ABSTRACT The phenomenal diversity of neuronal types in the central nervous system is achieved in part by the asymmetric division of neural precursors. In zebrafish neural precursors,

asymmetric dispatch of Sara endosomes (with its Notch signaling cargo) functions as fate determinant which mediates asymmetric division. Here, we found two distinct pools of neural

precursors based on Sara endosome inheritance and spindle-microtubule enrichment. Symmetric or asymmetric levels of spindle-microtubules drive differently Sara endosomes inheritance and

predict neural precursor lineage. We uncover that CAMSAP2a/CAMSAP3a and KIF16Ba govern microtubule asymmetry and endosome motility, unveiling the heterogeneity of neural precursors. Using a

asymmetric) are possible. SIMILAR CONTENT BEING VIEWED BY OTHERS ELONGATOR STABILIZES MICROTUBULES TO CONTROL CENTRAL SPINDLE ASYMMETRY AND POLARIZED TRAFFICKING OF CELL FATE DETERMINANTS

Article 27 October 2022 A CASZ1–NURD COMPLEX REGULATES TEMPORAL IDENTITY TRANSITIONS IN NEURAL PROGENITORS Article Open access 16 February 2021 SPINDLE POSITIONING AND ITS IMPACT ON

VERTEBRATE TISSUE ARCHITECTURE AND CELL FATE Article 22 June 2021 INTRODUCTION Asymmetric cell division (ACD) generates daughter cells with distinct cell fates, giving raise to cell

diversity during the development of tissues. During ACD, specific molecules, so-called cell fate determinants, are unequally distributed between daughter cells. We have previously shown that

Sara endosomes in dividing neural precursor (NP) cells of the zebrafish spinal cord could be asymmetrically segregated to one of the daughter cells during anaphase1. When NPs produce

asymmetric lineages (so called _n•p_ lineages), a daughter divides (_p_ fate) and the sibling differentiates into a neuron (_n_ fate). Asymmetric _n•p_ lineages are generated by asymmetric

cell division of the NP, which targets Sara endosomes and their Notch signaling cargo to one of the daughters, the one which will acquire the _p_ fate. In flies, asymmetric segregation of

Sara endosomes during sensory organ precursor (SOP) mitosis, relies on two key features of the system: (i) the asymmetric density of microtubules (MTs) composing the central spindle, and

(ii) the directed motility of Sara endosomes towards the plus end of MTs2. Importantly, this asymmetric Sara endosome targeting forecasts the fate of the SOP lineage. In vertebrates,

however, the mechanism leading to the asymmetric segregation of Sara endosomes remains unknown. Furthermore, unlike flies, our previous study of Sara endosome asymmetric segregation in NP

could not forecast whether its subsequent lineage is symmetric or asymmetric1. Prompted by these issues, here we investigate (i) whether the composition of MTs at the central spindle

underlies asymmetric motility of endosomes in zebrafish NP mitosis, (ii) whether the dynamic of MTs is predictive of the different types of NP lineages and (iii) what are the physical and

cell biological basis of the mechanism mediating Sara segregation. RESULTS CENTRAL SPINDLE MICROTUBULES DEFINE TWO DISTINCT TYPES OF NEURAL PRECURSORS To understand the mechanism behind

asymmetric targeting of Sara endosomes, we first studied the MTs of the central spindle during anaphase. We focused on somite 6 to 8 in embryos around 24 hours post-fertilization (hpf) (27 ±

 1 somite stage). We monitored the density of central spindle MTs by following a tagged version of human Double Cortin (DCX)3 75 ± 30 s after the onset of cytokinesis, which we define as the

moment of appearance of the cleavage furrow (see methods) (Fig. 1a). In particular, we studied whether there is an asymmetry of MT density between the two sides of the central spindle which

project into the two poles of the NP that will give rise to the two daughter cells. We discovered that the distribution of MT asymmetry 75 s after the onset of cytokinesis is bimodal in the

spinal cord (Fig. 1a, b). To validate the existence of two types of NPs according to microtubule density asymmetry, we performed clustering analysis. To determine whether there is more than

one pool in the population of NPs, we used the Akaike Information Criterion (AIC), a test comparing the precision of models having different numbers of clusters. We studied this with two

different clustering algorithms: Gaussian mixture model (GMM) using probability of distribution4 and K-mean model using centroid distances5 (colored dots in the x-axis of Fig. 1b; see also

methods and Fig. S1a–d). We used those two independent clustering methodologies to reinforce our analysis. Both algorithms yield the same number of clusters and cluster composition (Fig. 

S1a–d). Then, we used the Dunn-index6 (DI, from zero to one), a metric where high values indicate reliable clustering, to evaluate the goodness of clustering of each population. Figure 1b

(bottom) shows that NPs are clustered into two distinct populations (high Dunn-index of DI = 86.0%): one cluster with low levels of asymmetry (so-called “symmetric”) and one with higher

level of asymmetry. The asymmetric cluster represents 41% of the NPs and shows an average enrichment by 30 ± 5% in MT density in one of the poles. The two clusters are separated by a

threshold of 20% enrichment, which we use hereafter as a criterium to separate the two NP pools. We defined pole A as the pole with lower spindle-MT density and pole B, that with higher

spindle-MT density. In the asymmetric pool of NPs, enrichment raises during anaphase, peaks 75 s after cytokinesis onset and decays after that (Fig. 1c). SARA ENDOSOMES ARE ENRICHED

ASYMMETRICALLY IN THE NP POOL WITH ASYMMETRIC CENTRAL SPINDLE We have previously shown that also Sara endosomes can be dispatched asymmetrically during NP mitosis1. We then wondered whether

Sara asymmetry also defines two clusters of NPs and, if so, whether this correlates with the two pools of NPs according to their MT asymmetry. Figure 1d shows that, 75 s after cytokinesis

onset, Sara endosomes ratio between the two daughters also show a bimodal distribution in the spinal cord, with two clusters separated by a threshold of 1.5-fold enrichment (DI = 80.7%). In

the pool of asymmetric NPs, Sara endosomes are enriched by 1.8 ± 0.2 fold in one of the daughters. We then evaluated MT and Sara endosome enrichments simultaneously in the same NPs. There is

a tight correlation between the pool of NPs with MT asymmetry and Sara endosome asymmetry: if and only if an NP shows MT asymmetry above 20%, Sara is also enriched above 1.5-fold in the

daughter with lower MT density (pole A; Fig. 1e, f). Cluster analysis combining these two traits (MTs and Sara), also uncovers two pools which are reliably separated (DI = 88.7%; Fig. 1f).

The two pools correlate with the asymmetry of cortical Par3, a component of the Par complex which controls cell polarity and has previously been shown to be involved in asymmetric divisions

of zebrafish NPs7. Indeed, Par3 enrichment in one daughter also defines two reliable pools (DI = 80.9%), which themselves correlate perfectly with MT asymmetry (Fig. S1e, f). However, as

previously shown1, Par3 is not essential for Sara endosomes asymmetry (Fig. S1g). Consistently, we observed that both the symmetric and asymmetric pool of NPs, considering the distribution

of MT densities, are present in Par3 morphants (DI = 92.0%; Fig. S1h). THE ASYMMETRIC NP POOL GIVES RISE TO ASYMMETRIC LINEAGES What are the fates of these two types of NPs? It is well

established that, in the spinal cord, NPs divide a maximum of two times to produce three types of lineages: _n•p_, _n•n_ and _p•p_ which produce 3, 2 and 4 neurons, respectively1,7,8,9. In

_n•p_ lineages, the mother NP divides asymmetrically to produce a daughter that differentiates readily into a neuron (_n_ daughter) and a daughter which is a progenitor cell (_p_ daughter)

that divides again to produce two neuron daughters itself. In _n•n_ and _p•p_ lineages, the mother cell divides symmetrically to produce either two neurons (_n•n_) or two progenitor cells

that will give a lineage with four neurons (_p•p_). Lineage tracing was carried out by single-cell injection of mRNA encoding the photoconvertible protein pSMOrange10 in 32-cell-stage

transgenic embryos expressing GFP-DCX. We first imaged MTs at 27 ± 1 somite stage in the 6–8 somite region by following GFP-DCX signal in a mother NP during anaphase and then photoconverted

pSMOrange later in one of the daughters (Fig. 2a, b). Two days later, we determined the lineage from each daughter (Fig. 2c). _A posteriori_, to avoid bias, we analyzed the spindle-MT

enrichment of the mother NP to determine whether the mother division was symmetric or asymmetric and, if asymmetric, whether the photoconverted daughter was the one enriched in MTs. Figure 

2d shows that most _n•p_ lineages (13 out of 15 lineages, _p_ = 4.88E-04, binomial test) are generated by asymmetric mother NP divisions. Conversely, symmetric lineages (_n•n_ or _p•p_) are

significantly biased to be generated by symmetric NPs (12 out of 17, _p_ = 2.24E-02, binomial test; see also Fig. S1i). Furthermore, when the NP division is asymmetric, the daughter with a

lower density of MTs (pole A), is almost always acquiring the precursor fate and divides again (92.3%; 12 out of 13 NPs, _p_ = 1.22E-04, binomial test). Our observations indicate that there

are two distinct, non-overlapping populations of NPs in the spinal cord (symmetric _vs_ asymmetric according to MTs or Sara segregation) which give rise to different symmetric or asymmetric

lineages; in the asymmetric lineage, biased inheritance of Sara endosomes in a daughter forecasts her fate as a progenitor daughter cell, which undergoes another round of mitosis before

their two daughters differentiate into a neuron. ANALYSIS OF ENDOSOMAL MOTILITY ON SPINDLE-MICROTUBULES Because the symmetric/asymmetric motility of Sara endosomes forecasts the type of

lineages and the fate of daughter cells upon NP division, we then wondered what controls the motility of this organelle. We therefore first analyzed the dynamics of endosomal targeting in

these two NP populations by following mCherry-Sara and GFP-DCX (Fig. 3a). We automatically tracked Sara endosomes in dividing NPs using Trackmate11 together with a custom Matlab code (for

details, see Fig. S2a, b and methods). Sara endosome tracks were registered in time with respect to the onset of cytokinesis (_t_ = 0 s). Onset of cytokinesis was detected by a

characteristic tilting of the two poles of the dividing cell which happens at the same time as the formation of the cytokinetic cleavage furrow (Fig. S2b–f). Spatial registration of the

tracks was based on the position of the centrosomes and the midpoint between those, which corresponds to _x_ = 0 µm (Fig. S2a, b). Figure 3b, c show spatiotemporal density plots considering

Poisson statistics (see methods and Fig. S3a) for the tracks of NPs with symmetric and asymmetric Sara endosomes, as well as a randomized data set as a control (Fig. S3b–f). We first studied

the targeting of endosomes towards the central spindle region, defined as a 2 µm wide region around the midpoint (dashed boxes in Fig. 3b, c; Fig. S2a). This corresponds to the antiparallel

array of MTs of the central spindle, as monitored by localization of mCherry-MKLP112 (Fig. S3g). We analyzed central spindle targeting by ANOVA statistics (Fig. 3d–f and Fig. S3h, i), where

a _P_-value below 0.05 (red dots) in a particular time interval indicates that the density of endosomes has a statistically significant heterogeneity, i.e., it is increased or decreased in

a particular region compared to the rest of the cell. Endosomes are indeed recruited to the central spindle between −200 s and −100 s (Fig. 3a, d, g). After that, endosomes depart from the

central spindle (Fig. 3a, d, g). Similar ANOVA analysis comparing the enrichment between the two poles shows that, in asymmetric NPs, endosomes are enriched in pole A (with less MTs) from

_t_ = 0 s to _t_ = 150 s (Fig. 3c, f). In contrast, no asymmetry between the two poles can be detected in symmetric NPs after departure (Fig. 3b, e). Like in the case of MTs, in the NP pool

with asymmetric Sara endosomes, asymmetry raises during anaphase, peaks 75 s after cytokinesis onset and decays after that (Fig. 3h). It is worth noting that, because of this decay, if the

ratio of Sara endosomes is evaluated after 75 s, the two clusters merge (Fig. S4a, b), consistent with our own previous report1. This also explains that if evaluated later, the asymmetry of

Sara endosomes could not forecast whether the subsequent lineage was symmetric or asymmetric1. We have previously shown2, based on a theoretical model of plus-end directed endosomal motility

on an antiparallel, asymmetric MT overlap (like in the central spindle), that the steady-state endosome distribution is captured by the expression:

$$\frac{{{{{{{\rm{P}}}}}}}^{{{{{{\rm{A}}}}}}}}{{{{{{{\rm{P}}}}}}}^{{{{{{\rm{B}}}}}}}}=\frac{1+\Delta }{1-\Delta }\exp \left(\frac{2{{{{{{\rm{k}}}}}}}_{{on}}{{{{{\rm{\rho

}}}}}}{{{{{\rm{vl}}}}}}\Delta }{{{{{{\rm{D}}}}}}{{{{{{\rm{k}}}}}}}_{{off}}}\right)$$ (1) with \({P}^{{{\mbox{A}}}}\), \({P}^{{{\mbox{B}}}}\), the probabilities for an endosome to be in

either side of the antiparallel overlap; \(\rho =({\rho }_{A}+{\rho }_{B})/2\) and \(\varDelta =({\rho }_{A}-{\rho }_{B})/({\rho }_{A}+{\rho }_{B})\) with ρa, ρb, MT densities in pole A and

pole B, respectively; \({{{\mbox{k}}}}_{{{\mbox{on}}}}\),\({{{\mbox{k}}}}_{{{\mbox{off}}}}\), MT association/dissociation constants of the motor; \(v\), the endosome motor-driven velocity;

\({{\mbox{D}}}\), the diffusion coefficient of endosomes detached from MTs and \({{\mbox{l}}}\), the antiparallel overlap length. Based on Eq. 1, Fig. 3i shows how the fraction of endosomes

in pole A depends on the enrichment of MTs in pole B. To evaluate the expected endosomal asymmetry, we previously measured the values of all the key dynamic parameters in this system (see

methods, Figs. S3g; S4c–f). Thus, we studied the mean square displacement (MSD) of endosome tracks to measure the diffusion coefficient (D). The directed velocity of the endosomes (\(v\)),

the rates of unbinding (\({k}_{{off}}\)) and binding (\({k}_{{on}}\rho\)) of endosomes to MTs were measured from the duration of the episode in which endosomal movement is directed or

diffusive, respectively, and the length of the antiparallel array of MTs (l) by using mCherry-mKLP1 labeling. We estimated _D_ = (8.15 ± 0.46)10−3 μm2 s−1 (Fig. S4c, d), _v_ = (1.36 ± 

1.07)10−1 µm _s_−1, _l_ = (1.3 ± 0.1)µm (Fig. S3g), \({k}_{{off}}=(0.97\pm 0.33){s}^{-1}\) (Fig. S4e) and \({k}_{{on}}\rho =(0.057\pm 0.004){s}^{-1}\) (Fig. S4f). Plugging these experimental

parameters in Eq. 1, a 30% MT enrichment in pole B causes a 1.8-fold enrichment of Sara endosomes in pole A (Fig. 3i), which coincides precisely with the values of bias observed

experimentally in an independent experiment (_cf_. Fig. 1b, d). This suggests that plus-end endosomal motility on an antiparallel, asymmetric MT overlap can explain in quantitative terms the

asymmetry of endosomes observed in NPs of the spinal cord of zebrafish. Comparing the dynamics of Sara endosomes targeting in fly SOPs and zebrafish NPs, the time spent at central spindle

(residence time) and the timing of departure differ significantly. Indeed, in NPs, the residence time is 2.5-fold shorter than in SOPs (~500 s in SOP vs. ~205 s in NP; Fig. 3b–d, g).

Consequently, in NPs, the departure of Sara endosomes from the central spindle is almost completed when the cytokinetic cleavage furrow first appears (Fig. 3), while, in SOPs, Sara endosomes

departure and segregation is completed significantly later2. In addition, considering the physical parameters of Sara endosomes motility in SOPs or NPs plugged in Eq. 1, we found that for a

30% spindle-MT enrichment the asymmetry of Sara endosomes is ~2.4-fold higher in SOP than in NP. This difference of the level of endosomal asymmetry is due to the diffusion coefficients:

\(D=\left(2.1\pm 0.1\right){10}^{-3}\mu {m}^{2}{s}^{-1}\) in SOPs2 which is ~4-fold smaller than \(D=\left(8.15\pm 0.46\right){10}^{-3}\mu {m}^{2}{s}^{-1}\) in NPs. This lower levels of Sara

endosomes asymmetry in NP compared to SOPs could arise from the requirement of different NPs in the spinal cord to generate both symmetric and asymmetric progenies. This control requires

therefore more flexibility than in fly SOPs where endosomal segregation must be asymmetric for all the cells. Those differences for diffusion, residence times at the central spindle and

symmetry of the spindle shed light on species-specific adaptations in molecular mechanisms and temporal dynamics. KIF16BA IS THE MOTOR OF THE SARA ENDOSOMES What motor drives the plus end

motility of endosomes? In flies, Sara endosomes motility is mediated by the Klp98A kinesin2. In zebrafish, its homolog KIF16Ba, colocalizes with Sara endosomes (Fig. 4a). In _KIF16Ba_

morphants, when considering MT asymmetry, two populations of NPs were observed: the frequency of asymmetric NPs is similar to that in wildtype animals (44.4%; with DI = 77.8% for the

confidence of clustering; Fig. 4b). In contrast, no asymmetric Sara endosomes targeting was found in the population of NPs with asymmetric MTs (Fig. 4c–e). Injection of KIF16Ba mRNA in

_KIF16Ba_ morphant embryos rescued the asymmetric inheritance of Sara endosomes in asymmetric NPs (Fig. S5a). This is consistent with the idea that KIF16Ba is essential for the motility of

Sara endosomes. Indeed, MSD analysis shows that the movement of Sara endosomes in _KIF16Ba_ morphant NPs is merely diffusive (confined diffusion; see Methods), without a directed component

(Fig. 4f and Fig. S5b, c). As a consequence, Sara endosomes fail to be targeted first to the central spindle (Fig. 4g, h) and later, to be dispatched asymmetrically (Fig. 4b–e and Fig. S5d,

e). Indeed, comparison of the recruitment phase between WT and _KIF16Ba_ morphant uncovers a statistically significant difference of Sara endosomes percentage at central spindle (Fig. 4g;

two-sample Kolmogorov–Smirnov test, _p_ < 0.001). It is worth noting that, while endosomal enrichment in the central spindle is impaired in _KIF16Ba_ morphants, endosomes which are

located at the central spindle by chance, still depart from there like in wildtype (Fig. 4g, h and Fig. S5d, e). This suggests that the mechanism of departure involves phenomena other than

those implicating KIF16Ba. MACHINERY BEHIND THE GENERATION OF ASYMMETRIC CENTRAL SPINDLE We then studied the machinery responsible to achieve an asymmetric central spindle in asymmetric NPs.

It has previously been shown in flies that asymmetry of MTs in the central spindle is mediated by stabilization of the minus end of MTs by Patronin2, the fly ortholog of

Calmodulin-Regulated Spectrin-Associated Proteins (CAMSAPs) in vertebrates13. Some CAMSAPs are MT associated proteins which bind minus ends14. In zebrafish, based on sequence homology, we

found six CAMSAP proteins (Fig. S6a). Of these, CAMSAP2a and CAMSAP3a are found associated to central spindle MTs (Fig. 5a, c). In asymmetric NPs, CAMSAP2a is also enriched asymmetrically,

and it is symmetric in symmetric NPs (Fig. 5a, b). CAMSAP3a is polarized, appears mainly apically and associates with MTs (Fig. 5c and Fig. S6b, c). During mitosis, CAMSAP3a is distributed

asymmetrically in asymmetric NPs, similar to CAMSAP2a (Fig. 5d). Single morphants for _CAMSAP2a_ or _CAMSAP3a_ did not show a phenotype in the number of NPs which show asymmetric microtubule

density (Fig. S6d). Single CRISPR mutants for _CAMSAP2a_ or _CAMSAP3a_ (Fig. S6e, f) did not show asymmetric phenotype either (Fig. 5e). However, double _CAMSAP2a__-/-_; _CAMSAP3a__-/-_

mutants show a dramatic depletion of the pool of NPs with asymmetric MT density in the central spindle (Fig. 5e, see also Fig. S6g). A _CAMPSAP2a__-/-_ mutant which is morphant in addition

for _CAMSAP3a_ (_CAMSAP2a__-/-__; CAMSAP3a__MO_) shows the same MT phenotype as the double morphant or mutant and could be rescued by injection of mRNA for mCherry-CAMSAP2a or

mScarlet-CAMSAP3a (Fig. 5e). Double mutants and morphants also show somitogenesis and segmentation defects (Fig. S6h–k) as observed in other studies where Notch/Delta signaling was

impaired15,16,17. In _CAMSAP2a__-/-__; CAMSAP3a__MO_, Sara endosomes are targeted to the central spindle like in control cells (Fig. 5f, Fig. S7a–c), but are never targeted asymmetrically

(Fig. 5g–i) consistent with the depletion of the asymmetric pool of NPs (Fig. 5e). Taken together, these data uncover a scenario where CAMSAP2a and CAMSAP3a are enriched asymmetrically in

pole B in asymmetric NP cells, where they could stabilize spindle-MTs and locally enrich their density, consistent with previous reports2,13,18. This mediates asymmetric motility and

targeting of Sara endosomes to pole A. As a consequence of the lack of NPs with spindle-MTs and Sara endosomes asymmetries, the frequency of _n•p_ lineages is drastically reduced in

_CAMSAP2a__-/-__; CAMSAP3a__MO_ animals (Fig. 5j). This confirms in addition that the asymmetric NP neuroblasts generate _n•p_ lineages (Fig. 2d). DISCUSSION The generation of a complex

nervous system, such as that of vertebrates, is mediated by a plethora of mechanisms, of which ACD plays a prominent role. We found that in the spinal cord of zebrafish, diversity is

generated by the existence of distinct pools of NPs with different fates, giving rise to symmetric or asymmetric lineages. Those NP pools are characterized by the symmetry or asymmetry of

their central spindle during mitosis, which drive asymmetric targeting of Sara endosomes. Supporting this concept our key observations are: (i) NPs in the spinal cord can be clustered into

two distinct pools where central spindle is asymmetric or not (Fig. 1a, b), (ii) central spindle asymmetry forecasts whether a lineage will be symmetric or asymmetric (Fig. 2d), (iii) the

asymmetric dispatch of Sara endosomes is fully forecasted by the situation in the central spindle (Fig. 1e, f), (iv) while the asymmetry of central spindle and endosomes is forecasted by the

symmetry/asymmetry of Par3 (Fig. S1e, f), Par3 is dispensable to generate those asymmetries1 (Fig. S1g, h), (v) the motility of endosomes in the central spindle is directed by KIF16Ba (Fig.

 4g, h) and (vi) the asymmetry of the central spindle is mediated by asymmetry of CAMSAP proteins (Fig. 5a–e), (vii) asymmetric targeting of endosomes in an asymmetric central spindle

depends on the binding and processivity of the kinesin, the diffusion coefficient of endosomes and the length of the region containing antiparallel MTs in the central spindle (Fig. 3i). We

therefore established the existence of different pools of NPs with different fates, the origin of those pools and the physical and molecular mechanism of asymmetric dispatch of a signaling

organelle, the Sara endosomes. While we found that the existence of a distinct NP pool with central spindle asymmetry depends on the asymmetry of CAMSAP proteins, it is however unclear how

CAMSAP distribution is controlled. An interesting candidate is Katanin: a negative regulator of MT-minus-end stabilization; it forms a complex with CAMSAPs and counteracts the formation of

CAMSAP-decorated MT lattices19,20. Consistently, depletion of Katanin in zebrafish impairs the asymmetric cell division of NPs21. Therefore, it could be interesting to analyze if Katanin

depletion plays a role in the asymmetry of spindle-MT enrichment and Sara endosomes segregation. The apico-basal orientation of the NP division and its link to NP fate has previously been

studied in a number of reports22. Horizontal or oblique divisions induce different segregation of apically polarized proteins such as Par37,23. It is possible that other factors in the Par

asymmetry complexes are asymmetrically segregated during oblique divisions playing a role in cell fate determination24. We also uncover that, while Sara endosomes residence time at central

spindle is different between SOPs and NPs, their departure from central spindle takes a similar time (~200 s of departure event in both organisms). In SOPs, we found that departure is

mediated by Notch and its binding to Uninflatable and by phosphorylation of Sara itself 25,26. Since the shift between recruitment and departure dynamics happens earlier in NPs than SOPs, it

indicates that the temporal activation of the mechanisms controlling Sara endosomes motility is different between zebrafish and flies. In addition, we found that the plus end directed motor

KIF16Ba is not necessary for the final departure of endosomes from the central spindle (Fig. 4g). This is not too surprising, because the final departure must implicate the movement of

endosomes towards the minus end of MTs at the outer side of the central spindle. This points to Dyneins as candidates for this step, as recently proposed23. We however did not find a key

role for Dyneins on this step of departure (Fig. S8a–c). Indeed, _Dlic1_ morpholino injection did not prevent departure of Sara endosomes from the cleavage furrow during NP division.

Therefore, our results uncover that the physics of the asymmetric endosomal targeting mechanism, based on asymmetric MT cytoskeleton, is similar to those found in insects. This indicates

that this mechanism had been conserved since their last common ancestor, the _Urbilaterian_ more than 500 million years ago27 and emphasizes the fundamental importance of Sara endosomes

inheritance in cellular fate determination. The characterization of this mechanism allows to understand the neurogenesis and development of zebrafish central nervous system with potential

translation in mammals and human biology. METHODS ZEBRAFISH STRAINS AND MAINTENANCE Zebrafish strains husbandry was maintained as described in ref. 28 and in accordance of the Swiss

Veterinary Service law. Embryos were grown at 28 °C and their stage monitored by counting somites number. All experiments were performed on AB zebrafish background. Zebrafish strains were

produced by us or ordered from European Zebrafish Resource Center (Supplementary Table 1). DATABASE RESEARCH AND SEQUENCES To find Patronin homologs in Zebrafish, BLAST (NCBI) of Patronin

protein sequence (NCBI: ALT55646) was used against the Zebrafish proteome (taxon: 7955). Significant alignments were found for the different CAMSAP proteins: CAMSAP1a (NCBI: NP_001159727),

CAMSAP1b (NCBI: NP_001093471), CAMSAP2a (NBCI: NP_00103846), CAMSAP2b (NCBI: XP_009297074), CAMSAP3a (NCBI: XP_021330421), CAMSAP3b (NCBI: XP_003197845). Same methodology was used to find

Klp98A (NCBI: Q9VB25) homolog: KIF16Ba (NCBI: XP_009292601). Each protein sequences were run into SMART to predict CAMSAP domains (Fig. S6a). CDNA, PLASMIDS AND PRIMERS To obtain open

reading frames of _CAMSAP2a_, _CAMSAP3a_, _Pard3ab_, _Sara_, and _KIF16Ba_, a cDNA library was generated (SuperScript IV First-Strand Synthesis System, ThermoFisher) from 5dpf zebrafish

embryos (Trizol reagent, Invitrogen). AscI and FseI restriction enzymes were used to insert coding sequences in “pCS2 + Fluorophore” plasmids having a sp6 promoter site to produce mRNA.

Other plasmids were ordered on AddGene (https://www.addgene.org/) and modified to clone the sequences of interest into pCS2 backbone (Supplementary Tables 2 and 3). MRNA AND MORPHOLINO

INJECTIONS mRNA was in vitro transcribed using Sp6 polymerase (mMachine Invitrogen kit, ref. AM1340). A PV-820 Pico-injector (World Precision Instruments) and a Narashige micromanipulator

were used for microinjection. Every injection was performed in one-cell-stage embryos, except for mScarlet-CAMSAP3a, Par3-mCherry and pSMOrange mRNA which were injected at 32-cell-stage to

obtain a mosaic expression. ATG morpholinos targeting RNA starting sequences were designed and ordered from GenTools (Supplementary Table 4). Control MO were designed with 5 mismatches from

the original sequence. Morpholino injections were done in one cell stage embryos. Injected quantities were adjusted to have the highest possible concentration without severe toxicity (range

from 0.5 to 2 ng per injection). Rescue experiments for the spindle-MT asymmetry decrease observed in _CAMSAP2a__-/-__; CAMSAP3a__MO_ 24hfp Zebrafish (Fig. 5e) were performed by injection of

mix containing either _CAMSAP3__a_ MO (1.5 ng) + mCherry-CAMSAP2a mRNA (1 ng) + GFP-DCX mRNA (1 ng) or _CAMSAP3a_ MO (1.5 ng) + mScarlet-CAMSAP3a mRNA (1 ng) + GFP-DCX mRNA (1 ng) in

one-cell-stage _CAMSAP2a__-/-_ mutant embryos. Rescue experiment for Sara endosome asymmetric inheritance in _KIF16Ba_ MO embryos (Fig. S5a) was performed by injection of a mix containing

_KIF16Ba_ MO (0.8 ng) + KIF16Ba mRNA (1 ng) + mCherry-Sara mRNA (1 ng) in one-cell-stage _GFP-DCX_ transgenic embryos. Then 24hpf injected zebrafish were screened for positive GFP and

mCherry/mScarlet dual expressions and mounted for microscopy. GENERATION OF _CAMSAP_ CRISPR KNOCK OUT sgRNA targeting _CAMSAP2a_ coding sequence was selected with the help of CRISPR Scan29

(https://www.crisprscan.org/) to prevent off targeting. sgRNA targeting _CAMSAP3a_ was designed and produced at Merck (Merck sgRNA service) following the same rules of selection as

_CAMSAP2a_ sgRNA (Supplementary Table 5). Afterward, one-cell-stage AB zebrafish embryos were injected with 1 nl calibrated drop containing: 1.5 µl of sgRNA (0.5 µg/µl), 1.5 µl of Cas9

protein (5 µg/µl, ThermoFisher, ref. A50576) and 2 µl of nuclease free water. Mutant zebrafish were identified by genotyping (Sanger sequencing, https://www.fasteris.com/en-us/) to select

deletion mutations leading to a frameshift of the coding sequence and premature stop codon (Fig. S6e, f). Identified mutants were crossed through multiple generations until reaching

heterozygosity, homozygosity, or combinations of mutations. Combination of _CAMSAP2a__-/-_ and _CAMSAP3a__-/-_ mutations led to fish death. Therefore, double mutants were kept heterozygous

and embryos resulting from their cross were sequenced at 24hpf with a ZEG device30 and sorted according to genotype. GENERATION OF _GFP-SARA_ CRISPR KNOCK IN The technology used to obtain a

knock-in in _Sara_ gene was inspired by D. Grunwald work31. pKHR5 plasmid31 was modified to insert a GFP in front of _Sara_ exon 2. A FLP removable mVenus sequence expressed under the alpha

crystallin promoter _CryA_, allowing to sort the well injected embryos by looking at mVenus expression in the retina of embryos from 48 hpf on was also inserted. The following mix was

injected (1 nl drop) in one-cell-stage embryos: 2.5 µl of linearized donor plasmid (100 ng/µl), 1 µl of sgRNA (1 µg/µl), 0.6 µl of Cas9 (5 µg/µl, ThermoFisher, ref. A50576), 0.5 µl of phenol

red and 0.4 µl of nuclease free water. 48hpf injected embryos having green retina were analyzed by PCR, grown as F0 and in-crossed in groups (Supplementary Table 6). Then, resulting F1

embryos having a green retina were amplified inside and outside of the donor plasmid and crossed with WT embryos. When F3 homozygote zebrafish were obtained, a western blot analysis was

performed on 5dpf embryos to show the presence of GFP-Sara using a mouse anti GFP antibody (Roche, ref. 11814460001) (Fig. S6l). Input loaded volume was 10 µL (10/1,000 µl) and IP loaded

volume was 10 µL (10/35 µl). Gel was exposed during 5 min. STEREOMICROSCOPE Embryos were first dechorionated in fish water medium with 0.003% of Tricaine (Sigma, ref. A5040) to anaesthetize

them. Then, they were imaged with a Leica Stereomicroscope M80 equipped with a Leica IC80 HD camera. Somites numbers of individual embryo were manually counted to assess embryonic

developmental stage. EMBRYO MOUNTING Embryos were first dechorionated in fish water medium with 0.003% of Tricaine to anaesthetize them. Then, they were mounted in 1% low-melting point

agarose (Sigma, ref. A9414) with the spinal cord close to the coverslip. SPINNING DISK CONFOCAL MICROSCOPY Embryos were imaged on a 3i Marianas spinning disk confocal setup based on a Zeiss

Z1 stand with a x63 PLAN APO NA 1.4 oil immersion objective. Intensity of the laser was adjusted to avoid bleaching. Division of neural precursor cells are acquired in the 6–8 somite area of

zebrafish spinal cord on 10–13 µm depth with z-stacks of ΔZ = 0.8 µm and Δt = 15 s until the end of the cytokinesis. IMAGE ANALYSIS 5D hyper stack images (3D + Time + Channel) were exported

in Tiff files from SlideBook 6.0 software. Then, images were treated with ImageJ and Matlab software. Custom written codes (available upon request) were used to acquire and process the

data. SARA ENDOSOMES RATIO QUANTIFICATION Sara endosomes ratios were quantified using injection of mRNA coding for mCherry-Sara. Dividing NPs in the 6–8 somite area of Zebrafish spinal cord

were imaged (Fig. 1e). Z stacks containing all the visible dividing cell (Δz = 0.8 µm, depth = 8 µm) at _t_ = 75 ± 30 s (see time registration) were projected using sum intensity projection.

Two areas on both daughter cells and a third area in the cytosolic background were drawn and saved as regions of interest (ROI). Then a custom ImageJ code uses the third ROI to subtract

background intensity and calculates Sara endosomes ratio in pole A as follow:

$${Ratio}\,{of}\,{Sara}\,{endosomes}\,{in}\,{pole}\,A=\frac{{Total}\,{Intensity}\,{ROI}\,{pole}\,A}{{Total}\,{Intensity}\,{ROI}\,{pole}\,B}$$ SPINDLE MICROTUBULE ENRICHMENT QUANTIFICATION

Spindle-MT densities were quantified using GFP-DCX as marker of MTs. Except for analysis of _CAMSAP_ mutant fish where GFP-DCX mRNA was injected, the transgenic _GFP-DCX_ zebrafish strain

was used. The same acquisition parameters and methodology as Sara ratio quantification were used. Spindle-MT enrichment in pole B was calculated as follow: $$

{spindle}\,{MT}\,{enrichment}\,{in}\,{pole}\,B\\ = \frac{{Total}\,{Intensity}\,{ROI}\,{pole}\,B-{Total}\,{Intensity}\,{ROI}\,{pole}\,A}{{Total}\,{Intensity}\,{ROI}\,{pole}\,A}{{{{{\rm{\times

}}}}}} \times 100$$ The normalized enrichment of spindle-MT density in pole B: Δ, was calculated with the following equation: $$\Delta

=\frac{{Total}\,{Intensity}\,{ROI}\,{pole}\,B-{Total}\,{Intensity}\,{ROI}\,{pole}\,A}{{Total}\,{Intensity}\,{ROI}\,{pole}\,B+{Total}\,{Intensity}\,{ROI}\,{pole}\,A}$$ To measure spindle-MT

enrichment in _CAMSAP_ mutants, _CAMSAP2a_+/−; _CAMSAP3a__+/−_ zebrafish were incrossed to produce variety of mutant embryos. One-cell-stage embryos were injected with GFP-DCX mRNA and grown

at 28 °C for one day. Then, 24hpf embryos with unknown genotype were mounted for microscopy to image and quantify spindle-MT enrichment. Later, embryos were unmounted and sequenced to

assign measured spindle-MT enrichments with the different genotypes indicated in Figs. 5e and S6g. CAMSAP2A AND CAMSAP3A RATIOS QUANTIFICATIONS CAMSAP2a and CAMSAP3a ratios were quantified

using mCherry-CAMSAP2a and mScarlet-CAMSAP3a mRNA overexpression in one-cell-stage embryos and 32-cell-stage embryos respectively (Fig. 5a, c). The same acquisition parameters as Sara ratio

quantification and methodology were used. The ratio of CAMSAP2a/CAMSAP3a was calculated as follow:

$${CAMSAP}\,{ratio}\,{in}\,{pole}\,B=\frac{{Total}\,{Intensity}\,{ROI}\,{pole}\,B}{{Total}\,{Intensity}\,{ROI}\,{pole}\,A}$$ PAR3 RATIO QUANTIFICATION Par3 ratios were quantified using

overexpression of Par3-mCherry mRNA in 32-cell-stage embryos (Fig. S1e). Note that only the cortical expression of Par3 was quantified, therefore its cytoplasmic expression was not

considered to calculate the ratio. The same acquisition parameters as Sara ratio quantification and methodology were used. Par3 ratio in cell B was calculated as follow:

$${Par}3\,{ratio}\,{in}\,{cell}\,B=\frac{{Total}\,{Intensity}\,{ROI}\,{cell}\,B}{{Total}\,{Intensity}\,{ROI}\,{cell}\,A}$$ CLUSTERING ANALYSIS To cluster 1-dimension and 2-dimensions

datasets, we used a custom Matlab algorithm based on the Akaike Information Criterion (AIC), which compares Gaussian Mixture Models (GMM) of different cluster numbers fitted by natural

logarithm of the likelihood function. GMM models were compared for clustering dataset in 1 to 4 clusters. The model with the lowest AIC score corresponds to the most likely number of

clusters in the population. Using this number of clusters, we then used two methods of clustering: K-mean clustering, which separates clusters according to their mean and GMM, which uses

fitted gaussians to cluster the data. Finally, to measure the goodness of the clusters, we calculated their Dunn Index (DI), an algorithm to evaluate clustering based on mean and variance. A

high DI indicates a reliable clustering. TRACKING, SPATIAL REGISTRATION AND TIME REGISTRATION The movements of mCherry-Sara positive endosomes on spindle-MTs (labeled by GFP-DCX) were

tracked with two different acquisition parameters referred as “normal tracking” and “fast tracking”. * i. Normal tracking: Δz = 0.8 µm, depth = 3.2 to 6.4 µm and Δt = 15 s for endosome and

MT channels. * ii. Fast-tracking: Δz = 0.5 µm, depth = 3 µm and Δt ~ 0.75 s (1.35hz) for endosome channel and, Δz = 0.5 µm, depth = 3 µm and Δt = 15 s for MT channel. In order to have a

centrosome-to-centrosome horizontal axis, the dividing NPs were rotated on ImageJ before treatment. Trackmate plugin was used in ImageJ to track Sara endosome movements11. Trackmate

Laplacian of Gaussian (LoG) filter was applied on Sara endosome channel to detect 1 µm diameter objects. Quality filter, based on local maxima value and closeness to the specified diameter

was manually applied on each time point to discard low quality objects. Then, Trackmate Simple Lap Tracker was used to establish Sara endosome tracks. “Linking max distance” and “Gap closing

max distance” parameters were set to 2.7 µm for “normal tracking” and 2 µm for “fast tracking”. Those values are based on Sara endosomes size (~1 µm) and velocity in fly2

(\(v({fly})=0.173\pm 0.007{{{{{\rm{\mu }}}}}}m.{s}^{-1}\)) which is linked to the maximum traveling distance for an endosome per time frame. “Gap closing max frame gap” value was set to 4

frames. No Trackmate filter was applied on tracks. Sara endosome data generated by Trackmate are _x_ coordinate, _y_ coordinate, time frame, mean intensity, diameter and signal to noise

ratio (SNR) computed as \({SNR}=\frac{{Iin}-{Iout}}{{stdin}}\), where \({Iin}\) is the mean intensity inside the spot volume, \({Iout}\) is the mean intensity in a ring ranging from its

radius to twice its radius and \({stdin}\) is the standard deviation computed within the spot. In addition, the GFP-DCX channel was used to manually record the _x_ and _y_ coordinates of

centrosome in pole B (α), centrosome in pole A (ƴ) and cell center (β) for each time point (Fig. S2a, b). It is worth disclaiming that it was difficult to manually assign a precise position

for β before appearance of the cleavage furrow. Therefore, a correction was applied on the manually recorded β coordinate to have \(\Vert \overrightarrow{\alpha \beta }\Vert =\Vert

\overrightarrow{\beta \gamma }\Vert\) during metaphase and anaphase until clear appearance of the cleavage furrow defined as cell center. The coordinates of α, β and ƴ were used for the

spatial registration of Sara endosome coordinates in the “normal tracking dataset” and “fast tracking dataset”. To do the spatial registration, Sara endosome coordinates (E1), were

considered as the orthogonal projection of the location of an endosome into the line connecting either centrosome α or ƴ with the spindle center β set as origin. The orthogonal length

between the endosome location and projection was set as new _y_ coordinate and the length between the endosome projection and β was set as new _x_ coordinate (Fig. S2b). According to the

sign of the new _x_ coordinate, Sara endosomes were attributed to pole B (_x_ < 0) or pole A (_x_ > 0). Sara endosomes located at the undefined overlapping region around β were not

considered after appearance of the cleavage furrow. In addition, Trackmate generated data were filtered to not consider diameter values below 0.1 diameter quantile, SNR values below 0.1 SNR

quantile and \(\Vert \overrightarrow{E1{{{{{\rm{\beta }}}}}}}\Vert\) > 6 µm. It is worth noting that this spatial registration considers Sara endosome coordinates in 2D (from a maximum

projection of the movie having the same ΔZ above and below cell center). However, the coordinates of centrosome in pole B (α), centrosome in pole A (ƴ) and cell center (β) are not perfectly

co-planar (their _z_ coordinates are not exactly the same) and this might introduce some imprecisions in the determination of their distances in 2D. We estimated that the maximum angle

between the three coordinates was 32° for a 6.4 µm depth movie, introducing a _x_ coordinate error of 0.93 µm at the edge of the cell and 0.19 µm at the central spindle. Since in the density

analysis (see below) bins are of 0.5 µm, these errors are negligible. Time registration was only applied to the “normal tracking dataset”. We noticed that, in metaphase, α, β and ƴ are

aligned very precisely (the angle \(\widehat{\alpha \beta \gamma }\) is close to 180°), while, coinciding with the formation of the cytokinetic furrow, α, β and ƴ depart from this alignment

(Fig. S2c–f). We defined _t_ = 0 s as the time corresponding to the frame preceding the dealignment event (angle \(\widehat{\alpha \beta \gamma }\) decrease >10°, manually verified)

induced by the formation of the cytokinetic cleavage furrow. As a consequence of registering time this way, the \(\widehat{\alpha \beta \gamma }\) (_t_) traces collapse to an angle close to

180° before _t_ = 0 s and, after that, the traces showing the dynamics of \(\widehat{\alpha \beta \gamma }\) collapse into a single curve, showing that the timing and dynamics of dealignment

with respect to the formation of the cleavage furrow is robust. Then Sara endosomes data were treated following Derivery and al. methodology2 to calculate the physical parameters used in

Eq. 1 (see below). VELOCITY \(V\) ANALYSIS The fast-tracking dataset (see above) of Sara endosomes movements was used to measure endosomal velocity along _x_-axis. A custom Matlab code was

made to select segments within Sara endosome _x_ motility tracks where endosomal movement is for at least 5 consecutive time points in the same direction. Then, each segments were plotted

for _x_ position _versus_ time and fitted with a linear regression to obtain velocity. Mean velocity of each segments was computed and gave \(v=\left(1.36\pm 1.07\right){10}^{-1}{{{{{\rm{\mu

m}}}}}}\,{s}^{-1}\) (39 segments, _mean R_2 _fit_ _=_ 0.92 ± 0.049, 95% confidence). KOFF AND KONΡ ANALYSIS To measure the off rate (\({k}_{{off}}\)) and on rate \(({k}_{{on}}\rho )\) of

Sara endosomes from MTs at central spindle, the fast-tracking dataset was used. First, the dataset was treated with a custom Matlab code to keep tracks within an area of 3 µm by 3 µm

centered around β. An additional manual verification of the tracks was performed to discard bad quality tracks. Then “transport-segments” were identified among the selected tracks according

to the following criteria: * i. instantaneous speed of Sara endosome must be higher than 0.15 µm.s−1. Since the calculated velocity and diffusion are _v_ = (1.36 ± 1.07)10−1 µm s−1 and _D_ =

 (8.15 ± 0.46)10−3 μm2 s−1, this threshold decreases the probability to have a diffusion event in the “transport-segment”. * ii. the duration of a “transport-segment” must be at least three

frames long. * iii. Sara endosome motility must be in the same direction for all the frames in the “transport-segment”. After identification of “transport-segments”, the number of

“transport-segments” as a function of their duration was plotted. Decay time of the exponential fit indicated\(\,{k}_{{off}}=(0.97\pm 0.33){s}^{-1}\) (_R_2 = 0.99, 95% confidence; Fig. S4e).

Segments in between the “transport-segment” were called “diffusion-segments”. The number of “diffusion-segments” was binned (15 s bins) and represented as a function of their duration.

Decay time of the exponential fit indicated \({k}_{{on}}\rho\) = (0.057 ± 0.004)s−1 (_R_2 = 0.99, 95% confidence; Fig. S4f). MEAN SQUARE DISPLACEMENT ANALYSIS To determine the Mean Square

Displacement (MSD) of Sara endosome tracks, the normal-tracking dataset was used. Tracks were manually and automatically refined to keep tracks: * i. at least eight timeframes long * ii.

with a maximum gap time of 45 s, * iii. and within an area of 10 µm by 9 µm centered around β. Afterward, MSD analyzer package32 was used with a custom Matlab code to generate

\(MSD(t)=\langle (\varDelta {x}^{2})\rangle +\langle (\varDelta {y}^{2})\rangle\) of each individual track (Fig. S4c; S5b). Mean weighted \({MSD}\left(t\right)\) was computed using the

number of tracks and the number of averaged points as weight. Then, the mean _MSD(t)_ was fitted with the linear model: _MSD(t)_ = 4_Dt_ or the quadratic model: \(MSD(t)=4Dt+{v}^{2}{t}^{2}\)

(Fig. S4d and Fig. S5b, c). In control, the quadratic fit captures best the motion of endosomes (R2 = 0.99 for quadratic fit and R2 = 0.97 for linear fit, 95% confidence). In _KIF16Ba_

morphant, the quadratic fit gives a complex value for _t_ (as _t_ 2 < 0) indicating confined diffusion. This was confirmed by an anomalous fit of _KIF16Ba_ morphant MSD:

\({MSD}(t)=4D{t}^{{{{{{\rm{\alpha }}}}}}}\) where α < 1 is found. Therefore, KIF16Ba is essential for the directed motility of Sara endosomes beyond diffusion and only the linear fit of

_KIF16Ba_ morphant MSD was considered. Diffusion values were extracted from the corresponding fitting models. The mean of diffusion values found in control NPs (quadratic model) and

_KIF16Ba_ morphant NPs (linear model) was used to determine the Diffusion parameter \(D=\left(8.40\pm 0.42\right){10}^{-3}\mu {m}^{2}{s}^{-1}\) used in Eq. 1. ANTIPARALLEL CENTRAL SPINDLE

LENGTH ANALYSIS To quantify the length of the spindle-MT overlapping region, we used mCherry-MKLP1 overexpression in _GFP-DCX_ transgenic Zebrafish embryos (Fig. S3g). Dividing NPs were

imaged with the following parameters: Δz = 0.8 µm, depth = 8 µm and Δt = 15 s. mCherry-MLKP1 was only detectable after the appearance of the cleavage furrow and is located at the central

spindle region, shrinking according to the contraction of the spindle-MTs. mCherry-MKLP1 length, \(l=(1.3\pm 0.1){\mu m}\) (_n_ = 3 NPs from 3 independent Zebrafish) was manually measured on

ImageJ software using the line scan of a maximal z-projection at registered _t_ = 30 ± 15 s. QUANTIFICATION OF SARA ENDOSOMES PERCENTAGE AT CENTRAL SPINDLE To quantify Sara endosomes

percentage at central spindle (Figs. 3g, 4g, 5f), the normal tracking dataset was used and refined with a custom Matlab code to keep tracks: * i. at least five timeframes long, * ii. with a

maximum gap time of 30 s * iii. within an area of 10 µm by 9 µm centered around β Sara endosomes were considered at central spindle if their absolute _x_ normalized coordinate was inferior

to 1 µm. Percentage of Sara endosomes at central spindle was calculated with the following equation: $$ {Sara}\,{endosomes}\,{percentage}\,{at}\,{central}\,{spindle}\\ \quad

=\frac{{Number}\,{of}\,{Sara}\,{endosomes}\,{at}\,{central}\,{spindle}}{{Total}\,{number}\,{of}\,{Sara}\,{endosomes}}\times 100$$ Rob Campbell, shadedErrorBar function was used on matlab to

generate shades of the standard error. If Sara endosomes are homogeneously located in a dividing NP, their percentage at central spindle should be 20% because the 2 µm large region

considered as the central spindle region represents 20% of the 10 µm large total region (Fig. S2a). To statistically evaluate the difference of Sara endosome percentage at central spindle,

the data were divided into the homogenous (t from −285 s to −225 s), recruitment (t from −210 s to −105 s) and departure phases (t from −90 s to 150 s). Then a two-sample Kolmogorov–Smirnov

test was used to compare phases between control and mutant/morphant NPs. AUTOMATIC QUANTIFICATION OF SARA ENDOSOMES RATIO A custom Matlab code was used to automatically quantify Sara

endosomes ratio according to mCherry-Sara total intensity in the normal tracking dataset. Data were sorted to keep tracks within an area of 10 µm by 9 µm centered around β. Afterward, Sara

endosomes ratios were calculated for each time frame with the following equation:

$${Sara}\,{endosomes}\,{ratio}\,{in}\,{pole}\,A=\frac{{Total}\,{intensity}\,{of}\,{Sara}\,{endosomes}\,{in}\,{pole}\,A}{{Total}\,{intensity}\,{of}\,{Sara}\,{endosomes}\,{in}\,{pole}\,B}$$

Ratio above 1.5 or below 0.67 (1/1.5) were considered as an asymmetric inheritance of Sara endosomes in pole A or pole B, respectively. HEATMAP OF SARA ENDOSOMES DENSITY To generate the

spatiotemporal density plots (heatmaps, Fig. S3c, e, f; S5d; S7a; S8a), the normal tracking dataset was used and refined with a custom Matlab code to retain tracks (i) at least four

timeframes long, and (ii) within an area of 10 µm by 9 µm centered around β. Then, the number of Sara endosomes were counted in bins of Δ_x_ = 0.5 µm and Δt = 15 s (20 bins per time point)

and referred as \(N(x,t)\). Negative _x_ values correspond to pole B having more spindle-MTs and positive _x_ values correspond to pole A having less spindle-MTs. Each \(N(x,t)\) values were

normalized to the total number of Sara endosomes in the dataset and displayed as heatmap with a Red Hot colormap lookup table (LUT). To generate the randomized heatmap, we first calculated

\(\lambda (t)\) in the asymmetric dataset as: $$\lambda (t)=\frac{\mathop{\sum }\nolimits_{x}^{20}{{{{{\rm{N}}}}}}(x,{{{{{\rm{t}}}}}})}{20}$$ Then, for each time point, we generated 20

values from a Poisson distribution around \(\lambda (t)\) corresponding to randomized Sara endosome bin numbers. Each one of the 20 generated values was randomly assigned to a bin for each

time point and colored with the Red Hot colormap LUT to display the randomized heatmap of Sara endosomes density. This randomized heatmap was used to compare the distributions of Sara

endosomes densities coming from experimental data (Fig. S3c, d). POISSON STATISTICS AND DISTRIBUTION For each dataset, the expected Poisson statistics around λ(t) were calculated for each

\(N(x,t)\) values according to the following equation: $${P}_{{bin}(x,t)}=\frac{{\lambda (t)}^{{{{{{\rm{N}}}}}}(x,t)}\times {e}^{\lambda (t)}}{N(x,t)!}$$ Then, \({P}_{{bin}(x,t)}\) values

were normalized by dividing them with the maximum \({P}_{{bin}(x,t)}\) value of the time point. A custom LUT was applied to visualize the normalized \({P}_{{bin}(x,t)}\) values, with red

colors corresponding to a high number of endosomes, white colors to a number of endosomes close to λ(t) and blue colors to a low number of endosomes (Fig. S3a). Gradient of the custom LUT

was adjusted for each time point to go from λ-3 \(\sqrt{{{{{{\rm{\lambda }}}}}}}\) (blue) to λ + 3 \(\sqrt{{{{{{\rm{\lambda }}}}}}}\) (red) with \(\sqrt{{{{{{\rm{\lambda }}}}}}}\)

corresponding to the standard deviation of λ. ANOVA ANALYSIS FOR DENSITY COMPARISON To compare the effect of cell side location or cell center location on Sara endosomes mean densities, a

statistical ANOVA analysis was performed. For each time point, \(N\left(x,t\right)\) values were segmented according to their location: pole B (_x_ from −5 μm to 0 μm), pole A (_x_ from 0 μm

to 5 μm), cell sides (_x_ from −5 μm to −1 μm and 1 μm to 5 μm) and cell center (_x_ from −1 μm to 1 μm). Then segments were compared by ANOVA analysis to obtain a _p_-value rejecting (_p_ 

< 0.05) or confirming (_p_ > 0.05) the hypothesis that the means of the segments is the same. To further analyze the dynamic of ANOVA _p_-values in a dataset, we randomly shuffled the

twenty \(N\left(x,t\right)\) values of each time bin of a dataset and performed the same statistical ANOVA analysis as described above. We repeated this methodology 10,000 times and

established the frequency of having a similar dynamic of the _p_-values compared to the _p_-value dynamics observed in the different datasets (Fig. 3e, f). For comparison of the ANOVA

_p_-value dynamic in pole B _versus_ pole A mean Sara endosomes densities of the control asymmetric shuffled dataset, we found that 1.4% of the 10 000 randomly shuffled datasets had: no

_p_-value < 0.05 before _t_ = 0 s and four _p_-values < 0.05 after _t_ = 0 s. This low percentage indicates that the pattern observed in the _p_ value dynamic of the asymmetric dataset

is not coming from a random distribution. For comparison of ANOVA _p_-value dynamic in pole B _versus_ pole A mean Sara endosomes densities of the control symmetric dataset and randomized

dataset (from symmetric dataset), we found that 75% and 84% of the respective randomly shuffled datasets had at least one time point with a _p_-value < 0.05. This similarity of the 75%

and 84% frequencies indicates that the _p_-value dynamic of the control symmetric dataset is comparable to the _p_-value dynamic of the randomized dataset and the appearance of a single,

sporadic statistically significant time point is expected to happen by chance. PHOTOCONVERTIBLE PROBE AND LINEAGE mRNA coding for pSMOrange10 was injected in 32-cell-stage _GFP-DCX_

transgenic embryos (Fig. 2a, b). Embryos were grown in the dark until reaching 27 ± 1 somite stage. pSMOrange is expressed in the cytosol and its mosaic expression allows to precisely target

isolated dividing NP. A Phasor system (3i) was used to illuminate a 4 µm circular region (4 µm depth, ΔZ = 1 µm) with 2 × 30 pulses of 2% 405 nm UV laser resulting in a photoconversion of

pSMOrange. Note that precise calibration and optimization of the microscopy system must be done to properly photoconvert a cell without damaging it. After photoconversion, the region around,

above and below the photoconverted cell was scanned for undesired cells and bleached with a 100% intensity 543 nm laser to keep only the cell of interest. Then, embryos were released from

agarose and kept in dark for 48 h at 28 °C in a 0.003% phenylthiourea (PTU) solution to stop pigmentation. Finally, embryos were mounted and imaged again to find back the photoconverted cell

and its lineage (Fig. 2c, d). STATISTICS AND REPRODUCIBILITY All statistics were calculated using Matlab software with custom codes adapted from MathWorks functions (available upon

request). All statistical test significances were considered using an α of 0.05. No statistical methods were used to predetermine sample size. R² fit values were determined with 95%

confidence interval (Figs. 3i; 4f; Fig. S4d–f; S5c). ANOVA were made using one-way ANOVA (Figs. 3d–f; 4e, h; 5i; Fig. S3h, i; S7c; S8c). A two-samples Kolmogorov-Smirnov test (95%

confidence) was used to compare distribution of data (Fig. 4g). Chi-square tests (95% confidence) were used to compare percentages of asymmetric NPs (Fig. 5E; Fig. S6d) and percentages of

lineages (Fig. 2d; 5j). The following _p_-values from Chi-square tests were found: Fig. 2d *=0.0402 and **= 0.0011; Fig. 5e *= 0.0174 and **=0.0014; Fig. 5j *=0.0127; Fig. S1i *=0.0402 (all

_vs_ n.p), *=0.0145 and **=0.0021; Fig. S6d *=0.0401. Non indicated comparisons are non-significant. A log10 transformation was applied to the Sara ratio dynamic analysis (Fig. 3h; 4d; 5h).

AIC, GMM, K-mean and Dunn-Index analysis were made using Matlab functions (MathWorks). A maximum of 3 NPs per embryos were imaged, apart for the lineage analysis where 1 NP per embryo was

imaged. The experiments were not randomized and the investigators were not blinded to allocation during experiments, outcome assessment and analysis of the data. REPORTING SUMMARY Further

information on research design is available in the Nature Portfolio Reporting Summary linked to this article. DATA AVAILABILITY All data supporting the findings of this study are available

within the paper, Supplementary Information for Supplementary Figs. and Supplementary Tables, and Supplementary Data 1 for data point values and raw images used for analysis. All other data

are available upon request. CODE AVAILABILITY Custom codes used for analysis were made on Matlab 2021b and are available upon request. REFERENCES * Kressmann, S., Campos, C., Castanon, I.,

Fürthauer, M. & González-Gaitán, M. Directional Notch trafficking in Sara endosomes during asymmetric cell division in the spinal cord. _Nat. Cell Biol._ 17, 333–339 (2015). Article  CAS

  PubMed  Google Scholar  * Derivery, E. et al. Polarized endosome dynamics by spindle asymmetry during asymmetric cell division. _Nature_ 528, 280–285 (2015). Article  CAS  PubMed  Google

Scholar  * Icha, J., Kunath, C., Rocha-Martins, M. & Norden, C. Independent modes of ganglion cell translocation ensure correct lamination of the zebrafish retina. _J. Cell Biol_ 215,

259–275 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Makov, U. Mixture Models in Statistics. In _International Encyclopedia of the Social & Behavioral Sciences_,

9910–9915 (Elsevier, 2001). * Jin, X. & Han, J. K-Means Clustering. In _Encyclopedia of Machine Learning_ (eds. Sammut, C. & Webb, G. I.) 563–564 (Springer US, 2010). * Kumar, S. K.

& Raju, G. Study on different cluster validity indices. _Int. J. Appl. Eng. Res_ 13, 9364–9376 (2018). Google Scholar  * Alexandre, P., Reugels, A. M., Barker, D., Blanc, E. &

Clarke, J. D. W. Neurons derive from the more apical daughter in asymmetric divisions in the zebrafish neural tube. _Nat. Neurosci._ 13, 673–679 (2010). Article  CAS  PubMed  Google Scholar

  * Papan, C. & Campos-ortega, J. A. On the formation of the neural keel and neural tube Danio (Brachydanio) rerio. _Roux’s Arch. Dev. Biol._ 203, 178–186 (1994). Article  Google Scholar

  * Xiong, F. et al. Specified neural progenitors sort to form sharp domains after noisy Shh signaling. _Cell_ 153, 550–561 (2013). Article  CAS  PubMed  PubMed Central  Google Scholar  *

Subach, O. M., Entenberg, D., Condeelis, J. S. & Verkhusha, V. V. A FRET-facilitated photoswitching using an orange fluorescent protein with the fast photoconversion kinetics. _J. Am.

Chem. Soc._ 134, 14789–14799 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Tinevez, J. Y. et al. TrackMate: an open and extensible platform for single-particle tracking.

_Methods_ 115, 80–90 (2017). Article  CAS  PubMed  Google Scholar  * Rathbun, L. I. et al. Cytokinetic bridge triggers de novo lumen formation in vivo. _Nat. Commun._ 11, 1269 (2020).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Akhmanova, A. & Hoogenraad, C. C. Microtubule minus-end-targeting proteins. _Curr. Biol._ 25, R162–R171 (2015). Article  CAS 

PubMed  Google Scholar  * Atherton, J. et al. A structural model for microtubule minus-end recognition and protection by CAMSAP proteins. _Nat. Struct. Mol. Biol._ 24, 931–943 (2017).

Article  CAS  PubMed  PubMed Central  Google Scholar  * Lewis, J., Hanisch, A. & Holder, M. Notch signaling, the segmentation clock, and the patterning of vertebrate somites. _J. Biol._

8, 1–7 (2009). Article  Google Scholar  * Delaune, E. A., François, P., Shih, N. P. & Amacher, S. L. Single-cell-resolution imaging of the impact of notch signaling and mitosis on

segmentation clock dynamics. _Dev. Cell_ 23, 995–1005 (2012). Article  CAS  PubMed  PubMed Central  Google Scholar  * Venzin, O. F. & Oates, A. C. What are you synching about? Emerging

complexity of Notch signaling in the segmentation clock. _Dev. Biol._ 460, 40–54 (2020). Article  CAS  PubMed  Google Scholar  * Akhmanova, A. & Steinmetz, M. O. Microtubule minus-end

regulation at a glance. _J. Cell Sci._ 132, 1–7 (2019). Article  Google Scholar  * Jiang, K. et al. Microtubule minus-end regulation at spindle poles by an ASPM-katanin complex. _Nat. Cell

Biol._ 19, 480–492 (2017). Article  CAS  PubMed  PubMed Central  Google Scholar  * Jiang, K. et al. Structural basis of formation of the microtubule minus-end-regulating CAMSAP-Katanin

Complex. _Structure_ 26, 375–382.e4 (2018). Article  CAS  PubMed  Google Scholar  * Mishra-Gorur, K. et al. Mutations in KATNB1 cause complex cerebral malformations by disrupting

asymmetrically dividing neural progenitors. _Neuron_ 84, 1226–1239 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  * Casas Gimeno, G. & Paridaen, J. T. M. L. The symmetry

of neural stem cell and progenitor divisions in the vertebrate brain. _Front. Cell Dev. Biol._ 10, 1–21 (2022). Article  Google Scholar  * Zhao, X. et al. Polarized endosome dynamics engage

cytoplasmic Par-3 that recruits dynein during asymmetric cell division. _Sci. Adv._ 7, 1–15 (2021). Article  Google Scholar  * Watson, J. L. et al. Synthetic Par polarity induces

cytoskeleton asymmetry in unpolarized mammalian cells. _Cell_ 1–18 (2023). * Loubéry, S. et al. Uninflatable and notch control the targeting of sara endosomes during asymmetric division.

_Curr. Biol._ 24, 2142–2148 (2014). Article  PubMed  Google Scholar  * Loubéry, S. et al. Sara phosphorylation state controls the dispatch of endosomes from the central spindle during

asymmetric division. _Nat. Commun._ 8, 1–11 (2017). Article  Google Scholar  * De Robertis, E. M. & Tejeda-Muñoz, N. Evo-Devo of Urbilateria and its larval forms. _Dev. Biol._ 487, 10–20

(2022). Article  PubMed  Google Scholar  * Westerfield, M. _The zebrafish book. A guide for the laboratory use of zebrafish (Danio rerio)_. (University of Oregon Press, 2000). *

Moreno-Mateos, M. A. et al. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. _Nat. Methods_ 12, 982–988 (2015). Article  CAS  PubMed  PubMed Central  Google

Scholar  * Lambert, C. J. et al. An automated system for rapid cellular extraction from live zebrafish embryos and larvae: development and application to genotyping. _PLoS One_ 13, e0193180

(2018). Article  PubMed  PubMed Central  Google Scholar  * Hoshijima, K., Jurynec, M. J. & Grunwald, D. J. Precise editing of the Zebrafish genome made simple and efficient. _Dev. Cell_

36, 654–667 (2016). Article  CAS  PubMed  PubMed Central  Google Scholar  * Tarantino, N. et al. Tnf and il-1 exhibit distinct ubiquitin requirements for inducing NEMO-IKK supramolecular

structures. _J. Cell Biol._ 204, 231–245 (2014). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We thank T. Wagner, A. Ruffieux and M. Menoud for

technical help with the fish facility. We are very grateful to K. Kruse, C. Aumeier, M. Fürthauer, C. Seum and E. Derivery for support and critical reading of the manuscript. This work was

supported by grants from the Swiss National Science Foundation, the ERC (Sara and Morphogen), the NCCR Chemical Biology program, the DIP of the Canton of Geneva, the SNSF and the SystemsX

EpiPhysX to M.G.G. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Biochemistry, Faculty of Science, University of Geneva, 30 Quai Ernest Ansermet, Geneva, 1205, Switzerland

Clément-Alexis Richard, Carole Seum & Marcos Gonzalez-Gaitan Authors * Clément-Alexis Richard View author publications You can also search for this author inPubMed Google Scholar *

Carole Seum View author publications You can also search for this author inPubMed Google Scholar * Marcos Gonzalez-Gaitan View author publications You can also search for this author

inPubMed Google Scholar CONTRIBUTIONS C.A.R designed and performed the experiments, generated and interpreted the data and wrote the manuscript. C.Seum produced the _GFP-Sara_ CRISPR knock

in zebrafish. M.G.G. supervised the project, interpreted the data and wrote the manuscript. CORRESPONDING AUTHORS Correspondence to Clément-Alexis Richard or Marcos Gonzalez-Gaitan. ETHICS

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THIS ARTICLE CITE THIS ARTICLE Richard, CA., Seum, C. & Gonzalez-Gaitan, M. Microtubule polarity determines the lineage of embryonic neural precursor in zebrafish spinal cord. _Commun

Biol_ 7, 439 (2024). https://doi.org/10.1038/s42003-024-06018-7 Download citation * Received: 30 January 2024 * Accepted: 06 March 2024 * Published: 10 April 2024 * DOI:

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