V
I
E
W
> OPEN ACCESS

43. CC BY-SA 4.0
The impacts of open access are very broad
and affect many areas
Tennant, Masuzzo, F1000Research, 2016
Wikimedia Commons, Public Domain

44. CC BY-SA 4.0
Publish your work open access can bring you
enormous benefits
CC-BY Danny Kingsley & Sarah Brown

45. CC BY-SA 4.0
Do you want to engage with Open Science?
When possible, use and cite existing public data
Whenever feasible, share your research data through trusted
repositories. General-purpose repositories and domain-specific ones
are available on the web. Make sure you share metadata as well
If you use software code as part of your research cycle, release the
code. Specify the open source license you intend to use, and link the
readers to a stable repository that hosts the code
Post free copies of your research articles online. Most journals allow
this, sometimes after an embargo period of 6-12 months
Post preprints of your research manuscripts online, ideally at the same
time of official submission to a journal
When possible, choose an open access journal as venue for your
scientific articles. Keep in mind that subscription journals also offer an
open access solution, upon payment of extra fees
Masuzzo, PeerJ Preprints, 2017
https://doi.org/10.7287/peerj.preprints.2689v1

46. CC BY-SA 4.0
www.compomics.com
@compomics