Mouath Aouayeb

Wassim Hamidouche

Catherine Soladie

Kidiyo Kpalma

Renaud Seguier

Micro-Expression Recognition from Local Facial Regions

Keywords: Inner Brow Raiser AU 4, Brow Lowerer AU 6, Cheek Raiser AU 9, Nose Wrinkler AU 10, Upper Lip Raiser AU 12, Lip Corner Puller

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Human face analysis has attracted a wide interest of the computer vision research community with a potential scope of applications including healthcare, education, security, etc. The human face holds essential information for a machine to understand human reactions and feelings, and also for humans to better understand each other in interpersonal communication. Recently, particular attention is paid to a special type of expression called facial micro-expression (MiE) which may help to understand the feelings of a person.

The pioneering work on human facial expressions was published by Charles Darwin and Phillip Prodger [START_REF] Darwin | The expression of the emotions in man and animals[END_REF] in 1872 with a hypothesis that some facial expressions may appear to reflect the real emotion when someone tries to hide it. In 1966, Haggard and Isaacs [START_REF] Haggard | Micromomentary facial expressions as indicators of ego mechanisms in psychotherapy[END_REF] first discovered the phenomenon of MiE when studying films of communication between a therapist and a patient frame by frame, searching for non-verbal reactions. Later in 1969, Ekman et al. [START_REF] Ekman | Nonverbal leakage and clues to deception[END_REF] proved the existence of MiEs as quick, universal, and spontaneous facial expressions of some local regions of the face.

The analysis of MiE can be used for several applications like lie detection, pain or stress detection, and teaching assistance. MiEs, like any other facial expression, is a representation of facial muscles movements. Yet, they are hard to analyze since they are local and lasting only from 1/25s to 1/5s according to the study conducted in [START_REF] Ekman | Unmasking the Face: A Guide to Recognizing Emotions from Facial Clues. A spectrum book[END_REF]. A precision of 40% in MiE recognition was reported in [START_REF] Ekman | Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage[END_REF] for people who had passed professional training. To help reading these movements and encoding MiEs, a Facial Action Coding System (FACS) is used by P. Ekman based on the combination of Action Units (AUs). An AU is a small region of the face, controlled by one muscle or a group of muscles that react together producing a basic movement in the face. The FACS system was originally developed by Hjortsjö [START_REF] Hjortsjo | Man's Face and Mimic Language. Studen litteratur[END_REF] in 1969 and then adopted in 1978 by Ekman et al. [START_REF] Ekman | Facial Action Coding System: Investigator's Guide. Facial Action Coding System: Investigator's Guide[END_REF]. Figure 1 ORCID(s): 0000-0001-7411-4375 (M. Aouayeb) illustrates the FACS as defined by Ekman et al. in [START_REF] Ekman | Facial Action Coding System: The Manual on CD ROM[END_REF]. Ekman has also shown in [START_REF] Ekman | Facial Expressions of Emotion: an Old Controversy and New Findings[END_REF] that human emotions can be categorized into 7 basic classes including disgust, surprise, happiness, fear, anger, contempt, and sadness.

Figures 2(a) and 2(b) show examples of emotions in Macro-Expression (MaE) and MiE and their corresponding AU's code according to the FACS system. The deformation of AUs defines the facial expression. This deformation goes through three main steps: Onset, Apex, and Offset [START_REF] Ekman | Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage[END_REF]. The Onset represents the starting point of the expression, the Apex represents the maximum or the peak that the expression reaches in deformation, and the Offset is the instant when the expression vanishes away. Hence, the period of any facial expression is the time spent between the beginning of the Onset period and the end of the Offset period. Figure 3 shows an example of a deformation evolution through time (only 5 frames out of the sequence are shown1 ) for disgust emotion. In the Apex frame, where the peak of the deformation occurs, we can notice a clear contraction of muscles between eyebrows and eyes, which refers to the AU9.

Analogously to the progress of MaE recognition, which has been first tackled by handcrafted features then modern deep neural networks, MiE recognition methods have also evolved from hand-designed approaches [START_REF] Zhao | Dynamic texture recognition using local binary patterns with an application to facial expressions[END_REF][START_REF] Liong | Less is more: Micro-expression recognition from video using apex frame[END_REF][START_REF] Davison | Objective classes for micro-facial expression recognition[END_REF] to deep spatiotemporal neural networks [START_REF] Kim | Micro-expression recognition with expression-state constrained spatio-temporal feature representations[END_REF][START_REF] Quang | CapsuleNet for Micro-Expression Recognition[END_REF][START_REF] Wang | Microattention for micro-expression recognition[END_REF][START_REF] Verma | Learnet: Dynamic imaging network for micro expression recognition[END_REF] and hybrid approaches [START_REF] Gan | OFF-ApexNet on Micro-expression Recognition System[END_REF][START_REF] Liong | Shallow triple stream three-dimensional cnn (ststnet) for micro-expression recognition[END_REF][START_REF] Xia | Spatiotemporal recurrent convolutional networks for recognizing spontaneous microexpressions[END_REF][START_REF] Khor | Enriched long-term recurrent convolutional network for facial micro-expression recognition[END_REF]. However, while MaE recognition has seen dramatic gains in accuracy, improvements in MiE recognition have been more moderate. The main drawback of previous approaches for automatic MiE recognition is the use of the whole face while a MiE occurs only in small parts of the face. These solutions have mainly contributed to the architecture of models for features extraction and classification, which mostly deal with the low intensity and short duration characteristics of MiEs and not the locality character. Nevertheless, recent works [START_REF] Zhao | Necessary morphological patches extraction for automatic micro-expression recognition[END_REF][START_REF] Reddy | Spontaneous facial micro-expression recognition using 3d spatiotemporal convolutional neural networks[END_REF][START_REF] Zhao | An improved micro-expression recognition method based on necessary morphological patches[END_REF] pay attention to this locality feature. They apply either handcrafted, deep learning or hybrid approaches only on some selected regions, which may result in better performance. Since the MiEs are brief and local, and the avail-able MiEs datasets are very small and unbalanced between classes, the result is still less accurate than the results of MaEs recognition systems.

This paper studies a new model to automatically recognize MiE from local facial regions using a composite architecture of CNN and LSTM for spatiotemporal features extraction. Our proposed solution differs from other regionbased solutions since it associates a novel label for each region based on the definition of facial expressions on the FACS system illustrated in Figure 2. Also, a shallow CNN model is used instead of deep CNN, because a very deep architecture may eliminate the spatial features that are helpful for MiE analysis. The contributions of this work can be summarized as follows:

• We use a shallow CNN, combined with LSTM to treat the spatiotemporal information. By this architecture, we address the low intensity (CNN) and rapidity (LSTM) characters of facial Micro-Expression.

• The proposed architecture of CNN-LSTM is applied on 6 regions of the face to address the locality aspect.

• The training process of CNN (spatial part) is done using specialized labels for each region depending on the expression to aid the CNN learning more specific and useful features. Experiments on 3 spontaneous MiE datasets demonstrate the potential of our approach performance that exceeds the state-of-the-art methods.

The remainder of this paper is organized as follows. Section 2 presents the state-of-the-art solutions for MiE recognition. Section 3 investigates the proposed spatiotemporal architecture for MiE recognition. The performance of the proposed solution is assessed and compared to the best-performing solutions in Section 4. Finally, Section 5 concludes this paper.

Related Work

In this section, we review and discuss the state-of-the-art solutions for MiEs recognition. The overall pipeline, shown in Figure . classes. This probability can be parameterized using different methods ( ) = ( ( )) which leverage at all frames in the sequence to predict ( | ) as follows:

= ( | ), ∈ [1, … , ], = ( ) . ( ) = ({ , = 1, ⋯ , }). (1)
where represents the model used for spatiotemporal features extraction and represents the model used for the classification. Based on the proposed problem formulation, state-of-the-art works can be grouped into 4 categories including handcrafted, deep learning, hybrid, and region-based. These categories may lead to different approaches for MiE recognition.

Handcrafted approaches

The first group of solutions for MiE recognition rely on handcrafted models. The model includes spatial and temporal features. The extraction of the features is based either on appearance face information that describes the variation of pixel's intensity (texture) or on geometric face information like the shapes and the locations of facial landmarks. Zhao et al. [START_REF] Zhao | Dynamic texture recognition using local binary patterns with an application to facial expressions[END_REF] first introduced Local Binary Pattern on Three Orthogonal Planes (LBP-TOP) for features extraction from dynamic textures to analyze the MiE. The features are defined in the LBP-TOP histogram which is the concatenation of the Local Binary Pattern (LBP) histograms of the three orthogonal planes: XY, XT, and YT. Concatenating histograms in the computation of LBP-TOP leads to redundant information where each neighbor pixel is used more than once. Subsequently, Wang et al. [START_REF] Wang | Lbp with six intersection points: Reducing redundant information in lbp-top for micro-expression recognition[END_REF] proposed an improved version of LBP-TOP with six intersection points of the three orthogonal planes XY, XT, and YT. Moreover, LBP Mean Orthogonal Planes (LBPMOP) computes the LBP of average planes for three orthogonal planes to reduce the redundancy. Guo et al. [START_REF] Guo | Extended local binary patterns for efficient and robust spontaneous facial micro-expression recognition[END_REF] proposed Extended LBPTOP (ELBPTOP) that uses the second-order discriminative information in the radial and angular directions of a local path along with the normal LBP-TOP.

Chaudhry et al. [START_REF] Chaudhry | Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions[END_REF] were the first to propose Histogram of Oriented Optical Flow (HOOF) for human action recognition and it was then adopted by Davison et al. [8] to be one of the baseline methods for MiE recognition. Main Directional Mean Optical Flow (MDMO) proposed by Liu et al. [START_REF] Liu | A main directional mean optical flow feature for spontaneous microexpression recognition[END_REF] to describe local facial dynamics by extracting principal Optical Flow (OF) direction of some AUs. Furthermore, Liong et al. exploited the Bi-Weighted Oriented Optical Flow (Bi-WOOF) in [START_REF] Liong | Less is more: Micro-expression recognition from video using apex frame[END_REF].

Different from these methods, Mean Oriented Riesz Features (MORF) [START_REF] Duque | Mean oriented riesz features for micro expression classification[END_REF] is proposed by Duque et al.. It uses the Riesz pyramid to model the temporal evolution of the MiE in two frames called Mean Oriented Riesz (MOR) image pair, which is then used to build a histogram. Polikovsky et al. [START_REF] Polikovsky | Facial micro-expressions recognition using high speed camera and 3d-gradient descriptor[END_REF] have used a 3D-Histogram of Oriented Gradient (HOG) on 12 facial regions to recognize MiE. Lu et al. [START_REF] Lu | Motion descriptors for micro-expression recognition[END_REF] proposed the Fusion of Motion Boundary Histogram (FMBH) technique to extract features from the face. The FMBH is the combination of Motion Boundary Histograms (MBHs) which are based on the norms and the angles computed from the horizontal and the vertical components of the optical flow. More details about the MBH descriptor can be found in [START_REF] Dalal | Human detection using oriented histograms of flow and appearance[END_REF].

Deep learning approaches

Computer vision tasks like object detection and tracking, video classification, and image segmentation are widely addressed using deep learning techniques. Due to the high performance of deep learning models on these tasks, many researchers have tested and adopted deep learning architectures for MiE analysis. State-of-the-art deep learning solutions are usually based on variants of CNN or a combination of CNN and Recurrent Neural Network (RNN).

The first deep learning solution for MiE recognition was proposed by Patel et al [START_REF] Patel | Selective deep features for micro-expression recognition[END_REF] relying on a CNN. They used the ImageNet-VGG-f CNN [START_REF] Chatfield | Return of the devil in the details: Delving deep into convolutional nets[END_REF] pre-trained on ImageNet dataset with a CNN trained on facial expressions datasets (CK+ [START_REF] Lucey | The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression[END_REF] and SPOS [START_REF] Pfister | Differentiating spontaneous from posed facial expressions within a generic facial expression recognition framework[END_REF]) to extract features. Since then, numerous deep learning solutions [START_REF] Reddy | Spontaneous facial micro-expression recognition using 3d spatiotemporal convolutional neural networks[END_REF][START_REF] Verma | Learnet: Dynamic imaging network for micro expression recognition[END_REF][START_REF] Kim | Micro-expression recognition with expression-state constrained spatio-temporal feature representations[END_REF] are proposed. Reddy et al. [START_REF] Reddy | Spontaneous facial micro-expression recognition using 3d spatiotemporal convolutional neural networks[END_REF] proposed a MicroExpSTCNN architecture that is based on 3D-CNN. Lateral Accretive Hybrid Network (LEAR-Net) was proposed by Verma et al. [START_REF] Verma | Learnet: Dynamic imaging network for micro expression recognition[END_REF] which is based on a CNN with an accretion layer to refine the salient expression features by accretion of the learning capability of the network. One of the best derivatives of RNN is LSTM. An architecture of CNN combined with LSTM has been proposed by Kim et al. [START_REF] Kim | Micro-expression recognition with expression-state constrained spatio-temporal feature representations[END_REF]. The CNN enables extracting the spatial features and the LSTM extracts the temporal features from the spatial features. While most computer vision tasks have seen dramatic gains in accuracy, improvements in MiE video analysis have been more moderate. This is mainly caused by the lack of MiE datasets to train deep architectures.

Hybrid approaches

Since previous categories don't achieve expected performance, many hybrid systems have been proposed. Hybrid solutions [START_REF] Gan | OFF-ApexNet on Micro-expression Recognition System[END_REF][START_REF] Liong | Shallow triple stream three-dimensional cnn (ststnet) for micro-expression recognition[END_REF][START_REF] Khor | Enriched long-term recurrent convolutional network for facial micro-expression recognition[END_REF] are based on a combination of handcrafted and deep learning methods. A typical method of Optical Flow (OF) and its variants are usually employed with CNN for features extraction. Liong et al. [START_REF] Gan | OFF-ApexNet on Micro-expression Recognition System[END_REF] proposed Off-ApexNet as a hybrid solution. They consider only two frames to represent the MiE: Onset and Apex. Then, for feature extraction, they compute OF from the two considered frames and pass the computed features to a CNN. Later, Shallow Triple Stream Three-dimensional CNN (STSTNet) [START_REF] Liong | Shallow triple stream three-dimensional cnn (ststnet) for micro-expression recognition[END_REF] was proposed as an improvement of the Off-ApexNet method. The authors added to the horizontal and vertical OF, the strain of the OF. Recently, Khor et al. [START_REF] Khor | Enriched long-term recurrent convolutional network for facial micro-expression recognition[END_REF] proposed the Enriched Long-term Recurrent Convolutional Network (EL-RCN) which can be summarized into three main steps. First, they compute the optical flow and the optical strain. Then, they suggest two different methods for learning spatial features. One method is called spatial dimension enrichment, where the spatial features Φ are the output of the VGG-16 [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] model trained on the concatenation of the image, the optical flow, and the optical strain. The other proposed method for spatial features extraction is called temporal dimension enrichment that trains three VGG-16 networks on the image, the optical flow, and the optical strain and then concatenates the outputs to form the spatial features Φ . Finally, an LSTM is applied to the extracted spatial features (Φ or Φ ) for temporal learning and ends with a Fully Connected Layer (FCL) for the classification. Recently, Xia et al. [START_REF] Xia | Spatiotemporal recurrent convolutional networks for recognizing spontaneous microexpressions[END_REF] introduced Spatiotemporal Recurrent Convolution Network (STRCN) as a solution for MiE recognition. They described two versions of the solution. One version is called STRCN-A based on appearance connectivity where the image is represented in a one-dimensional vector and thus, the sequence by a 2D-matrix that will be fed to the STRCN block. The other version is STRCN-G with geometricbased connectivity where an OF is applied and the output is fed to the STRCN. The STRCN block is based on recurrent CNN.

The advantage of the methods in this category is the use of handcrafted methods to facilitate the spatiotemporal features extraction and the classification with the used deep learning architecture.

Region-based approaches

In contrast to previous cited approaches which focused on improving the feature extraction model, the contributions of region-based solutions address the locality character in the preprocessing step. Instead of using the whole face for MiE analysis, other researchers consider using some particular regions. Hence, enforce the system to extract more relevant and robust spatial features. Such region-based methods have achieved state-of-the-art performance. Ekman et al. [START_REF] Ekman | Facial Action Coding System: The Manual on CD ROM[END_REF] have already identified six different regions that are left and right (eye+eyebrow), the nose, the two cheeks, and the mouth. Based on these regions, Zhao el al. [START_REF] Zhao | Necessary morphological patches extraction for automatic micro-expression recognition[END_REF] have selected manually 18 regions of interest called Active Patchs (APs). From these APs, they identified Necessary Morphological Patches (NMPs) by giving a weight for each AP. The weights are calculated using an entropy-weight method based on their extracted features by LBP-TOP. An improved version to identify the NMPs was proposed by Zhao et al. [START_REF] Zhao | An improved micro-expression recognition method based on necessary morphological patches[END_REF]. Instead of choosing manually 18 APs, they divide the six regions into small blocks to get 106 APs and apply the Random Forest (RF) algorithm on the features extracted from APs by LBP-TOP and OF to select the NMPs.

Based on the advantages offered by region-based approaches, our proposed system proceeds on Regions Of Interests (ROIs) and uses deep learning techniques to extract spatiotemporal features. As can be seen in the following section, our formulation differs in that we also adopt a different label for each region when extracting the spatial features. The label vector is given based on the FACS system and the work of [START_REF] Zhao | An improved micro-expression recognition method based on necessary morphological patches[END_REF].

Proposed Solution

This section describes the proposed approach. An overview of our architecture is illustrated in Figure 6. This work aims to learn robust spatiotemporal features from local facial regions of MiE sequence and classify them into the corresponding labels.

Region definition

The region selection is based on two steps. First, we identify the face and then we crop the 6 ROIs identified by Ekman et al. [START_REF] Ekman | Facial Action Coding System: The Manual on CD ROM[END_REF] based on the FACS. Figure 5 shows the locations of the selected regions and the included AUs. To identify the face, the dlib algorithm2 is used to detect the 68 facial landmarks. Based on these points we crop the face from the entire image. Then, two steps are needed to extract the regions from the face. First, remove all global movements from all frames by referring to the first frame of each sequence: in the used dataset the subjects are static (no global movements), thus it's unnecessary to align the faces. Secondly, we identify 6 blocks corresponding to the selected regions and crop them. Instead of one sequence of MiE we have 6 sequences: one sequence for each region. And thus, = [ , = 1, … , ], where is the sequence of region and is the number of regions ( is set to 6 in this study).

Since the used datasets have different face sizes and knowing that the size of each region is proportional to the size of the face, the size of each region will be different from one dataset to another. To overcome this issue, we resize each region into a fixed size of {(80 × 100), (80 × 120), (60 × 60), (60×160)}, for respectively the left and the right {eye + eyebrow}, the nose, the right and left cheeks, and the mouth.

Spatial features extraction

The goal of the spatial model is to extract relevant spatial features from each region. To perform efficient training, we give a different emotion label for each region. As shown in Figure 7, a different region may express different emotional states. The regions and their corresponding labels are summarized in Table 1. If the region is responsible for that emotion, based on the work proposed by Zhao et al. in [START_REF] Zhao | An improved micro-expression recognition method based on necessary morphological patches[END_REF], the same emotion label is given. Otherwise, another label is given to the corresponding region, which should refer to "No Reaction" or a neutral state. It is clear, from Table 1, that the {eyes + eyebrows} and the mouth are the regions responsible for most MiEs emotions. However, the cheeks and the nose are regions that can be important for some reactions as well as to differentiate between two emotions. A drawback of using different labels for each region is the complexity to tune the hyperparameters of the model to fit all the regions. Since the MiE datasets used in our experiments are small, using the proposed labels may help the model to learn faster but also the size of the network is important for this issue. According to [START_REF] Nguyen | Anomaly detection in video sequence with appearance-motion correspondence[END_REF][START_REF] Xia | Revealing the invisible with model and data shrinking for composite-database micro-expression recognition[END_REF], deep CNN architecture may eliminate the features that are helpful for MiE recognition. Hence, the considered CNN architecture, shown in Figure 8, is much shallower (only 6 convolutional layers) than state-of-the-art models relying on InceptionNet [START_REF] Szegedy | Going deeper with convolutions[END_REF] or ResNet [START_REF] He | Deep residual learning for image recognition[END_REF]. The proposed CNN model consists of two main parts. The first part includes two sequential blocks, the first block is composed of 4 convolution layers with a filter size of 5 × 5 and the second block consists of 8 convolution layers with a filter size of 3 × 3. The second part is composed of a maxpooling layer and 4 parallel blocks of 16 convolution layers MER from Local Facial Regions each, with filter size, respectively, 1 × 1, 3 × 3, 5 × 5 and 7 × 7. Each convolution block is followed by a max-Pooling layer. The outputs of the last parallel max-pooling layers are then concatenated and fed to an average-pooling layer, followed by FCLs to perform the training of the convolution part. The output of the average-pooling layer represents the spatial features. However, to reduce the size of the spatial features, we take the output of the third layer of the FCL, which contains only 20 neurons.

In our formulation, we denote the input ( ) as the frame of the sequence , as the convolution operation with filters of size × ( 0 = ), for the max-pooling layer with size 2 × 2,

for the global average pooling layer, and for FCL with neurons. The output of the spatial model is defined in Equation ( 2):

= ( ) ( (
where ( ) is the number of classes of the region , ( ) indicates the spatial features to be saved of the ℎ frame of the region , and ⊕ is the concatenate operation.

After each convolutional or fully connected layer, we use Rectified Linear Unit (ReLU) as an activation function except for the last FCL we use Softmax. Since the model contains FCLs, it is more vulnerable to the overfitting problem. Overfitting is detected when the performance of the model on the test set is too far from the performance on the train set. To solve this problem, a Dropout layer is used. This latter chooses randomly a percentage of neurons to be ignored during the training which forces the deep learning model to learn more robust features that are useful in conjunction with many different random subsets of the other neurons. In this work, we use Dropout with = 0.5.

To train the spatial model for each region as a multi-label classification problem we used focal loss ( ) [START_REF] Lin | Focal loss for dense object detection[END_REF] instead of categorical cross-entropy. The loss for the training is performed by Equation (3):

 = ( ( ( ) ( ( ))), ( )), (3)
where ( ) and ( ) are, respectively, the extracted spatial features for all the frames and the ground truth label of region .

One advantage of such loss is that it deals with the problem of high unbalanced classes. The Categorical Cross-Entropy (CCE) loss function for multi-label classification is expressed in Equation ( 4):

( , ) = - ∑ log( ), (4)
where specifies the ground truth class and ∈ [0, 1] is the model estimated probability for the class with label . The modified Cross-Entropy (CE) or the focal loss function is given by Equation ( 5):

( , ) = - ∑ (1 -) log( ). (5)
In the above ∈ [0, 1] is a weighting factor for class set by inverse class frequency to contribute the imbalance between classes but it does not differentiate between hard and easy classification task of samples. That's why another modulating factor (1 -) is introduced to the CE loss function, where the >= 0 factor is called the focusing parameter. The goal of the temporal model, presented in Figure 9, is to extract spatiotemporal features ( ) (Equation ( 6)) from a sequence of spatial features ( ( ), ∈ [1.. ]) of the region . The proposed temporal model is LSTM, and thus we have to pad with zeros all the input sequences to the same length .

Temporal model

=

( 1 ( ), .., ( )).

As an activation function for the output of the LSTM, a Leaky Rectified Linear Unit (LeakyReLU) is used. After that, a dropout is used with = 0.2 to protect the model from overfitting.

Finally, we concatenate all the extracted spatiotemporal features from all the regions and feed them to the classification network. This latter is composed of a FCL with 256 neurons, followed by LeakyReLU and a dropout with = 0.5, then another FCL with neurons followed by a activation function. The output of the classification network is expressed by Equation [START_REF] Davison | SAMM: A Spontaneous Micro-Facial Movement Dataset[END_REF].

= ( 256 (⊕ , ∈ [1, … , ])). ( 7
)
The classification and the temporal model are trained together with also the focal loss function.

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MER from Local Facial Regions

Experimental Results

In this section, we assess the performance of the proposed MiE recognition model. We start by introducing the datasets, then we ablate various design choices to show the contributions of the used model's parameters. Next, we present the results under different protocols and conditions to validate the performance of the model and finally we compare our method to the existing state-of-the-art methods.

Datasets

Experiments are conducted on three benchmark datasets of spontaneous MiE including SMIC [START_REF] Li | A Spontaneous Micro-expression Database: Inducement, Collection and Baseline[END_REF], CASME II [START_REF] Yan | CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation[END_REF] and SAMM [START_REF] Davison | SAMM: A Spontaneous Micro-Facial Movement Dataset[END_REF]. These datasets are publicly available and used on the state-of-the-art proposed solutions. Hereafter, we report details of each datasets including size, number of classes, participants and video resolution.

SMIC

The Spontaneous Micro-Expression (SMIC) dataset includes spontaneous micro-expressions elicited by emotional movie clips. It contains 164 sequences from 16 different subjects recorded by a high-speed (HS) camera at 100 frames per second (fps). The sequences are recorded also with a normal speed camera at 25 fps of both visual (VIS) and nearinfrared (NIR) light range. So we have three datasets referred as SMIC-HS, SMIC-VIS, and SMIC-NIR for the same MiE recorded under different conditions. SMIC provides sequences with a facial resolution of (190 × 230) and corresponding to only 3 classes: Negative, Positive, and Surprise.

CASME II

The Chinese Academic of Science Micro-Expressions II (CASME II) dataset is the largest dataset of spontaneous MiE with 2473 sequences from 35 participants with 5 classes: Happiness, Disgust, Repression, Surprise and Sadness, plus the Other class. The sequences are recorded with high temporal and spatial resolutions of 200 fps and 280 × 340, respectively.

SAMM

The Spontaneous Micro-Facial Movement (SAMM) has the largest amount of different ethnicities (13 ethnicities) and age distribution (Mean age = 33.24). The video sequences are recorded with a high-resolution camera with 200 fps and contain the 7 basic classes of emotion: Disgust, Surprise, Happiness, Fear, Anger, Contempt, and Sadness. The dataset includes 159 sequences from 32 participants. It has the highest spatial resolution (400 × 400 pixels) among these three datasets. Further, this dataset's focus is not on the emotional labels but on the objective AUs so that all the sequences are FACS-coded and include the Onset, Apex, and Offset frames.

Table 2 summarizes the three datasets of spontaneous MiEs.

These datasets are very interesting since they afford close cases to reality with spontaneous MiEs sequences. However, they are small datasets compared to Extended Cohn-Kanade (CK+) or FER-2013 datasets for MaE recognition, that's why they are combined to form a bigger dataset.

Experimental settings

Since the three considered datasets have different conditions (number of classes, frame rate, dimensions), to combine them into a larger dataset, we used two proposed methods: with 3 classes based on the MEGC 2019 [START_REF] See | MEGC 2019 -The Second Facial Micro-Expressions Grand Challenge[END_REF] test conditions and with 5 classes based on AUs [START_REF] Davison | Objective classes for micro-facial expression recognition[END_REF]. Table 3 summarizes the MEGC conditions, where the number of classes is reduced to 3 (negative, positive, and surprise) to create a new larger dataset, named FULL, that combines SMIC, SAMM and, CASME II. This dataset has 442 sequences with 250 for negative class, 109 for positive class and 83 for surprise. The conditions for the distribution of the 5 classes based on AUs are described in [START_REF] Davison | Objective classes for micro-facial expression recognition[END_REF] and the obtained distribution of classes is summarized in Table 4. The new distribution of classes is used to create a new dataset by fusing only CASMEII and SAMM. The fusion is done after reassigning the sequences of CASMEII and SAMM datasets to the 7 AU classes presented in Table 4. However, only 5 classes are preserved, the other two classes (VI and VII) are removed because the VI class contains only a very few samples and the "Others" class (VII) denotes movements that are not suited for the other cat- egories and referred to no emotion state. In this evaluation, 4 metrics are used including accuracy, F1-score, UAR, and UF1. The UF1 and the UAR are given by Equations ( 8) and ( 9), respectively.

1 = 1 ∑ =1 1 , 1 = 2 2 + + , (8)
where , and are respectively true positive, false positive and false negative for class and is the number of classes.

= 1 ∑ =1 , = (9)
where is the number of samples and is the accuracy rate of class .

Ablation study

Hereafter is presented an analysis to demonstrate the contribution of each component in the global performance of the proposed method. The experiments of this study are conducted using Leave-One-Subject-Out Cross-Validation (LOSO-CV) evaluation method under the MEGC conditions.

Effects of the ROIs selection

To demonstrate the importance of the selected regions we test the model with the whole face, with only some regions, and with all the regions. Table 5 gives the result of the test with different regions and proves the importance of the selected areas. It shows that using all the regions outperforms the other configurations with the whole face, without cheeks, or without the nose. We have gained about 7% accuracy using regions compared to the whole face and that's due to the two following facts: the focus on the relevant areas for MiE recognition and the use of specific labels for each selected region. From Table 5, we can also conclude that the less are the regions that contain an emotion the more those regions are important. For example, disgust emotion can be detected in the nose but also in different regions like the eyes, and the mouth, contrary to the happiness which can be expressed only with the mouth and cheeks. Therefore, the mouth and the cheeks are more important than the nose resulting in lower accuracy without cheeks than without the nose.

Impact of the proposed CNN

In the proposed solution, the second contribution is the use of a special model of CNN as described in Section 3. Contrary to classical CNN models, we used a shallower architecture inspired by the architecture of the InceptionNet [START_REF] Szegedy | Going deeper with convolutions[END_REF]. Table 6 compares the performance of the proposed model with a classic CNN illustrated in Figure 10. It shows that the proposed CNN model outperforms the classic one.

Loss function

As a loss function for spatial and temporal models, we use two different functions: categorical cross-entropy loss and focal loss [START_REF] Lin | Focal loss for dense object detection[END_REF]. Table 7 gives the result with the categorical cross-entropy loss and with focal loss. The results with

Comparison and discussions

The proposed model is evaluated with two cross-validation methods: the LOSO-CV method and the Cross dataset (CDB) method. In LOSO-CV, each folder contains more than one sequence of MiE of a person that didn't appear in the other folders. The LOSO-CV ensures subject-independent evaluation. For the cross dataset method, the training set will be from a dataset and the test set from another dataset. For example, the training maybe on CASME II and the test on SAMM. The second method is good to evaluate the model but the provided datasets are too small for the training especially when using a complex architecture of CNN and LSTM which may lead to a bad performance. Tables 10 and11 show the performance of the proposed model with the LOSO-CV method and under the two conditions (with 3 classes based on the MEGC 2019 conditions or with 5 classes on the AU objective classes) while Tables 12 and13 present the performance with cross dataset method.

From Table 10 and Table 11 we can notice that the best performance of our model is reached on the CASME II dataset. This can be explained by the characteristics of the CASME II dataset with 200 fps (100 fps for SMIC) and RGB color mode (Grayscale for SAMM). The same observation can be made in CDB validation (Table 12 and Table 13) where the proposed method outperforms other cases when CASME II is the test set that can support the hypothesis. However, when the CASME II is used as the training set (Table 12), we can notice better performance on SAMM, which is a 200 fps and grayscale dataset, than on SMIC, which is 100 fps and RGB dataset. This latter can be an argument to explain how the temporal information is more beneficial for MiE recognition than the color scale of the image which is a part of the spatial information.

The proposed solution has been compared with other MiE recognition algorithms in different conditions. The comparison is summarized in Tables 8 and9 which show that the proposed framework performs the best among different approaches. Table 8 shows the performance of our method, with LOSO-CV and under MEGC conditions, comparing with baselines and recent methods, that obtain high accuracy on the four datasets. The results indicate that our method outperforms handcrafted, deep learning, and hybrid approaches for almost all datasets (ICE-GAN [START_REF] Yu | Ice-gan: Identity-aware and capsule-enhanced gan for micro-expression recognition and synthesis[END_REF] has the highest performance on SAMM).

As expected, results in Table 9 with LOSO-CV and under 5 classes based AU conditions are less accurate. We moderately improve the accuracy for three datasets (CASME II & SAMM, CASME II, SAMM) compared to other stateof-the-art methods.

Conclusion

In this paper, we have presented a novel spatiotemporal deep learning solution for MiE recognition from local facial regions. We have reported details and tests that support the used techniques and parameters. The main contributions of the proposed solution are the use of some particular regions of the face instead of the whole face and the extraction of spatiotemporal features from these regions with a deep learning architecture that combines CNN and LSTM. The solution is tested on different datasets and different distributions of classes and with different evaluation methods and metrics. The experiments showed that the proposed solution has a good performance that exceeds state-of-the-art solutions.

In this paper, we focused only on the recognition task without spotting and a static method for region cropping is used. Thus, in future work, we will extend the study by developing a framework that detects the active region, spots and then recognizes the MiE.

  (a) Examples of Macro-Expressions with their corresponding action units (AUs). The images are from Cohn-Kanade (CK) dataset. (b) Examples of Micro-Expressions with their corresponding action units (AUs). Figures from [5].

Figure 2 :

 2 Figure 2: Macro-and Micro-Expressions in FACS system.

Figure 3 :

 3 Figure 3: A sequence of 5 frames representing the evolution of a micro-expression from Onset up to Apex an then Offset.

•

  We test the proposed solution on two different distribution of datasets : the Micro-Expression Grand Challenge (MEGC) 2019 challenge dataset with 3 combined data bases (CASMEII, SAMM, SMIC) and 3 classes (Positive, Negative and Neutral), and the AUbased datasets (CASMEII and SAMM) with 5 classes based on AU.

  4, has two main parts: spatiotemporal features extraction and classification. Given a fixed set of classes 1 , 2 , … , and a MiE sequence composed of frames ( 1 , 2 , … , ), the goal of MiE recognition as a video sequence classification problem is to identify the class to which the MiE sequence belongs. In other words, we are interested in the class with the highest probability ( | ) among the probabilities of

Figure 4 :

 4 Figure 4: Pipeline of state-of-the-art micro-expression recognition task.

Figure 5 :

 5 Figure 5: Illustration of the ROIs with corresponding AU [51].

Figure 6 :

 6 Figure 6: An overview of our proposed solution.

Figure 7 :

 7 Figure 7: Identification of emotional state location [51].

Figure 8 :

 8 Figure 8: The architecture of the proposed shallow CNN inspired from the InceptionNet [41].

Figure 9 :

 9 Figure 9: Temporal model: the architecture of the LSTM applied on a sequence of spatial features (SF) of the region r. 0 , ℎ 0 : cells initialization. More details of the LSTM are in [23].

Figure 10 :

 10 Figure 10: The architecture of a classic CNN the left eye region.

J

  o u r n a l P e -p r o o f Journal Pre-proof MER from Local Facial Regions

Table 1

 1 The ROIs and their corresponding emotions.

	eyes and eyebrows* nose cheeks* mouth
	Happiness
	Disgust
	Surprise
	Repression
	Sadness
	Contempt

* Right and left side

Table 2

 2 Summary of three publicly available datasets (SMIC, CASME II, SAMM) of spontaneous MiEs.

		SMIC	CASME II SAMM
	# Sequences 164	247	159
	Participants	16	26	32
	Resolution	640 × 480 640 × 480	2040 × 1088
	Face	190 × 230 280 × 340	400 × 400
	Frame rate	100	200	200
	FACS coded	No	Yes	Yes
	# Classes	3	5	7
	Mean Age	26.7	22.03	33.24
	Ethnicities	3	1	13

Table 3

 3 Summary of the MEGC 2019 conditions to get the FULL dataset[START_REF] See | MEGC 2019 -The Second Facial Micro-Expressions Grand Challenge[END_REF]

	Emotion Class SMIC CASME II SAMM FULL
	Negative	70	88 †	92 ⊕	250
	Positive	51	32	26	109
	Surprise	43	25	15	83
	Total	164	145	133	442
	† Negative class of CASME II consists of samples from its original emotion classes of Disgust and Repression, ⊕ Negative class of SAMM consists of samples from its original emotion classes of Anger, Contempt, Disgust, Fear and Sadness.

Table 4

 4 The total number of movements assigned to the new classes for both SAMM and CASME II.

		Class CASME II SAMM Total
		I II III IV V VI VII	25 15 99 26 20 1 69	24 13 20 8 3 7 84	49 28 119 34 23 8 153
		Total	255	159	414
	Table 5 Evaluation of the model with different regions on the FULL dataset.
	regions	whole face no cheeks no nose all regions
	Accuracy UAR UF1	0.85 0.84 0.84	0.89 0.84 0.84		0.91 0.84 0.85	0.92 0.89 0.89

Table 6

 6 Evaluation of the proposed model depending on the CNN architecture on the FULL dataset.

	Metrics / CNN Simple CNN proposed model
	Accuracy UAR UF1		0.85 0.79 0.79	0.92 0.89 0.89
	Table 7 Evaluation of the proposed model depending on loss function on the FULL dataset.
	Loss Function	CE	Focal Loss: = 1∕2, = 2 [26]
	Accuracy UAR UF1	0.91 0.87 0.87	0.92 0.89 0.89

Table 8

 8 LOSO-CV performance of the proposed method, baselines and the recent methods ( * references from the MEGC 2019 challenge) handcrafted approach, † hybrid approach, ⊕ deep learning approach.

	Models (bold: 1 ; blue: 2 ) LBP-TOP [39] * ⋄ Bi-WOOF [28] * ⋄ OFF-ApexNet [17] †	FULL UF1 UAR 0.58 0.57 0.62 0.62 0.71 0.70	SMIC UF1 UAR 0.20 0.52 0.57 0.58 0.68 0.66	CASME II UF1 UAR UF1 UAR SAMM 0.70 0.74 0.39 0.41 0.78 0.80 0.52 0.51 0.87 0.86 0.54 0.53
	Micro-Attention [43] ⊕ ATNet (Fusion) [34] ⊕ Quang et al. [37] * ⊕ Zhou et al. [52] * † Liong et al. [27] * † Liu et al. [29] * † LFM-based (CNN+LSTM) [3] † ICE-GAN [48] ⊕ Our proposed method ⊕	0.50 0.63 0.65 0.73 0.73 0.78 0.77 0.85 0.90	0.49 0.61 0.65 0.72 0.76 0.78 0.75 0.84 0.90	0.47 0.55 0.58 0.66 0.68 0.74 0.72 0.79 0.88	0.46 0.54 0.58 0.67 0.70 0.75 0.71 0.79 0.88	0.53 0.79 0.70 0.86 0.83 0.82 0.87 0.87 0.98	0.51 0.77 0.70 0.85 0.86 0.82 0.84 0.86 0.98 0.78 0.81 0.40 0.34 0.49 0.48 0.58 0.59 0.58 0.56 0.65 0.68 0.77 0.71 0.67 0.60 0.85 0.82

⋄

Table 9

 9 LOSO-CV performance of the proposed method and state-of-the-art based on the 5 classes of AUs on CASME II and SAMM datasets ( * references from[START_REF] Davison | Objective classes for micro-facial expression recognition[END_REF]).

	Models (bold: 1 ; blue: 2 ) LBP-TOP * ⋄ HOOF * ⋄ HOG3D * ⋄ ELRCN [19] †	CASME II & SAMM F1-score Accuracy F1-score Accuracy F1-score Accuracy CASME II SAMM 0.40 0.52 0.51 0.67 0.38 0.44 0.40 0.52 0.56 0.69 0.33 0.42 0.27 0.43 0.51 0.69 0.22 0.34 0.41 0.57 ----
	Micro-Attention [43] ⊕ Our proposed method ⊕	0.66 0.83	0.76 0.86	-0.84	-0.88	-0.82	-0.79

⋄ handcrafted approach, † hybrid approach, ⊕ deep learning approach.

Table 10

 10

	Performance of the proposed method under the MEGC with LOSO-CV.
	Dataset	UF1 UAR
	FULL SMIC part CASME II part 0.98 0.90 0.90 0.88 0.88 0.98 SAMM part 0.78 0.81
	the focal loss are better since it is more compatible with a scenario of imbalanced data among classes.

Table 11

 11 Performance of the proposed method in 5 classes based on AUs with LOSO-CV.

	Dataset	UF1 Accuracy
	CASME II + SAMM 0.83 CASME II part 0.84 SAMM part 0.82	0.86 0.88 0.79

Table 12

 12 Cross dataset performance of the proposed method under the MEGC challenge.

	Train set	Test set	Accuracy F1-score
	CASME II & SMIC CASME II & SAMM SMIC SAMM SMIC & SAMM CASME II	0.89 0.75 0.92	0.83 0.75 0.91
	CASME II CASME II SMIC SMIC SAMM SAMM	SMIC SAMM CASME II SAMM CASME II SMIC	0.70 0.87 0.75 0.79 0.90 0.75	0.71 0.79 0.79 0.75 0.88 0.76
	Table 13 Cross dataset performance of the proposed method in 5 classes based on AU.
	Train set	Test set	Accuracy F1-score
	CASME II SAMM SAMM CASME II	0.75 0.82	0.59 0.69

A Micro-Expression sequence labeled disgust in CASME II dataset[START_REF] Yan | CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation[END_REF]

J o u r n a l P r e -p r o o fJournal Pre-proof

http://dlib.net/face_landmark_detection.py.html

J o u r n a l P r e -p r o o f

247 samples was reported by the authors[START_REF] Yan | CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation[END_REF] while in the publicly available dataset, the number of samples is about 255 samples.

J o u r n a l P r e -r o f

J o u r n a l P e -r o o f


Nina Schweizer

Laurence Haren

Ricardo Viais

Cristina Lacasa

Ilaria Dutto

Andreas Merdes

Jens Lüders
email: jens.luders@irbbarcelona.org

Sub-centrosomal mapping identifies augmin-gTuRC as part of a centriole-stabilizing scaffold

Keywords: centriole, augmin, gTuRC, microtubules, ciliogenesis

Centriole assembly and maintenance are crucial for the formation of cilia and for the formation of centrosomes that function as microtubule organizing centers (MTOCs).

Centriole biogenesis and MTOC

function both require the microtubule nucleator g- tubulin ring complex (gTuRC). The widely accepted view is that gTuRC localizes to the pericentriolar material (PCM), where it nucleates microtubules. gTuRC has also been observed at centriolar regions that lack PCM, but the significance of these findings is unclear. Here we have used expansion microscopy to map spatially and functionally distinct sub-populations of centrosomal gTuRC including in the centriole lumen. Luminal localization is mediated by augmin and both complexes are linked to the centriole inner scaffold through POC5. Disruption of luminal localization impairs centriole stability and cilia assembly, defects that are also observed in gTuRC mutant fibroblasts derived from a patient suffering from microcephaly with chorioretinopathy. These results identify a novel, non-canonical role of augmin-γTuRC in the centriole lumen that is linked to human disease.

Introduction

Centrioles, which are at the core of the centrosome and template the formation of cilia, are formed by nine sets of microtubules that are arranged in a circular fashion so that they form the wall of a cylinder. In human cells, wall microtubules of mature centrioles are organized as triplets and doublets in the proximal and distal cylinder, respectively. Triplets consist of one inner, complete microtubule, the so-called A-tubule, and two incomplete Band C-tubules that share part of their wall with the adjacent A-and B-tubule, respectively. Doublets consist of only A-and B-tubules [START_REF] Winey | Centriole structure[END_REF] . In cycling cells formation of new centrioles is initiated during S phase and occurs laterally at mother centrioles [START_REF] Nigg | Once and only once: mechanisms of centriole duplication and their deregulation in disease[END_REF] . The origin of centriolar wall microtubules is not clear. The finding that the nucleator gTuRC is required for centriole biogenesis [START_REF] Haren | NEDD1-dependent recruitment of the gamma-tubulin ring complex to the centrosome is necessary for centriole duplication and spindle assembly[END_REF][START_REF] Lüders | GCP-WD is a gamma-tubulin targeting factor required for centrosomal and chromatin-mediated microtubule nucleation[END_REF][START_REF] Bahtz | GCP6 is a substrate of Plk4 and required for centriole duplication[END_REF][START_REF] Kleylein-Sohn | Plk4-induced centriole biogenesis in human cells[END_REF][START_REF] Cota | MZT1 regulates microtubule nucleation by linking γTuRC assembly to adapter-mediated targeting and activation[END_REF] and the observation that gTuRC-shaped structures cap the minus-end of Atubules [START_REF] Guichard | Procentriole assembly revealed by cryo-electron tomography[END_REF] , suggest that at least the A-tubules arise by nucleation. During S/G2 phase daughter centrioles elongate and, after passing through mitosis, are converted to centrosomes through acquisition of PCM [START_REF] Wang | The conversion of centrioles to centrosomes: essential coupling of duplication with segregation[END_REF] . The PCM is the canonical site of gTuRC localization, where it has a well-established role as a nucleator of microtubules that extend into the cytoplasm during interphase and are incorporated into the spindle during mitosis 10-12 . Electron microscopy (EM) and, more recently, super resolution microscopy have revealed localization of gTuRC subunits also at the subdistal appendages [START_REF] Tassin | Characterization of the human homologue of the yeast spc98p and its association with gamma-tubulin[END_REF][START_REF] Chong | Super-resolution microscopy reveals coupling between mammalian centriole subdistal appendages and distal appendages[END_REF] and in the lumen of mother centrioles [START_REF] Bahtz | GCP6 is a substrate of Plk4 and required for centriole duplication[END_REF][START_REF] Fuller | The core of the mammalian centriole contains gamma-tubulin[END_REF][START_REF] Moudjou | gamma-Tubulin in mammalian cells: the centrosomal and the cytosolic forms[END_REF][START_REF] Sonnen | 3D-structured illumination microscopy provides novel insight into architecture of human centrosomes[END_REF] , but their roles at these sites were not investigated.

During ciliogenesis, the mother centriole is transformed into a basal body and templates formation of the axoneme, a microtubule-based scaffold structure that is at the core of cilia. However, axoneme microtubules are believed to not require nucleation but originate from elongation of the doublet microtubules in the distal basal body wall [START_REF] Ishikawa | Ciliogenesis: building the cell's antenna[END_REF] .

Here we have re-evaluated the long-standing view that gTuRC is a component of the PCM and that its centrosomal role is to nucleate microtubules. We found that gTuRC is distributed as functionally distinct sub-populations on the outside and, in complex with augmin, in the lumen of centrioles. Luminal augmin-gTuRC does not nucleate microtubules but contributes to centriole integrity, maintaining the ability of centrioles to template formation of cilia.

Results

gTuRC forms separable centrosomal sub-populations

To identify potential centrosomal sub-populations of gTuRC and elucidate whether these may have distinct functions, we analyzed the centrosomal localization of the gTuRC targeting factor NEDD1 and of the core subunits g-tubulin and GCP4 by expansion microscopy (ExM). gTuRC subunits localized on the outer surface of both mother and daughter centrioles, visualized with anti-acetylated tubulin antibodies, in some cases displaying enrichment in the proximal part of mother centrioles. In addition, all three proteins were found in the centriole lumen (Fig. 1a,c,d,e). This localization pattern was fundamentally different from that of the bona-fide PCM components CDK5RAP2 and pericentrin, which associated only with the outer, proximal part of mother centrioles (Fig. 1a). To re-evaluate the paradigm that centrosomal microtubules are nucleated in the PCM, we analyzed microtubule regrowth after cold-induced depolymerization. Whereas microtubules could not be detected in cold-treated cells, after a few seconds of warming microtubules were nucleated in close proximity of centriole cylinders (Fig. 1b).

Microtubules grew preferentially from the proximal surface of mother centrioles, but were also observed along the entire centriole wall including at distal ends. Thus, gTuRC and nucleation activity are generally associated with the outer surface of centrioles, with some enrichment in the region of the PCM.

gTuRC co-localizes with augmin in the central lumen of centrioles Next, we focused on luminal gTuRC. Interestingly, we found that subunits of the augmin complex, which recruits gTuRC to spindle microtubules during mitosis [START_REF] Lawo | the 8-subunit human Augmin complex, regulates centrosome and spindle integrity[END_REF][START_REF] Goshima | Augmin: a protein complex required for centrosome-independent microtubule generation within the spindle[END_REF] , colocalized with gTuRC in the centriole lumen during interphase. Colocalization was observed for both endogenous augmin subunits and EGFP-tagged recombinant versions (Fig. 1c, Supplementary Fig. 1a). By comparing the localizations of HAUS6 and NEDD1 relative to SAS-6, which marks the cartwheel structure at the proximal end of daughter centrioles [START_REF] Guichard | The Rise of the Cartwheel: Seeding the Centriole Organelle[END_REF] , and centrin, a marker for the distal lumen of centrioles [START_REF] Paoletti | Most of centrin in animal cells is not centrosome-associated and centrosomal centrin is confined to the distal lumen of centrioles[END_REF] , we found that in all cases NEDD1 and HAUS6 were found distal to the SAS-6 signal (Fig. 1d) and proximal to the bulk of the centrin signal (Fig. 1e). These results show that apart from gTuRC that is recruited to the outside of centrioles, a separate pool colocalizes with augmin in the central region of the centriole lumen.

Lumen recruitment of augmin and gTuRC occurs late during centriole elongation

Interestingly, newly formed daughter centrioles frequently lacked luminal augmin and gTuRC, even though gTuRC was visible on the outside of the daughter cylinder (Fig. 1e).

Consistent with this, only a minor fraction of all daughter centrioles identified by centrin labeling, was also positive for luminal HAUS6 or NEDD1 staining (Fig. 1f). Measuring the distance between centrin foci of mother and daughter centrioles as a proxy for daughter centriole length revealed that these centrioles were more elongated than daughter centrioles that lacked these proteins (Fig. 1g), suggesting that augmin and gTuRC localize to the lumen late during centriole biogenesis. Corroborating this result, accumulation of HAUS6 in the lumen of daughters coincided with poly-glutamylation, a tubulin modification that occurs selectively on the C-tubules of triplet microtubules [START_REF] Gambarotto | Imaging cellular ultrastructures using expansion microscopy (U-ExM)[END_REF] and that became detectable only after daughters had reached a substantial length (Supplementary Fig. 1b).

The most robust luminal HAUS6 signal was observed in mature centrioles, where it was confined to the proximal/central cylinder, marked by poly-glutamylation (Supplementary Fig. 1b).

Together our results indicate that gTuRC localizes first to the outside of newly formed daughter centrioles and subsequently, during centriole elongation, accumulates with augmin in the centriole lumen (Fig. 1h).

Centriole outer wall recruitment of gTuRC depends on CEP192

Previous work implicated CEP192 in the recruitment of gTuRC to centrosomes [START_REF] Zhu | The mammalian SPD-2 ortholog Cep192 regulates centrosome biogenesis[END_REF][START_REF] Gomez-Ferreria | Human Cep192 is required for mitotic centrosome and spindle assembly[END_REF] and identified the targeting factor NEDD1 as proximity interactor of CEP192 [START_REF] Gupta | A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface[END_REF][START_REF] Firat-Karalar | Proximity interactions among centrosome components identify regulators of centriole duplication[END_REF] , but did not distinguish between distinct sub-centrosomal sites. Super resolution microscopy detected CEP192 along the outer walls of mother and daughter centrioles (Fig. 2b) [START_REF] Sonnen | Human Cep192 and Cep152 cooperate in Plk4 recruitment and centriole duplication[END_REF] . To reevaluate CEP192's role in gTuRC centrosome recruitment, cells were transfected with siRNA, synchronized in mitosis with the Eg5 inhibitor STLC and then, bypassing cell division, released into G1 by CDK1 inhibition (Fig. 2a). This setup avoided adverse effects on centrosomes from duplication failure [START_REF] Zhu | The mammalian SPD-2 ortholog Cep192 regulates centrosome biogenesis[END_REF] , and enriched interphase cells with fully elongated, CEP192-depleted centrioles. We found that CEP192 depletion removed NEDD1 specifically from the outside of centrioles, whereas the luminal pool of NEDD1 appeared unaffected (Fig. 2b,c).

Lumen recruitment of gTuRC requires augmin

Considering their colocalization (Fig. 1c), we asked whether luminal recruitment of gTuRC required augmin. Using the same experimental setup as before, we depleted cells of HAUS6. Strikingly, centrioles lacking HAUS6 also lacked g-tubulin in the lumen (Fig. 2d), whereas g-tubulin signals on the outside of centrioles could still be detected (Fig. 2d). Importantly, we never observed HAUS6-negative centrioles that were positive for luminal g-tubulin. We additionally analyzed gTuRC lumen localization in mouse hippocampal neurons in which Haus6 had been knocked out post-mitotically. We previously showed that during neuronal culture PCM-associated g-tubulin is strongly downregulated and the remaining signal is centriole-associated [START_REF] Sánchez-Huertas | Non-centrosomal nucleation mediated by augmin organizes microtubules in post-mitotic neurons and controls axonal microtubule polarity[END_REF] . Indeed, in neurons at nine days in vitro (DIV) both HAUS6 and g-tubulin displayed centriolar localization. Strikingly, both proteins were largely absent from centrioles in conditional Haus6 KO neurons (Supplementary Fig. 2b,c), indicating that residual centrosomal gTuRC in differentiated neurons is luminal and recruited by the same augmin-dependent mechanism as in cycling cells. Since previous work showed that gTuRC targeting is broadly mediated by NEDD1 [START_REF] Haren | NEDD1-dependent recruitment of the gamma-tubulin ring complex to the centrosome is necessary for centriole duplication and spindle assembly[END_REF][START_REF] Lüders | GCP-WD is a gamma-tubulin targeting factor required for centrosomal and chromatin-mediated microtubule nucleation[END_REF] , we also tested depletion of NEDD1. In this condition NEDD1 and g-tubulin were absent from both the outside and the lumen of centrioles (Fig. 2e,f). Thus, outer wall localization of gTuRC requires CEP192, luminal gTuRC localization depends on augmin, and the targeting factor NEDD1 is required for recruitment to both sites.

Centriolar augmin interacts with the inner scaffold protein POC5

To learn more about the roles of augmin and gTuRC in the centriole lumen, we performed biotin proximity labeling using HAUS6 fused to the BirA biotin ligase as bait and identified centriole-specific, biotinylated proteins by mass spectrometry (Fig. 3a, Supplementary Fig. 3a,b). This approach identified POC5, a centrin-binding protein and component of a scaffold structure at the luminal surface of centrioles proposed to protect against mechanical stress [START_REF] Dantas | Calcium-binding capacity of centrin2 is required for linear POC5 assembly but not for nucleotide excision repair[END_REF][START_REF] Azimzadeh | hPOC5 is a centrin-binding protein required for assembly of fulllength centrioles[END_REF][START_REF] Guennec | A helical inner scaffold provides a structural basis for centriole cohesion[END_REF] . Consistent with this, augmin subunits were previously found as proximity interactors in cells expressing POC5-BirA as bait [START_REF] Gupta | A Dynamic Protein Interaction Landscape of the Human Centrosome-Cilium Interface[END_REF] . POC5 was present in the lumen of mother centrioles and accumulated in the lumen of daughter centrioles after these had reached a significant length (Fig. 3b), resembling the behavior that we had observed for luminal augmin and gTuRC. POC5 and g-tubulin were confined to the same central luminal region, but in end-on views g-tubulin appeared to localize more interior than POC5 (Fig. 3b).

POC5 is required for luminal recruitment of augmin-gTuRC

Since we occasionally observed daughter centrioles that were positive for POC5, but lacked g-tubulin in the lumen (Fig. 3b), we tested whether POC5 may function upstream of augmin and gTuRC lumen recruitment. Using the mitotic arrest-release approach, we found that centrioles depleted of POC5 also lacked luminal HAUS6 and g-tubulin, whereas both proteins were always present at centrioles in control cells (Fig. 3c,d). Previous analysis by cryo-electron tomography (cryo-ET) showed that the inner scaffold is a periodic, helical structure, lining the inner centriole wall [START_REF] Guennec | A helical inner scaffold provides a structural basis for centriole cohesion[END_REF] , likely composed of repeating units of scaffold protein complexes. Thus, scaffold proteins may tend to self-associate. Indeed, POC5 exogenously expressed in human cells forms filamentous structures in the cytoplasm that associate with other centriole proteins [START_REF] Dantas | Calcium-binding capacity of centrin2 is required for linear POC5 assembly but not for nucleotide excision repair[END_REF] . We confirmed this observation and found that these ectopic assemblies were also labeled with antibodies against HAUS6 and g-tubulin (Fig. 3e, Supplementary Fig. 3d). Together, these findings demonstrate that POC5, through interaction with augmin, recruits gTuRC to the inner centriole scaffold.

POC5 and augmin promote centriole integrity

The inner scaffold was suggested to confer stability on centrioles [START_REF] Guennec | A helical inner scaffold provides a structural basis for centriole cohesion[END_REF] . When we quantified the number of centrioles at the end of the duplication cycle, by counting of centrin foci in mitotic cells, we found that POC5 depletion had no effect on the number of centrioles (Supplementary Fig. 4a). We also did not observe any change in centriole number after HAUS6 depletion (Supplementary Fig. 4b). However, when mitotic duration was extended up to ~18 h by treatment with the Eg5 inhibitor STLC, centriole numbers in POC5 and HAUS6 RNAi cells declined: whereas 70-80% of control cells still had the expected number of at least 4 centrin foci, this number was observed in only ~35% of POC5 depleted cells and ~50% of HAUS6 depleted cells (Fig. 4a,b,c,d), suggesting centriole destabilization. Time course experiments further revealed that the decline in centriole numbers correlated with the time spent in mitosis (Fig. 4e). We also assayed centriole stability in the non-cancer RPE1 cell line. To avoid p53-dependent G1 arrest caused by mitotic defects [START_REF] Lambrus | A New Mode of Mitotic Surveillance[END_REF] , we induced cell cycle exit by serum withdrawal immediately after transfection of siRNA. After HAUS6 was efficiently depleted, we added serum for cell cycle re-entry, and STLC for prolonged mitotic arrest. Similar to U2OS cells, albeit less pronounced, centrioles in HAUS6-depleted RPE1 cells were also destabilized (Supplementary Fig. 4c).

Curiously, during prolonged mitotic arrest the number of centrioles in control cells also slightly decreased (Fig 4e). We speculated that this was due to premature loss of the cartwheel, which normally occurs at mitotic exit in a PLK1-dependent manner [START_REF] Wang | The conversion of centrioles to centrosomes: essential coupling of duplication with segregation[END_REF][START_REF] Arquint | STIL microcephaly mutations interfere with APC/Cmediated degradation and cause centriole amplification[END_REF] . Indeed, in the presence of the PLK1 inhibitor BI2536 the cartwheel component SAS-6 was retained at daughter centrioles during prolonged mitotic arrest (Supplementary Fig. 4d,e).

Counting of centrioles in PLK1-inhibited cells after prolonged mitotic arrest revealed that there was no significant difference in the percentage of cells with 4 or more centrioles between control and HAUS6 RNAi samples (~76% and ~69%; Fig. 4e), suggesting that centriole destabilization specifically affected cartwheel-less daughter centrioles.

The time-dependent disappearance of centrin foci in mitotically arrested cells may indicate complete centriole disassembly or merely loss of their distal, centrin-containing compartment. To distinguish between these possibilities, we quantified foci of centrobin, which localizes to the outer wall of daughter centrioles, in a central region [START_REF] Wang | A distal centriolar protein network controls organelle maturation and asymmetry[END_REF][START_REF] Zou | Centrobin: a novel daughter centriole-associated protein that is required for centriole duplication[END_REF] . In contrast to the reduction in centrin foci, a similar percentage of control and HAUS6 depleted cells had the expected number of at least two centrobin foci (~88% versus ~83%) (Fig. 4f,g), demonstrating that centrioles did not completely disassemble.

POC5 and gTuRC are required for ciliogenesis

While centriole destabilization was only observed upon prolonged mitosis, we hypothesized that cilia assembly, which relies on the elongation of microtubule doublets at the distal tip of mother centrioles, might naturally be sensitive to centriolar defects caused by the absence of luminal augmin-gTuRC. Since we were not able to efficiently remove HAUS6 from mother centrioles, we tested depletion of POC5, the scaffold protein most proximal to luminal HAUS6 (Fig. 3a). RPE1 cells were treated with control or POC5 siRNA and serum-starved to induce ciliogenesis. Whereas 67% of control cells had a cilium, only ~30% of POC5-depleted cells that lacked POC5 on both centrioles were ciliated (Fig. 5a,b). Since POC5 depletion was shown to cause G1 arrest in RPE1 cells [START_REF] Azimzadeh | hPOC5 is a centrin-binding protein required for assembly of fulllength centrioles[END_REF] , which could interfere with assaying ciliogenesis, we repeated the experiment in RPE1 p53 KO cells. In this case ~55% of control cells were ciliated, whereas only ~14% of cells that lacked POC5 on both centrioles possessed a cilium (Fig. 5b), confirming that loss of POC5 from the centriole lumen impairs ciliogenesis.

Mutations in gTuRC subunits have been linked to developmental defects including primary microcephaly and retinopathy [START_REF] Martin | Mutations in PLK4, encoding a master regulator of centriole biogenesis, cause microcephaly, growth failure and retinopathy[END_REF][START_REF] Scheidecker | Mutations in TUBGCP4 alter microtubule organization via the γ-tubulin ring complex in autosomal-recessive microcephaly with chorioretinopathy[END_REF][START_REF] Maver | Rare missense TUBGCP5 gene variant in a patient with primary microcephaly[END_REF][START_REF] Puffenberger | Genetic mapping and exome sequencing identify variants associated with five novel diseases[END_REF][START_REF] Mitani | Bi-allelic Pathogenic Variants in TUBGCP2 Cause Microcephaly and Lissencephaly Spectrum Disorders[END_REF] . We hypothesized that some of the clinical manifestations may also involve centriole destabilization and impaired ciliogenesis. To address this, we analyzed the ability of GCP4 mutant fibroblasts, obtained from a patient diagnosed with microcephaly and chorioretinopathy [START_REF] Scheidecker | Mutations in TUBGCP4 alter microtubule organization via the γ-tubulin ring complex in autosomal-recessive microcephaly with chorioretinopathy[END_REF] , to assemble cilia after serum starvation. Strikingly, only ~20% of GCP4 mutant fibroblasts were ciliated compared to ~80% of control fibroblasts (Fig. 5c,d). Additionally, cilia in patient fibroblasts were significantly shorter (Fig. 5e). Importantly, impaired ciliogenesis was not the consequence of defective centriole duplication (Fig. 5f). Moreover, mother centrioles had acquired subdistal appendages, as determined by the presence of ODF2 and ninein (Fig. 5c), indicating proper maturation. Thus, in GCP4 mutant cells centrioles form and mature, but are defective in supporting cilia assembly and growth.

Discussion

Here we have identified novel, non-canonical roles of augmin and gTuRC in the centriole lumen that are independent of their previously described functions in microtubule nucleation. In addition to nucleating microtubules, both protein complexes are linked to the centriole inner scaffold through POC5, contributing to centriole integrity. POC5 was recently mapped to the innermost region of the scaffold, a position well suited for anchoring augmin-gTuRC [START_REF] Guennec | A helical inner scaffold provides a structural basis for centriole cohesion[END_REF] . Augmin is known to directly interact with microtubules through its HAUS8 subunit [START_REF] Hsia | Reconstitution of the augmin complex provides insights into its architecture and function[END_REF] and this interaction is independent of its ability to recruit gTuRC [START_REF] Hsia | Reconstitution of the augmin complex provides insights into its architecture and function[END_REF][START_REF] Song | Mechanism of how augmin directly targets the γ-tubulin ring complex to microtubules[END_REF] . Thus, it is tempting to speculate that augmin, apart from interacting with POC5, may also directly bind and stabilize microtubules of the centriole wall. Curiously, shape and dimension of native and reconstituted augmin [START_REF] Hsia | Reconstitution of the augmin complex provides insights into its architecture and function[END_REF][START_REF] Song | Mechanism of how augmin directly targets the γ-tubulin ring complex to microtubules[END_REF] have striking similarity to unassigned Y-and L-shaped linker structures that are connected to A-and B-tubules of the centriole wall and protrude into the lumen [START_REF] Li | Three-dimensional structure of basal body triplet revealed by electron cryo-tomography[END_REF][START_REF] Greenan | Insights into centriole geometry revealed by cryotomography of doublet and triplet centrioles[END_REF][START_REF] Hamel | Identification of Chlamydomonas Central Core Centriolar Proteins Reveals a Role for Human WDR90 in Ciliogenesis[END_REF] . However, further work is needed to more precisely map the configuration of luminal augmin.

Considering the periodicity of the inner scaffold structure [START_REF] Guennec | A helical inner scaffold provides a structural basis for centriole cohesion[END_REF][START_REF] Li | Three-dimensional structure of basal body triplet revealed by electron cryo-tomography[END_REF] and our observation that augmin and gTuRC are distributed along the entire length of the central lumen, one can speculate that multiple copies of augmin and gTuRC may adopt a stacked configuration at the inner centriole wall. Since both augmin and gTuRC are large, multisubunit complexes, they would be expected to form prominent structures in the centriole lumen. Indeed, several EM and cryo-ET studies have described unidentified densities including ring-shaped structures in the centriole lumen [START_REF] Li | Three-dimensional structure of basal body triplet revealed by electron cryo-tomography[END_REF][START_REF] Greenan | Insights into centriole geometry revealed by cryotomography of doublet and triplet centrioles[END_REF][START_REF] Paintrand | Centrosome organization and centriole architecture: their sensitivity to divalent cations[END_REF][START_REF] Ibrahim | Electron tomography study of isolated human centrioles[END_REF] . The fact that these are not consistently observed may indicate sensitivity of augmin and gTuRC to the conditions used for sample preparation. While the precise arrangement of luminal augmin and gTuRC remains to be determined, we showed that their specific loss from the lumen impairs centriole integrity and ciliogenesis, similar to defects observed after depletion of WDR90, which functions more upstream in the recruitment of scaffold proteins [START_REF] Hamel | Identification of Chlamydomonas Central Core Centriolar Proteins Reveals a Role for Human WDR90 in Ciliogenesis[END_REF] . Based on our results, augmin and gTuRC may be considered components of the inner scaffold, which may thus extend farther into the centriole lumen than previously anticipated. Our findings also suggest that the integrity of the entire extended scaffold structure is required for its centriole-stabilizing and ciliogenesis-promoting function.

Importantly, the impairment of ciliogenesis caused by displacement of luminal augmin-gTuRC is also observed in patient-derived gTuRC mutant fibroblasts. Thus, phenotypes previously ascribed to augmin or gTuRC deficiency may not solely be related to their function as microtubule nucleators, but also to their luminal roles in promoting centriole integrity.

Methods

Cell culture and treatments. U2OS cells and hTERT RPE1 cells were cultured in DMEM and DMEM/F12 (Invitrogen), respectively, with 10% fetal bovine serum (FBS) and PenStrep (both Gibco). Parental cell lines were obtained from ATCC, hTERT RPE1 p53 -/cells were provided by Meng-Fu Bryan Tsou [START_REF] Wang | De novo centriole formation in human cells is error-prone and does not require SAS-6 self-assembly[END_REF] . U2OS cells stably expressing POC5-GFP, EGFP-HAUS8, EGFP-HAUS6 or BirA-HAUS6 were generated by transfection of the appropriate expression plasmid, followed by either the selection with 1 mg/ml geneticin (Gibco) or by FACS. Human fibroblasts were derived from skin biopsis from a control individual (WT) and a patient with GCP4 mutations (AII-1) [START_REF] Scheidecker | Mutations in TUBGCP4 alter microtubule organization via the γ-tubulin ring complex in autosomal-recessive microcephaly with chorioretinopathy[END_REF] and cultured in DMEM with 15% FBS and PenStrep. All cell lines were kept in a 37°C incubator with 5% CO2 and a humidified atmosphere. For the induction of ciliogenesis, cells were incubated in DMEM without FBS for 48 hours. G2 arrest or mitotic exit was induced with 10 µM RO-3306 (Sigma). Mitotic arrest was induced with 10 µM STLC (Sigma). PLK1 was inhibited with 100 nM BI2536 (Adooq Bioscience). Neuron cell culture. For obtaining embryonic hippocampal tissue, timed pregnant mice were sacrificed by cervical dislocation. Cell cultures were prepared from e17.5-18.5 mouse embryos as described previously [START_REF] Sánchez-Huertas | Non-centrosomal nucleation mediated by augmin organizes microtubules in post-mitotic neurons and controls axonal microtubule polarity[END_REF] . Briefly, tissue was dissected in Hank´s solution (Merck), incubated in 0.25% trypsin (Life Technologies) and 1 mg/ml DNAse (Roche) for 15 min at 37°C and dissociated into single cells by gentle pipetting. Cells were seeded on poly-D-lysine coated glass cover slips in DMEM (Invitrogen) with 10% FBS and PenStrep (both Gibco). 2 h after plating, the medium was replaced with Neurobasal medium with 0.6% Glucose, 2% B27, Glutamax (all Life Technologies) and PenStrep (Gibco) and cells were kept in a 37°C incubator with 5% CO2 and a humidified atmosphere. At 3 DIV, 1 μM cytosine arabinoside (Sigma) was added to the medium.

Generation of mouse

Immunofluorescence microscopy and expansion microscopy. Cells were grown on poly-L-lysine-or poly-D-lysine-(neurons) coated coverslips and fixed with methanol at -20°C for a minimum of 15 min or with 3.7% paraformaldehyde at 37°C, followed by methanol at -20°C as described for the microtubule regrowth assays. To visualize centrioles with α-tubulin or acetylated α-tubulin, cells were incubated on ice for 0.5 h to depolymerize cytoplasmic microtubules before fixation. To remove cytoplasmic background (stainings of centrioles for α-tubulin or EGFP-HAUS6) cells were preextracted in ice-cold PHEM (60 mM PIPES, 25 mM HEPES, 10 mM EGTA, 2 mM MgCl2) pH 6.9 with 0.1% Triton X-100 for 1-2 min before fixation. Fixed cells were washed with PBS and blocked in PBS-BT (PBS, 3% BSA, 0.1% Triton X-100) for 1 h at RT, followed by the incubation with primary antibodies in PBS-BT either for 1 h at RT or overnight at 4°C.

After washes in PBS-T (PBS, 0.1% Triton X-100) cells were incubated with secondary antibodies and 0.5 μg/ml DAPI (where appropriate) in PBS-BT for 1 h at RT. Cells were washed in PBS-T and either mounted in ProLong Gold Antifade (Thermofisher) on glass slides or further processed for ExM, as described previously [START_REF] Tillberg | Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies[END_REF] Subsequently, membranes were blocked and probed with antibodies.

Antibodies. Generation of rabbit polyclonal antibodies against HAUS6 (WB, 1:2000, IF, 1:1000 or 1:500 (ExM)) and GCP4 (ExM, 1:100) has been described previously [START_REF] Cota | MZT1 regulates microtubule nucleation by linking γTuRC assembly to adapter-mediated targeting and activation[END_REF][START_REF] Lecland | The dynamics of microtubule minus ends in the human mitotic spindle[END_REF] . Other antibodies used in this study were: mouse anti-γ-tubulin (TU-30, Exbio; IF, 1:500 or 1:250 (ExM)), rabbit anti-γ-tubulin R75 [START_REF] Julian | gamma-Tubulin participates in the formation of the midbody during cytokinesis in mammalian cells[END_REF] (1:1000), rabbit anti-a-tubulin (ab18251, Abcam; ExM, 1:300), mouse anti-acetylated a-tubulin (clone 6-11B-1, Merck; ExM, 1:250), mouse antiacetylated a-tubulin (T7451, Sigma; IF, 1:1000), mouse anti-polyglutamylated tubulin (GT335, AdipoGen; ExM, 1:250), rabbit anti-NEDD1 [START_REF] Lüders | GCP-WD is a gamma-tubulin targeting factor required for centrosomal and chromatin-mediated microtubule nucleation[END_REF] (ExM, 1:250), rabbit anti-HAUS5 [START_REF] Lawo | the 8-subunit human Augmin complex, regulates centrosome and spindle integrity[END_REF] (ExM, 1:100), rabbit anti-pericentrin [START_REF] Lüders | GCP-WD is a gamma-tubulin targeting factor required for centrosomal and chromatin-mediated microtubule nucleation[END_REF] (ExM, 1:250), rabbit anti-GFP (A6455, Invitrogen;

ExM, 1:250), chicken anti-GFP (GFP-1020, Aves Labs; ExM, 1: 1:250), mouse anti-centrin 1 (clone 20H5, Millipore; IF, 1:500 or 1:250 (ExM)), rabbit anti-POC5 (A303-341A, Bethyl

Laboratories WB, 1: 2500, IF: 1:500 or 1:250 (ExM)), mouse anti-SAS-6 (sc-81431, Santa Cruz; IF, 1:100 or 1:50 (ExM)), mouse anti-centrobin [START_REF] Ogungbenro | Centrobin controls primary ciliogenesis in vertebrates[END_REF] (IF, 1:500), rabbit anti-ninein [START_REF] Srsen | Centrosome proteins form an insoluble perinuclear matrix during muscle cell differentiation[END_REF] (IF, 1:100), rabbit anti-ODF2 (43840, Abcam; IF, 1:500), mouse anti-GAPDH (sc-47724, Santa Cruz Biotechnology; WB, 1:10000). Alexa-Fluor-488-, Alexa-Fluor-568-and Alexa-Fluor-647-conjugated, cross-adsorbed secondary antibodies were obtained from Thermo Fisher

(1:500 or 1:100 (ExM)). Streptavidin Alexa Fluor 594 was obtained from Invitrogen

(1:5000). Horseradish-peroxidase-coupled secondary antibodies for WB were obtained from Jackson ImmunoResearch Laboratories (1:5000).

Plasmids. The EGFP-HAUS8 expression plasmid was provided by Laurence Pelletier [START_REF] Lawo | the 8-subunit human Augmin complex, regulates centrosome and spindle integrity[END_REF] .

The POC5-GFP expression plasmid was obtained from Ciaran Morrison [START_REF] Dantas | Calcium-binding capacity of centrin2 is required for linear POC5 assembly but not for nucleotide excision repair[END_REF] . The plasmid expressing EGFP-HAUS6 was generated by cloning HAUS6 cDNA into pCS2-EGFP using AscI and Fse1 restriction sites. Site-directed mutagenesis was used to render EGFP-HAUS6 RNAi-insensitive (HAUS6 591A>G; 594T>C; 597G>A; 600G>C). A plasmid for expression of BirA-HAUS6 was generated by subcloning RNAi-insensitive HAUS6 into pCDNA5 FLAG-BirA R118G (provided by Brian Raught) [START_REF] Comartin | CEP120 and SPICE1 cooperate with CPAP in centriole elongation[END_REF] . Subsequently, FLAG-BirA R118G -HAUS6 was amplified by PCR and inserted into pEGFP-N1, replacing EGFP.

Microtubule regrowth assay. U2OS cells were grown on poly-L-lysine-coated coverslips and incubated on ice for 40 min to depolymerize cytoplasmic microtubules. For microtubule regrowth, coverslips were transferred into 3.7% paraformaldehyde at 37°C for 1 min, followed by the incubation in methanol at -20°C for a minimum of 15 min. For the negative control (no microtubule regrowth), cells were incubated in 3.7% paraformaldehyde on ice for 10 min, followed by the incubation in methanol at -20°C for a minimum of 15 min.

Transfection of plasmid and siRNA. Lipofectamine 2000 (Invitrogen) was used for the transfection of expression plasmids. Depletion of CEP192, HAUS6, NEDD1 and POC5 was performed by transfecting cells with the following siRNA oligonucleotides (Sigma) CEP192, 5'-AAGGAAGACAUUUUCAUCUCU-3'; HAUS6, 5'-CAGUUAAGCAGGUACGAA-3'; NEDD1, 5'-GCAGACAUGUGUCAAUUUGTT-3'; POC5, 5'-CAACAAAUUCUAGUCAUA-3'; using Lipofectamine RNAiMAX (Invitrogen). siRNA oligos against luciferase (5'-UCGAAGUAUUCCGCGUACG-3') were used as control.

Centrosome isolation and BioID. BioID from centrosomes was performed as described previously [START_REF] Firat-Karalar | Proximity interactions among centrosome components identify regulators of centriole duplication[END_REF] . Briefly, ten 15 cm dishes of U2OS cells stably expressing BirA-HAUS6 or parental U2OS cells (negative control) were incubated in culture medium containing 50 μM Biotin (Bio Basic) overnight. Subsequently, the medium was replaced and cells were incubated in culture medium containing 5 µg/ml nocodazole (Sigma) for 1 h. Cells were then washed with ice-cold HB buffer (20 mM HEPES pH 7.8, 5 mM K-acetate, 0.5 mM MgCl2, 0.5 mM DTT) and protease inhibitors (Roche) and incubated in 3 ml HB buffer for 10 min at 4ºC. Cells were scraped from the plate and transfered into a 15 ml dounce homogenizer. After homogenization the lysate was centrifuged at 3000 rpm at 4ºC for 5 min to pellet nuclei. Supernatant was collected and the pellet was washed again with HB buffer and centrifuged. Both supernatants were combined and 0.1% Triton X-100 was added, before the lysate was centrifuged at 3000 rpm at 4ºC for 5 min. Supernatant was collected and loaded onto a sucrose gradient (discontinuos sucrose gradient prepared with 5 ml of 70%, 3 ml of 50% and 3 ml of 40% sucrose) in Ultra-Clear Beckman tubes and centrifuged at 26000 rpm in a SW32Ti rotor for 1h at 4ºC. 500 µl fractions were collected and centrosome enrichment was determined by analyzing 10 µl of each fraction by WB using anti-g-tubulin and anti-centrin antibodies. Lumos™ Tribrid (Thermo Scientific). The mass spectrometer was operated in a datadependent acquisition mode, survey MS scans were acquired with a resolution of 120,000 (defined at 200 m/z), and lock mass was defined at 445.12 m/z in each scan. In each scan the top speed (most intense) ions were fragmented by CID and detected in the linear ion trap. The ion count target values for survey and MS/MS scans were 400,000 and 10,000, respectively. Target ions already selected for MS/MS were dynamically excluded for 15 s.

Spray voltage in the NanoMate source was set to 1.60 kV. RF Lens was tuned to 30%.

Minimal signal required to trigger MS to MS/MS switch was set to 5,000. The spectrometer was working in positive polarity mode and singly charge state precursors were rejected for fragmentation. Database searching was done with Proteome Discoverer software v2.1.0.81 (Thermo) using Sequest HT search engine and SwissProt Human release 2018 01 and manually introduced contaminants database and user proteins. Searches against targeted and decoy database were used for determining the false discovery rate (FDR).

Search parameters for trypsin enzyme specificity allowed for two missed cleavage sites, oxidation in M and acetylation in protein N-terminus. Peptide mass tolerance was 10 ppm and the MS/MS tolerance was 0.6 Da. Peptides with q-value lower than 0.1 and FDR < 1%

were considered as positive with a high confidence level.

For the quantitative analysis contaminant identifications were removed and unique peptide spectrum matches of protein groups identified with Sequest HT were analyzed with SAINTexpress-spc v3.11 [START_REF] Teo | SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software[END_REF] . High confidence interactors were defined as those with Bayesian false discovery rate BFDR ≤ 0.02.

Statistical analysis and replication of experiments.

Statistical analysis was performed using Prism 7 software. For quantifications of the accumulation of HAUS6/NEDD1 at distinct centriolar sites (Fig. 1f), centriole numbers (Fig. 4b,d,e,g, Supplementary Fig. 4e), the mean of independent experiments was first determined and statistics were performed on the entirety of the obtained means. Normality of data distribution within a data set was tested with a D'Agostino-Pearson normality test (Fig. 1g, Fig. 5e, Supplementary Fig. 2c) or a Shapiro-Wilk normality test (Fig. 1f, Fig. 4b,d,e,g, Fig. 5. b,d, Supplementary Fig. 4c), depending on sample size. Significances between two data sets were determined using an unpaired two-tailed Student's t-test (Fig. 1f,g, Fig. 4b,d,e,g, Fig. 5b,d,e, Supplementary Fig. 4c,e) or a Mann-Whitney test (Fig. 4e (data set with BI2563), Supplementary Fig. 2c).

The results, together with the number of independent experiments and sample sizes, are reported in the figures and figure legends. Protein localizations/dependencies and microtubule regrowth along the centriole wall has been confirmed in at least two independent experiments, except for CEP192 wall localization and its loss upon CEP192 RNAi (Fig. 2b). The displayed images are representative examples. The BioID (Fig. 3a, Supplementary Fig. 3b) was performed once.

GTAGGTGGAAATTCTAGCATCATCC- 3 '

 3 strains. A neuronal specific Haus6 conditional KO mouse strain was generated by crossing Haus6 floxed (Haus6 fl ) mice 50 (RBRC09630, Accession No. CDB1354K (http://www2.clst.riken.jp/arg/mutant%20mice%20list.html) with B6.Tg(Actl6b-Cre)4092Jiwu/J mice (Jackson Laboratories). Mouse strains were maintained on a mixed C57BL/6 background in strict accordance with the European Community (2010/63/UE) guidelines in the Specific-Pathogen Free (SPF) animal facilities of the Barcelona Science Park (PCB). All protocols were approved by the Animal Care and Use Committee of the PCB/University of Barcelona (IACUC; CEEA-PCB) and by the Departament de Territori I Sostenibilitat of the Generalitat de Catalunya in accordance with applicable legislation (Real Decreto 53/2013).Mice genotyping. DNA was extracted from tail biopsies by digesting biopsies with 0.4 mg/mL Proteinase K in 10 mM Tris-HCl, 20 mM NaCl, 0.2% SDS and 0.5 mM EDTA overnight at 56°C, followed by DNA precipitation with isopropanol. Genotyping was were used as internal PCR controls.

  : cells were washed with PBS and subsequently incubated in 0.1 mg/ml Acryloyl X (Life Technologies) in PBS at RT overnight. Cells were washed in monomer solution (1 x PBS, 2 M NaCl, 2.5% acrylamide (Bio-Rad), 0.15% Methylenbisacrylamide (Santa Cruz), 8.625% sodium acrylate (Sigma) and embedded in monomer solution containing 0.2% APS and 0.2% TEMED. Gels were polymerized for 2 h at 37°C and then digested with 8 U/ml Proteinase K (Invitrogen) in 50 mM Tris-HCl pH 8, 1 mM EDTA, 1 M NaCl, 0.5% Triton X-100 for 4 h at 37°C. Gels were expanded in MilliQ (expansion factor ~4) and mounted on poly-L-lysine-coated coverslips for imaging.Images were acquired with an Orca AG camera (Hamamatsu) on a Leica DMI6000B microscope equipped with a 1.4 NA 100× oil immersion objective. AF6000 software (Leica) was used for image acquisition and blind deconvolution. Alternatively, images were acquired with an MRm camera on an Axiovert 200M (Carl Zeiss) using a 1.4 NA 63× Plan Apo objective and Axiovision software (Fig.5c). Images were processed in ImageJ and Photoshop (Adobe) and represent maximum projections of a deconvolved stack or a single section, respectively. Image J was used for the quantification of fluorescence intensities. Images were acquired with constant exposure settings and pixel grey levels of the focused z plane were measured within a region of interest (ROI) encompassing a single centriole. Background fluorescence was measured adjacent to the ROI and subtracted.Western blotting (WB). Cells were washed in PBS and lysed in 50 mM HEPES, pH 7.5, 150 mM NaCl, 1 mM MgCl2, 1 mM EGTA, 0.5% NP-40 and protease inhibitors (Roche) for at least 10 minutes on ice. Extract was cleared by centrifugation and subjected to SDS PAGE, followed by the transfer of proteins to PVDF membranes by tank blotting.

Figure 1 aFigure 1 Figure 2

 112 Figure 1

Figure 3

 3 Figure 3

Figure 4 POC5

 4 Figure 4

Figure 5

 5 Figure 5

  Figure S1

  RO-3306 was added to the culture medium for ~18 h to arrest cells in G2. RO-3306 was washed out and STLC was added to arrest cells in mitosis for defined time points. In some cases, BI2536 was added together with STLC, as indicated. (f) Control RNAi and HAUS6 RNAi U2OS cells arrested in mitosis with STLC stained for centrin (red), centrobin (green) and DNA (DAPI, blue). The experimental design is depicted schematically. ~52 h after siRNA transfection, STLC was added to the culture medium for ~18 h. (g) Quantifications of the percentage of control RNAi or HAUS6 RNAi U2OS cells with ≥ 4 centrin foci or ≥ 2 centrobin foci after spending up to ~18 h in mitosis.Error bars represent standard deviations from the mean obtained from three independent experiments (% of cells with ≥ 4 centrin foci, control RNAi: 74.3 ± 5.5%, HAUS6 RNAi: 53.0 ± 3.0%, p < 0.01; % of cells with ≥ 2 centrobin foci, control RNAi: 87.7 ± 4.2%, HAUS6 RNAi: 83.3 ± 5.1%; mean ± SD, 300-504 cells per condition and experiment, p = 0.32). *** p < 0.001, ** p < 0.01; n.s., not significant. Bar (all panels), 10 µm (merge with DAPI) or 2 µm (insets depicting centrioles).

	Figure 4 POC5 and augmin promote centriole stability. (a) Control RNAi and POC5 RNAi experiment, p = 0.100). The experimental design is depicted schematically. ~52 h after
	U2OS cells arrested in mitosis with STLC stained for centrin (red), POC5 (green) and DNA siRNA transfection,
	(DAPI, blue). The experimental design is depicted schematically. ~52 h after siRNA
	transfection, STLC was added to the culture medium for ~18 h. (b) Quantifications of the
	percentage of control RNAi or POC5 RNAi U2OS cells with ≥ 4 centrioles (centrin foci)
	after spending up to ~18 h in mitosis. Error bars represent standard deviations from the
	mean obtained from three independent experiments (control RNAi: 78 ± 0.7%, POC5
	RNAi: 34.7 ± 10.6%, mean ± SD, 239-337 cells per condition and experiment, p < 0.001).
	(c) Control RNAi and HAUS6 RNAi U2OS cells arrested in mitosis with STLC stained for
	centrin (red), HAUS6 (green) and DNA (DAPI, blue). The experimental design is depicted
	schematically. ~52 h after siRNA transfection, STLC was added to the culture medium for
	~18 h. (d) Quantifications of the percentage of control RNAi or HAUS6 RNAi U2OS cells
	with ≥ 4 centrioles (centrin foci) after spending up to ~18 h in mitosis. Error bars represent
	RO-3306 wash-out
	RO-3306 standard deviations from the mean obtained from four independent experiments (control
	RNAi: 71.8 ± 4.9%, HAUS6 RNAi: 49.5 ± 3.9%, mean ± SD, 232-335 cells per condition
	and experiment, p < 0.001). (e) Quantifications of the percentage of control RNAi or
	HAUS6 RNAi U2OS cells with ≥ 4 centrioles (centrin foci) after spending different times in
	mitosis, in the presence or absence of PLK1 inhibitor BI2536. Error bars represent
	standard deviations from the mean obtained from three to six independent experiments
	(control RNAi (0.5 h): 81 ± 6.2%, HAUS6 RNAi (0.5 h): 79.3 ± 6.5%; mean ± SD, 3
	experiments, 154-329 cells per condition and experiment, p = 0.765; control RNAi (2 h):
	80.7 ± 4.2%, HAUS6 RNAi (2 h): 70.7 ± 5.5%, mean ± SD, three experiments, 125-250
	n.s. cells per condition and experiment, p = 0.071; control (24 h): 66.7 ± 6.4%, HAUS6 RNAi
	(24 h): 48.8 ± 4.6%; mean ± SD, 6 experiments, 115-350 cells per condition and
	**
	experiment, p < 0.001; control RNAi (24 h, BI2536): 76.0 ± 3.5%, HAUS6 RNAi (24 h,
	BI2536): 69.3 ± 4.0%; mean ± SD, 3 experiments, 110-311 cells per condition and

Acknowledgements

Author contributions

NS designed experimental strategies, performed most of the experiments, prepared figures and contributed to manuscript writing. LH performed experiments with patient fibroblasts. RV generated conditional Haus6 KO mice and prepared neuronal cultures. CL supervised animal experiments and performed the BioID experiment. ID performed some of the POC5 RNAi experiments in RPE1 p53 -/-cells. AM supervised experiments with patient fibroblasts. JL supervised the study, proposed experimental strategies, and contributed to manuscript writing.

Competing interests

The authors declare that they have no competing interests.

Supplementary Figure 1 Augmin localizes to the centriole lumen late during the cell cycle. (a) Centrioles of parental U2OS cells or U2OS cells stably expressing EGFP-HAUS8 in ExM stained for acetylated α-tubulin (red) and HAUS5 (green) or GFP (EGFP-HAUS8, green). (b) Centrioles of U2OS cells in ExM stained for α-tubulin (red) and polyglutamylated tubulin (green) or HAUS6 (red) and poly-glutamylated tubulin (green). Yellow arrowheads point to daughter centrioles that lack HAUS6/poly-glutamylation, white arrowheads point to poly-glutamylated daughter centrioles (that have HAUS6 in the lumen). Bar (all panels), 2 µm. Cartoons illustrate centriole configurations in the corresponding panels, dark grey = mother centriole, light grey = daughter centriole.