Automatic quantification of microtubule dynamics enables

RESEARCH ARTICLE
Cytoskeleton, May 2011 68:266–278 (doi: 10.1002/cm.20510)
C
V
2011 Wiley-Liss, Inc.
Automatic Quantification of Microtubule Dynamics
Enables RNAi-screening of New Mitotic Spindle Regulators
Lucia Sironi,1,2† Jérôme Solon,1,3† Christian Conrad,1 Thomas U. Mayer,2
Damian Brunner,1,4 and Jan Ellenberg1*
1
European Molecular Biology Laboratory (EMBL), Cell Biology and Biophysics Unit, Meyerhofstrasse 1, Heidelberg, Germany
University of Konstanz, Department of Biology, Universität Strasse 10, Konstanz, Germany
3
Centre of Genomic Regulation (CRG), Cell and Developmental Biology, C/ Dr. Aiguader 88, Barcelona, Spain
4
University of Zürich, Institute of Molecular Life Sciences, Winterthurerstrasse 190, Zürich, Switzerland
2
Received 2 November 2010; Revised 3 February 2011; Accepted 2 March 2011
Monitoring Editor: George Bloom
The genetic integrity of every organism depends on the
faithful partitioning of its genome between two daughter cells in mitosis. In all eukaryotes, chromosome segregation requires the assembly of the mitotic spindle, a
bipolar array of dynamic microtubules. Perturbations
in microtubule dynamics affect spindle assembly and
maintenance and ultimately result in aberrant cell divisions. To identify new regulators of microtubule
dynamics within the hundreds of mitotic hits, reported
in RNAi screens performed in C. elegans, Drosophila
and mammalian tissue culture cells [Sonnichsen et al.,
2005; Goshima et al., 2007; Neumann et al., 2010], we
established a fast and quantitative assay to measure
microtubule dynamics in living cells. Here we present
a fully automated workflow from RNAi transfection,
via image acquisition and data processing, to the quantitative characterization of microtubule behaviour.
Candidate genes are knocked down by solid-phase
reverse transfection with siRNA oligos in HeLa cells
stably expressing EB3-EGFP, a microtubule plus end
marker. Mitotic cells are selected using an automatic
classifier [Conrad et al., 2011] and imaged on a spinning disk confocal microscope at high temporal and
spatial resolution. The time-lapse movies are analysed
using a multiple particle tracking software, developed
in-house, that automatically detects microtubule plus
ends, tracks microtubule growth events over consecutive frames and calculates growth speeds, lengths and
lifetimes of the tracked microtubules. The entire assay
Additional Supporting Information may be found in the online
version of this article.
†
These authors contributed equally to this work.
*Address correspondence to: Jan Ellenberg, Cell Biology and
Biophysics Unit, European Molecular Biology Laboratory
(EMBL), Meyerhofstr. 1, D-69117 Heidelberg, Germany. E-mail:
jan.ellenberg@embl.de
Published online 22 March 2011 in Wiley Online Library
(wileyonlinelibrary.com).
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provides a powerful tool to analyse the effect of essential mitotic genes on microtubule dynamics in living
cells and to dissect their contribution in spindle assembly and maintenance. V 2011 Wiley-Liss, Inc.
C
Key Words:
mitosis, RNAi screening, microtubule dynamics, EB3, quantitative microscopy, multiple particle tracking
Introduction
M
icrotubules are an indispensable component of the
cytoskeleton and therefore play a critical role in
many cellular processes such as cell division, cell motility,
shape maintenance and intracellular trafficking. Microtubules are polarized polymers that in vitro stochastically
switch between phases of growth and shrinkage, a phenomenon known as dynamic instability [Mitchison and
Kirschner, 1984]. It is possible to describe such behaviour
with the following four parameters: the rate of growth,
the rate of shrinkage and the transition frequencies
between growing and shrinking (a catastrophe event) and
between shrinking and growing (a rescue event) [Desai
and Mitchison, 1997]. Purified microtubules can selfassemble in vitro under certain conditions, but in cells
due to the presence of many microtubule-associated
factors, grow more rapidly and undergo transitions more
frequently and this results in a high microtubule turnover
rate and therefore in the cell’s ability to respond very fast
to functional and environmental changes [Kline-Smith
and Walczak, 2004]. Such a requirement is particularly
evident in mitosis when a cell has to rearrange entirely its
microtubule network from a relatively uniform distribution into a highly regulated bipolar structure named the
mitotic spindle in which chromosomes are captured at
their kinetochores and biorented at the spindle equator
prior to their segregation [Wittmann et al., 2001].
Measuring microtubule dynamics in living cells is not a
trivial task: fluorescently labelled tubulin can be either
microinjected [Waterman-Storer and Salmon, 1997; Yvon
and Wadsworth, 1997] or stably expressed in cells as a
GFP-fusion protein [Rusan et al., 2001]. This has allowed
the measurement of microtubule dynamics at the cell periphery where, because of a less dense microtubule network, individual microtubule ends can be imaged. To
facilitate the detection of microtubule ends it is also possible to create artificial systems, such as cytoplasts, by cell
enucleation, in order to obtain samples with reduced
thickness and microtubule density [Komarova et al.,
2002]. Additionally using uncaging or photobleaching
methods it is possible to either selectively highlight a subset of microtubules [Mitchison et al., 1998] or to create a
dark area in which newly growing labelled microtubules
can be easily followed [Komarova et al., 2002].
Systematic analyses of microtubule behaviour in the
centre of the cell, however, are possible only with the use of
microtubule plus end markers. Microtubule plus end tracking proteins (þTIPs) are a family of evolutionary conserved
proteins that associate with the ends of the growing microtubules where they regulate microtubule dynamics and promote the attachment of microtubules to different cellular
structures [Akhmanova and Steinmetz, 2008]. End-binding
protein 1 and 3 (EB1 and EB3) selectively bind growing
microtubule plus ends with a high turnover rate [Dragestein et al., 2008] giving rise to a characteristic comet-like
structure that, if tracked over time, allows to measure tubulin polymerization rates of individual microtubules. If these
markers are combined with high-resolution confocal microscopy it is possible to measure microtubule dynamics in
living cells unbiased by geometrical constraints or cell cycle
stages [Piehl and Cassimeris, 2003; Srayko et al., 2005].
Because manual tracking can be labour intensive and a
very subjective method, depending on the quality of the
data and the microtubules that are selected for tracking,
we, and very recently also others [Matov et al., 2010],
have developed methods to automatically extract microtubule growth rates from time-lapse movies of cells expressing a microtubule plus end marker. Unlike manually
biased analysis, automatic tracking allows us to track all
kinds of microtubules, short or long-lived, stable or
dynamic, in the centre or at the periphery of the cell.
Here we describe our integrated and fully automated
workflow that combines solid phase RNAi transfection and
high-resolution time-lapse spinning disk microscopy of
EB3-EGFP with an automatic multiple-particle tracking
software that detects all microtubule ends, reconstitutes
their growth trajectories and calculates velocities, lengths
and lifetimes of most growth events occurring during the
time-lapse sequence. We demonstrate the sensitivity of our
assay by showing that mild perturbations of microtubule
dynamics, induced by low doses of microtubule poisons,
are truly detected by the automatic tracking method.
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We then exploited this powerful assay to identify genes
that alter microtubule dynamics in mitosis and therefore
provide a mechanistic explanation for spindle assembly
and maintenance phenotypes inferred from genome-wide
screens. We chose to screen 20 candidate genes that scored
as ‘mitotic’ in the Mitocheck screen, a genome-wide
RNAi screen designed to identify all mammalian genes
essential for mitosis [Neumann et al., 2006; Neumann
et al., 2010].
We depleted the selected genes using solid-phase reverse
transfection techniques [Erfle et al., 2008] and then with
the help of ‘Micropilot’ software [Conrad et al., 2011],
trained a classifier to automatically select mitotic cells for
each knockdown and execute an imaging protocol at
high temporal resolution. The time-lapse sequences were
then analysed with the automatic tracking software and
velocities, lengths and lifetimes of the microtubule tracks
were computed.
In addition to obtaining the first quantitative analysis for
known mitotic genes such as NUSAP1, TPX2, CCDC5,
KIF18, we could identify two uncharacterized genes,
C11orf38 and CCDC9, that like our positive control, chTOG, strongly reduced growth rates and track lengths.
Based on the microtubule dynamics parameters computed, the 20 genes could also be clustered into phenotypic families: genes that strongly affect microtubule
dynamics (the hits of the screen), genes that reduce in a
milder fashion microtubule dynamics, genes that increase
microtubule dynamics and genes that do not affect microtubule dynamics.
The results show that the assay we developed allows
robust and fully automated analysis of microtubule
dynamics in live interphase and mitotic cells and can now
be applied to systematically identify and characterize the
function of microtubule regulators in comprehensive
perturbation studies using RNAi mediated knockdown or
small molecular inhibition.
Material and Methods
Cell line, Inhibitor Treatments, Rnai Reverse
Transfection Protocols
A HeLa ‘Kyoto’ cell line (the parental ‘Kyoto’ cell line
was a kind gift from Prof. Shuh Narumiya) stably
expressing EB3-EGFP (the plasmid pEB3-EGFP was a
kind gift from the Pepperkok lab) was manually selected
according to standard protocols and maintained at
0.5 mg/mL G418 (Sigma Aldrich) in DMEM, supplemented with 10% FCS and antibiotics. For imaging, the
medium was exchanged to DMEM without phenol red,
supplemented with 25 mM HEPES-KOH, pH 7.3 and
20% FCS. The picked clone behaves like the parental
cell line, as confirmed by FACS analysis (Supporting
Information Fig. S1).
Automatic Quantification of Microtubule Dynamics
267 n
Nocodazole (Calbiochem, Darmstadt, Germany; stock
10 mg/mL in DMSO) and taxol (Sigma, St Louis, MO;
stock 10 mM in DMSO) were diluted to the final concentrations (80, 160, 330 nM for nocodazole; 20, 50, 100
nM for taxol) directly in imaging media. Imaging was
started 15 min after drug addition to allow complete
microtubule turnover in the presence of the drugs.
Chemically synthesized 21 nt RNA duplexes were provided by Ambion (Applied Biosystems, see Table I for
oligo sequences). Eight-well Labtek chambers (Nalge
Nunc, Rochester, NY) were coated manually with the
source siRNA transfection solution for solid-phase transfection, following previously described protocols [Erfle
et al., 2007]. Each cell chamber contained one control
sample (with scrambled oligo) and seven different knockdowns. The genes were grouped on different Labteks (that
we called chips) depending on the timing of phenotype
occurrence. We prepared eight replicas of each chip to
minimize transfection differences between successive
experiments. Cells were seeded into the chambers 24 to
48 hr prior imaging, and then imaged for the following
12 hr that corresponded to the maximal phenotype
appearance, as observed in the primary screen.
Table I. Sequence of siRNA Oligos (Ambion,
Applied Biosystems)
siRNA sequence (50 ->30 )
Gene
ch-TOG
KIF11/EG5
AURKA
BUB1
C11ORF38
CCDC9
CCDC5
CENPE
CEP135
INCENP
KIF18
NDEL1
NUSAP1
PLK1
PLK4
TACC3
TPX2
TUBG2
TUBGCP6
GGUGUUGUAAGUAAGGUGUtt
GGAAUUGAUUAAUGUACUCtt
GGUCCAAAACGUGUUCUCGtt
GGAAUUCAAAACCAGGCUGtt
GCACUAGACUCACUCUUUGtt
CCGUAAGAAAGCUGAACUUtt
GCAAGUGAAUACGAGUCAGtt
GGAAUUAAAGGCUAAAAGAtt
GCUACAAGAAAAGUUAGCAtt
GGAGAAGAAGAAGCAGAUUtt
GCUGGAUUUCAUAAAGUGGtt
GCUAGUUGAAUUCCAGGAAtt
GGUGCAAGACUGUCCGUGUtt
GGUGGAUGUGUGGUCCAUUtt
GGGAUGUUGUAUCUUCAUUtt
GGUUCGAAGAGGUUGUGUAtt
GGGCAAAACUCCUUUGAGAtt
CCGAGAAGCAGAUGGAAGUtt
GGCUGAAAGAACAAUUUGUtt
Automatic Multiple-Particle Tracking
Prior to the tracking the raw data was prepared in ImageJ
(NIH, http://rsb.info.nih.gov/ij/): the cell was cropped out
from the full frame and a Gaussian blur filter (radius ¼ 2)
was applied. The multiple-particle tracking software performs four tasks: particle detection, track reconstitution,
broken tracks reconnection and parameter computation.
Particle Detection
Each microtubule plus end (intensity maximum) is
searched, using a scrolling window that covers, pixel by
pixel, the entire frame. In each window, the pixel of
maximum of intensity is detected and considered as a
microtubule plus end if the value of the maximum is
higher than n times the global noise of the image (n is
defined by the user). The size of the scrolling window
(also defined by the user, for the screen we chose a
window size of 7 pixels) is a critical parameter as it
searches for only one particle within its area and consequently it defines the minimum distance at which two
particles can be found. In a second step, in an iterative
process, the positions of the maxima are readjusted by
re-detecting each maximum in a window centred on the
already determined maximum. The final coordinates of
the maxima are refined fitting 2D Gaussian curves to
the intensity profiles. The accuracy on the maxima position, determined using a fixed quantum dot, is 140 nm
in x and y, when the images have a signal-to-noise ratio
of 8.
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Track Reconstitution
Trajectories are reconstituted by linking particles from successive time points, for which a global Energy U, based on
the distance between each particles on two consecutives
frames, is minimized (the algorithm was adapted from
Sbalzarini and Koumoutsakos [2005]):
Uij ¼ ðXti Xtþ1j Þ2 þ ðYti Ytþ1j Þ2
This energy minimization takes into account appearing
and disappearing particles if the distance after minimization between two particles is higher than a critical
distance.
Broken Tracks Reconnection
The length of the EB3-EGFP comets correlates with
microtubule growth speeds [Bieling et al., 2007]. Growth,
such as shortening, is in vivo variable in terms of velocity
and duration and is often interrupted by phases in which
microtubules are quiescent, neither growing nor shrinking,
called pauses [Shelden and Wadsworth, 1993]. For this
reason we observe that the EB3 signal at the microtubule
ends fluctuates and can transiently disappear (data not
shown). When the latter occurs the tracks are terminated
unless re-growth, and consequently EB3 re-accumulation,
occurs within a defined time interval. Such tracks are
defined as broken and computationally reconnected if the
end of the first track and the beginning of the second lie
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within a 10-pixel radius (0.8 lm) and the two events
occur within five time points (2 s).
Parameter Calculation
For each track, average speed, length and lifetime are calculated. The instant velocity (velocity between two consecutive time points) of a growing microtubule plus end
fluctuates dramatically (dashed line in Supporting Information Fig. S2), probably as a result both of the local
environment (i.e. availability of free tubulin or presence of
spatial constraints in the crowded cytoplasm) and the
uncertainty associated with the maxima detection. In order
to reduce the error to 20% of the measurement, we computed velocities over a minimum distance of 600 nm. If
on average a particle moves with a velocity of 18 lm/min
(300 nm/s), it travels 600 nm in 2 s, which correspond to
five time points. We therefore define the step5-velocity,
the velocity of the growing end calculated between time
points 1 and 6, time points 2 and 7, 3 and 8 and so on
(black line in Supporting Information Fig. S2). The average velocity of a track is then defined as the mean of all
the step5-velocities of the track (red line in Supporting Information Fig. S2). The length of a track is the mean of
the step5-displacements divided by 5 (that gives a calculated step1-displacement), multiplied by the number of
time points the microtubule end lives. The lifetime of a
track is the number of time points the track lives multiplied by the time interval (0.4 s) of the time-lapse
sequence. As in vivo a growth event can end either
because of a catastrophe or a pause event, the lifetime of a
track should not be taken as an indication of the catastrophe frequency [Shelden and Wadsworth, 1993].
Speeds, length and lifetimes are then displayed for each
cell as histograms of all tracks (Figs. 1f and 2b).
From each histogram a characteristic value, which
allows us to compare different cells, is then calculated.
To do so we measured the median of the speed distributions and we fitted the track length and the track
lifetime distributions with exponential decays and calculated the characteristic track length and track lifetime of
each cell. Because of statistical reasons it was not always
possible to fit the track length and track lifetime distributions of the screening data. Therefore for these samples we measured the medians of track length and track
lifetime distributions for each cell (Supporting Information Fig. S3).
The tracking results were evaluated by comparing track
velocities extracted from the automatic tracking with manual tracking performed on the same tracks for wild type
and drug treated cells: independently of the experimental
conditions we measured a difference of less than 2 lm/
min between the manual and the automatic scoring.
The multiple-particle tracking software was written in
Matlab (Mathworks).
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Imaging Conditions
High-Resolution Imaging
EB3-EGFP expressing cells were imaged with a a-Plan
Fluar 100, 1.45 N.A. oil objective lens (Carl Zeiss,
Jena, Germany) on a Perkin Elmer Ultraview ERS system
(version 2.0.0.009_EMBL, the special software version of
the Ultraview system allowed communication with the
system during the scan), equipped with a Yokogawa spinning-disk confocal unit (CSU10), an EM-CCD camera
(Hamamatsu) and a 100 mW 488 nm laser line. Cells
were cultured in #1 LabTek chambers (Nalge Nunc,
Rochester, NY) and imaged at 37 C on the microscope
stage. 2D time-lapse sequences of single cells were
recorded for 60 s, t ¼ 400 ms.
For the Screen
In an 8-well cell chamber, six locations per well were
manually selected. The ‘Micropilot’ software [Conrad
et al., 2011] provided an autofocus routine for the multiple location mode in the Perkin Elmer Ultraview system. Following the autofocus, ‘Micropilot’ then started a
fast low-resolution prescanning routine in which each
location was imaged and classified, using the trained
classifier. ‘Micropilot’ was trained to recognize mitotic
centrosomes by providing many examples of previously
recorded centrosomes with identical image processing
and microscope settings. When during the prescanning
procedure a centrosome was identified, ‘Micropilot’
reconfigured the microscope settings and initiated the
high temporal resolution imaging on the location
selected. Once this task was completed ‘Micropilot’
resumed the prescanning mode from the first prescan
position, skipping the last positive classified position for
the following 10 prescan cycles, until another centrosome
was identified.
The mitotic cells automatically detected and imaged
with the above procedure are mostly prometaphase cells,
where typically only one centrosome is in focus and a
bipolar spindle, stably horizontal, is not yet fully
assembled.
Hierarchical Clustering of Hits
All depletions are represented by the three values (track
speed, track length and track lifetime), which are the
mean values from different cells treated with identical
conditions. In order to define a pair wise distance
between genes all three parameters were normalized by
the values of the scrambled treated samples. The
heatmap was created in R (http://www.r-project.org)
using the ‘manhattan’ distance matrix. The colours
were taken from colorbrewer (Brewer, Cynthia A., http://
colorbrewer2.org/).
Automatic Quantification of Microtubule Dynamics
269 n
Fig. 1. Automatic tracking of microtubules in interphase. (a) Raw single frame taken from a time-lapse sequence of an interphase
HeLa cell stably expressing EB3-EGFP (scale bar, 5 lm). (b) Maximum intensity projection of all time points (time projection) from
a 60 s sequence (time resolution ¼ 0.4 s). (c) Enlargement of selected time points of the region marked with a red box in (a). The
white and the red arrowheads indicate two distinct growing microtubules plus ends (scale bar, 2 lm). (d) Graphical output of the
tracked microtubules: the tracks are displayed with a rainbow of colours to distinguish individual microtubules. The insert contains
an enlargement of the boxed region: the x,y coordinates of all time points of the blue track are represented as dots. (e) Overlay of the
detected tracks in (d) onto the time projection in (b). (f ) Histograms of the average speeds, lengths and lifetimes of the tracks in (d).
The median of the distribution of average growth speeds is 17.6 lm/min. The distribution of the lengths and lifetime are fitted with
exponential decays, to calculate a characteristic track length k (1.6 lm) and a characteristic lifetime s (3.7 s). In the insert the same
distributions are plotted in semi-logarithmic scale.
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Fig. 2. Automatic tracking of microtubules in mitosis. (a) Panel containing a raw single frame from a time-lapse sequence of a mitotic HeLa cell stably expressing EB3-EGFP (scale bar, 5 lm); the time projection from a 60 s sequence; the graphical output of the
tracked microtubules and the overlay of the detected tracks onto the time projection of the same sequence. (b) Histograms of the average speeds, lengths and lifetimes of the tracks in (a). The median of the distribution of the average growth speeds is 13.1 lm/min.
The distribution of the lengths and lifetime are fitted with exponential decays, and a characteristic track length k (0.9 lm) and a
characteristic lifetime s (2.5 s) are calculated.
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Automatic Quantification of Microtubule Dynamics
271 n
Results
EB3-EGFP as a Marker for Microtubule
Dynamics
To visualize individual microtubules we generated a HeLa
Kyoto stable cell line expressing EB3-EGFP (Enhanced
Green Fluorescence Protein). EB3-EGFP, like previously
reported [Mimori-Kiyosue et al., 2000], localizes
throughout the cell cycle only to the plus ends of growing microtubules (Fig. 1a) and accumulates at the centrosome (Fig. 2a) [Berrueta et al., 1998; Mimori-Kiyosue
et al., 2000; Morrison and Askham, 2001], from early
prophase to the end of mitosis, concurrently with the
increase of microtubule nucleation rates at the centrosome [Piehl et al., 2004].
To quantify microtubule dynamics in vivo we recorded
60 s-2D-time-lapse sequences with high spatial (100
magnification) and high temporal resolution (as high as
150-ms time interval), on a spinning-disk confocal microscope (Fig. 1c, Video 1).
Automatic Tracking of Microtubules in
Interphase
Because both the number of microtubule ends in a cell
and the number of samples that we intended to probe
with this assay are large, we developed an automatic multiple-particle tracking software (see Material and Methods
for detailed description) that detects microtubule ends,
reconstitutes microtubule tracks and quantifies microtubule growth speeds, track lengths and lifetimes. These
parameters give a quantitative readout of microtubule dynamics in live cells.
The graphical output of the reconstituted tracks from a
60 s time-lapse of an interphase cell is displayed in Fig.
1d: the tracks are distributed in a radial fashion from the
centre to the periphery of the cell, longer in the centre,
shorter and more densely packed at the periphery. This is
consistent with previous observations that suggested that
most interphase microtubules are nucleated at the centre
of the cell, grow persistently and rapidly by addition of
tubulin dimers at the plus ends [Komarova et al., 2002]
and, when they reach the cell margins, undergo fluctuations in length due to rapid switching between growing
and shrinking [Sammak et al., 1987].
To control that most tracks are correctly detected and
reconstituted, the track representation was superimposed
onto the time projection of the movie (Fig. 1e).
The number of tracks per cell, extracted from a 60 s
time-lapse sequence, varies between 200 and 500, depending on the size of the cell. This value does not contain
tracks shorter than five time points (2 s) that are discarded
because of the necessity to measure displacements and
velocities over a minimum distance (see Material and
Methods). The number of very short tracks is therefore
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underestimated but, at the same time, false tracks, due to
camera shot noise, are removed.
For each track we calculated the average speed, the total
length and the lifetime (see Material and Methods) and
plotted the histograms of these parameters (Fig. 1f ). We
then calculated the median of the distribution of the
speeds, and we fitted the distributions of the track lengths
and the track lifetimes with exponential decays (see insert
plots in semi-logarithmic scale), as the probability for a
microtubule to switch from a growing state to a shrinking
state is equal at every time point [Verde et al., 1992]. For
the interphase HeLa cell in Fig. 1, the median of the
microtubule average growth speed is 17.6 lm/min, the
characteristic track length (k) is 1.6 lm and the characteristic lifetime (s) is 3.7 s.
Automatic Tracking of Microtubules in Mitosis
In a similar fashion the automatic tracking software
allowed us to measure microtubule dynamics also in mitotic cells (Fig. 2a, Video 2). While in interphase cells
we focused at the bottom of the cells where most microtubules, due to physical constraints, tend to grow parallel
to the surface of the cell chamber (and to the imaging
plane), in mitotic cells we chose the plane of focus that
contains one or both centrosomes at the beginning of
the time-lapse. For this reason the number of growth
events detected in the same time interval are slightly
reduced (data not shown). As the centrosomes are positioned in the centre of the cell, microtubules can enter
the plane of focus both from above and below and grow
with a range of angles with respect to the imaging plane
and this results on average in lower growth speeds and
shorter track lengths and track lifetimes (Fig. 2b). For
the mitotic HeLa cell in Fig. 2, the median of microtubule average growth speed is 13.1 lm/min, the characteristic track length (k) is 0.9 lm and the characteristic
lifetime (s) is 2.5 s.
Dose Response of Microtubule Poisons on
Interphase Microtubule Dynamics
Next we tested the assay developed on cells treated with
low doses of nocodazole and taxol.
Nocodazole and taxol are very well characterized microtubule poisons: their effects differ depending on the concentration used. At high concentration (>1 lM),
nocodazole binds to tubulin and rapidly depolymerises
microtubules in cells [Wadsworth and McGrail, 1990],
while taxol stabilizes microtubules [Jordan et al., 1993],
increasing the polymer mass in cells, and inducing microtubule bundling [Turner and Margolis, 1984]. On the
contrary at low concentration (10–100 nM), without
affecting the mass of polymerized tubulin, both drugs alter
microtubule dynamics, blocking the cells in mitosis and
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suppressing chromosome oscillations [Jordan et al., 1991,
1992].
EB3-EGFP expressing HeLa cells were treated with a
range of nocodazole (80, 160 and 330 nM) or taxol (20,
50 and 100 nM) concentrations and 10 interphase cells
for each condition were analysed.
The effects of the perturbations can easily be visualized
from the time projections of the time-lapse sequences
(Fig. 3a): compared to untreated cells, the number of
tracks upon nocodazole or taxol addition is reduced. In
particular in taxol-treated cells the tracks are very few,
very short and located preferentially at the periphery of
the cell. Such a dramatic effect may be a result of the
kinetics of the drug uptake by the cells. Taxol was indeed
reported to accumulate rapidly in cells 200- to 700-fold
over the media concentration, while equilibrium requires
several hours [Yvon et al., 1999]. As the time-lapse
sequences were recorded shortly after the addition of the
drugs to the media, the reduction of growing microtubule
plus ends is most likely due to taxol’s microtubule stabilizing and bundling effect.
The tracks in the nocodazole treated cells are distributed homogenously in the cell but the intensity profiles
are not uniform: microtubule polymerization occurs in
short bursts interrupted by long pauses [Vasquez et al.,
1997] during which the intensity of EB3 at the plus ends
is reduced (see the kymograph of a representative track in
Fig. 3b). On the contrary, in taxol treated cells, microtubule polymerization is slower than in the untreated sample
(the slope of the kymograph is steeper) but persistent,
characterized by a constant association of EB3 with the
growing end.
The initial qualitative observations (Figs. 3a and 3b)
could be confirmed and quantified with the automatic
tracking. In Fig. 3c representative tracks are plotted: both
treatments dramatically affect the length of tracks and, in
the case of nocodazole, it is possible to observe that many
consecutive time points are clustered together suggesting
either very slow polymerization speeds or long pausing
events.
The data from all the analysed cells are summarized in
Fig. 3d: both nocodazole and taxol reduce tubulin polymerization rates in a dose dependent manner. Nocodazole, at low concentrations, was shown to suppress
dynamics both at plus and minus ends, by lowering
growth and shrinking speeds and by increasing the percentage of time spent pausing [Vasquez et al., 1997].
Indeed we confirm such observation: slower growth
speeds, shorter tracks and increase of pausing events, suggest that microtubule dynamicity is suppressed. The
quantification of taxol effects also supports the more dramatic phenotype: the efficient and fast uptake of the
drug combined with slow equilibration times [Yvon
et al., 1999] resulted in a stronger impact on polymerization rates and track lengths.
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RNAi Screen of Candidate Mitotic Genes to
Dissect Their Role in Mitotic Microtubule
Dynamics
Having proved the efficiency and sensitivity of our tracking procedure, we then used it to screen for genes that in
mitosis directly affect microtubule dynamics. We integrated both the imaging and the analysis, above described,
into an RNAi-based screen in which mitotic cells were
automatically selected and imaged at high temporal
resolution.
We selected as candidate genes positive hits from the
Mitocheck genome-wide screen [Neumann et al., 2006;
Walter et al., 2010; Neumann et al., 2010] that either
could affect microtubule behaviour because they associate
with spindle microtubules (such as NUSAP1, TPX2 and
KIF18) or because they bind to structures that regulate
microtubule dynamics, such as centrosomes or kinetochores (PLK1, AURKA, PLK4, CENPE, BUB1), or are
unknown genes whose phenotypes are consistent with a
function in microtubule regulation (see Table I for complete list of candidate genes).
The knockdowns were performed by solid-phase reverse
transfection of siRNA oligos [Erfle et al., 2007] in multiwell cell chambers (Fig. 4a), in order to monitor several
depletions in one experiment. Even though the mitotic
index of cycling adherent cells is as low as 5%, the identification of the mitotic cells was facilitated because EB3EGFP strongly accumulates at the centrosomes from late
G2 to the end of mitosis (Fig. 2a). We were therefore able
to use the centrosomes as a characteristic feature to train
‘Micropilot’ [Conrad et al., 2011] to automatically identify mitotic cells. The ‘Micropilot’ software was developed
to replace the human expertise in identifying the desired
cells and, following the detection, to reconfigure the
microscope settings to launch high-resolution imaging
assays (in our application a 60 s-time-lapse sequence with
a time resolution of 0.4 s; see flow chart in Fig. 4b).
This allowed us to acquire many time-lapse sequences
of mitotic cells knocked down for different genes in fully
automated overnight experiments. All the data was then
fed into the tracking analysis and microtubule growth
speeds, track lengths and lifetimes were automatically
measured for all depletions (Fig. 4c and Supporting Information Fig. S3). Out of the 20 genes depleted, three
genes scored as positive hits: ch-TOG, C11orf38 and
CCDC9. All three knockdowns reduce significantly (P <
0.0001) microtubule growth speeds (Fig. 4c) and track
lengths (Supporting Information Fig. S3). ch-TOG is a
member of the conserved XMAP215/Dis1 family of
microtubule associated proteins [Popov and Karsenti,
2003; Gard et al., 2004] and therefore expected to be a
key regulator of microtubule dynamics in mammalian
cells. XMAP215 binds to centrosomes [Popov et al.,
2002; Cassimeris and Morabito, 2004] and to microtubule plus ends where it stabilizes and stimulates
Automatic Quantification of Microtubule Dynamics
273 n
Fig. 3. Dose response to microtubule poisons of interphase microtubule dynamics. (a) Time projections of time-lapse sequences
of a control cell (untreated) or cells treated with 330 nM nocodazole or 50 nM taxol (scale bar, 5 lm). (b) Kymographs of representative tracks from control or treated cells (scale bar, 1 lm). (c) Graphical representation of tracked microtubules from control or treated
cells. Green and red dots represent respectively the first and the last time points. (d) Charts showing the difference of average growth
speeds, track lengths and lifetime for control (light bars) and cells treated (grey bars) with increasing concentrations of nocodazole (80,
160 and 330 nM) or taxol (20, 50 and 100 nM). Each bar represents the mean with the standard deviation from 5 to 10 cells. [Color
figure can be viewed in the online issue which is available at wileyonlinelibrary.com.]
microtubule plus end growth [Kinoshita et al., 2002].
Also in vitro studies have suggested that XMAP215 participates in the process of tubulin polymerization by binding
to microtubule ends and catalyzing the addition of up to
25 tubulin dimers [Brouhard et al., 2008]. Since depletion
of ch-TOG by siRNA in cells was shown to result in disorganized spindle poles and aberrant spindle morphologies
[Gergely et al., 2003; Cassimeris and Morabito, 2004;
Cassimeris et al., 2009], we can now confirm that such a
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Sironi et al.
phenotype is a result of a direct effect on microtubule dynamics. Most ch-TOG-depleted cells were indeed not able
to assemble a functional spindle and remained blocked in
mitosis (data not shown). C11orf38 and CCDC9 are, on
the contrary, new uncharacterized orfs: validating these
genes as bona fide regulators of microtubule dynamics
and characterizing the molecular mechanism by which
they carry out this function will now be a very interesting
goal of future investigations.
CYTOSKELETON
Fig. 4. RNAi screen of candidate mitotic genes to dissect their role in mitotic microtubule dynamics. (a) Example of one of the
RNAi chips used for the screen. Eight-well Labtek chambers were coated with the source siRNA transfection solution for solid-phase transfection
for one control sample (with scrambled siRNA oligo) and seven different knockdowns (in this example siRNA oligos against ch-TOG, KIF11/EG5,
CCDC9, CENPE, C11orf38, TPX2 and NUSAP1). In an overnight experiment six locations per condition were imaged. (b) Flow chart describing
how the imaging was automated using the ‘Micropilot’ software. Starting at location 1, a single image was recorded and classified. If no mitotic cell
was found the same procedure was repeated at location 2 (and at consecutive locations), while if a mitotic cell was found a 60 s-time-lapse sequence
with a time resolution of 0.4 s was recorded. After recording the high-temporal sequence, the multi-location experiment is resumed at location 1.
(c) Box plot of the distributions of the medians of the average growth speeds (calculated as in Figs. 1f and 2b) for cells treated with scrambled siRNA
oligos or siRNA oligos against 19 different mitotic genes (see Table I for list). The number of samples for each condition varies between 10 and 30
cells and each cell is treated as an independent measurement. (d) Heat map of the analysed genes: the parameters (track speed, lengths and lifetimes)
calculated from the tracking were used for phenotypic clustering of the 19 mitotic genes (the parameters for scrambled treated cells were normalized
to 1). The colour code represents a decrease (blue colours) or an increase (orange colour) in speeds, lengths and lifetimes, while scrambled treated
cells and no phenotypes are represented in white. The dendrogram corresponding to the hierarchical clustering is shown on the left.
CYTOSKELETON
Automatic Quantification of Microtubule Dynamics
275 n
We then clustered the genes with similar phenotypes by
associating each gene with a vector, defined by the computed parameters and by calculating pair wise differences.
From the resulting heat map (Fig. 4d) we observed that
the 20 genes screened were clustered into two main
groups: a group containing the hits that strongly affect
(dark blue colours) all of the parameters measured; a second group that contains the rest of the genes that, if
depleted, induce milder effects or have no phenotype.
Within the latter group the genes are clustered into three
subgroups: CENPE, INCENP, PLK1 and KIF11/EG5
form a group together with scrambled treated samples;
TPX2, TUBGCP6, TACC3, CCDC5, TUBG2 and
NUSAP1, when depleted, reduce microtubule dynamics
while on the contrary PLK4, BUB1, KIF18, NDEL1,
CEP135 and AURKA, when depleted, increase microtubule speeds, lengths and in some cases lifetimes.
TPX2, TUBGCP6, TACC3, CCDC5, TUBG2 and
NUSAP1, despite the fact that they do not alter significantly tubulin polymerization rates, have an impact on the
lengths or the lifetimes of a growth event (light blue colours), suggesting a possible role in microtubule stabilization. Indeed this agrees with previous literature: CCDC5 is
a component of the HAUS complex that was shown to regulate centrosome and spindle integrity either by acting as a
microtubule stabilizer or a microtubule nucleator [Goshima et al., 2008; Lawo et al., 2009]; TUBG2 and
TUBGCP6 are c-tubulin subtypes and therefore most
probably involved in microtubule nucleation; NUSAP1
and TPX2 are both nuclear factors that associate with the
spindle in a Ran-dependent fashion [Ribbeck et al., 2006]
and were shown to be required for microtubule assembly
around the chromosomes [Groen et al., 2009]. We classify
this subgroup as the microtubule nucleating and stabilizing
genes.
PLK4, BUB1, KIF18, NDEL1, CEP135 and AURKA
define on the contrary a subgroup of microtubule destabilizing genes. Surprisingly all genes of this subgroup were
reported to be involved in controlling mechanisms either
in centrosome biogenesis [Kleylein-Sohn et al., 2007; Mori
et al., 2007] or in microtubule attachment at kinetochores
[Mayr et al., 2007; Logarinho and Bousbaa, 2008].
Discussion
This study provides the tools to automatically and efficiently screen mitotic genes for microtubule regulators:
candidate genes are depleted by RNAi in cells expressing
EB3-EGFP that is used as a marker to track individual
microtubule growth events; mitotic cells are selected by
intensity-based classification algorithms and imaged at
high resolution; time-lapse sequences are analysed by a
multiple-particle tracking software and, based on the data
quantification, genes are then clustered into groups with
phenotypic similarities. The integrated workflow of all the
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Sironi et al.
different methods offers the scientific community a very
useful screening method to classify known or uncharacterized genes into a class of in vivo microtubule regulators.
The classification as a mitotic gene in the Mitocheck
screen did not imply any gene function: genes were
defined as ‘mitotic’ by visual observation of chromosomes,
but whether and how they have a role in mitosis has to
still be addressed. With our screen we go a step further,
we identify mitotic genes that have a specific function in
mitosis: regulation of microtubule dynamics.
Amongst the 20 candidate genes tested, two scored as
positive hits: C11orf38 and CCDC9. Like ch-TOG (our
positive control) both unknown orfs greatly reduced
microtubules growth speeds, lengths and lifetimes and
resulted in severe mitotic phenotype: all cells imaged
where not able to assemble a functional spindle and complete mitosis.
We are confident that we truly identify genes that regulate microtubule dynamics since ch-TOG scored positively
and well-known mitotic genes, such as EG5 or PLK1, that
regulate spindle formation either by organizing assembled
anti-parallel microtubule or by phosphorylation of key mitotic substrates, did not score as hits. We are also confident that the assay is sensitive because controlled
perturbations in microtubule dynamics with low doses of
drugs are efficiently detected.
Why genes, such as NUSAP1 or TPX2, did not have a
strong influence on microtubule dynamics could be due
to the fact that either their effect is indirect, for example
by controlling the function of other microtubule regulators, or to the redundancy of the mitotic apparatus: many
positive and negative microtubule regulators act in a concerted fashion to promote spindle formation and maintenance so that depletion of one factor does not affect
dramatically the entire system.
Acknowledgments
We thank all members of the Mitocheck project for their
help in setting up the screen, all members of the Ellenberg
and Mayer laboratories, especially Dr. Peter Lenart and
Dr. Sebastien Huet, for valuable suggestions and discussions. L.S. was supported by the Human Frontier Science
Programme Long-Term fellowship LT00319/2004. J.E.
acknowledges funding by the European Commission
within the MitoSys and SystemsMicroscopy consortia
(FP7/2007-2013-241548 and FP7/2007-2013-258068).
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