SUPPLEMENTARY MATERIAL...2 M. Deforet et al. - Automated velocity mapping of migrating cell...

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M. Deforet et al. - Automated velocity mapping of migrating cell populations (AVeMap)

Automated velocity mapping of migrating cell populations (AVeMap)

Maxime Deforet, Maria Carla Parrini, Laurence Petitjean, Marco Biondini, Axel Buguin, Jacques

Camonis, Pascal Silberzan

SUPPLEMENTARY MATERIAL

Nature Methods: doi:10.1038/nmeth.2209

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Supplementary Figure 1 : Principle of the PIV technique : The original image is first split in

subwindows. The cross correlation of each subwindow at time t with its counterpart at time t+t is

then performed and the maximum of the cross correlation identified as the average displacement

between the two windows, at subpixel resolution. The velocity is then deduced knowing the time

interval t between two successive images. This operation is iterated for all the windows of the initial

image and for all the images of the video yielding the dynamic evolution of the velocity field. Note

that there is usually an overlap between the windows.

These particular images have been acquired on Madine Derby Canine Kidney (MDCK) cells in phase

contrast.


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a)

Velocity (µm/h) Persistence /

Order parameter

n mean SD SEM mean SD SEM

Tracking

analysis

Control 32 24.5 5.0 0.9 0.81 0.10 0.02

siWave2 32 14.2 3.2 0.6 0.27 0.21 0.04

PIV Analysis Control 40 16.87 2.28 0.36 0.77 0.08 0.012

siWave2 40 6.26 0.72 0.12 0.10 0.08 0.013

b)

Supplementary Figure 2 : Wave2 protein depletion and comparison of cell tracking and AVeMap

analyses. a) Validation of Wave2 protein depletion. HEK-HT cells were transfected with siLuc

(control) or siWave2 and subjected to lysis. Lysates were analyzed with the indicated antibodies.

Quantification was performed by image analysis with Odyssey Infrared Imager (Licor). Protein

expression levels, after normalization of protein loads using GAPDH (glyceraldehyde-3-phosphate

dehydrogenase), are expressed as percentage of the control. b) Measurements reported in Fig. 2b

and Fig. 2c. Values measured by manual cell tracking (velocity and persistence) or AVeMap (velocity

and order parameter) on the experiments shown in Supplementary Videos 1 and 2 are reported with

statistical analysis. The analysis was limited here to the border cells. Note that n for cell tracking is

the number of cell tracked, while n for PIV is the number of images in the video. SD is standard

deviation; SEM is standard error of mean.


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Supplementary Figure 3: Direct comparison between AVeMap and cell tracking velocities.

Velocities are measured by PIV (AVeMap) (white arrows) and manual tracking (red arrows) on the

edge cells. Note the similarities between the two velocity fields at the edge.

HEK-HT control cells. Bar is 50 µm; Arrow is 50µm·h-1.


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Supplementary Figure 4: Persistence and order parameter. a) trajectory of a migrating cell. The

trajectory over 12 h is shown in Cyan. The persistence is classically defined as the ratio d / l where l is

the contour length of the trajectory and d, the end-to-end distance. This value is then averaged over

several of these trajectories. Note that only border cells are analyzed this way. b) The order

parameter S is < cos> where is the angle between the local velocity and the average direction of

migration which is perpendicular to the average wound border (line (C1)) and the averaging is

performed within the area of interest as defined by the distance to the edge. (see Supplementary

Discussion 1). The line (C2) is the actual border. One then gets a dynamical mapping of the

directionality of the migration. Bar is 20 µm, arrow is 15 µm·h-1.


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a)

Computed average distance ± Standard deviation (pixels)

L = 0 pixel L = 1 pixel L = 2 pixel L = 3 pixel L = 4 pixel

= 0 pixel 0 ± 0 1 ± 0 2 ± 0 3 ± 0 4 ± 0

= 1 pixel 1.6 ± 0.7 1.7 ± 0.7 2.4 ± 0.7 3.2 ± 0.7 4.2 ± 0.7

= 2 pixel 2.6 ± 1.2 2.7 ± 1.2 3.1 ± 1.2 3.8 ± 1.2 4.5 ± 1.2

= 3 pixel 3.6 ± 1.7 3.7 ± 1.7 4.0 ± 1.7 4.5 ± 1.7 5.1 ± 1.7

b)

Supplementary Figure 5: Pointing errors in manual Cell Tracking (computer simulations). We

assume that the displacement of a cell in a time intervalt (elementary step) is L (in pixels) and that

the pointing error before clicking is ± in both directions. The gray lines delineate the pixels. The

black pixels in a) are the “real” or “ideal” positions. The gray pixels are the pixels within of the true

position. Here, we deal with situations where is of the same order than L (or even larger) and the

usual approximations used for error analysis and error propagation do not hold. In particular, the

average measured distance is not L.

The table b) lists the computed average distances for various L and . This distance is first calculated

for all the points within of the “ideal” point (gray pixels in a)). These values are then averaged with

no weighting and further averaged over the angle . The computed average distance largely

overestimates the real value given by L as previously reported 1. This effect is all the more important

since L is small and large. This overestimation of the distances results in the overestimation of the

velocity by manual tracking measurement.


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Supplementary Figure 6 : Distributions of the migration directions for control (a) and siWave2 (b)

cells. The distributions have been normalized to one in each case (note the difference in scales

between the two histograms). The resulting order parameters are indicated in both cases. The

distributions were calculated for all the displacements of the border (50 µm width) and over a

duration of 8 h for the control cells and 20 h for the Wave2-depleted cells ( ≈ 36,000 vectors for the

control and ≈ 88,000 vectors for the siWave).


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t = 0 t = 16 h Velocity Order parameter

HEK-HT

Control

HEK-HT

siWave2

(2 nM)

HEK-HT

siWave2

(5 nM)

A549

control

A549

TGF1

10 ng·mL-1

Supplementary Figure 7 : AVeMap analysis of migrating HEK and A 549 monolayers.

For the HEK-HT cells, the transfection with siRNA against siWave2 at a concentration of 2 nM

(yielding a partial depletion of Wave2 with residual 23 % gene expression (data not shown)) shows an

intermediate behavior between control and transfection performed at 5 nM (14 % gene expression

(Supplementary Fig. 2a)). Note that the velocity depends only weakly on Wave2 depletion level

while the order parameter is much more affected. Analysis time: 10 h.

The A549 cells respond to TGF1 by losing cell-cell adhesion junctions and acquiring invasive

properties 2. As a result, they show a marked augmentation of their velocity. We observe that this is

accompanied by a decrease of the order parameter indicating that they switch to a more random

migration situation. Analysis time: 2 h.

Bar = 500 µm

The original videos can be found as Supplementary. Videos 1-5.


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Supplementary Figure 8: Correlation of wound closure and productive velocity. This graph results

from the analysis of the 67 videos of Ref. 3. The "productive velocity" VP is defined as VP = V S. VP

therefore combines the velocity and orientation information. The wound closure is the large scale

healing velocity (i.e. the effective velocity of the edge). The correlation between these two quantities

is apparent on this graph (Pearson correlation coefficient = 0.70).


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Supplementary Figure 9 : Comparison of the Area Score (AS) 3 and the AVeMap productive velocity

(V S). In Ref. 3, AS < 0.75 was associated with “accelerated” migration (red symbols) and AS > 2 to

“impaired” migration (blue symbols). The solid black circle is the control (AS = 1). The two limits are

represented by the horizontal dash lines. Some genes not satisfying to this criterion were also

manually analyzed and classified into the accelerated or impaired group. The symbol code is given in

Supplementary Table 1. It is identical to Fig. 2g.

A 10% criterion on the productive velocity VP = V S as determined by AVeMap (vertical dash lines)

shows a good overlap with the AS criterion since most of the accelerated red genes coincide with VP >

1.86 µm·h-1 and most of the impaired blue genes are characterized by VP < 1.52 µm·h-1. We

emphasize here that the AVeMap technique does not require any manual analysis. Using AVeMap,

the genes SGK3, NRP1, VEGFB, PTPRO, ADCK4, RHOA or NEDD9 that did not satisfy to the Area Score

criterion were correctly identified directly from the videos. We note that AVeMap and AS

classifications disagree for a few genes (for instance, NEK8, DUSP18, TAF1, ARHGAP26…), which

would therefore need to be analyzed again. Endpoint AS measurements and the videos used to

access VP result from two different sets of experiments. The observed discrepancies can be

attributed to the different fields of view analyzed on these two experiments (several cm for the area

scores measurements vs. a few 100 µm in the videos).


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SYMBOL Alias GO Phenotype Symbol in Fig. 2g and Supp. Fig. 9

Productive velocity (pix/h)

Velocity (pix/h)

Order parameter

Accelerated

CDH3 P-cadherin Adhesion A: minimal adhesion, front–rear

polarity

2.73 (acc) 10.13 (+) 0.27 (-)

CTNND1 p120-catenin Migration, Adhesion 4.38 (acc) 8.22 (+) 0.53 (+)

DOCK6 ZIR1

B: transient adhesion, front–rear polarity

4.00 (acc) 15.48 (+) 0.26 (-)

PRKACA PKACA Migration 1.96 (acc) 8.09 (+) 0.24 (-)

ADCK4 COQ8

C: minimal adhesion, erratic migration, poor or non-existent front–rear polarity

2.58 (acc) 15.61 (+) 0.17 (-)

CDC14C CDC14B2

3.37 (acc) 15.69 (+) 0.21 (-)

CSNK1E CK1e

-2.22 (imp) 18.62 (+) -0.12 (-)

CSNK1G2 CK1g2

4.86 (acc) 21.80 (+) 0.22 (-)

DDEF1 ASAP1 Migration 3.72 (acc) 18.70 (+) 0.20 (-)

NEK8 NEK12A

0.51 (imp) 11.57 (+) 0.04 (-)

PRKCH PKC-ε

8.62 (acc) 51.81 (+) 0.17 (-)

STK40 SHIK

2.19 (acc) 24.76 (+) 0.09 (-)

STYXL1 DUSP24

3.40 (acc) 24.24 (+) 0.14 (-)

PFN2 Profilin

15.56 (acc) 33.27 (+) 0.47 (+)

PPP1R1B DARPP32

4.11 (acc) 12.25 (+) 0.34 (-)

TPD52L3 NYD-SP25

7.81 (acc) 23.01 (+) 0.34 (-)

GJA1 CX43

6.51 (acc) 13.40 (+) 0.49 (+)

CDC2L5 CHED

D: adhesive, large protrusions

3.70 (acc) 7.35 (+) 0.50 (+)

MYLK MLCK Migration 4.36 (acc) 8.55 (+) 0.51 (+)

FMN1 Formin Adhesion 1.96 (acc) 5.12 (+) 0.38 (nd)

PTPRO PTPU2

2.42 (acc) 5.85 (+) 0.41 (nd)

SRPK2 SFRSK2

1.84 (nd) 5.15 (+) 0.36 (nd)

DUSP18 DUSP20

0.80 (imp) 2.99 (-) 0.27 (-)

ADAM17 TACE

E: adhesive, collective, medium protrusions

4.22 (acc) 7.73 (+) 0.55 (+)

ADCK1

4.23 (acc) 7.15 (+) 0.59 (+)

LTK TYK1 F: adhesive, collective, small compact

cells

3.99 (acc) 9.68 (+) 0.41 (nd)

VEGFC Flt4-L Migration 2.08 (acc) 5.71 (+) 0.36 (nd)

NEDD9 CASL Migration, Adhesion 3.17 (acc) 5.99 (+) 0.53 (+)

TAF1 TAF2A

G: not significantly distinguishable from control

0.91 (imp) 7.41 (+) 0.12 (-)

NF1 WSS Migration 1.53 (nd) 5.52 (+) 0.28 (-)

CTNNB1 β-catenin Migration, Adhesion

Unique

1.88 (acc) 7.09 (+) 0.27 (-)

RHOA ARHA Migration, Adhesion 3.25 (acc) 8.15 (+) 0.40 (nd)

RIOK2

2.49 (acc) 7.27 (+) 0.34 (nd)

ACVR1 ALK2 Migration 4.84 (acc) 9.10 (+) 0.53 (+)

CONTROL 1.69 4.44 0.38

Impaired

CSNK2A2 CK2A2

A: weak adhesion, erratic migration,

unpolarized

0.91 (imp) 7.86 (+) 0.12 (-)

PTPN6 SHP1

0.79 (imp) 7.78 (+) 0.10 (-)

CAMK2B CAM2 B: dynamic adhesions, vertical

migration

0.58 (imp) 5.86 (+) 0.10 (-)

DMPK DM

0.03 (imp) 5.05 (+) 0.01 (-)

VEGFB VEGFL Migration 1.24 (imp) 5.12 (+) 0.24 (-)

ENPP5

C: dynamic adhesion, larger cells

0.65 (imp) 5.08 (+) 0.13 (-)

LOC390975

-0.05 (imp) 4.02 (nd) -0.01 (-)

RSU1 RSP-1 Adhesion 0.02 (imp) 4.05 (nd) 0.00 (-)

IKBKE IKKE

D: cells become stretched during migration

1.45 (imp) 6.71 (+) 0.22 (-)

ACP5 TRAP

1.09 (imp) 5.43 (+) 0.20 (-)

PRKCE PKCε

1.48 (imp) 4.68 (nd) 0.32 (-)

ARHGAP26 GRAF

3.75 (acc) 6.06 (+) 0.62 (+)

PIK3R5 p101-P13K

E: not significantly distinguishable from control

1.10 (imp) 6.66 (+) 0.17 (-)

ALS2CR2 PAPK

0.42 (imp) 6.42 (+) 0.07 (-)

CDC2L1 p58

0.93 (imp) 5.19 (+) 0.18 (-)

CRK CRKII Migration, Adhesion 0.17 (imp) 5.02 (+) 0.03 (-)

LIMS1 PINCH Migration 0.25 (imp) 4.07 (nd) 0.06 (-)

C9orf98 DDX31

1.1 (imp) 4.06 (nd) 0.27 (-)

DUSP6 MKP3

0.60 (imp) 3.78 (-) 0.16 (-)

EPHB2 DRT

-0.32 (imp) 2.84 (-) -0.11 (-)

TLN1 TLN Migration, Adhesion -0.13 (imp) 4.88 (+) -0.03 (-)

SGK3 CISK

0.96 (imp) 5.86 (+) 0.16 (-)

FLJ25006

E: not significantly distinguishable from control (larger cells)

0.14 (imp) 4.65 (nd) 0.03 (-)

MAP3K11 MLK3

1.37 (imp) 5.36 (+) 0.26 (-)

NRP1 NRPP Migration, Adhesion 1.07 (imp) 4.64 (nd) 0.23 (-)

VIM Vimentin Migration 0.19 (imp) 5.29 (+) 0.04 (-)

IGF1R CD221

E: not significantly distinguishable from control (very small protrusions)

0.22 (imp) 2.99 (-) 0.07 (-)

ITGB1 CD29 Migration, Adhesion 0.09 (imp) 3.57 (-) 0.02 (-)

AKT1 PKB Migration 0.34 (imp) 3.41 (-) 0.10 (-)

PHPT1 PHP14

1.14 (imp) 4.08 (nd) 0.28 (-)

ACTB Actin Migration Unique

0.73 (imp) 4.09 (nd) 0.18 (-)

FES FPS Adhesion -0.60 (imp) 3.57 (-) -0.17 (-)


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Supplementary Table 1: Gene-by-gene comparison of the AVeMap and Cell Tracking analyses.

AVeMap analysis of the migration of the cells selected by the end-point screen of Ref. 3. The 4 first

columns are from Ref. 3 as is the classification “accelerated” and “impaired” (respectively, red and

blue parts of the table. Control is yellow). Column 5 lists the symbol and color codes of Figure 2g and

Supplementary Fig. 9. The “productive velocity” (column 6) is the product V S where these

parameters are measured with the AVeMap software (acc=accelerated, imp=Impaired, nd=not

significantly different from control). In Red, the results that are different from the Area Score

analysis. The two following columns (“velocity” and “order parameter”) give the corresponding

values and the comparison of these values with the control ((+) larger, (-) smaller, (nd) not

significantly different. A 10% threshold was used here). Negative S values correspond to a retraction

of the edge of the monolayer.


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Monolayer

Dissociated cells AVeMap does not perform well on fully dissociated cells.

A good tracking algorithm may be more adapted.

Confluence Only confluent monolayers can be analyzed. Avoid holes

or incomplete monolayers.

Scratch

The scratch wound should be as regular as possible. It

should be roughly aligned with the vertical direction (by

an alignment during acquisition or by rotating the images

afterwards) before analysis.

Defects Avoid movies for which migration is impaired by defects

(debris ...) or a poor definition of the initial edge.

Image

acquisition

Contrast

Well-contrasted images are better but this is not a critical

parameter. The algorithm filters the images before

treatment.

Gain, Exposure

Time

Keep the same values for these two parameters for all

frames during image acquisition.

Time interval

Trade-off with window size (see below). The critical

parameter is the displacement between successive

frames. A smaller time interval allows to use smaller

windows and therefore increases the spatial resolution.

PIV parameters

Window size

Has to be chosen so that the typical displacement

between successive frames is of the order of ¼ of the

window size. If unsure, try different sizes and compare

results.

Overlap As a rule of thumb, use an overlap of 0.5. This parameter

allows a redundancy of the computation.

Supplementary Table 2: Guidelines on experimental conditions and calculation parameters for the

use of AVeMAp Some trial and error may be necessary before running large scale assays.


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Supplementary Discussion 1: Differences between the cell tracking persistence (P) and the

AVeMap order parameter (S).

S and P are different in nature. For a given tracked cell, the persistence is calculated by the ratio of

the end to end distance of the trajectory of a cell to the total length of this trajectory: ⁄

(Supplementary Figure 4). Describing this trajectory by the succession of elementary displacements

, pi can be explicitly expressed as ‖∑

‖

∑ ‖ ‖

where F is the number of frames. P is then the

average of pi over N cells :

∑

. This last average corresponds to a space average. For

practical reasons, only border cells are routinely analyzed this way.

The calculation of S proceeds from the PIV images. The software first measures the average value of

the cosine of the angles of all the elementary displacements in the region of interest of the image

with respect to the normal to the average front edge that we characterize by its elementary vector

(this direction is also the average direction of displacement) : ⟨ ⟩

∑

‖ ‖

(Supplementary Figure 4). These values are then time-averaged over the time-course of an

experiment (F frames) :

∑ ⟨ ⟩

.

S and P are therefore different ways to characterize the persistence of the movement. In particular,

in the case of very tortuous displacements, the definition of S allows it to include negative values of

the cosine, corresponding to backward motions sometimes observed for cells of very small

productive motility (see for instance Supplementary Fig. 6). P, in contrast, is a ratio of norms and

remains positive whatever the orientation of the displacements. Therefore, these two quantities are

difficult to compare directly.

Nevertheless, both parameters convey the same type of information relatively to the actual

trajectories of cells relatively to the most direct route and, except for very particular situations, they

should follow the same trends.


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Supplementary discussion 2: Generation of virtual trajectories by integration

We generate virtual trajectories in the following way: we first fix an origin and obtain the first point

of the trajectory by calculating the displacement at the origin by a bilinear interpolation of the local

velocity field. This process is then reiterated until obtaining the whole trajectory.

The Figure below is an overlay of actual trajectories of tracked cells and virtual trajectories obtained

by this integration.

Overlay of trajectories measured by tracking (Red) and calculated by integration of the velocity field

obtained by PIV (virtual trajectories) (Blue) on control HEK-HT cells. Bar = 50 µm. 15 min between

each point. The phase contrast image corresponds to the common origin of both trajectories. The

virtual trajectories result from integration of the velocity using a bilinear interpolation. Although very

close, the virtual trajectories are slightly smoother than the ones obtained by CT because of the

pointing errors. (details of PIV: overlap 0.75, window size 32, mesh size 10.32 µm (8 pixels) between

two computed velocities).

We have then computed the velocity and persistence on the tracked and virtual trajectories using the

standard procedure used in cell tracking (Supp Fig. 4a). Results are summarized in the Table below

(Errors are SDs):

Velocity (µm/h) Persistance

Tracked trajectories 24.5 ± 5.0 0.78 ± 0.06

Virtual trajectories 15.6 ± 5.1 0.89 ± 0.10

Results are therefore very consistent (with the expected overestimation of V and underestimation of

P for the tracked trajectories because of the pointing error).

Even though AVeMap can compute both order parameter S and persistence P, for practical use, we

tend to favor S rather than P since, to our opinion, it accounts better for very tortuous trajectories

(by definition, S can change sign whereas P cannot).


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References

1. Huth, J. et al. Significantly improved precision of cell migration analysis in time-lapse video microscopy through use of a fully automated tracking system. BMC cell biology 11, 24(2010).

2. Kasai, H. et al. TGF-beta1 induces human alveolar epithelial to mesenchymal cell transition (EMT). Respiratory research 6, 56(2005).

3. Simpson, K.J. et al. Identification of genes that regulate epithelial cell migration using an siRNA screening approach. Nature cell biology 10, 1027-38(2008).


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