Abstract
Centrioles form centrosomes and cilia. In most proliferating cells, centrioles assemble through canonical duplication, which is spatially, temporally and numerically regulated by the cell cycle and the presence of mature centrioles. However, in certain cell-types, centrioles assemble de novo, yet by poorly understood mechanisms. Here, we established a controlled system to investigate de novo centriole biogenesis, using Drosophila melanogaster egg explants overexpressing Polo-like kinase 4 (Plk4), a trigger for centriole biogenesis. At high Plk4 concentration, centrioles form de novo, mature and duplicate, independently of cell cycle progression and of the presence of other centrioles. We show that Plk4 concentration determines the kinetics of centriole assembly. Moreover, our results suggest Plk4 operates in a switch-like manner to control the onset of de novo centriole formation, and that distinct biochemical kinetics regulate de novo and canonical biogenesis. Finally, we investigated which other factors modulate de novo centriole assembly and reveal that PCM proteins promote biogenesis, likely by locally concentrating critical components.
Introduction
“(…) the problem which has interested cytologists and embryologists for many years, namely, whether an ordinarily self-duplicating body may, under certain conditions, seem to be created de novo.” (Dirksen, 1961): On The presence of centrioles in artificially activated sea urchin eggs.
It was not long after their discovery in cells in the late 1890’s (by Boveri and van Beneden), that scientists began proposing that centrioles were not always assembled through duplication (Harvey, 1936; Yatsu, 1905). The fascinating discovery that such an elaborate yet fully functional structure can form without a template, raised a variety of questions regarding the regulation of organelle biogenesis, many of which stay pertinent to this date. And while much effort has contributed to our current understanding of the regulation of pro-centriole assembly next to an already mature, mother structure, much less is known regarding the “unguided” de novo centriole formation.
Centrioles are cylindrical microtubule (MT)-based structures that assemble centrosomes and cilia in eukaryotic cells. The animal centrosome is typically composed of two centrioles, surrounded by Pericentriolar Material (PCM), a membrane-less compartment, which contains hundreds of proteins organised within distinct domains, that are responsible for anchoring and nucleating MTs (see (Joukov and Nicolo, 2019) for a thorough review).
Centriole biogenesis is usually tightly regulated to ensure a correct organelle copy number and prevent a variety of human diseases, including cancer and microcephaly (Bettencourt-Dias et al., 2011; Godinho and Pellman, 2014; Godinho et al., 2014; Levine et al., 2018; Marteil et al., 2018; Lopes et al., 2018). In proliferating cells, centriole biogenesis occurs through a canonical pathway synchronous with cell-cycle progression, called centriole duplication. Accordingly, centrioles begin assembling at G1-S transition, whereby a single procentriole forms at the proximal side of each of the two mother centrioles (reviewed in (Nigg and Holland, 2018; Breslow and Holland, 2019). During mitosis, centrioles undergo centriole-to-centrosome conversion through the recruitment of Cep135/Bld10, Cep295/Ana1 and Cep152/Asterless (Asl), becoming competent for duplication in the next cell-cycle (Fu et al., 2016; Izquierdo et al., 2014; Wang et al., 2011; Tsuchiya et al., 2016). After mitosis, one centrosome is segregated to each daughter cell. This process entails that the location, timing and number of procentrioles assembled in cycling cells is determined by older/mature centrioles (Banterle and Gönczy, 2017; Breslow and Holland, 2019).
Polo-like kinase 4 (Plk4) is a major player in centriole biogenesis in most animal cells (Bettencourt-Dias et al., 2005; Habedanck et al., 2005; Kleylein-Sohn et al., 2007). Depletion or inhibition of its kinase activity prevents centriole formation, while overexpression leads to the formation of multiple centrioles (Bettencourt-Dias et al., 2005; Habedanck et al., 2005; Wong et al., 2015). Plk4 activity and function is regulated by its concentration, which is known to be very low in human cultured cells (Bauer et al., 2016). As a result, the concentration of active Plk4 must be well-regulated to maintain a correct centriole number and normal cell-cycle progression. Full Plk4 activity is accomplished by trans-autophosphorylation of a conserved T-loop residue within its catalytic domain, which triggers kinase activation through a positive feedback mechanism (Lopes et al., 2015). It is still not fully understood how Plk4 acquires basal catalytic activity, but it is likely that other centrosomal proteins regulate this process, such as its substrates Ana2 and Asl (Klebba et al., 2015b; a; Moyer et al., 2015; Zitouni et al., 2016; Mclamarrah et al., 2018; Boese et al., 2018; Aydogan et al., 2019). Moreover, at high concentration, Plk4 self-assembles into nanoscale condensates in Xenopus extracts and in human cultured cells, which may be important for centriole assembly (Montenegro Gouveia et al., 2018; Yamamoto and Kitagawa, 2019; Park et al., 2019).
Centrioles can also form de novo in a variety of cell-types (reviewed in (Nabais et al., 2018)), but the regulation of this process remains largely unknown. De novo centriole assembly occurs naturally in organisms that lack centrosomes and generate centrioles to nucleate motile cilia, such as land plants that produce ciliated sperm (Renzaglia and Garbary, 2001), several unicellular organisms that alternate between non-flagellated and flagellated life-cycle states and in animal multiciliated cells, where many centrioles are produced at once (Dingle and Fulton, 1966; Aldrich, 1967; Fulton and Dingle, 1971; Grimes, 1973a; b; Mir et al., 1984; Al Jord et al., 2014; Meunier and Azimzadeh, 2016; Fritz-Laylin et al., 2016; Mercey et al., 2019a; b; Zhao et al., 2019) Furthermore, centrosomes form de novo in parthenogenetic insects that develop without fertilisation (Riparbelli et al., 1998; Tram and Sullivan, 2000; Riparbelli and Callaini, 2003). In most animals, centrioles are lost during female oogenesis and are provided by the sperm upon fertilisation, as they are needed for embryo development (Rodrigues-martins et al., 2008; Varmark et al., 2007). However, Nasonia vitripennis and Muscidifurax uniraptor wasps (Riparbelli et al., 1998; Tram and Sullivan, 2000; Ferree et al., 2006), Drosophila mercatorum flies (Riparbelli and Callaini, 2003) and Acyrthosiphon pisum aphids (Riparbelli et al., 2005) can reproduce in the absence of fertilisation. In these cases, multiple centrosomes form spontaneously in the egg at late stages of meiosis, two of which are captured for spindle formation and embryo development, thus replacing the centrioles that are otherwise inherited from the sperm (Tram and Sullivan, 2000).
Centrioles can also form de novo in cells that undergo physical, chemical or genetic perturbations. Proliferating cells are capable of assembling centrioles de novo, but only after their centrosomes have been physically or chemically removed (Khodjakov et al., 2002; La Terra et al., 2005; Uetake et al., 2007). Chlamydomonas reinhardtii carrying a mutated centrin copy has defects in centriole segregation giving rise to progeny without centrioles that, within few generations, reacquire centrioles de novo (Marshall et al., 2001). Although in these cases there is no strict control over the number of centrioles formed, it has been proposed that resident centrioles negatively regulate de novo centriole biogenesis (Marshall et al., 2001), and that such inhibitory effect can be accomplished by having a single centriole in the cell (La Terra et al., 2005; Lambrus et al., 2015).
In Drosophila tissue culture cells, evolutionary conserved centriolar components, such as Sas6, Sas4 and Bld10, are critical for both canonical and de novo assembly (Rodrigues-Martins et al., 2007), suggesting that centrioles assembled by both pathways share their core composition but perhaps differ in their triggering. Despite the wide spread circumstances in which centrioles form de novo, the regulation and role of older centrioles on this process have not been addressed. This is in part due to the lack of a controlled model system suitable for high-resolution time-lapse imaging and amenable to experimental perturbations.
In this study, we investigated the spatio-temporal regulation behind de novo centriole assembly, including the effect of pre-assembled centrioles on the biogenesis of new ones, by developing a new experimental system that enabled tracking this process visually. Plk4 upregulation drives de novo centriole biogenesis in unfertilised Drosophila melanogaster eggs (Rodrigues-Martins et al., 2007; Peel et al., 2007). The fly egg is ideal to study centriole assembly since all the proteins necessary for the first centrosome and nuclear cycles are maternally inherited and, in the absence of fertilisation, centrioles are not present. Therefore, centrosomes detected in unfertilised eggs result from de novo assembly and not from duplication from paternally inherited centrioles. Here, we accomplished, for the first time, fast live-imaging of de novo centriole assembly with high spatial resolution, using single-egg cytosolic explants (Telley et al., 2013; de-Carvalho et al., 2018). We show that, at high Plk4 concentration, centrioles form de novo and then become competent to duplicate, and that both pathways are concurrent. We present a combination of experiments and mathematical modelling that reveal that de novo centriole formation occurs independent of pre-existing centrioles. These results contradict the existing view that resident centrioles generate signals that inhibit de novo biogenesis. We demonstrate that Plk4 modulates the kinetics of centriole assembly in a concentration-dependent manner that is suggestive of a switch-like molecular mechanism. Finally, we find that the PCM, in particular Gamma-tubulin, regulates de novo biogenesis, suggesting that a local environment of concentrated centriolar and PCM components is required for de novo centriole assembly.
Results
An assay to investigate centriole biogenesis live with high spatio-temporal resolution
De novo centriole assembly has remained poorly studied in live samples due to the lack of a suitable system where the process can be triggered and documented in a timely-manner. Overexpressing Polo-like kinase 4 (Plk4) drives de novo bona fide centriole biogenesis, validated by Electron Microscopy (EM), in unfertilised Drosophila melanogaster eggs (Rodrigues-Martins et al., 2007), but the onset of the process and its spatio-temporal dynamics was unknown. Reasons behind this knowledge gap are mostly imaging-related, for the axial depth is optically limited and greatly impaired by the light scattering properties of the egg yolk. Therefore, it is currently impossible to visualise events that take place deep inside the fruit-fly egg, which would otherwise be the ideal system to address critical questions concerning centriole biogenesis.
We set up a cell-free assay that resolves these limitations by generating cell cortex-free micro-scale explants that can be fully imaged, while retaining the native characteristics of the cytoplasm in vivo (Fig. 1A, (Telley et al., 2013; de-Carvalho et al., 2018). Using this assay, we observed de novo centriole biogenesis, at high spatio-temporal resolution (Fig. 1B,C and Suppl. Movie 1). Germline-specific Plk4 overexpression triggers the formation of multiple centrioles in cytoplasmic explants, demonstrating that post-meiotic Drosophila melanogaster egg extracts are competent for centriole biogenesis, recapitulating what we had previously observed in the egg (Fig. 1C; (Rodrigues-Martins et al., 2007). Therefore, these extracts offer a powerful assay to investigate the regulation of centriole assembly.
Centrosomes formed in the explants are stable at least within the first hour of the process, since we never observed centriole elimination during our time-lapse recordings. We tested several fluorescent protein fly lines in the explants, namely Ana1-Tomato, GFP-Plk4, Asl-mCherry, Spd2-GFP and Sas6-GFP. We chose Spd2-GFP as our routine centrosome reporter because its fluorescence signal was brighter and more photostable across explants than all the others tested. As a result, most experiments in this study were conducted using this protein reporter.
De novo formed centrioles mature and acquire the ability to duplicate in the absence of cell cycle progression
It was previously proposed that in both human cells (La Terra et al., 2005; Lambrus et al., 2015) and Drosophila eggs (Rodrigues-Martins et al., 2007), centrioles that form de novo can then duplicate in a canonical fashion. However, this was never confirmed directly and raises some questions; since centriole duplication is thought to depend on centriole maturation, a process called centriole-to-centrosome conversion (Wang et al., 2011; Izquierdo et al., 2014; Fu et al., 2016; Chang et al., 2016) and known to be coupled to cell cycle progression, which does not occur in eggs (Horner et al., 2006; Vardy and Orr-Weaver, 2007; Deneke et al., 2019). Thus, we first asked whether de novo formed centrioles can recruit Ana1 and Asterless (Asl), required for centriole-to-centrosome conversion, followed by the recruitment of Plk4 and bona fide centriole duplication (Fig. 2 and Suppl. Movies 2A–D). Surprisingly, we observed that recently born centrioles recruit signature molecules characteristic of centriole maturation, such as Ana1 (Fig. 2C) and Asl (Fig. 2D), in the absence of mitosis (Wang et al., 2011; Izquierdo et al., 2014; Fu et al., 2016; Chang et al., 2016). Moreover, the trigger of biogenesis, Plk4, is also recruited to the centrioles (Fig. 2A).
Next, we investigated whether centrioles that formed de novo also duplicate, as predicted by their ability to mature and recruit Plk4. In our assay, single centrioles are first detected as radially symmetrical intensity spots with Gaussian intensity profile (Fig. 2A,B,C, right). Over time, a single Spd2 Gaussian intensity profile can evolve into a mixture of at least two Gaussian distributions, consistent with the presence of more than one diffraction-limited centriole and canonical duplication (intensity line profiles in Fig. 2D). Since image resolution by conventional confocal microscopy is diffraction-limited, we proceeded to validate centriole duplication using higher resolution techniques. Validation by EM had previously been performed in intact eggs overexpressing Plk4, confirming the assembly of structurally normal centrioles (Rodrigues-Martins et al., 2007). Currently, it is not possible to accomplish EM validation in cytoplasmic droplets since the egg explants are imbedded in halocarbon oil, which is not compatible with sample processing. Therefore, we resorted to 3D-Structured Illumination Microscopy (SIM), which has approximately twice the spatial resolution of confocal microscopy. Spd2–GFP visualised by 3D-SIM imaging forms a ring at the centre of the microtubule aster, with an inner diameter of about 230-320 nm when viewed in cross section (Suppl. Fig. 1, insets). Previous studies have demonstrated that Spd2 also forms toroids at the centrosome in Drosophila syncytial embryos, whereby Spd2 projections extend from a central hollow structure, which presumably contains a single centriole (Conduit et al., 2015). In addition, in our experiments smaller structures form adjacent to older centrioles which previously formed de novo, demonstrating the onset of canonical duplication is concomitant with de novo biogenesis in this system (Suppl. Fig. 1, Insets).
In 97% (66/68) of our time-lapse recordings captured by confocal microscopy, we observed the duplication of the first centriole within 2 to 3 minutes after its de novo assembly (Fig. 2D, scatter-plot). Finally, we asked whether centrioles are fully converting to centrosomes, maturing also in their ability to nucleate MTs. Indeed we observed that as they age, centrioles continue incorporating centrosomal proteins and increase their MTOC capacity, which is reported by the intensity of the microtubule-associated protein Jupiter (Fig. 2D, bottom).
Centriole-mediated regulation of centriole biogenesis
Interpretation of earlier experiments led to the model that existing centrioles play a dominant role in centriole assembly and negatively regulate de novo centriole biogenesis, and that this inhibitory effect can be accomplished by a single centriole in the cell (Marshall et al., 2001; La Terra et al., 2005; Uetake et al., 2007; Lambrus et al., 2015). Whether centrioles can release an inhibitory signal is unknown. On the other hand, it has been suggested that centrioles can act as catalysers of centriole biogenesis, by concentrating centriole components and therefore preventing biogenesis elsewhere (Marshall et al., 2001; Lopes et al., 2015).
We asked whether the appearance of the first centriole can prevent further de novo formation. Surprisingly, despite the assembly of centrioles and their duplication, we continue to see de novo formation (see timeline in Fig. 2D), challenging the view that existing centrioles have a context-independent inhibitory effect in centriole biogenesis. To further test this in more detail, we analysed the spatio-temporal regulation of de novo biogenesis, at high Plk4 concentration, by assessing if centrioles impact the place and timing of other de novo events (Fig. 3A, 4A and Suppl. Fig. 2). Once the first centrosome had formed, we assessed if older centrioles affect the biogenesis of others, e.g. by promoting (triggering effect) or repressing (inhibitory effect) the assembly of new ones.
We did not observe a statistical difference in the pairwise inter-event distance between the first four centrioles formed de novo (Kruskal-Wallis mean rank test) (Fig. 3B, Suppl. Fig. 2). However, we noticed that new centrioles form, on average, more than 10 μm away from previous ones, regardless of centriole rank and droplet size (Fig. 3B,C), raising the question whether this process is spatially random and if there is any spatial regulation (e.g. an inhibitory effect) imposed by older centrioles on the birth of neighbours. To test these hypotheses we generated stochastic models with similar geometric constraints as the cytosolic explants, allowing us to compare observed and simulated data. By measuring the inter-event distances between four random events, independent and uniformly distributed within 3-dimensional spaces of similar geometry, we can derive that observations in explants do not significantly deviate from the random simulations (Fig. 3D). According to our measurements, older centrioles have only a short-range effect on the biogenesis of new centrioles, promoting canonical duplication in very close proximity, but not determining the place of de novo assembly elsewhere in the cytosol. Hence, on the scale of tens of micrometers centrioles behave as independent entities in the initial stages of de novo assembly. Our results strongly suggest that de novo centriole biogenesis is not affected by the mere presence of other centrioles. It is possible that biochemical changes at the level of the entire cytoplasm allow for stochastic de novo centriole formation. To obtain more insight we went on to study the temporal kinetics of de novo biogenesis.
The kinetics of de novo biogenesis
We measured the time for the first four de novo centrioles to appear in the explants (Fig. 4A). We detected, on average, a long lag-phase until the birth of the first de novo event, after which the process seemingly accelerated, presenting rates of de novo centriole biogenesis in the range of one every two minutes (Fig.s 4B). Assuming independent events with a constant rate, computer simulations predict that all inter-event times should follow a similar distribution. As depicted in Fig. 4C, not all of the observed inter-event distributions were within the confidence interval of the simulation. Moreover, the difference was more noticeable with higher number of centrioles (Fig. 4C). Maximum Likelihood Estimation of birth rate indicated a linear increase with centriole number (Fig. 4D). Altogether, our results demonstrate that the de novo centriole formation rate accelerates in time and may comprise two distinct phases. In an initial lag phase preceding the formation of the first centriole(s), the probability of centriole assembly is very low. In the subsequent phase, most events seem to occur almost simultaneously. Such kinetics is reminiscent of a bistable process. Cell-cycle transitions typically show bistability; they rely on accumulation of a signal or activating enzyme, and the moment a critical transition occurs the kinetics becomes essentially irreversible and independent of the signal. This is true for the G2/M transition, which is driven by high Cdk1 activity. Low Cdk1-Cyclin B activity in interphase can drive S-phase onset and G2 progression, whereas high Cdk1-Cyclin B activity triggers mitotic entry. The difference between interphasic and mitotic Cdk activity likely relies on their concentration and phosphorylation thresholds required to activate substrates and drive the respective cell-cycle transition (Gutierrez-Escribano and Nurse, 2015; Swaffer et al., 2016; Godfrey et al., 2017). Here, we observe a burst in centriole biogenesis, after which all centrosomes are retained in the explants suggesting that the transition underlying their assembly is irreversible (Tyson and Novak, 2001; Charvin et al., 2009) We thus hypothesise that multiple foci centriole biogenesis are generated in the cytosol by the action of the bistable molecular switch arising from the stochastic Plk4 concentration and activity.
Plk4 concentration modulates the kinetics of centriole assembly
Little is known about the regulation of Plk4 activity and the onset of centriole biogenesis. Full Plk4 activity is accomplished by trans-autophosphorylation of a conserved T-loop residue within its catalytic domain, which triggers kinase activation through a positive feedback mechanism (Lopes et al., 2015). Consequently, the expected kinetics of Plk4 activation may greatly depend on local concentration and on overcoming a critical threshold (Fig. 5A). As found in other kinases, Plk4 possesses an autoinhibitory mechanism; once synthesised it is autoinhibited by a cis-interaction between its L1 linker and activation loop (T-Loop). Autoinhibition is relieved upon Plk4 homodimerisation through its Polo-box (PB) domain 3 and autophosphorylation of residues within L1 (Klebba et al., 2015a). Moreover, Plk4 binding to the substrate Stil/Ana2 increases Plk4 phosphorylation within its T-loop (Moyer et al., 2015; Zitouni et al., 2016). Therefore, Plk4 auto-phosphorylation and interaction with Stil may provide spatial and temporal regulation of Plk4 kinase activity in cells, activating Plk4 preferentially at the centrosome where it is concentrated. Moreover, Plk4 ability to form large order oligomers (“condensates”), may be important for the onset of centriole biogenesis (Montenegro Gouveia et al., 2018; Leda et al., 2018; Shohei and Kitagawa, 2018; Park et al., 2019). We hypothesised that, in the case Plk4-driven centriole biogenesis is based on a positive feedback mechanism, the initiation of biogenesis is concentration-dependent and relies on overcoming a critical, local threshold in kinase activity (Fig. 5A). In addition, if the process is bistable, we expect the kinetics to remain fast once the critical transition has occurred, provided there is enough activator (Plk4) in the system. To test this, we established a titration assay for Plk4 concentration using egg cytoplasm. Wildtype eggs have all the components, except for Plk4, presumably at similar concentrations as Plk4–overexpressing eggs. Thus, mixing egg cytoplasm from these two genetic backgrounds dilutes only Plk4 within a range of full overexpression and endogenous levels. We measured the temporal kinetics of de novo centriole biogenesis for a series of dilutions. We found that all tested Plk4 dilutions – 0.5, 0.33 and 0.16 relative concentration – delay the onset of de novo centriole assembly (Fig. 5B). The delay is dilution dependent; centrosome formation occurs within all explants at the highest Plk4 concentration, saturating within 25 min. Saturation is not reached within the observation time at lower Plk4 concentrations, and the onset of de novo centriole assembly occurs progressively later with increasingly lower Plk4 concentration (Fig. 5C). Moreover, stochastic simulations taking into consideration Plk4 trans-auto-phosphorylation and dephosphorylation, were in agreement with most of the observed data (Suppl. Fig. 4). Our results suggest the presence of a concentration dependent Plk4 threshold, modulated by the activity of a putative counteracting phosphatase. As a consequence, the kinetics of Plk4 activity and centriole biogenesis is non-linear as previously hypothesised in Lopes et al. 2015. Our results also show that the time from the first to the second biogenesis event does not differ between high overexpression and dilutions (Fig. 5C, Suppl. Fig. 5), suggesting that Plk4-driven centriole assembly relies on such a switch-like molecular process. Our experiments provide the first evidence in vivo that Plk4 triggers de novo centriole biogenesis through a positive feedback mechanism marked by a critical threshold of Plk4 concentration.
Our results lead to the hypothesis that under endogenous conditions, Plk4 concentration in cells is very low and undergoes limited oligomerisation in the cytosol, which can prevent auto-activation until the sperm centriole enters the egg and locally concentrates Plk4. However, the concentration and the oligomerisation state of Plk4 in the cytoplasm have never been studied in Drosophila. Therefore, we decided to investigate these biochemical parameters in the early fly embryo using Fluorescence Correlation Spectroscopy (FCS).
Plk4 regulation under endogenous conditions
FCS is a technique with single molecule sensitivity, therefore ideal for quantification of low abundance proteins present at nanomolar to picomolar concentrations inside the cell. Previously, this technique has been used to determine the oligomerisation state of another centriolar protein, Sas6, in human U2OS cells (Keller et al., 2014). Moreover, Plk1, also a member of the Polo-like kinase family, has been studied by FCS in human RPE1 cells (Mahen et al., 2011). FCS measurements revealed distinct diffusion coefficients for Plk1 in the cytoplasm, which correlated with its kinase activity during different cell-cycle stages (Mahen et al., 2011).
Therefore, we conducted in vivo FCS to determine Plk4 concentration, diffusion and oligomerisation in syncytial fly embryos in which Plk4 is at endogenous levels and both allelles were tagged with a fluorescent reporter by CRISPR (Suppl. Fig. 7 and Suppl. Movie 3). Despite the very low concentration, we could detect bursts of mNeonGreen– Plk4 fluorescence above background signal, which was assessed in control flies expressing only RFP-Tubulin (Suppl. Fig. 9A). More importantly, the mNeonGreen–Plk4 traces generated clear autocorrelation function (ACF) curves, whereas the background fluorescence measured in RFP–Tubulin expressing embryos did not autocorrelate (Suppl. Fig. 9B). For mNeonGreen–Plk4, the normalised ACF were best fitted, with minimal residuals, to a two-component diffusion model, and this fit was corroborated by the distribution obtained from the Maximum Entropy Method (MEM) fit (Fig. 6A, Suppl. Table 4). Two fractions of diffusing mNeonGreen–Plk4 were detected in the cytoplasm: one diffusing at 17.17 µm2/s which is similar to the fluorophore mNeonGreen alone (Suppl. Fig. 8D) and another, slower fraction diffusing at 1.49 µm2/s (Fig. 6A, Suppl. Table 4). While the first fraction probably refers to Plk4 monomers, the second cannot be explained by homo-oligomerisation alone, suggesting that a fraction of Plk4 may associate with quasi-immobile substrates in the cytosol.
Next, we calculated the total concentration of mNeonGreen–Plk4 in the cytosol and determined its oligomeric state using the brightness of injected mNeonGreen monomer as calibration (Suppl. Fig. 8). We confirmed that Plk4 concentration in the cytosol is very low, around 7.55 nM, and an estimate for diffusion in the cytosol suggests coexistence of monomeric and oligomeric form (Fig. 6B). More precisely, 30.1% of diffusing Plk4 is detected as a monomer, while around 69.9% forms low-order oligomers, likely dimers and at most tetramers (Fig. 6B). Altogether, the FCS results indicate that Plk4 is indeed a very low abundance protein that undergoes limited oligomerisation within the cytoplasm, in early-developing Drosophila embryos. Thus, the nanomolar concentration of Plk4 may be insufficient to trigger de novo centriole assembly.
The change in the kinetics of de novo centriole assembly in response to Plk4 concentration allied to the current body of knowledge in the centrosome field, collectively suggest that centriole formation is critically regulated by timely concentration of centrosomal molecules in one single place (Rale et al., 2018; Takao et al., 2019). But what initiates the concentration of these centrosomal molecules? Recent studies suggest that the PCM may play an important role.
PCM components promote the early steps of centriole de novo assembly
In D. melanogaster cultured cells, co-depletion of the centriolar protein Ana2 and the PCM component D-Pericentrin–like protein (D-Plp) additively impair centriole biogenesis, indicating that two alternative pathways – a centriolar and a PCM-mediated – may be at play (Ito et al., 2019). Moreover, in mouse ependymal cells without centrioles and specialised electron-dense deuterosomes that can feed centriole assembly, a correct number of centrioles can form de novo within Pericentrin rich areas (Mercey et al., 2019b). To test the role of the PCM in de novo centriole assembly, we started by performing perturbation experiments in Drosophila DMEL cultured cells, since it is easier to knock down several genes in vitro than in the organism. To create an assay for de novo centriole assembly, we depleted centrioles through successive cell divisions in the presence of RNAi against Plk4. As cells proliferate in the absence of centriole duplication, centriole number is progressively reduced. This is followed by a recovery period, without RNAi against Plk4, where Plk4 translation is resumed and centrioles assemble de novo (Rodrigues-Martins et al., 2007).
After RNAi against Plk4, we further depleted PCM components, while allowing Plk4 translation to recover (Fig. 7A), which is sufficient to drive centriole de novo assembly in the mCherry (mCh)-treated control cells (Fig. 7B,C, and Suppl. Fig. 10). After 10 days, only 3% of the cells treated with RNAi against Plk4 had centrioles, whereas in the mCherry-treated control about 85% of the cells had at least one centriole, as expected (Rodrigues-Martins et al., 2007). Cells depleted of centrioles were then treated for four days with RNAi against PCM components including: Cnn + Asl + D-Plp + Spd2 together (referred to as “All PCM”), previously shown to be essential for PCM maintenance (Pimenta-Marques et al., 2016), and the downstream PCM protein, γ-tubulin, which is known to be important for MT nucleation across species and contribute for centriole duplication in C. elegans embryos and human cells (Dammermann et al., 2004; Kleylein-Sohn et al., 2007). While cells treated with control mCherry dsRNA recovered centriole number within 4 days after ceasing Plk4 dsRNA treatment (indicating that centrioles formed de novo), only 15-20% of the cells treated with dsRNA against “All PCM” had centrioles (Fig. 7C). Moreover, de novo centriole formation was impaired by γ-tubulin 23C depletion, whereby only 34–42% of Plk4 depleted cells recovered a normal centriole number (Fig. 7C, Suppl. Fig. 10). This result implies that Gamma-tubulin, a critical PCM component necessary for microtubule nucleation at the centrosome, is important for de novo centriole biogenesis. We proceeded to validate this observation in vivo and generated fly lines expressing shRNA against Gamma-tubulin 23C and Gamma-tubulin 37C (a maternally expressed gene, mostly abundant in early fly development (Tavosanis et al., 1997), under control of the UASp/Gal4 system. Fertilised eggs laid by females overexpressing the shRNA targeting Gamma-tubulin 37C do not develop (Suppl. Table 7) and unfertilised eggs display spindle defects similar to those previously shown in oocytes from Gamma-tubulin 37C mutant females (yellow asterisks in Fig. 7D and in Suppl. Fig. 11) (Tavosanis et al., 1997), indicating this RNAi construct is likely functional. We collected unfertilised eggs expressing RNAi targeting Gamma-tubulin 23C and/or 37C, while simultaneously overexpressing Plk4, under control of the V32-Gal4 driver. In the control, centrioles form de novo in 73% (22/30) of the eggs overexpressing Plk4 alone (Fig. 7D,E and Suppl. Fig. 11). On the other hand, in the case of recombinant Gamma-tubulin 23C + 37C RNAi flies overexpressing Plk4, only 26% (14/54) of their eggs show centrioles, while individual Gamma-tubulin knock-downs display intermediate phenotypes (Fig. 7D,E and Suppl. Fig. 11). Therefore, Gamma-tubulin depletion seems to impair de novo centriole assembly in vivo too.
Discussion
De novo centriole assembly is widely documented across the eukaryotic tree of life. Numerous studies report its incidence and even its relationship with life-history traits in particular groups (Mizukami and Gall, 1966; Aldrich, 1967; Grimes, 1973a; b; Mir et al., 1984; Renzaglia and Garbary, 2001; Idei et al., 2013), but they have not addressed how de novo assembly is regulated in living cells and what the contribution of older centrioles to this process is. With the workflow here implemented, we demonstrate that cytosolic explants from post-meiotic D. melanogaster eggs overexpressing Plk4 are competent of centriole biogenesis, offering the opportunity to investigate centriole formation at high spatio-temporal resolution by confocal fluorescence microscopy (Fig. 1). In these explants, Plk4 triggers stochastic formation of multiple, stable centrioles. Our assay allowed us to study several important open questions regarding the regulation of de novo centriole biogenesis.
How is the timing of biogenesis regulated?
Our current knowledge supports the need for extrinsic timely cues, provided by the cell cycle regulation, to control the centriole cycle (Wang et al., 2011; Izquierdo et al., 2014; Fu et al., 2016; Tsuchiya et al., 2016). However, here we observed that de novo formed centrioles can undergo time-dependent centriole-to-centrosome conversion and maturation, incorporating Ana1, Asl, Spd2 and Plk4. Consequently, approximately 2–3 minutes after being born, centrioles nucleate more microtubules and can duplicate (Fig. 2, insets, Suppl. Fig. 1, insets). Given that unfertilised eggs are not progressing through the cell cycle (Horner et al., 2006; Vardy and Orr-Weaver, 2007; Deneke et al., 2019), our findings suggest that centriole de novo formation, maturation and duplication can occur even without cell cycle transitions, in particular without having to undergo mitosis. Surprisingly, we also observed that the duplication time is similar for the first centrosomes assembled de novo at high (undiluted) and lower (diluted) concentration of Plk4 (Suppl. Fig. 6). This indicates that, despite the absence of a typical cell-cycle “clock”, canonical biogenesis is both spatially and temporally robust. Hence, we propose that distinct intrinsic “clocks” regulate de novo and canonical biogenesis, with de novo biogenesis being more sensitive to Plk4 concentration.
Our data suggests that a switch-like transition mediated by Plk4 activity occurs in the cytoplasm. Evidence for such molecular mechanism is supported by the change in the kinetics of de novo centriole biogenesis following the delay in assembly of the first de novo event, modulated by concentration of Plk4 (Fig. 4D, 5C and Suppl. Fig. 4). Theoretical modelling and simulations indicate that the rate of de novo centriole assembly accelerates with time, following the rise in Plk4 activity (Suppl. Fig. 6). The sensitivity to the dilutions of Plk4 expression agrees with the non-linear kinetics of Plk4 trans-autoactivation in the cytosol by Lopes et al. 2015, suggesting that the burst in biogenesis occurs once a critical activity threshold is overcome (also proposed by Lambrus et al. 2015 for the regulation of canonical duplication). Moreover, Plk4 may need to oligomerise to promote centriole assembly. Consistent with this we observe, oligomeric forms of Plk4 in the cytoplasm at extremely low concentrations of Plk4 (Fig. 6).
Centrosomal proteins are highly enriched in intrinsically disordered regions, coiled-coil domains and phosphorylation sites, which are critical for protein interactions and oligomerisation, therefore promoting assembly of protein scaffolds (Santos et al., 2013; Kuhn et al., 2014). For example, Sas6 self-assembly into homodimers is at the heart of the universal 9-fold symmetry (Nakazawa et al., 2007; Breugel et al., 2011; Kitagawa et al., 2011; Guichard et al., 2017). At high concentration, Xenopus Plk4 forms supramolecular scaffolds that bind other centrosomal proteins and nucleate MTs (Montenegro Gouveia et al., 2018). In human cells, the association of Plk4 into condensates was shown to be mediated by disordered regions within Plk4 (Yamamoto and Kitagawa, 2019) and regulated by autophosphorylation (Montenegro Gouveia et al., 2018; Yamamoto and Kitagawa, 2019; Park et al., 2019).
In switch-like processes, critical thresholds exist that, whenever crossed, result in an irreversible transition. We suspect that the concentration of active Plk4 increases over time at multiple sites in the cytosol, overcoming the activity of counteracting factors and driving centriole biogenesis almost simultaneously in independent locations in the explants. We have demonstrated a concentration-dependent delay in the onset of de novo centriole biogenesis upon Plk4 dilution in wild-type extract (Fig. 5B), while the inter-event time is much less affected by the cytoplasmic dilutions (Fig. 5C, Suppl. Fig. 5) and the spatial dynamics still fall within random predictions at lower Plk4 overexpression (Suppl. Fig. 3). Our dilution experiments suggest that time-dependent localised concentration of Plk4 and, perhaps, association into higher-order structures drives de novo centriole biogenesis at multiple locations in the cytoplasm. Once a critical threshold in molecular concentration is locally crossed, Plk4-driven centriole assembly is irreversibly catalysed.
Which factors can help to locally increase the concentration of centriole components?
Besides local Plk4 concentration, other factors may play a role in regulating the location of de novo centriole assembly. For instance, MTs likely participate to the localisation of some components at the centrosome through molecular motor based transport. Furthermore, the PCM was shown to be important for canonical centriole biogenesis (Dammermann et al., 2004; Pelletier et al., 2004; Kemp et al., 2004; Delattre et al., 2006; Kleylein-Sohn et al., 2007) and recent studies in multicilliated cells propose that, in the absence of centrioles or specialised deuterosomes, centrioles can form within PCM clouds (Mercey et al., 2019b). De novo centriole biogenesis has been described to occur within Pericentrin and Gamma-tubulin-rich foci in vertebrate somatic cells (Khodjakov et al., 2002). We have also hypothesised that, in our system, the early steps of de novo centriole assembly occur within a MT and PCM-rich environment. In agreement, our PCM perturbation experiments support an important role for the PCM, in particular its downstream component Gamma-tubulin, in de novo centriole assembly (Fig. 7, Suppl. Fig.s 10 and 11). The PCM may generate protein scaffolds in the cytoplasm where centriolar proteins bind with higher affinity, therefore locally concentrating these molecules and forming stable seeds for centriole biogenesis. Moreover, Gamma-tubulin promotes MT nucleation, which may attract more components via motor-based transport or through entrapment of proteins with MT-binding capacity, such as Plk4 (Montenegro Gouveia et al., 2018). These manifold properties of the PCM may promote centriole biogenesis within biochemically-confined environments in the cytoplasm.
Do centrioles influence the de novo assembly of others?
Previous studies had suggested that once centrioles form de novo in cells without centrioles, any other events of biogenesis would be “templated”, i.e., follow the canonical pathway (Marshall et al., 2001; La Terra et al., 2005; Uetake et al., 2007; Lambrus et al., 2015). This appears to be the case in Naegleria gruberi, where the first basal body assembles de novo but the second duplicates from the first (Fritz-Laylin et al., 2016), in acentriolar somatic human cells (La Terra et al., 2005; Uetake et al., 2007; Lambrus et al., 2015) and in green algae (Marshall et al., 2001). Together, these studies suggest that centrioles negatively regulate the de novo pathway and play a dominant role in biogenesis by recruiting the centrosomal components that limit biogenesis. In fly egg explants, we observed that centrioles continue to form de novo long after the first centriole has assembled and duplicated (Fig. 2). Both pathways – de novo formation and canonical duplication – co-occur within the same cytoplasmic compartment, indicating that “older” centrioles and their duplication do not prevent biochemically de novo centriole assembly, even at lower Plk4 overexpression (Fig. 5 and Suppl. Fig. 5). Thus, it appears that these pathways are not inherently mutually inhibitory in the fly germline.
We then wondered whether centriole assembly has a negative impact on the birth of other centrioles, for instance by changing the molecular composition of the cytoplasm. Addressing this problem required comparing our observations with random simulations results obtained under similar spatial geometries. This comparison strongly indicates that the first de novo events are spatially independent, suggesting that recently formed centrioles have only a very short-range effect, if any, on the biogenesis of new centrioles. They promote duplication at the centrosome but do not impact the place where new centrioles assemble de novo elsewhere in the cytosol (Fig. 3D and Suppl. Fig. 3). We cannot exclude the possibility that shortly after forming, centrioles are still immature and therefore incapable of inhibiting de novo biogenesis. However, this hypothesis seems unlikely given their ability to duplicate. Another explanation may be that the overall concentration of Plk4 in our system is so high that over-rides any possible spatial regulation, but our modelling suggests that the location of de novo centriole biogenesis remains random even at lower overall Plk4 concentration (“0.16”, Suppl. Fig. 4). Our results provide further support that spatio-temporal (local) concentration of Plk4 must be well-regulated in cells to form an exact number of centrioles, since their presence is not necessarily enough to ensure centrioles can only form in the vicinity of existing ones.
How do our results fit with what naturally occurs in vivo and in nature?
A previous study had estimated 1200–5000 Plk4 molecules per cell in asynchronous human cells, from which around 70 molecules are loaded at the centrosome (Bauer et al., 2016). We generated flies labelled with mNeonGreen at Plk4 genomic loci by CRISPR (Suppl. Fig. 7) and confirmed that endogenous diffusing pool of Plk4 is present at very low concentration and undergoes limited self-association in the cytosol in early fly embryos (Fig. 6B). These properties of Plk4 in the cytosol are unfavourable for centriole de novo assembly, ensuring that centrioles form in the right place by canonical biogenesis. Our measurements help building a quantitative framework for the transition of Plk4 molecules from the cytoplasm to the centriolar compartment, which ultimately controls centriole biogenesis.
Finally, we wonder to what extent our findings in D. melanogaster relate to the naturally occurring parthenogenetic development in other organisms, including some species of wasps, flies and aphids (Riparbelli et al., 1998; Tram and Sullivan, 2000; Riparbelli and Callaini, 2003; Riparbelli et al., 2005; Ferree et al., 2006). In those cases, multiple functional centrosomes form spontaneously in the egg during meiosis, two of which assemble the first mitotic spindle and trigger normal development. In the case of D. mercatorum, the centrosomes that assemble de novo can also duplicate and they do so in a cell-cycle dependent manner (Riparbelli and Callaini, 2003). It would be relevant to determine if the burst in centrosome assembly coincides with an increase in global Plk4 concentration or activation in the egg of these species. Just like in our system, a highly variable number of MTOCs are assembled, suggesting the presence of a weak control mechanisms against de novo centriole formation in the germline, once the eggs enter meiosis. Further studies aimed at documenting centrosome birth dynamics and their maturation in these natural systems may find more about the principles that govern de novo centriole formation and their conservation throughout species evolution.
In oocytes from some parthenogenetic hymenoptera, maternal centrosomes form de novo close to cytoplasmic organelles highly enriched in Gamma-tubulin called accessory nuclei (Ferree et al., 2006). Moreover, centrosome ablation in vertebrate CHO cells is followed by accumulation of Gamma-tubulin and Pericentrin in nuclear-envelope invaginations, hours before bona-fide centrioles are detected (Khodjakov et al., 2002). Interestingly, if treated with nocodazole, acentriolar CHO cells are no longer capable of assembling centrioles de novo (Khodjakov et al., 2002). Therefore, our work besides substantiating previous studies, further suggests that the organisation of PCM-rich foci likely represent the first steps and are essential for de novo centriole assembly.
Despite a profound knowledge in the field concerning localisation and interaction of centrosomal molecules, and how these interactions change during the cell cycle, there is still a vast array of processes to uncover regarding the regulation of centriole assembly. For example, it remains important to investigate scaffold formation in vivo and how thresholds in activity of molecules affect formation, as these thresholds might also regulate canonical centriole duplication and perhaps other critical transitions in organelle assembly. It is yet unclear how PCM and MTs contribute to the early onset of centriole formation. Understanding how these – activity threshold and sensitivity to them, as well as PCM and MT-rich micro-environments – go awry may allow uncovering one putative mechanism by which centriole number deregulation arises in human diseases, since an increase in number of PCM-rich foci possibly promotes assembly of supernumerary centrioles.
Materials and Methods
Fly work and sample preparation
D. melanogaster stocks and husbandry
All D. melanogaster stocks used in this study are listed in Suppl. Table 1. Transgenic mNeonGreen-Plk4 flies were generated in-house by CRISPR/Cas9-mediated gene editing (Port et al., 2014). Twenty base-pairs guide RNAs (gRNA) targeting the N-terminal region of Plk4, with 5’ BbsI-compatible overhangs, were ordered as single-stranded oligonucleotides (Sigma-Aldrich). The complementary oligonucleotides were annealed, phosphorylated and cloned into BbsI-digested pCFD3-dU6:3gRNA expression plasmid (from Simon Bullock, MRC, Cambridge, UK). A plasmid DNA was designed for homologous recombination-mediated integration of mNeonGreen between the 5’UTR and the first coding exon of Plk4. 1-kbp long 5’ and 3’ homology arms were PCR-amplified from genomic DNA isolated from y1,M{nanos-Cas9.P}ZH-2A,w* flies (Suppl. Table 2) (BDSC# 54591). The mNeonGreen coding sequence was PCR amplified from plasmids (Suppl. Table 2). All fragments were sub-cloned into the pUC19 plasmid (Stratagene) using restriction enzymes: 5’ Homology Arm - NdeI and EcoRI; Fluorescent tag + linker - EcoRI and KpnI; 3’ Homology Arm KpnI and XbaI. Synonymous mutations were performed on the homology arms, removing the protospacer-adjacent motif (PAM) sequence from the donor plasmid to prevent re-targeting. The final donor template for homologous recombination-mediated integration was composed of a fluorescent reporter and a short flexible linker (see sequence in Suppl. Table 2), flanked by 1-kbp homology arms. Two circular plasmids – pCFD3-Plk4_gRNA and mNeonGreen template – were co-injected into nos-Cas9 embryos (BDSC# 54591 (Port et al., 2014)). Injected flies (F0) were crossed to a balancer strain and single-fly crosses were established from their offspring (F1). The resulting F2 generation was screened for positive integrations by PCR, using primers dmPLK4 5UTR 3 FW and dmPLK4 1exon Rev (Suppl. Table 3). Homozygous mNeonGreen-Plk4 and pUb–RFP–β2-Tubulin flies (gift from Yoshihiro Inoue, (Kitazawa et al., 2014)) were crossed, establishing a stable stock.
We also generated flies expressing short hairpin RNAs (shRNA) against gamma-tubulin 37C and 23C under the UASp promoter and crossed them with the V32-Gal4 (w*; P{maternal-αtubulin4-GAL::VP16}V2H, kindly provided by Daniel St Johnston), at 25°C, to knock-down both genes in the female germline. To generate gamma-tubulin 37C and 23C constructs, sense and antisense oligos for each target gene were annealed and cloned into pWALIUM22, using NheI and EcoRI restriction enzyme sites (Suppl. Table 6). Each construct was inserted into different landing sites on the third chromosome by PhiC31 integrase-mediated recombination (Suppl. Table 6). Germline-specific Plk4 overexpression was accomplished by crossing flies carrying the pUASp–Plk4 construct (Rodrigues-Martins 2007) and the V32-Gal4, at 25°C.
Centrosomes were visualised using the following centrosomal reporters: i) pUb-Spd2–GFP (homemade construct, injected at BestGene Inc.); ii) Ana1–tdTomato (gift from Tomer Avidor-Reiss, (Blachon et al., 2008); iii) pUASp–GFP–Plk4 (homemade construct, injected at BestGene Inc.); iv) Asl-mCherry (gift from Jordan Raff, (Conduit et al., 2015)), in combination with either endogenous Jupiter–GFP (BDSC# 6836) or endogenous Jupiter–mCherry (gift from Daniel St Johnston, (Lowe et al., 2014)), as reporters for centrosomal microtubule nucleation.
Flies were maintained at 25°C in vials supplemented with 20 mL of culture medium (8% molasses, 2.2% beet syrup, 8% cornmeal, 1.8% yeast, 1% soy flour, 0.8% agar, 0.8% propionic acid, and 0.08% nipagin).
Testing UASp-RNAi lines for developmental lethality
To test for lethality effects of γ-tubulin 37C and γ-tubulin 23C shRNAs alone and recombined, each line was crossed to V32-Gal4 flies. Female progeny carrying the Gal4 and shRNA was crossed to w1118 males (10 females x 5 males per vial, 4 independent crosses) and the number of pupae in each vial was counted 9-10 days after each transfer (3 technical repeats were performed). See results in Suppl. Table 7.
Embryo/Egg collections
For embryo collections, 3–4 days old female and male flies were transferred to a cage coupled to a small apple juice agar plate (25% apple juice, 2% sucrose, 1.95% agar and 0.1% nipagin), supplemented with fresh yeast paste. Embryos were collected for 1h and aged for half-an-hour. For unfertilised egg collections, around a hundred 5-7 days old virgin females were placed in the cage and 20 minutes collections were performed. All cages were maintained at 25°C, under 50–60% humidity. The embryos or eggs were dechorionated in 7% Sodium Hypochlorite solution (VWR), washed thoroughly in milliQ water, aligned and immobilised on clean, PLL-functionalised coverslips, using a thin layer of heptane glue. Samples were covered with Voltalef grade H10S oil (Arkema).
Preparation of micropipettes and functionalised coverslips
High Precision 22×22 glass coverslips No 1.5 (Marienfeld) were cleaned for 10 min in 3M Sodium Hydroxide, followed by 4 dip-and-drain washes in milliQ water. Next, they were sonicated for 15 min in “Piranha” solution (H2SO4 and H2O2 (30% concentrated) mixed at 3:2 ratio), followed by two washes in MilliQ water, once in 96% ethanol and twice again in milliQ water for 5 min each. Coverslips were spin-dried and subsequently treated for 20 minutes with Poly-L-Lysine (PLL) solution 0.01 % (Sigma-Aldrich), followed by multiple dip-drain-washes in MilliQ water. The coverslips were spin-dried and stored in a clean and dry rack.
Glass capillaries (0.75mm inner diameter, 1 mm outer diameter; Sutter Instrument) were forged into glass needles by pulling them on a vertical pipette puller (Narishige PC-10), using a one-step pulling protocol, at about 55% heating power. Using a sharp scalpel, the tip of the capillary was cut, generating micropipettes with 30-35 µm diameter pointed aperture (Telley et al., 2013).
Single egg extract preparation
Cytoplasmic extraction from individual unfertilised eggs and explant deposition onto the surface of PLL-coated coverslips was performed on a custom-made micromanipulation setup coupled to an inverted confocal microscope, as previously described in (Telley et al., 2013) and (de-Carvalho et al., 2018). The size of the explants was manually controlled in order to produce droplets measuring between 40 - 80 μm in diameter and approximately 10 μm in height, allowing fast time-lapse imaging of the entire explant volume.
Egg immunostaining and imaging
Unfertilised eggs overexpressing Plk4 and knocked down for γ-tubulin were collected from 5–7 days old virgin females for 2h at 25°C, and aged at 25°C for 4 hours. Protocol was conducted according to (Riparbelli and Callaini, 2005). Briefly, aged eggs were rinsed in MilliQ water + 0.1% Tween, dechorionated in 7% Sodium Hypochlorite solution (VWR) and washed extensively with MilliQ water. Using a metal grid, dechorionated eggs were transferred into a scintillation flask containing 50% ice-cold Methanol + 50% Heptane. The vitelline membrane was removed by vigorously shaking the eggs for 3 min. Devitellinised eggs sunk to the bottom of the lower Methanol phase and were then collected into a 1.5 ml eppendorf and fixed for 10 minutes in Methanol at −20°C. Following fixation, the eggs were rehydrated in Methanol:PBS series (70:30%, 50:50% and 30:70%) for 5 min each, washed twice in PBS for 10 min and incubated for 1 hour in D-PBSTB (1x Dulbecco’s PBS, with 0.1% Triton X-100 and 1% BSA), at RT. Primary antibody incubations were performed overnight at 4°C, with the following antibodies: rabbit anti-Bld10 (dilution 1:500; gift from Tim Megraw, The Florida State University, USA); rat anti-tubulin YL1/2 (dilution 1:50; Biorad) and guinea-pig anti-Ana1 (dilution 1:500; kindly provided by Jordan Raff), diluted in D-PBSTB. Eggs were washed extensively in D-PBSTB and incubated with secondary antibodies for 2h at RT - donkey anti-rabbit Alexa 555 (dilution 1:1000; Molecular Probes), goat anti-rat Alexa 488 (dilution 1:1000; Jackson Immunoresearch Laboratories) and donkey anti-guinea pig Alexa 647 (dilution 1:1000; Jackson Immunoresearch Laboratories) in D-PBSTB. Eggs were washed twice in PSB with 0.1% Triton X-100, twice in PBS and mounted onto coverslips in Vectashield mounting media (Vector Laboratories).
Imaging was conducted on a Nikon Eclipse Ti-E microscope equipped with a Yokogawa CSU-X1 Spinning Disk confocal scanner and a piezoelectric stage (Physik Instrumente) with 220 µm travel range. 0.3 µm optical sections were recorded with a EMCCD Photometrics 512 camera using a Plan Fluor 40x 1.30 NA oil immersion objective, controlled with Metamorph 7.5 software. 491 nm, 561 nm and 640 nm laser lines were used to excite the secondary antibodies. Egg counts were tested with a Chi-square test against the null-hypothesis that the outcome is random. Then, each test condition was compared to the control condition with a 2-proportions Z-test under H0 that the proportions of eggs with centrioles are equal versus HA that the proportion in the test is smaller. The significance level for multiple testing was Bonferroni corrected. Significance level was p=0.01.
Image acquisition, processing and analysis
Time-lapse explant imaging on the spinning disk confocal microscope
Centriole formation was followed by time-lapse imaging in droplets initially devoid of centrosomes. Explants were imaged at room temperature using a Plan Apo VC 60x 1.2 NA water objective. 0.45 µm thick optical sections were acquired with an EMCCD Andor iXon3 888 camera using a Yokogawa CSU-W1 Spinning Disk confocal scanner equipped with a piezoelectric stage (737.2SL, Physik Instrumente), installed on a Nikon Eclipse Ti-E microscope. Unless stated differently, dual-colour (488 nm and 561 nm excitation laser lines), 15 seconds time-lapses of the explant volume were recorded with Andor IQ3 software.
Image processing
Multi-stack, time-lapse calibrated images were deconvolved with Huygens (Scientific Volume Imaging, The Netherlands) using a Point Spread Function (PSF) automatically calculated from the data set and run in batch mode, for each channel separately. 32-bit deconvolved images were converted to 16-bit and processed using Fiji (NIH (Schindelin et al., 2012)). Selected stills from the time-lapse acquisitions were processed with Photoshop CS6 (Adobe). Graphic representations were performed using using GraphPad Prism software (Version 5.0) and the final figures were assembled in Illustrator CS6 (Adobe).
Centrosome tracking
Centrosomes were tracked using the Fiji Plug-in TrackMate v3.5.1 (Jaqaman et al., 2008). Centrosomes were identified by the Spd2–GFP localisation at the centre of mass of the microtubule aster. Relying on this criteria, we performed the TrackMate analysis sequentially, starting with the Jupiter-mCherry channel. First, we applied a 3D Gaussian Blur filter to the images (sigma = 0.7 pixels), facilitating the particle detection on TrackMate using the Laplacian of Gaussian algorithm. The microtubule asters were automatically detected inside spheres of approximately 0.7 µm in radius, adjusting the threshold value for each time-lapse video independently. Next, the first four de novo formed asters were manually tracked from the list of detected particles. A corrected XYZT coordinate matrix of the first de novo events was saved for each video and imported to MatLab R2016b (The MathWorks, Inc.). MatLab was used to build a 3D binary mask with spheres of radius r (where r ≥ microtubule aster size), centred at the detected coordinate points. This allowed bypassing incorrect particle detection caused by the large number of green auto-fluorescent yolk particles of intermediate signal intensity, therefore excluding them from the analysis early on. The resulting 3D masks were concatenated into 4D hyperstacks, using the Bio-Formats importer plugin in FIJI. The Spd2–GFP images were multiplied by the corresponding 4D binary masks, resulting in a 4D image retaining the pixel intensity values solely within the Jupiter-mCherry ROIs. Next, we used TrackMate to detect centrioles within spheres of 0.3 µm radius, combining sub-pixel localisation and a Median filter. After detection, the particles were manually tracked. The final centrosome tracks were exported as an Excel MS spreadsheet.
Statistics and mathematical modelling
Centrosome tracking data was imported in R version 3.4.1 for further analysis and modelling. The data was analysed in two ways: one aiming at identifying possible spatial constraints in the positioning of the centrioles relative to each other within the droplet at the time a centrosome is formed (neglecting time), while the other aimed at understanding temporal constraints (neglecting space). The data was analysed statistically, and simulations were performed in an effort to understand the underlying principles. The details regarding sample size, statistical tests and descriptive statistics are indicated in the respective figure legends and in the main text.
The experimental data was compared to simulated data by calculating the empirical cumulative distributions of each dataset (one experimental and 100 simulated – each consisting of 68 droplets) using the function ecdf from the stats package; and overlapping the median and 95% confidence interval (from the quantiles 0.025 to 0.975) of the simulated datasets’ cumulative distributions with the corresponding empirical distribution from the experimental dataset. Random numbers were generated using the function runif from the stats library.
For the spatial analysis, each time a new centriole appeared, the 3D pairwise distances between centrioles was calculated and labelled according to appearance relative to prior centrosomes in the droplet. This allowed keeping track of event order and, if any spatial effect of existing centrosomes on the appearance of a new centrosome was present, we would be able to detect a difference in their pairwise distances. To test this, the function kruskal.test of the stats library was used to perform the Kruskal-Wallis rank sum test on the pair-wise distances and labels. To complement this analysis, we decided to compare the distributions of pairwise distances with those expected by a spatially null model whereby centrosomes appear randomly across the available space in the droplet. To simulate this null model, sets of random points were simulated in sections of semi-spheres of similar geometry as each of the experimental droplets, characterised by height h and diameter d. To this effect, a height z was generated which satisfied – where q1 was a random number between 0 and 1 – by applying the optim function from the stats library with the “Brent” method, starting with z = 0. This ensured that the z coordinate was selected proportionally to the area of the circle it specifies. The two extremes, z = 0 and z = 1, correspond to the lowest and highest point of the droplet, respectively. Subsequently, the coordinates x and y were generated, within the respective circle at height z, by generating a random angle θ between 0 and 2π, and a random number q2 between 0 and 1, resulting in x = r cos(θ) and y = r sin(θ), where and . The pairwise distances between simulated points were calculated in the same way as for the experimental data, and the respective empirical cumulative distributions were computed and compared to the experimental empirical distribution, as described above.
For the temporal analysis, the waiting times between centrosome births were calculated from the data and labelled according to which centrosome had just formed. Accounting for a possible change of centrosome birth rate as a function of the number of existing centrosomes, centrosome birth rates were estimated from each of the observed distributions of waiting times by Maximum Likelihood using the fitdistr function from the MASS library. The experimental data was then compared with a temporal null model whereby centrosomes form at a constant rate in time, irrespective of the existence of other centrosomes and of the volume of the droplet. To this effect, random samples of Poisson distributed waiting times were generated using the rexp function of the stats library, using the rate estimated from the waiting times between the appearance of the first and second centrosomes. The empirical cumulative distributions of these waiting times were compared to those from experimental data, as described above.
The trans-autophosphorylation of Plk4 was modelled following Lopes et al., 2015. Briefly, it is assumed that Plk4 protein is produced with constant source rate s in basal activity form B. The phosphorylation of this B form in the T-loop results in a form A1 with higher catalytic activity. The phosphorylation of the A1 form the degron converts it to a A2 that is targeted for proteasome increasing its degradation rate but that keeps the same catalytic activity. The phosphorylation at the T-loop is catalysed by either low activity B form or the high activities A1 and A2 forms, while only the later are assumed to phosphorylate the degron of other Pkl4 forms. Both phophorylation reactions can be reverse by the constant activity of a phosphatase. We neglected the first order phosphorylation term in Lopes et al. (2015)
The dynamics of the three Plk4 forms is described by the following set of differential equations: with A = A1 + A2.
The rate of de novo centriole formation in the explant is assumed to be proportional Plk4 activity (aA + bB) and therefore the probability that a droplet has no centrioles F decreases in time according to:
The system of four differential equations was solved numerically using the function ode of the package deSolve in the software R.
The stochastic solutions for the same set of reactions were obtained by the Gillespie algorithm as implemented in the function ssa of the package GillespieSSA in R. Each simulation corresponded to a droplet where the Plk4 trans-autophosphorylation was simulated independently. The biosynthesis of the first centriole was simulated as a single reaction event that removes a single “precursor” F with a propensity f(aA + bB)F. The simulated explant is assumed to form one centriole upon this event.
The model in differential equation and stochastic versions was used to reproduce the temporal evolution of the number of explants containing at least under different concentrations of Plk4. Experimentally four activity levels of Plk4 were obtained by mixing the cytoplasm of eggs overexpressing Plk4 and wildtype, in different proportions with expected activities relative to the overexpressing egg of 1.0, 0.5, 0.33, and 0.12 (Fig. 5B and Suppl. figure 6). The corresponding levels of Plk4 activity were defined in the model through the source parameter s = K, K/2, K/3, K/6. The value of K and the remaining parameters were adjusted by solving the ordinary differential equations for variable F and visually comparing (1-F) with the experimental time course of the frequencies of explants with at least one centriole (Suppl. figure 6). The adjusted parameters were then used to simulate the stochastic kinetics. The parameter values of the solutions illustrated in Supplemental figure 6 were: K = 0.01Nmin−1, a = 1.0/Nmin−1, b = 0.01/Nmin−1, c = 1.0/Nmin−1, p = 0.45min−1, d0 = d1 = 0.01min−1, d2 = 0.38min−1, f = 0.34. The value of N was set to 2000 molecules for the Gillespie simulations and to the unit in the ordinary differential equations.
3D-Structured Illumination Microscopy
Cytoplasmic droplets were imaged with a Plan Apo 60x NA 1.42 oil objective on a GE HealthCare Deltavision OMX system, equipped with two PCO Edge 5.5 sCMOS cameras and 488 nm and 568 nm laserlines. Spherical aberrations were minimised by matching the refractive index of the immersion oil to that of the cytosol, providing the most symmetrical point spread function. 15 seconds, multi-stack time-lapses were acquired, with 0.125 μm Z-steps and 15 frames (three angles and five phases per angle) per Z-section. Images were reconstructed in Applied Precision’s softWorx software and processed using Fiji (NIH, (Schindelin et al., 2012)). Selected stills were assembled into final figures with Photoshop CS6 (Adobe).
Biochemistry
mNeonGreen purification
The mNeonGreen coding sequence was cloned with an N-terminus Streptavidin-Binding Peptide (SBP)-Tag and a flexible linker, into the pETMz expression vector (gift from the EMBL Protein Expression & Purification Facility, Heidelberg, Germany), between NcoI and BamHI restriction sites. The 6xHis-Z-tag-TEV-SBP-linker-mNeonGreen protein was expressed in BL21 (Rosetta) Competent E. coli at 25°C for 5 hours. The grown liquid culture was harvested and centrifuged at 4000 rpm for 25 minutes, at 4°C. The pellet was ressuspended in ice-cold lysis buffer containing 50 mM K-Hepes (pH 7.5), 250 mM KCl, 1mM MgCl2, 1 mM DTT, 7 mM of Imidazole, 1x DNaseI and 1x Protease inhibitors. The sample was applied to a pre-chilled French-press, equilibrated with Lysis buffer, and run twice at a constant pressure (around 12kPa). The cell lysate was collected in a flask on ice and ultracentrifuged at 4°C for 25 min at 50000 rpm using a Ti-70 rotor (Beckman). The protein purification was done through affinity chromatography on a Ni-column (HiTrap chelating HP column 1 ml, GE HealthCare). The column was loaded with a filtered solution of 100 mM nickel chloride, washed extensively with milliQ water and equilibrated with wash buffer (50 mM K-Hepes (pH 7.5), 250 mM KCl, 1mM MgCl2, 1 mM DTT, 7 mM of Imidazole). The clarified lysate was applied to the column (at 1.5 ml/min), followed by 200 ml wash buffer. The protein was eluted at 1.5 ml/min with elution buffer: 50 mM K-Hepes (pH 7.5), 250 mM KCl, 1mM MgCl2, 1 mM DTT, 400 mM of Imidazole. 1 ml sample fractions were collected and kept at 4°C. The most concentrated samples were pooled together and their N-terminus 6xHis-Z-tag was cleaved with TEV protease overnight at 4°C by treating with 150U TEV/mg of protein. The following day, the cleaved protein was passed through a column for size-exclusion chromatography to remove contaminants, the cleaved tag and the TEV protease (with Tiago Bandeiras at IBET, Oeiras, Portugal). Additionally, the elution buffer was exchanged to a storage buffer: 50 mM K-Hepes (pH 7.8), 100 mM KCl, 2 mM MgCl2, 1 mM DTT, 1 mM EGTA. The HiLoad Superdex 75 16/60 (GE HealthCare) gel filtration column was equilibrated with storage buffer for 1hour. The sample was spun at 15000 rpm for 15 min at 4°C and the clear fraction was applied to the gel filtration column coupled to an AKTA device at 1 ml/min. The cleaved mNeonGreen protein was concentrated approximately 5 times using Amicon 10K Centrifugal filters. Pure glycerol was added at 5% v/v and small aliquots were snap-frozen in liquid nitrogen and stored at −80°C.
Plk4 titration in cytoplasmic extract
Plk4 dilution was accomplished by mixing cytoplasm from flies with different genetic composition. Unfertilised eggs collected from females overexpressing Plk4 in the germline (genotype: V32-Gal4/ pUb-Spd2–GFP; Jupiter-mCherry/pUASp-GFP–Plk4) were homogenised in unfertilised eggs from females without the transgenic pUASp element (genotype: V32-Gal4/ pUb-Spd2–GFP; Jupiter–mCherry), where all components are at wild-type levels, specifically diluting overall Plk4 concentration in the cytoplasm. Different final Plk4 concentrations were achieved by mixing Plk4 overexpression:wildtype eggs at the following ratios: 6:0 (“1” relative Plk4 concentration, control); 3:3 (“0.5” relative Plk4 concentration); 2:4 (“0.33” relative Plk4 concentration) and 1:5 (“0.16” relative Plk4 concentration). Small droplets were produced from the cytoplasmic mixtures and images were acquired for 40 minutes. All time-lapse acquisitions within this section were performed at 1 minute time-interval with 0.45 µm optical sections, using a Plan Apo VC 60x 1.2 NA water objective.
Fluorescence Correlation Spectroscopy (FCS) data acquisition and analysis
Standard rhodamine 6G calibration
All FCS measurements were performed on a point-scanning confocal microscope (Zeiss LSM780 Confocor3) equipped with a UV-VIS-IR C Achromat 40X 1.2 NA water-immersion objective and a gallium arsenide detector array wavelength selected between 491-561nm. Before each experiment the system was aligned using a high concentration and calibrated using a low concentration Rhodamin 6G solution in water. The known diffusion coefficient of rhodamine 6G (410 µm2/s) (Majer and Zick, 2015) allowed us to determine the lateral beam waist (wxy = 232 nm) and the structure factor (S = 5.77) of the focused laser (Point Spread Function, PSF). The resultant volume of illumination is calculated through:
The values for wxy and S were used as constants in the subsequent model-based fittings of the autocorrelation functions (ACF) and the volume was used to calculate the concentration (see below).
Calibration with purified mNeonGreen
mNeonGreen fluorescent tag was first measured in a cytoplasm-compatible buffer. Fluorescence intensity in time (I(t)) was recorded as 6 iterations of 10s. Each 10s trace was autocorrelated into an ACF, G(τ), using the Zeiss onboard autocorrelator which calculates the self-similarity through:
Here <> denotes the time-average, dI(t)=I(t)-<I(t)> and τ is called the timelag. The resulting G(τ) curves of the fluorophores in buffer were readily fitted using a regular 3D diffusion model: where N reflects the number of moving particles in the confocal volume and GT(τ) is the correlation function associated to blinking/triplet kinetics:
Where T is the fraction of molecules in the dark state and τt the lifetime of the darkstate. GD(τ) is the correlation function associated to diffusion which in this case is simple Brownian diffusion in 3D:
These fittings allowed us to measure the number of molecules in the confocal volume and therefore their brightness (<I(t)> / N) together with the characteristic diffusion times (τD).
The above model fit is based on the assumption that there are only two characteristic timescales generating the ACF. In order to get a model free estimate of the number of timescales involved we used a Maximum Entropy Method based fitting (MEMfit) of the combined and normalised ACFs of each experiment. MEMfit analyses the FCS autocorrelation data in terms of a quasicontinuous distribution of diffusing components making it an ideal model to examine the ACF of a highly heterogeneous system without prior knowledge of the amount of diffusing species.
To be able to quantify the brightness of individual fluorescent tags in an embryo the purified mNeonGreen was injected into pUb-RFP-β2-Tubulin dechorionated embryos. An anomalous coefficient had to be included to fit the resultant ACF:
For simple Brownian diffusion a = 1 and the fit function is identical to the one used to fit the fluorophores in buffer. However, for fluorophores injected into the cytosol of embryos the fitting algorithm gave an anomalous coefficient of a = 0.8. An anomalous coefficient smaller than 1 indicates constrained diffusion and could be caused by the more crowded environment in the yolk. In addition, the large amount of (uncorrelated) autofluorescence generated by the yolk leads to an underestimation of the brightness therefore requiring a background correction factor. The background values were determined per excitation power from embryos lacking the Plk4 reporter. If the background itself does not autocorrelate it has no influence on the obtained timescales in the data. Nevertheless, the background will impact the absolute number, N, and consequently also the calculated brightness. Therefor, all the measurements were background corrected during via:
Where BG is the measured background from embryos lacking the reporter fluorophore. Consequently the corrected brightness was calculated as:
Finally, any 1 millisecond-binned intensity trace that contained changes in average intensity (most likely arising from yolk spheres moving through the confocal spot during the measurement) were discarded from further analysis.
mNeonGreen-Plk4 measurements in embryos
For the measurements of mNeonGreen-Plk4, embryo staging was done based on the pUb-RFP-β2-Tubulin reporter. We chose embryos at blastoderm stage, in division cycles 10 or 11. Before each FCS acquisition series, a large field-of-view image of the embryo was acquired. Six different, 10 seconds long intensity traces were measured at the inter-nuclear cytoplasmic space of the syncytium. The 10s measurement was long enough to obtain sufficient passage events and short enough to avoid each trace to be contaminated by events that do not arise from mNeonGreen-Plk4 diffusing in the cytosol.
From these measurements, the MEMfit method on the normalised ACF indicates three timescales for the tagged-Plk4 molecules. A first timescale of 5-50 µs corresponding to the triplet state dynamics that were similarly found in both the buffer as well as from fluorophores injected in the embryo. A second timescale of about 0.8ms, most likely coming from the diffusion of a Plk4 monomer (see similarity to mNeonGreen monomer in cytosol). And a third timescale of diffusion that is much slower, 9ms. In order to fit the ACFs the diffusional part of the fit function was associated with two components:
The fraction f corresponds to the fast diffusing Plk4. The Diffusion Coefficient of each of the components can be calculated from the diffusion timescales τD via:
In vitro experiments
Drosophila melanogaster cell culture
Drosophila (DMEL) cells were cultured in Express5 SFM (GIBCO, USA) supplemented with 1x L-Glutamine-Penicillin-Streptomycin. Double-stranded RNA (dsRNA) synthesis was performed as previously described (Bettencourt-Dias et al., 2004). 2 million cells were plated and treated for 12 days with 40 μg dsRNA against Plk4 or mCherry (control), replacing the dsRNA every 4 days. Cells were fixed at day 10 to confirm centriole depletion and treatment with dsRNA agains PCM was initiated. Cells were then treated for 6 days with different amounts and combinations of dsRNA: 80 μg mCherry alone, 20 μg of individual PCM components – Cnn, Asl, D-Plp, Spd2 or γ-tubulin 23C – or combinations of two – Cnn + Spd2 or Cnn + D-Plp – or four components – Cnn + Asl + D-Plp + Spd2 (referred to as ‘All PCM’). Primers used for dsRNA synthesis are listed in Suppl. Table S5.
Immunostaning and imaging of D. melanogaster cultured cells
DMEL cells were plated onto clean glass coverslips and allowed to adhere for 1 hour and 30 min. The media was removed and cells were fixed at −20°C for 10 min in chilled methanol. Cells were permeabilised and washed in D-PBSTB (1x Dulbecco’s Phosphate Buffered Saline pH 7.3, with 0.1% Triton X-100 and 1% BSA) for 1 hour. Cells were incubated overnight at 4°C with primary antibodies – rat anti-Sas4 (dilution 1:500) kindly provided by David Glover (University of Cambridge, UK) and rabbit anti-CP110 (dilution 1:10000; Metabion) – diluted in D-PBSTB. Cells were washed in D-PBSTB and incubated for 1hour 30 min at room temperature with secondary antibodies – donkey anti-rat Alexa 555 (dilution 1:1000; Molecular Probes) and donkey anti-rabbit Alexa 647 (dilution 1:1000; Jackson Immunoresearch Laboratories) – and DAPI (dilution 1:200) in D-PBSTB. Cells were washed and mounted with Dako Faramount Aqueous Mounting Medium (S3025, Agilent).
Cell imaging was conducted on a Nikon Eclipse Ti-E microscope equipped with a Yokogawa CSU-X1 Spinning Disk confocal scanner. Images were recorded with a EMCCD Photometrics 512 camera. Optical sections of 0.3 µm thickness were acquired with a Plan Apo 100x 1.49 NA oil immersion objective using a piezoelectric stage (737.2SL, Physik Instrumente), controlled by Metamorph 7.5 software. Centriole number was scored in 300 cells per treatment, per independent experiment. Data is presented as average (with standard error mean, S.E.M.) of two independent experiments. We tested all counts with a Chi-square test against the null-hypothesis that the outcome is random. Then, each 16d test condition was compared to the 16d mCherry control condition with a 2-proportions Z-test and H0 that the proportions of cells with centrioles are equal versus HA that the proportion in the test is smaller. The significance level for multiple testing was Bonferroni corrected. Significance level was p = 0.01. All images were processed with ImageJ (NIH, USA) and Adobe Photoshop CS6 (Adobe Systems, USA), and the final figures were assembled in Adobe Illustrator CS6 (Adobe Systems, USA).
Author contributions
Conceptualization: CN, IAT, MBD
Methodology: CN, JdC, IAT (egg explant assay); CN, TvZ (FCS measurements)
Software: DP, JC (design and implementation of model simulations)
Validation: CN, JdC, DP, TvZ, SM, JC, IAT, MBD
Investigation: CN, JdC (performing data collection egg explants); CN, TvZ (performing data collection in FCS measurements); DP, JC (collecting in silico data)
Analysis: CN, IAT (experimental data from egg explants, eggs and cell culture); TvZ, SM (FCS data); DP, JC (theoretical model)
Resources: CN (CRISPR fly line, vectors and plasmid design, recombinant protein purification); PD (genotyping); IAT (design of micromanipulation microscope)
Visualization of data: CN, DP, TvZ, IAT
Writing – original draft: CN
Writing – review and editing: CN, SM, JC, IAT, MBD
Supervision and coordination: IAT, MBD
Competing interests
The authors declare no competing interests for this study.
SUPPLEMENTARY FIGURES
SUPPLEMENTARY TABLES
SUPPLEMENTARY TIME-LAPSE MOVIES
Movie 1 (support to Figure 1): Centriole biogenesis in a Drosophila melanogaster egg explant. Time-lapse movie of a droplet of cytosolic extract isolated from an unfertilised Drosophila egg overexpressing Plk4, acquired on a spinning-disk confocal microscope. The movie is a Z-projection. Centrioles are absent in the first time point and form de novo throughout the experiment detected as spots (Spd2, in green) associated with microtubule asters (magenta), reported by the microtubule associated protein Jupiter. Time (min:sec) is shown at the top left.
Movies 2A–D (support to Figure 2): Centrioles assemble de novo, recruit different centrosomal molecules and duplicate. Time-lapse movies of droplets of cytosolic extract from non-cycling unfertilised Drosophila eggs overexpressing Plk4, acquired on a spinning-disk confocal microscope. Videos are Z-projections showing centriole biogenesis reported by different centrosomal proteins in green – Plk4 (A), Ana1 (B), Asl (C) and Spd2 (D) – and the microtubule-associated protein Jupiter (magenta). The larger green blobs result from yolk autofluorescence, highly noticeable in the Plk4 movie. Time (min:sec) is shown at the top left of each 4 movie.
Movie 3 (support to Figure 6): mNeonGreen-Plk4 localisation in a syncytial Drosophila embryo. Time-lapse movie of an embryo expressing homozygous mNeonGreen-Plk4 (endogenously labeled by CRISPR, in green) and RFP-Tubulin (magenta), acquired on a spinning-disk confocal microscope, through nuclear cycles 10-13. The movie is a bleach-corrected intensity projection. Time (min:sec) is shown at the top left.
Acknowledgments
We would like to thank the Central Imaging and Flow Cytometry Facility (CIFF) at the National Centre for Biological Sciences (NCBS) in Bangalore, where all FCS experiments were performed.
We acknowledge the technical support of IGC’s Advanced Imaging Facility (AIF), in particular Gabriel Martins and Nuno Pimpão Martins. IGC’s AIF is supported by the national Portuguese funding ref# PPBI-POCI-01-0145-FEDER-022122, co-financed by Lisboa Regional Operational Programme (Lisboa 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER) and Fundação para a Ciência e a Tecnologia (FCT, Portugal).
We thank IGC’s Fly Facility and Fly Transgenesis Facility supported by Congento (LISBOA-01-0145-FEDER-022170), co-financed by Lisboa Regional Operational Program (Lisboa 2020) under the Portugal 2020 Partnership Agreement through the European Regional Development Fund (FEDER) and Fundação para a Ciência e a Tecnologia (FCT, Portugal). Transgenic fly stocks were obtained from Bloomington Drosophila Stock Center (NIH742 P40OD018537).
We acknowledge financial support from Boehringer Ingelheim Fonds PhD Fellowship awarded to C. Nabais, Human Frontiers Science Program (HFSP) Young Investigator Grant (RGY0083/2016) awarded to I.A. Telley supporting J. de–Carvalho, the Fundação para Ciência e a Tecnologia (FCT) supporting I.A. Telley (Investigador FCT IF/00082/2013), the EU FP7-PEOPLE-2013-CIG (N° 818743) awarded to I.A. Telley, ERC-2010-StG-261344-CentriolStructure&Number and an ERC-2015-CoG-683258-Birth&Death awarded to M. Bettencourt-Dias, and the Gulbenkian Foundation (FCG). T.S. van Zanten acknowledges an EMBO fellowship (ALTF 1519-2013) and a NCBS Campus fellowship. S. Mayor acknowledges a JC Bose Fellowship from DST (Government of India), support from the NCBS-Max Planck Lipid Centre, a grant from HFSP RGP0027/2012, and support from Wellcome Trust/DBT India Alliance Margdarshi Fellowship (IA/M/15/1/502018).
We thank Tiago Bandeiras at Instituto de Biologia Experimental e Tecnológica (IBET), Oeiras, for the gel filtration chromatography conducted in his facility with Micael Freitas.
We thank Tomer Avidor-Reiss, Daniel St Johnston, Yoshihiro Inoue and Jordan Raff for sharing transgenic fly lines. We thank David Glover, Jordan Raff and Tim Megraw for providing antibodies.
We thank members of the Cell Cycle Regulation lab at IGC for giving feedback to earlier versions of the manuscript.
Footnotes
↵§ co-lead authors