Ketty Favre
email: ketty.favre@univ-rennes1.fr

Muriel Pressigout

Eric Marchand

Luce Morin

A Plane-based Approach for Indoor Point Clouds Registration

Iterative Closest Point (ICP) is one of the mostly used algorithms for 3D point clouds registration. This classical approach can be impacted by the large number of points contained in a point cloud. Planar structures, which are less numerous than points, can be used in well-structured man-made environment. In this paper we propose a registration method inspired by the ICP algorithm in a plane-based registration approach for indoor environments. This method is based solely on data acquired with a LiDAR sensor.

A new metric based on plane characteristics is introduced to find the best plane correspondences. The optimal transformation is estimated through a two-step minimization approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration.

Experiments on the Autonomous Systems Lab (ASL) dataset show that the proposed method enables to successfully register 100% of the scans from the three indoor sequences. Experiments also show that the proposed method is more robust in large motion scenarios than other state-of-the-art algorithms.

I. INTRODUCTION

In robotics, registration of 3D point sets is a key issue in localization applications. The trend for autonomous vehicles makes it a widely searched field. Nowadays, 3D LiDARs are becoming cheaper and more frequently used. They have proven their efficiency in localization applications. The raw data generated by a 3D LiDAR are 3D point clouds, meaning a set of 3D points representing the coordinates of the physical point hit by the laser in the sensor reference frame.

One of the most popular approaches in robotics to register 3D point clouds is the well-known Iterative Closest Point (ICP) algorithm [START_REF] Besl | A method for registration of 3-D shapes[END_REF]. It allows to compute the rigid transformation (rotation and translation) that links a source and a target 3D point cloud. To do so, each point from the source point cloud is paired with its closest point in the target one. Then the 3D rigid transformation that minimizes the distance between paired points is estimated. This is achieved within an iterative scheme until the residual error has reached the desired threshold. A survey presenting ICP variants are given in [START_REF] Rusinkiewicz | Efficient variants of the ICP algorithm[END_REF] and [START_REF] Pomerleau | A Review of Point Cloud Registration Algorithms for Mobile Robotics[END_REF].

This introduction provides a short synthesis of the ICP variants focused on the different distances that can be used in order to estimate the 3D rigid transformation that registers two point clouds. The point-to-point distance is first introduced in [START_REF] Besl | A method for registration of 3-D shapes[END_REF]. Then, the point-to-plane distance is presented in [START_REF] Chen | Object modelling by registration of multiple range images[END_REF], proven to be more robust and faster to converge than the point-to-point one. A linear resolution of this minimization can be found in [START_REF] Low | Linear least-squares optimization for point-toplane ICP surface registration[END_REF] using the small angle approximation to solve the optimization problem. In [START_REF] Fitzgibbon | Robust Registration of 2D and 3D Point Sets[END_REF], the problem is kept non-linear and solved using a Levenberg-Marquardt approach integrating robust estimators. In [START_REF] Magnusson | Beyond points: Evaluating recent 3D scan-matching algorithms[END_REF], Normal Distribution Transform (NDT) takes into account local surface structures around each point and does not match individual points unlike common ICP variants. In Generalized-ICP (G-ICP) [START_REF] Segal | Generalized-ICP[END_REF], the local neighborhood of points is used in order to assimilate this structure to small planar patches. As in point-to-plane approaches, the local normals of the target point cloud are taken into account but also the ones of the source point cloud. It can be assimilated to plane-to-plane registration.

In [START_REF] Taguchi | Point-plane SLAM for hand-held 3D sensors[END_REF] and [START_REF] Grant | Efficient Velodyne SLAM with point and plane features[END_REF], the approaches exploit the planar surfaces of man-made environments with a plane-to-plane distance. Those approaches are interesting to use in indoor environments when enough planes are available. However, segmenting planes can be time consuming. In [START_REF] Poppinga | Fast plane detection and polygonalization in noisy 3D range images[END_REF] and [START_REF] Pathak | Fast 3D mapping by matching planes extracted from range sensor point-clouds[END_REF], the 3D data of the sensor are used as range images. The neighborhood relation of the pixels is then used to segment the planes in a region growing scheme. It also includes a polygonalization of the planes in surface models. A region growing process based on smoothness is introduced in [START_REF] Rabbani | Segmentation of point clouds using smoothness constraints[END_REF]. In [START_REF] Fischler | Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography[END_REF], [START_REF] Pathak | Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping[END_REF] and [START_REF] Taguchi | Point-plane SLAM for hand-held 3D sensors[END_REF], RANSAC approaches are used in order to fit points to planar patches.

Matching planes after the segmentation is another challenging task. In [START_REF] Grant | Efficient Velodyne SLAM with point and plane features[END_REF], the data rate acquisition is supposed very high, which leads to low relative translation between scans. Thus, planes with the projections of the origin of the sensor close to each other and almost parallel are matched. In [START_REF] Chen | PLADE: A Plane-Based Descriptor for Point Cloud Registration With Small Overlap[END_REF], a plane/line descriptor is proposed to establish structure correspondences. Attributes of the planes and the constraints between them are used in [START_REF] Zong | A Fast and Accurate Planar-Feature-Based Global Scan Registration Method[END_REF].

Algorithms such as G-ICP [START_REF] Segal | Generalized-ICP[END_REF], point-to-plane ICP [START_REF] Low | Linear least-squares optimization for point-toplane ICP surface registration[END_REF] and NDT [START_REF] Magnusson | Beyond points: Evaluating recent 3D scan-matching algorithms[END_REF] while being efficient for fine registration are sensitive to large motion and usually need a good initialization of the rigid transformation in order to converge. Moreover G-ICP can be slow to perform registration as is shown further in this article. In this paper, we exhibit a framework designed for

P P pi-1 pi-1 ni-1 di-1 s s t t t pi t pi+1 t ni t ni+1 t di di+1 pi s pi+1 s
Fig. 1. Point-to-plane distance d ⊥ i as described in [START_REF] Low | Linear least-squares optimization for point-toplane ICP surface registration[END_REF]. In red the surface on which the target point cloud lies and the surface normals related to its points. In blue the surface where lies the source point cloud to register. plane-to-plane registration in indoor environment. This method is denoted New Accurate Plane-based ICP (NAP-ICP). The proposed algorithm is robust to large motion, thus it is less sensitive to initialization than other evaluated state-of-the-art algorithms. The main contributions of this article are:

• an efficient score metric for finding best plane correspondences; • a two-step minimization method from coarse to fine registration based on plane features; • an algorithm performing fast and accurate registration in challenging datasets; • a method robust to large motion or inaccurate initialization. First the variants of the distance to minimize are presented. After the methodology of the proposed method is described with a section related to each step of the algorithm. Then the experiments and their results are presented. Finally a conclusion and perspectives are given.

II. ICP VARIANTS

In the following sections, source and target points will be respectively denoted s P = { s p i } N i=1 and t P = { t p j } M j=1 . The target point cloud is fixed. The goal is to find the rigid transformation t T s that best fits the source to the target. This transformation is defined as follows:

t T s = t R s t t s 0 3×1 1 (1)
with t R s and t t s respectively a 3×3 rotation matrix and a 3×1 translation vector. Each point s p i of the source is matched with its closest point t p i in the target. Then the rigid transformation minimizing a distance metric is estimated and these two steps are iterated until a threshold is reached. In the original ICP algorithm [START_REF] Besl | A method for registration of 3-D shapes[END_REF] the distance metric to be minimized is the Euclidean point-to-point distance.

As corresponding points from one scan to another may not be exactly identical but may lie on the same surface, it is better to choose, as in [START_REF] Chen | Object modelling by registration of multiple range images[END_REF], to minimize the point-to-plane distance (Fig. 1), defined by:

d ⊥ i = t n i • ( t T s s p i -t p i ) 2 (2)
with t n i the surface normal computed from t p i neighborhood.

In order to reduce the number of input data in the optimization step, the plane-to-plane distance is used. A plane Π(ρ, n) is given by the equation n p = ρ, where ρ is the distance from the origin of the sensor in the direction of the unit plane normal n. The distance between two corresponding planes ([9], [10]), with s Π i ( s ρ i , s n i ) the source plane and t Π i ( t ρ i , t n i ) the target one, is given as follows:

d Π i = t R s s n i -t n i [ t R s s n i ] t t s + s ρ i -t ρ i (3)
where s n i and t n i are the normal to s Π i and t Π i respectively and s ρ i and t ρ i their respective distance to the origin of the sensor in the target frame.

III. METHODOLOGY

This section describes each step of the proposed NAP-ICP algorithm. The framework is given in Fig. 2. Similarly to the classical ICP algorithm the method iteratively performs the matching step and the minimization step. However in the proposed method the first features to be matched are planes. Once matched, the rigid transformation minimizing the plane-to-plane distance is estimated. After the plane-toplane registration is performed, an additional point-to-plane registration is done. An example of registration using the proposed method is given in Fig. 3. Each step of the framework is described further in this article: Fig. 3. Example of the registration between two point clouds (scans 3 and 4 from Apartment sequence from ASL dataset [START_REF] Pomerleau | Challenging data sets for point cloud registration algorithms[END_REF]). The overlap between scans is small, yet the proposed method succeeds in registering the two point clouds accurately. In white the target point cloud -In green the source point cloud. Left: before registration -Right: after registration.

• the score metric for finding best plane correspondences is detailed in section III-C; • robust plane-to-plane registration is described in section III-D; • the additional point-to-plane minimization leading to finer registration is detailed in section III-E.

A. Preprocessing

In order to perform plane-to-plane registration as fast and accurately as possible preprocessing is sometimes needed. To speed up computation time, target and source scans are subsampled using a voxel grid of a given resolution. Also, to avoid points from the acquisition system or operators to be part of the point cloud, all points closer than 50cm to the sensor are discarded.

B. Plane extraction

The first step in a plane-based registration (besides preprocessing) is to extract planar structures. In the presented results, planes are extracted using a region growing segmentation based on [START_REF] Rabbani | Segmentation of point clouds using smoothness constraints[END_REF], using the Point Cloud Library (PCL) [START_REF] Rusu | 3D is here: Point Cloud Library (PCL)[END_REF]. In this approach, the points in a neighborhood with a small angle difference between normals are considered to be on the same smooth surface and are gathered in a cluster. Each cluster represents a plane. The normals are estimated by performing a Principal Component Analysis (PCA) on the neighborhood of the concerned points [START_REF] Rusu | 3D is here: Point Cloud Library (PCL)[END_REF]. An example of the obtained plane segmentation is given in Fig. 4. Fig. 4. Plane segmentation with region growing approach. Left: input point cloud (from ASL dataset [START_REF] Pomerleau | Challenging data sets for point cloud registration algorithms[END_REF]) -Right: plane extraction result using region growing [START_REF] Rabbani | Segmentation of point clouds using smoothness constraints[END_REF]. Each extracted plane is in a different color. Red points are outliers.

C. Plane matching

Once the planes are segmented, the next step is matching each source plane to the closest one in the target point cloud. For each extracted plane s Π i in the source, a list of planes in the target that are potential matches for the source plane is made, called target candidates. Each target candidate t Π j is given a score within the range [0; 1]. It is computed from the following features:

• the distance between the projections of the origin on source plane and target plane d o , expected to be close to 0:

d o = s ρ i s n i -t ρ j t n j 2 (4)
• the distance between the centroids of source and target planes d c , expected to be close to 0:

d c = s pi -t pj 2 (5)
with s p and t p the centroids of s Π i and t Π j respectively; • the area ratio between the planes S r , expected to be close to 1 as planes are expected to have similar areas:

S r = min( s S i , t S j ) max( s S i , t S j ) (6)
with s S i and t S j the area of source and target planes respectively; • the dot product of the normals of the planes φ n , expected to be close to 1 as planes are expected to be almost parallel:

φ n = s n i • t n j (7)
Each feature is normalized between [0; 1] and weighted, which leads to a score defined as follows:

score = α • do + β • dc + γ • (1 -Ŝr ) + δ • (1 -φn ) (8)
with . denoting the normalized value, and the weights α, β, γ and δ subject to:

α + β + γ + δ = 1 (9)
Parameters α, β, γ, δ were chosen empirically to fixed values for all experiments such as: α = 0.35, β = 0.4, γ = 0.1 and δ = 0.15.

A target plane is considered as a valid matching candidate if it respects the following condition: score < t score [START_REF] Grant | Efficient Velodyne SLAM with point and plane features[END_REF] Some matched planes following the previous condition happen to be too far from each other, hence their centroids are distant. They are discarded with the following condition:

d c > t centroid ( 11
)
If the distance d c between the centroids is bigger than a threshold t centroid , the planes are too far from each other to be a valid correspondence.

Also, matched planes are supposed to have similar areas. To avoid matched planes with a notable difference in area, another condition is added:

S r > t S (12)
If the area ratio S r is smaller than a threshold t S , the correspondence is discarded. The valid pairs of matched planes form the list of correspondences between source and target planes. The resulting list is used for the transformation estimation. As not only the correspondence with the smallest score are kept but all correspondences respecting the previous conditions, the list may contain several occurrences of the same source plane, with different target planes and vice-versa.

D. Plane-to-plane registration

Now that the set of plane correspondences is built, the planeto-plane distance minimization that estimates the rigid transformation linking source to target planes can be computed.

Closed-form plane-to-plane optimization method: In this section a closed-form minimization of the plane-to-plane distance is presented. The derivation is similar to the one presented in [START_REF] Taguchi | Point-plane SLAM for hand-held 3D sensors[END_REF] without the point-to-point correspondences.

Corresponding planes are denoted s Π i ( s ρ i , s n i ) and t Π i ( t ρ i , t n i ). Similarly to the point-to-point problem in [START_REF] Arun | Least-squares fitting of two 3-d point sets[END_REF] and [START_REF] Lorusso | A Comparison of Four Algorithms forEstimating 3-D Rigid Transformations[END_REF] the rotation and translation are decoupled.

The rotation estimation is obtained by minimizing:

N i=1 = t R s s n i -t n i 2 (13)
First, a 3 × 3 correlation matrix H is built such as:

H = N i=1 s n i t n i (14)
Its Singular Value Decomposition (SVD), H = UDV, is computed. The optimal rotation matrix tR s is given by:

tR s = VU (15)
To compute the translation the following equation is minimized:

N i=1 t n i t t s + s ρ i -t ρ i 2 (16)
corresponding to solving the linear system A t t s = b where

A =    t n 1 . . . t n N    , b =    t ρ 1 -s ρ 1 . . . t ρ N -s ρ N   
The least-squares solution of this problem is given by t t s = A + b where A + is the pseudo-inverse of A.

RANSAC process: As the previously created correspondence list may contain outliers, it is important to discard them as they can lead to divergence in the minimization step. To do so, a RANSAC process is applied to make the minimization more robust.

Only three non-parallel planes are needed in the source and target respectively. Each sample of the RANSAC algorithm is selected respecting this condition.

Gauss-Newton plane-to-plane minimization: The plane correspondences identified as inliers by the RANSAC process are given as input of the Gauss-Newton approach.

This method requires a minimal representation of the transformation to be estimated t T s . Such a representation is defined by a 6 dimensional vector denoted q = ( t t s , θu) where θ and u are the angle and the axis of the rotation t R s .

The plane-to-plane error has to be minimized such that:

q = argmin q N i=1 d Π i 2 (17)
Equation ( 17) can be solved using the Gauss-Newton algorithm. Solving it consists in minimizing the cost function E(q) = e(q) :

e(q) = n(q) -n ρ(q) -ρ (18)
with n(q) = (..., t R s s n i , ...), n = (..., t n i , ...), ρ(q) = (..., [ t R s s n i ] t t s + s ρ i , ...) and ρ = t ρ i the error vector of the distance between the target point cloud and the source point cloud transformed with the previous estimated transformation.

The first order Taylor approximation gives:

e(q + δq) ≈ e(q) + J(q)δq [START_REF] Rusu | 3D is here: Point Cloud Library (PCL)[END_REF] where J(q) is the Jacobian of e(q) in q.

With the Gauss-Newton method, the solution consists in minimizing E(q + δq) with: E(q + δq) = e(q + δq) ≈ e(q) + J(q)δq (20)

The minimization problem can be solved by an iterative least-squares approach which gives: δq = -λJ(q) + e(q) [START_REF] Lorusso | A Comparison of Four Algorithms forEstimating 3-D Rigid Transformations[END_REF] where λ is a coefficient in ]0, 1] and J + is the pseudo-inverse of the Jacobian J.

The pose is then updated at each iteration:

q k+1 = q k ⊕ δq = exp δq q (22)
where ⊕ denotes the composition over se(3) obtained via the exponential map. Its associated 4N ×6 Jacobian matrix J stacks each Jacobian matrix J i :

J i = 0 3×3 [ t R s s n i ] × -[ t R s s n i ] 0 1×3 (23)
with [x] × the skew matrix of a vector x.

E. Point-to-plane Registration

To ensure an accurate registration, a finer step is added to find the best expected rigid transformation. To do so a pointto-plane registration is added at the end of the process. A robust non-linear minimization of the point-to-plane distance is presented. Each point s p i from s P is matched to its closest point t p i in t P according to the Euclidean distance. Then the rigid transformation that registers source to target point cloud is computed by minimizing the point-to-plane distance (Eq. 2).

The principle is the same as section III-D with:

q = argmin q N i=1 d ⊥ i 2 (24)
Its Jacobian J i is defined by:

J i = -t n i t n i [ s p i ] × (25)
If outliers are present in the dataset the minimization problem becomes unstable. Using the Gauss-Newton method for the minimization allows to introduce M-estimators, a class of robust functions, in the algorithm to discard outliers [START_REF] Comport | Real-time markerless tracking for augmented reality: the virtual visual servoing framework[END_REF]. Denoting ρ(.) a robust function, q becomes:

q = argmin q N i=1 ρ(d ⊥ i ) (26)
The introduction of M-estimators can be implemented as an Iteratively Re-weighted Least Squares (IRLS) where the error to minimize is defined by:

e ρ (q) = D(e(q)) (27)
and δq = -λ(DJ(q)) + De(q) (28

)
where D is a N × N diagonal matrix containing the weights that reflect the confidence in the data.

IV. EXPERIMENTS

To evaluate the efficiency, accuracy and robustness of the proposed method, experiments in different scenarios are held. The indoor sequences of the ASL Dataset [START_REF] Pomerleau | Challenging data sets for point cloud registration algorithms[END_REF] are used. One of the main advantages of this dataset is that each sequence comes with the ground truth poses measured for each scan with millimeter precision. As the proposed NAP-ICP is designed to register point clouds in man-made environments, the outdoor sequences containing various types of surfaces (thus not planar) were discarded. All sequences were recorded using a Hokuyo UTM-30LX.

• Apartment: This sequence is designed to evaluate algorithm robustness to outliers coming from dynamic elements (e.g. moved furniture). The sequence was captured moving the sensor on a 2D plane in an apartment. It is a very structured scene (walls, ceiling, floor). The sequence is composed of 44 scans of about 365,000 points. Metrics: As in [START_REF] Pomerleau | Comparing ICP variants on real-world data sets Open-source library and experimental protocol[END_REF], the accuracy of the tested metrics is evaluated with the Euclidean distance ∆ t between the estimated transformation and the ground truth for translation and the geodesic distance ∆ r for rotation:

∆ t = t ts -t t * s (29) ∆ r = arccos trace( t R * s -1 t Rs ) -1 2 (30)
with t ts and t Rs the estimated translation and rotation, t t * s and t R * s the ground truth translation and rotation respectively. The thresholds to estimate a successful registration are 0.1m for translation and 2.5 • for rotation as suggested in [START_REF] Magnusson | Beyond points: Evaluating recent 3D scan-matching algorithms[END_REF]. Note that rotation and translation errors are presented separately but a result is valid only if both rotation and translation errors are smaller than their respective threshold.

A. Impact of several minimization steps

This experiment aims to show the need for the point-toplane registration step in NAP-ICP algorithm. As can be seen in Fig. 2 the estimation of the rigid transformation is performed in two successive steps. First a plane-to-plane minimization is performed (section III-D) followed by a pointto-plane registration (section III-E).

In Fig. 5, the impact of the steps can be observed with curves representing the cumulative probabilities errors on translation and rotation. The more top-left the curve the better the algorithm performs. The expected behavior is to attain 1 (meaning all scans of the sequence are registered) before the error threshold is reached. If so, it means that 100% of the scans of the sequence are successfully registered (according to the threshold previously). If 1 is not reached before the threshold, it means that the registration error is too large to be considered successful. On the plots, one can observe that for each sequence, the plane-to-plane registration gives a good initialization of the rigid transformation but is still far from ground truth. For instance, considering the Apartment sequence, only 41% scans are well registered regarding translation error (81% success rate in rotation). On ETH and Stairs, regarding rotation, even if the plane-to-plane registration gives results sufficient to reach the expected threshold, the addition of the point-to-plane proves to give a more accurate estimation.

For each tested sequence, the point-to-plane ICP step addition achieves a 100% success rate in rotation and translation. This proves the ability of the plane-to-plane minimization to give a result close enough to what is expected in order to obtain an accurate registration with a robust point-to-plane registration.

In Figure 6 all scans of Apartment sequence are registered in a mapping intention with the full algorithm. Each scan is registered with the previous one using the previously found transformation as initialization 1 .

B. Comparison with state-of-the-art Algorithms

In the following experiment, NAP-ICP is compared to three state-of-the-art registration algorithms in terms of accuracy and computation time.

• G-ICP [START_REF] Segal | Generalized-ICP[END_REF] has three major parameters. Maximum iterations is set to 10, Euclidean fitness epsilon is set to 10 -6 and maximum correspondence distance is set to 0.8m. • NDT, with the steps recommended in [START_REF] Magnusson | Beyond points: Evaluating recent 3D scan-matching algorithms[END_REF]. Transformation epsilon is set to 10 -3 , step size 0.1, maximum iteration 5, first step resolution 1.0m, second step resolution 2.0m, third step resolution 1.0m and last step 0.5m. The points of the map are colored regarding their height (blue being the lowest value, green in between and yellow the highest). In white the ground truth trajectory. In purple dots the trajectory computed with the plane-to-plane registration only. In red dots the trajectory computed with the full proposed NAP-ICP (combination of plane-to-plane and point-to-plane registration).

Ceiling has been removed to ease visualization.

• Point-to-plane ICP [START_REF] Chen | Object modelling by registration of multiple range images[END_REF], with the PCL implementation (denoted ICP-PCL), similarly to G-ICP has three major parameters. Maximum iterations is set to 100, Euclidean fitness epsilon is set to 10 -6 and maximum correspondence distance is set to 0.8m. Accuracy: To evaluate accuracy, equations (29) and (30) are used with all tested algorithms. The results of this experiment are summarized in Table I.

Globally, the proposed NAP-ICP algorithm gives more accurate results than G-ICP, NDT and ICP-PCL. On Apartment, ETH and Stairs sequences NAP-ICP achieves a 100% rate of successful registration. G-ICP, NDT and ICP-PCL give a 100% success rate on ETH sequence. However, their results on Stairs are not as good, even if still satisfying. On Apartment NAP-ICP outperforms the state-of-the-art algorithms. G-ICP achieves 75% of successful registrations, 77% for NDT and only 43% for ICP-PCL. This sequence includes large rotations (38% of the sequence is composed of motion with more than ±35 • rotation on yaw axis) and G-ICP, NDT and ICP-PCL

Fig. 2 .

 2 Fig. 2. The proposed NAP-ICP overview.

  (a) Apartment sequence (b) ETH sequence (c) Stairs sequence

Fig. 5 .

 5 Fig. 5. Cumulative probabilities of translation and rotation errors for each step addition of the algorithm. Left: translation error (in meters) on the horizontal axis. The vertical bar represents a threshold (0.1m) for successful registration. Right: rotation error (in degrees) on the horizontal axis. The vertical bar represents a threshold (2.5 • ) for successful registration.

Fig. 6 .

 6 Fig.6. 3D mapping of the Apartment sequence using the proposed method. The points of the map are colored regarding their height (blue being the lowest value, green in between and yellow the highest). In white the ground truth trajectory. In purple dots the trajectory computed with the plane-to-plane registration only. In red dots the trajectory computed with the full proposed NAP-ICP (combination of plane-to-plane and point-to-plane registration). Ceiling has been removed to ease visualization.

  This sequence aims to evaluate robustness of registration to repetitive elements. This scene was captured in a long hallway, following a straight path. It is composed of a wall, a curved ceiling and numerous pillars and arches which are repetitive elements. The sequence is composed of 35 scans of about 191,000 points.• Stairs: This sequence aims to evaluate robustness to rapid variations in scanned volumes. It starts in a long corridor, then a small staircase is crossed and finally the last scan is captured outside of the building. The path in the staircase shows that considering only 2D paths is not valid. The sequence is composed of 30 scans of about 191,000 points.

• ETH:

The authors gratefully acknowledge the financial support of the French Ministry of Higher Education, Research and Innovation and from the European Interreg Project ADAPT.

• preprocessing of the 3D point clouds is briefly discussed in section III-A; • plane extraction is described in section III-B;

A short video presenting the mapping process can be found at: https://youtu.be/CL9gulE68rU

sometimes struggle to find the right solution when our method succeeds. More detailed results with rotation and translation errors are presented in Fig. 7 for all sequences with curves representing the cumulative probabilities errors on translation and rotation for each tested algorithm. The most significant feature of the proposed method in this experiment is its robustness to large motion scenarios (especially rotations) in comparison with other algorithms.

Computation time: No speed optimization are performed in NAP-ICP, however it is important to estimate the performances of the proposed method at this point. The experiments were held on a desktop computer with an Intel Xeon W-2133, 3.6GHz CPU and 32GB RAM. Processing time for the tested algorithms on each sequence is detailed in Table II. For each method, it includes all steps from point clouds preprocessing to transformation estimation. On all sequences NDT is the fastest algorithm, followed by ICP-PCL. NAP-ICP method is slower than the aforementioned algorithms but compensate with its accuracy. G-ICP is the slowest method to handle this dataset. V. CONCLUSION AND PERSPECTIVES

In this paper, NAP-ICP, an efficient plane-based registration algorithm for indoor 3D point clouds, is presented. This proposed method is based solely on LiDAR data.

A new metric based on planes characteristics is proposed to efficiently find the best plane correspondences. The robust plane-to-plane minimization followed by a point-to-plane minimization reaches 100% of successful registration on the tested sequences. Experiments show that NAP-ICP performs better than other state-of-the-art algorithms in well-structured environments (more specifically with large motion initialization between scans). They also showed that NAP-ICP algorithm is not only accurate but also fast. As it was not the main goal of this study, there is still room for optimization.

A more thorough study about weighting parameters for plane matching will be proposed in future works. This method also needs to be improved to handle outdoor urban environments.


Nathan Carriot

Benoît Paix

Stephane Greff

Bruno Viguier

Jean-François Briand

Gérald Culioli
email: culioli@univ-tln.fr

Stéphane Greff

Integration of LC/MS-based molecular networking and classical phytochemical approach allows in-depth annotation of the metabolome of non-model organisms -The case study of the brown seaweed Taonia atomaria

Keywords: Molecular networking, Metabolomics, Lipidomics, UHPLC-MS/MS, Macroalga, Taonia atomaria

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

Graphical abstract 1. Introduction

Marine chemical ecology is a field of research that aims to address the role of chemicals in marine ecosystems. Indeed, molecules expressed by marine organisms have preponderant ecological functions, e.g. foraging, defense or reproduction [START_REF] Hay | Marine chemical ecology: chemical signals and cues structure marine populations, communities, and ecosystems[END_REF]. There is a growing interest in this field of study because it benefits from many recent technical advances including those of environmental metabolomics [START_REF] Kuhlisch | Metabolomics in chemical ecology[END_REF].

Metabolomics is the latest in the so-called "omics" sciences that brings together genomics, transcriptomics and proteomics [START_REF] Tautenhahn | An accelerated workflow for untargeted metabolomics using the METLIN database[END_REF]. Ultimately, in such a research area the aim is to decipher the metabolome, which represents the whole set of low-molecular-weight organic compounds present in an organism or more generally in a biological sample, and to evaluate and interpret its variations [START_REF] Oliver | Systematic functional analysis of the yeast genome[END_REF]. The metabolome is constituted by all quantifiable metabolites and appears to be highly complementary with other "omics" sciences for a global systems biology approach [START_REF] Dittami | A metabolic approach to study algal-bacterial interactions in changing environments[END_REF][START_REF] Monte | Improved drug therapy: triangulating phenomics with genomics and metabolomics[END_REF][START_REF] Patti | Meta-analysis of untargeted metabolomic data from multiple profiling experiments[END_REF].

Untargeted LC-MS-based metabolomic studies are currently the most common ones but metabolite identification is generally the limiting factor when interpreting the resulting data [START_REF] Chaleckis | Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics[END_REF].

Indeed, the diversity of the metabolism is vast in nature and metabolites implied in the discrimination between sample groups often remain unidentified [START_REF] Vaniya | Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics[END_REF]. With an estimation of less than 2% of mass spectra which can be annotated from untargeted studies, the characterization of the chemical structures is the most challenging step of the metabolomic workflow [START_REF] Da Silva | Illuminating the dark matter in metabolomics[END_REF].

Knowing that each LC-MS analysis generates a large number of spectra [START_REF] Scheubert | Significance estimation for large scale metabolomics annotations by spectral matching[END_REF][START_REF] Kind | Qualitative analysis of algal secretions with multiple mass spectrometric platforms[END_REF], spectral identification of each compound, one by one, using classical dereplication tools with reference databases (DBs) such as NCBI, Metlin, NIST or MassBank, is a too time-consuming approach.

For the past few years, MS/MS molecular networking (MN) has been proposed as a new emerging tool to optimize this dereplication process [START_REF] Yang | Molecular networking as a dereplication strategy[END_REF]. Based on the idea that MS/MS fragmentation patterns are similar for molecules sharing close chemical structures, the Global Natural Products Social Molecular Networking (GNPS) Web-platform (http://gnps.ucsd.edu) allows to generate MNs where compounds are linked according to their molecular relatedness [14][START_REF] Nothias | Bioactivity-based molecular networking for the discovery of drug leads in natural product bioassay-guided fractionation[END_REF][START_REF] Aron | Reproducible molecular networking of untargeted mass spectrometry data using GNPS[END_REF]. The resulting MNs show then diverse clusters composed by several m/z features (defined as "nodes") characterized by their MS/MS data. The clusters generated in a MN could then gather metabolites belonging to a same chemical family or sharing closely related chemical structures.

In this study, the use in a first step of a restricted MN will allow to gather compounds having very similar chemical structures in order to facilitate their annotation. Indeed, to unlock clusters and to putatively identify all nodes of a cluster, two situations are expected: (i) if a compound is identified, all nodes of a cluster could be annotated using mass differences from this "seed node" [START_REF] Duncan | Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species[END_REF][START_REF] Ramos | Natural products targeting strategies involving molecular networking: different manners, one goal[END_REF][START_REF] Grim | Staring into the void: demystifying microbial metabolomics[END_REF], (ii) if all compounds remain unidentified, the annotation can be done by determining the common structural characteristics of the nodes thanks to the comparison of their MS/MS spectra. The enlargement of the MN is then considered in order to continue the annotation of the metabolome by using less restrictive parameters for the connection between nodes.

The selected model in this study was Taonia atomaria, a brown alga commonly reported along the Mediterranean and North-Eastern Atlantic coasts. To date, several types of chemical compounds, mainly sesquiterpenes [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF][START_REF] Jerković | Chemical diversity of headspace and volatile oil composition of two brown algae (Taonia atomaria and Padina pavonica) from the Adriatic Sea[END_REF][START_REF] Rosa | Sesquiterpenes from the brown alga Taonia atomaria[END_REF][START_REF] Tringali | Sesquiterpenes and geranylgeranylglycerol from the brown algae Taonia lacheana and Taonia atomaria f. ciliata: their chemotaxonomic significance[END_REF], meroditerpenes [START_REF] Pellegrini | Chimiotaxonomie et marqueurs chimiques dans les algues brunes[END_REF][START_REF] Tziveleka | Marine polyprenylated hydroquinones, quinones, and chromenols with inhibitory effects on leukotriene formation[END_REF][START_REF] González | Marine natural products of the atlantic zone-V: The structure and chemistry of taondiol and related compounds[END_REF][START_REF] González | Atomaric acid, a new component from Taonia atomaria[END_REF][START_REF] Abatis | Atomarianones A and B: two cytotoxic meroditerpenes from the brown alga Taonia atomaria[END_REF] and geranylgeranylglycerol (GGG) [START_REF] Tringali | Sesquiterpenes and geranylgeranylglycerol from the brown algae Taonia lacheana and Taonia atomaria f. ciliata: their chemotaxonomic significance[END_REF], have been characterized from the genus Taonia by natural products chemistry. Because of its morphology and distribution, this algal species represents a model of choice for studies in chemical ecology, and more particularly for those dealing with interactions between algae and their associated microbiota. For this reason and in order to carry out metabolomic analyses, the metabolome of this alga needs to be better characterized.

In this context, the aim of the present study was to develop an optimal workflow dedicated to the annotation of a largely unexplored metabolome of a non-model species, namely T. atomaria.

The proposed workflow was based on three successive steps: (i) a classical phytochemical approach permitting to isolate chemical standards which can then be used as seed nodes, initiating a "in-house" MS/MS DB ; (ii) a GNPS networking approach with high restrictiveness allowing only the most structurally-related metabolites to cluster together and to enrich the DB, and (iii) a GNPS networking approach with less restrictive parameters allowing to gather other related metabolites in order to complete the DB.

The present approach is expected to significantly extend our knowledge on algal metabolomes through the MS/MS fragmentation pattern of lipid families presumed to be new and at the same time to propose new solutions for the annotation of the metabolome of little-studied species.

Experimental

The metabolome of T. atomaria was investigated through an original workflow (Fig. 1)

including the analysis of its main chemical constituents by natural products chemistry and the building of several less and less restricted MNs coupled to DB queries and MS/MS data analysis.

Chemicals

Algal material

Isolation of chemical standards from T. atomaria 2.3.1. Extraction of the algal material and fractionation of the crude extracts by Flashchromatography

The collected algal samples were air-dried for 48h in the dark at room temperature (22 ± 3°C), crushed and weighed. The dried algae (140g) were extracted using a mixture of MeOH/CH2Cl2

(1:1, v/v; 3 × 500 mL). The extracts were combined, filtered (filter paper), concentrated in vacuo, and weighted (25g; extraction yield: 17.9%, w/w).

A part of the resulting crude extracts (4.7 g) was submitted to fractionation by flash chromatography (Spot Flash system; Armen Instruments, Saint Ave, France) on a reversedphase column (SuperVarioFlash D40-RP18 model, 40-63 µm, 84g; Merck, Darmstadt, Germany). For fractionation, the extracts were dissolved in 25 mL MeOH and mixed with 15g of reversed-phase silica gel (Sepra C18e, 50 µm; Phenomenex, Le Pecq, France), dried under reduced pressure and loaded on a guard column on the top of the column. The fractionation was monitored by UV detection at 205 nm. For the flow rate, a linear ramp was used from 2 to 32 mL/min during 2 min and then this final flow rate was maintained throughout the entire experiment. The separation process was carried out using a ternary eluent system: H2O (solvent A), MeOH (solvent B) and CH2Cl2 (solvent C). After an initial isocratic step with 90% of A and 10% of B from 0 to 3 min, a linear gradient up to 100% of B from 3 to 13 min, an isocratic step with 100% of B from 13 to 18 min, and a linear gradient up to 100% of C from 18 to 29 min, an isocratic step with 100% of C was finally used from 29 to 40 min. This process led to 39 fractions which were dried in vacuo, weighted and stored at -20°C (more details are given in Supporting Information, Table S1).

Purification and structural characterization of chemical standards from T. atomaria

For sixteen of the 39 fractions obtained by flash chromatography, selected according to available quantities, an aliquot (5 to 10 mg) was solubilized in the appropriate deuterated solvent (CDCl3 or CD3OD) and analyzed by 1 then came back to 5% ACN over 0.01 min and was maintained 1.99 min, for a total run time of 16 min (See Supporting information Fig. S1 for the chromatograms of these sixteen fractions).

The MS acquisition was performed in positive ionization and full scan range (m/z 50 to 1200 at a frequency of 2 Hz) modes. The following MS conditions were used: nebulizing gas (N2) pressure: 0.4 bars, drying gas (N2) flow: 4 L/min, drying temperature: 180°C, capillary voltage:

4.5 kV. The mass spectrometer was calibrated with a solution of formate/acetate forming clusters at the beginning of the sequence run and the same solution was automatically injected before each sample for internal mass calibration. MS/MS acquisition experiments were conducted by collision induced dissociation (CID) on each sample. Three major precursor ions were used and, after the acquisition of three MS/MS spectra per precursor ion, the corresponding m/z value was excluded from the precursor ion selection for 1 min.

MS/MS molecular networking using GNPS

Raw UHPLC-MS/MS data were converted into ".mzXML" files using DataAnalysis software (version 4.3; Bruker Daltonics). The files corresponding to the sixteen fractions previously selected (See Part 2.3.2.) were grouped into six folders corresponding to six polarity ranges.

The Global Natural Products Social Molecular Networking online workflow (GNPS; http://gnps.ucsd.edu) was used to create MNs of related compounds based on the similarity of their MS/MS fragmentation patterns [14]. Three MNs (MN-1 to MN-3, from the most to the least restrictied MN) were then generated using various values of "Minimum cosine score (CS)"

(a similarity score between two MS/MS spectra allowing the creation of a link between them), "Minimum matched fragment ions (MF)" (the minimum number of common fragment ions that are shared by two MS/MS spectra in order to be connected by an edge in the MN) and "Network

TopK" (the maximum number of neighbor nodes for a single node) (Table 1). Data from GNPS were imported into Cytoscape (version 3.7.0) using nodes to represent each spectrum (each m/z feature) and edges to represent the similarity of MS/MS fragmentation between two connected nodes [14]. A specific color was attributed to each node according to the chemical family of the annotated metabolite or to the corresponding RT. Width of edges connecting each node was proportional to the spectral similarity using CS as a default parameter.

Metabolite annotation in the MNs

The strategy was to focus on clusters with the highest number of nodes. In such clusters, each node could be tentatively annotated by one or several of the following possibilities: (1) during the computational process dedicated to the construction of MNs on the GNPS platform, fragment similarity searches were performed in several public DBs (e.g. MassBank, HMDB, MoNA, Metlin…) for each m/z feature and the results were directly available into the MNs subsequently generated by Cytoscape, (2) MS data (accurate mass and fragmentation pattern) of a specific metabolite can also be compared with those found in other DBs (PubChem, ChemSpider…) using various tools [START_REF] Tautenhahn | XCMS Online: A web-based platform to process untargeted metabolomic data[END_REF], (3) MS data and RT of the detected m/z features were compared with those of pure compounds isolated from T. atomaria (See Part 2.3.2.), and (4) in the case of absence of a hit in a DB, the MS data of all the nodes in a same cluster were carefully analyzed to highlight specific common fragments or mass losses which could allow the identification of a specific chemical family in relation to the data available in the literature or through mass fragmentation pattern analysis.

In all these cases, once one m/z feature was annotated in a cluster, the main part of the corresponding cluster could be annotated from this seed node according to similarity of MS/MS spectra and to accurate masses differences between nodes.

Results and discussion

Isolation of chemical standards from T. atomaria: a "phytochemical approach"

Previous studies related to compounds isolated from extracts of the brown alga T. atomaria allowed the isolation and the structural characterization of GGG [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF][START_REF] Tringali | Sesquiterpenes and geranylgeranylglycerol from the brown algae Taonia lacheana and Taonia atomaria f. ciliata: their chemotaxonomic significance[END_REF][START_REF] Amico | R)-1-O-Geranylgeranylglycerol from the brown alga Dilophus fasciola[END_REF] and other terpenes (mainly germacrane and cadinane sesquiterpenoids) [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF][START_REF] Jerković | Chemical diversity of headspace and volatile oil composition of two brown algae (Taonia atomaria and Padina pavonica) from the Adriatic Sea[END_REF][START_REF] Rosa | Sesquiterpenes from the brown alga Taonia atomaria[END_REF][START_REF] Tringali | Sesquiterpenes and geranylgeranylglycerol from the brown algae Taonia lacheana and Taonia atomaria f. ciliata: their chemotaxonomic significance[END_REF][START_REF] Amico | R)-1-O-Geranylgeranylglycerol from the brown alga Dilophus fasciola[END_REF][START_REF] Amico | Sesquiterpenes based on the cadalane skeleton from the brown alga Dilophus fasciola[END_REF].

In this study, after the fractionation by flash-chromatography of the crude extracts of T.

atomaria and the purification by semi-preparative HPLC of the main chemical components from a selection of fractions, eight metabolites were characterized. Seven of these compounds were already described from T. atomaria: cadina-4( 14),5-diene, cubebol, 4-epi-cubebol, (+)gleenol, germacra-4(15),5,10( 14)-trien-9-ol, (1S, 5E, 7S) 1-acetoxygermacra-4(15),5,10( 14)triene and geranylgeranylglycerol [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Rosa | Sesquiterpenes from the brown alga Taonia atomaria[END_REF][START_REF] Tringali | Sesquiterpenes and geranylgeranylglycerol from the brown algae Taonia lacheana and Taonia atomaria f. ciliata: their chemotaxonomic significance[END_REF] (Fig. S2). An additional compound was identified as dictyol A, a diterpene previously reported from the Phaeophyceae Dictyota dichotoma [START_REF] Fattorusso | Dictyol A and B, two novel diterpene alcohols from the brown alga Dictyota dichotoma[END_REF] but described here for the first time from T. atomaria (Fig. S3-S5). As Dictyota spp. are morphologically and taxonomically close to T. atomaria and in order to avoid a possible contamination of the crude extracts studied here, the presence of this compound was confirmed by LC-MS/MS on specific crude extracts obtained within a single thallus of T. atomaria.

The MS data and RT of these eight compounds allowed to initiate the elaboration of an in-house DB dedicated to this algal model.

Annotation of the metabolome of T. atomaria using a restricted MN (MN-1)

The building of a MN (named MN-1) with very restrictive parameters allowed to obtain a simplified MN keeping only strong similarities between nodes. Three parameters have been specifically optimized to have a more restricted MN: (i) the minimum CS which was fixed at 0.85 (default value: 0.7) for a higher similarity between the MS/MS spectra of two connected nodes, (ii) the minimum matched fragment ions (MF) was set at 10 (default value: 6) and

(iii) the Network TopK value was 7 (default value: 10). Many studies using MNs for annotation of the metabolome of microorganisms use low values of CS and MF [START_REF] Crüsemann | Prioritizing natural product diversity in a collection of 146 bacterial strains based on growth and extraction protocols[END_REF][START_REF] Saurav | Antimicrobial activity and bioactive profiling of heterocytous cyanobacterial strains using MS/MS-based molecular networking[END_REF] in order to increase the size of the clusters by inducing the clustering of less similar MS/MS spectra. This strategy allows to obtain a lot of links between nodes and less unbound nodes.

Our proposed strategy for annotation is completely opposite, the creation at first of a constrained network will give small clusters with highly correlated nodes which would allow an easier and faster determination of their characteristic MS/MS fragmentation patterns. The enlargement of the network, by using less restrictive parameters values, would connect some unbound nodes in clusters already annotated and should allow their quick annotation.

The building of a highly restricted MN (MN-1) showed the occurrence of ten main clusters named 1A to 1J which were carefully analyzed (Fig. 2).

Annotation of known lipid classes: Clusters 1A to 1E

During the building process of MN-1, the only m/z feature matching within a DB (MoNA) belonged to cluster 1A and was identified as a triacylglycerol (TG). This node at m/z 794.7233 ([C49H96NO6] + , calc. m/z 794.7232,  -0.1 ppm; [M + NH4] + ) was annotated as TG (14:0/16:0/16:1) (See Supporting information for MS/MS data and fragmentation pattern, Fig. S6) [START_REF] Samburova | Analysis of triacylglycerols and free fatty acids in algae using ultra-performance liquid chromatography mass spectrometry[END_REF]. Just using mass differences from this seed node (Fig. 2), thirteen other TGs were readily detected in the neighboring nodes and their annotation was confirmed after examination of their MS/MS data. RT constitutes an essential parameter when analyzing lipids. In reversed phase chromatography, retention increases with the number of carbon atoms and decreases with the number of unsaturations, in a homologous series. In that respect, a RT-based representation of MN-1 (See supporting information, Fig. S7) confirmed that these nodes belong to the same lipid family. Interestingly, several nodes connected to TGs in the same cluster showed a lower retention: a similar MS/MS fragmentation pattern and lower molecular masses were then used to easily annotated these nodes as diacylglycerols (DGs). It's noteworthy that a TG which was connected to other TGs or DGs always bore with these nodes two common acyl chains [e.g. TG (14:0/16:0/16:1) and TG (15:0/16:0/16:1), TG (16:0/16:0/18:1) and DG (16:0/16:0) or TG (16:0/18:1/18:2) and DG (16:0/18:1)]. In cluster 1A, a detailed analysis of the MS data of the remaining non-annotated nodes allowed to gather them in two subgroups. Some of these nodes showed a characteristic neutral loss of 261 Da which is typical of the loss of the polar sulfoquinovosyl group from the ammoniated adduct of sulfoquinovosyldiacylglycerols (SQDGs) [START_REF] Popendorf | Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS[END_REF]. Seven of these lipids were then identified in cluster 1A, including the node at m/z 834.5396 ([C43H80NO12S] + , calc. m/z 834.5396,  0.0 ppm; [M + NH4] + ) annotated as SQDG (16:0/18:3) (See Supporting information for MS/MS data and fragmentation pattern, Fig. S6). MS data of nodes of the second subgroup were characterized by a neutral loss of m/z 197 which allowed to annotate them as monogalactosyldiacylglycerols (MGDGs) [START_REF] Popendorf | Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS[END_REF][START_REF] Isaac | New mass-spectrometry-based strategies for lipids[END_REF].

For example, the node at m/z 746.5770 ([C41H80NO10] + , calc. m/z 746.5777,  0.9 ppm;

[M + NH4] + ) was annotated as MGDG (14:0/18:1) (See Supporting information for MS/MS data and fragmentation pattern, Fig. S6). Ultimately, all the nodes of cluster 1A were annotated.

The corresponding metabolites belonged to four lipid classes but the acyl chains of all these glycerolipids were mainly C16:0, C16:1 and C18:1. It can be noticed that MGDG (16:0/18:2) and MGDG (16:1/18:1) corresponded to a same single node on the network because of their identical m/z value and their similar RT and MS fragmentation pattern.

Interestingly, seven nodes with characteristic RT of TGs were observed in cluster 1B (See supporting information, Fig. S8) and the detailed analysis of their MS data confirmed their annotation. As reported for cluster 1A, the same approach allowed to annotate the other nodes as DGs and MGDGs. However, the MS data of the node at m/z 976.5977 ([C53H86NO15] + , calc.

m/z 976.5992, Δ 1.6 ppm; [M + NH4] + ) matched with none of the chemical families previously defined in this MN. This m/z feature was then annotated as a digalactosyldiacylglycerol, DGDG (18:4/20:5) (See Supporting information for MS/MS data and proposed fragmentation pattern, Fig. S8) [START_REF] Popendorf | Molecular ion-independent quantification of polar glycerolipid classes in marine plankton using triple quadrupole MS[END_REF][START_REF] Isaac | New mass-spectrometry-based strategies for lipids[END_REF]. It could be pointed out that glycerolipids of cluster 1B were composed by longer FAs (eg. C18:4, C20:4 and C20:5) than those found in metabolites of cluster 1A.

A third cluster (1C) consisted of nodes, which showed a characteristic MS/MS fragment ion at m/z 236.1493 corresponding to the raw formula [C10H22NO5] + . A comparison of these MS data with those from the literature indicated that this fragment ion is typical of mono-and diacylglycerylhydroxymethyl-N,N,N-trimethyl-ß-alanines (MGTAs and DGTAs) [START_REF] Kind | Qualitative analysis of algal secretions with multiple mass spectrometric platforms[END_REF]. This class of betaine lipids has already been observed in T. atomaria [START_REF] Paix | Temporal covariation of epibacterial community and surface metabolome in the Mediterranean seaweed holobiont Taonia atomaria[END_REF][START_REF] Paix | A multi-omics analysis suggests links between the differentiated surface metabolome and epiphytic microbiota along the thallus of a Mediterranean seaweed holobiont[END_REF] and such glycerolipids can be differentiated from the very similar mono-and diacylglyceryl-N,N,Ntrimethylhomoserines (MGTSs and DGTSs) via the absence of a characteristic mass loss of m/z 87 [START_REF] Roche | Betaine lipids in chlorarachniophytes[END_REF]. Fifteen MGTAs were identified in cluster 1C using the MGTA (14:0) at m/z 446.3481 ([C24H48NO6] + , calc. m/z 446.3476,  2.0 ppm) as a seed node. (See Supporting information for MS/MS data and proposed fragmentation pattern, Fig. S9). These last fragment ions observed in all the MS/MS spectra of the nodes found in cluster 1C were thus characteristic of MGTAs (but also of DGTAs) and allowed to group these lipids within a single cluster.

A fourth cluster (1D) gathered nodes among which some showed a characteristic neutral loss of 261 Da on their MS/MS spectra. Based on the annotation previously carried out for the SQDGs and considering their MS and MS/MS data, the compounds of this cluster were annotated as sulfoquinovosylmonoacylglycerols (SQMGs). For example, the node at m/z 600.3411 ([C27H54NO11S] + , calc. m/z 600.3412,  0.1 ppm; [M + NH4] + ) was thus annotated as SQMG (18:1) (See Supporting information for MS/MS data and proposed fragmentation pattern, Fig. S10). Six other SQMGs were identified in cluster 1D together with several structurally related lipids: three monogalactosylmonoacylglycerols (MGMGs) and a monoacylglycerol (MG). It can be noticed that in this restricted MN the SQMGs appeared in a separate cluster than the SQDGs (only found in cluster 1A) showing that the fragment ions due to the acyl chains were in this context more important for the gathering than those of the sulfoquinovosyl part.

A further cluster (1E) was composed by five compounds belonging to a known lipid family.

All these compounds showed in their MS/MS spectrum a characteristic fragment ion at m/z 184.0733 which corresponded to the head polar group of phosphatidylcholines (PCs) [START_REF] Suárez-García | Development and validation of a UHPLC-ESI-MS/MS method for the simultaneous quantification of mammal lysophosphatidylcholines and lysophosphatidylethanolamines in serum[END_REF].

As an example, the node with m/z 542.3237 ([C28H49NO7P] + , calc. m/z 542.3241,  0.8 ppm;

[M + H] + ) was annotated as lyso-PC (20:5) (See Supporting information for MS/MS data and proposed fragmentation pattern, Fig. S11). Thus, using further fragment ions, such as acylium ions, it was possible to identify the five other nodes as four lyso-PCs and a PC.

Putative annotation of new lipid classes: Clusters 1F to 1J

a) Geranylgeranylglycerol derivatives (Clusters 1F and 1G)

Geranylgeranylglycerol (GGG), previously isolated using the "phytochemical approach", was identified in cluster 1F using the experimental data (RT, MS and MS/MS data) obtained from the chemical standard. In this situation, this known metabolite served as a seed node to annotate the rest of this cluster as no other metabolites matched with a hit. Thanks to the MS fragmentation of the precursor adduct ion of GGG at m/z 382.3322 [C23H44NO3] + , calc.

m/z 382.3316,  -1.6 ppm; [M + NH4] + ), many of the resulting fragment ions observed on the MS/MS spectrum of GGG were also found in the MS/MS spectra of the other compounds of this cluster (Fig. 3). More particularly, the characteristic fragment ion at m/z 273.2577 [C20H33] + corresponding to the geranylgeranyl (GG) moiety was observed for all nodes of the cluster 1F.

After a detailed analysis of their MS/MS data, most of the nodes of this cluster have been were also observed allowing its annotation as MGGG (18:4). Seven other nodes of the cluster with acyl chains varying from C16 to C20 with various unsaturations levels were then easily identified in the same way. In the same cluster, six other nodes showed molecular formulae lacking one oxygen atom which were inconsistent with a substitution of GGG by an acyl chain but in accordance with the occurrence of an ether-linked chain. For example, in addition to 3). Five other nodes with a similar fragmentation pattern were putatively annotated as monoalkyl derivatives of GGG in cluster 1F.

characteristic
Another cluster (1G) was composed of m/z features which showed a similar fragmentation pattern than those of cluster 1F, the main differences in their MS/MS data being a greater number of oxygen atoms in their molecular formulae and a characteristic ion fragment at

c) Fulvellic acid derivatives (Clusters 1I and 1J)

Two other clusters (1I and 1J) were composed by nodes which showed molecular formulae and MS/MS fragmentation pattern, more particularly the characteristic fragment ion at m/z 85.0284

[C4H5O2] + , corresponding to those of mono-and diacylglycerols substituted by a C4H5O2 additional chemical group. These findings were in accordance with the chemical structure of fulvellic acid derivatives (i.e. acylglycerols bearing a methacrylic acid moiety) which have been previously described from Japanese samples of the brown alga Sargassum fulvellum [START_REF] Kusumi | A novel type of glycerides bearing a methacrylic acid moiety from the brown alga, Sargassum fulvellum[END_REF]. Thus, we proposed to putatively annotate these compounds as fulvellic acid derivatives even if the lack of MS/MS data in the literature did not allow their unambiguous characterization.

In more detail, the seven nodes of cluster 1I were described as monoacyl fulvellic acid

Assessment of the annotation carried out using the restricted MN-1

This MN approach allowed to quickly identify chemically related compounds gathered in a same cluster because these metabolites showed common MS fragment ions and/or similar MS/MS fragmentation patterns. Indeed, as soon as a metabolite was annotated (seed node), the chances of annotating the whole cluster were very strong. In the case of MN-1, nearly 90% of the metabolites belonging to a cluster where a seed node has been identified were annotated.

Nevertheless, it should be pointed out that to isobaric molecules [e.g. DG (16:1/18:1) & DG (16:0/18:2)] constituted one limitation of this type of approach as they are commonly detected in a same node.

Finally, the careful analysis of MN-1 led to the annotation of 147 metabolites distributed in eighteen chemical classes (Fig. 2). Even if the nodes belonging to the major clusters were annotated, certain compounds and/or clusters are still to be determined. Then, the use of less restricted MNs would allow to include them within a cluster where most of the nodes were already annotated, which could facilitate their identification.

Further annotation of the metabolome of T. atomaria using less restricted MNs (MN-2 and MN-3)

The previous MN (MN-1) was based on restrictive parameters that only permitted to highlight strong similarities between the MS/MS spectra of metabolites within a same cluster. In order to further enrich the chemical DB of T. atomaria, the metabolites previously identified by the phytochemical approach and the analysis of MN-1 were incorporated into a less restricted MN (MN-2; See Supporting information , Fig. S15), and then an even less restricted (MN-3) network (Fig. 5). Reducing this restriction allowed certain unbound metabolites (self-loop nodes) to be gathered in clusters containing known compounds. Their annotation became easier by highlighting fragment ions and MS/MS fragmentation pathways which were common with previously identified nodes.

Moreover, the use of less restricted MNs allowed the gathering of nodes belonging to close chemical classes in a same cluster. Thus, MN enlargement with MN-2 and MN-3 brought together two clusters (1A and 1B), which were separated in MN-1, in a single cluster (2A-B and 3A-B, respectively) gathering several classes of structurally related lipids (DGs, TGs, MGDGs and SQDGs). In the same way, the two clusters 1F and 1G comprising various GGG derivatives grouped together (3F-G). Moreover, several terpenes, some of which have been identified in the phytochemical approach, namely dictyol A, (1S, 5E, 7S) 1-acetoxygermacra-4(15),5,10( 14)-triene and germacra-4(15),5,10( 14)-trien-9-ol, which appeared previously in MN-1 as self-loop nodes, were found clustered with the MGGGs in MN-2 (cluster 2G) and in MN-3 (cluster 3F-G). These clusters also allowed to confirm the annotation of other terpenes, such as retinol, retinal, and dictyolene identified through online DBs and trans-calamenene and 1,4-peroxymuurol-5-ene already isolated by our group from this algal species and referenced in our in-house DB, which appeared as self-loop nodes in MN-1. This strategy made it possible to annotate 61 additional metabolites very easily and to further enrich the DB. Finally, a total of 210 metabolites (Table 2) were putatively identified within the metabolome of T. atomaria using the workflow described in this study.

Taxonomical and ecological significance of the annotated metabolites of T. atomaria

In line with previous observations on T. atomaria from the French Mediterranean coasts [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF][START_REF] Paix | Temporal covariation of epibacterial community and surface metabolome in the Mediterranean seaweed holobiont Taonia atomaria[END_REF][START_REF] Paix | A multi-omics analysis suggests links between the differentiated surface metabolome and epiphytic microbiota along the thallus of a Mediterranean seaweed holobiont[END_REF], no meroditerpenes were observed here either with the phytochemical approach or using MNs. In these previous studies and in the present one, the main isolated terpenes were GGG and sesquiterpenes with cadinane, germacrane or spiroaxane carbon skeletons. The same trend was also observed for specimens of T. atomaria collected in the Adriatic Sea (Croatia) [START_REF] Jerković | Chemical diversity of headspace and volatile oil composition of two brown algae (Taonia atomaria and Padina pavonica) from the Adriatic Sea[END_REF][START_REF] Rosa | Sesquiterpenes from the brown alga Taonia atomaria[END_REF], or for samples of Taonia pseudociliata from the Gulf of Catania (Sicily, Italy) (previously identified as Dilophus fasciola, [START_REF] Tringali | Sesquiterpenes and geranylgeranylglycerol from the brown algae Taonia lacheana and Taonia atomaria f. ciliata: their chemotaxonomic significance[END_REF][START_REF] Amico | R)-1-O-Geranylgeranylglycerol from the brown alga Dilophus fasciola[END_REF][START_REF] Amico | Sesquiterpenes based on the cadalane skeleton from the brown alga Dilophus fasciola[END_REF]) and no meroditerpenes were reported either in these studies.

However, several meroditerpenes were identified in previous works focusing on algal species reported as Taonia atomaria from Canary Islands (e.g. taondiol and atomaric acid) [START_REF] González | Marine natural products of the atlantic zone-V: The structure and chemistry of taondiol and related compounds[END_REF][START_REF] González | Atomaric acid, a new component from Taonia atomaria[END_REF], or from the Aegean Sea (e.g. taondiol, atomaric acid, sargaquinone, and atomarianones A and B) [START_REF] Tziveleka | Marine polyprenylated hydroquinones, quinones, and chromenols with inhibitory effects on leukotriene formation[END_REF][START_REF] Abatis | Atomarianones A and B: two cytotoxic meroditerpenes from the brown alga Taonia atomaria[END_REF][START_REF] Nahas | Radicalscavenging activity of Aegean Sea marine algae[END_REF]. In these studies, which did not report any sesquiterpenes or GGG, such a chemical composition appeared to be strikingly close to those of Stypopodium species widely reported for their meroditerpenes production [START_REF] Gerwick | Ichthyotoxic and cytotoxic metabolites of the tropical brown alga Stypopodium zonale (Lamouroux) Papenfuss[END_REF][START_REF] Rovirosa | Isoepitaondiol, a diterpenoid of Stypopodium flabelliforme and the insecticidal activity of stypotriol, epitaondiol and derivatives[END_REF][START_REF] Wessels | A new tyrosine kinase inhibitor from the marine brown alga Stypopodium zonale[END_REF][START_REF] Gil | Inhibition of inflammatory responses by epitaondiol and other marine natural products[END_REF][START_REF] Sabry | Neurotoxic meroditerpenoids from the tropical marine brown alga Stypopodium flabelliforme[END_REF]. For instance, atomaric acid and taondiol have been previously isolated from Stypopodium zonale [START_REF] Soares | Variation on diterpene production by the Brazilian alga Stypopodium zonale (Dictyotales, Phaeophyta)[END_REF] while epi-taondiol and sargaquinone were described from Stypopodium flabelliforme [START_REF] Areche | Stereostructure reassignment and absolute configuration of isoepitaondiol, a meroditerpenoid from Stypopodium flabelliforme[END_REF]. Stypopodium and Taonia are morphologically and phylogenetically close genera among the Dictyotaceae family [START_REF] Paula | What are and where are the bioactive terpenoids metabolites from Dictyotaceae (Phaeophyceae)[END_REF] and, as already suggested [START_REF] Soares | Variation on diterpene production by the Brazilian alga Stypopodium zonale (Dictyotales, Phaeophyta)[END_REF], the hypothesis of a botanical misidentification of Stypopodium spp. samples as T. atomaria could be a plausible scenario to consider. However, more thorough chemotaxonomic studies are necessary to confirm this hypothesis and an integrative approach coupling morphological, phylogenetic and metabolomics analyses could provide the information needed to clarify this situation.

In an ecological point of view, sesquiterpenes and GGG produced by T. atomaria have demonstrated anti-adhesion activities against several epiphytes including diverse bacterial strains [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF] and barnacle larvae [START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF], with gleenol and GGG showing the strongest activities.

Through a specific extraction procedure developed for this seaweed [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF], these two compounds were also characterized as main components of the surface metabolome of T. atomaria [START_REF] Othmani | Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis[END_REF][START_REF] Othmani | Settlement inhibition of marine biofilm bacteria and barnacle larvae by compounds isolated from the Mediterranean brown alga Taonia atomaria[END_REF][START_REF] Paix | Temporal covariation of epibacterial community and surface metabolome in the Mediterranean seaweed holobiont Taonia atomaria[END_REF][START_REF] Paix | A multi-omics analysis suggests links between the differentiated surface metabolome and epiphytic microbiota along the thallus of a Mediterranean seaweed holobiont[END_REF]. These studies brought evidence that these chemical compounds may be implied in the regulation of the epibiosis at the algal surface. Sesquiterpenes have been noticed mainly in apical parts, where the meristem -which is composed of cells involved in the algal growthis located. In line with the optimal defense theory which assumes that chemical resources are allocated within the organism to maximize the plant fitness [START_REF] Mckey | The distribution of secondary compounds within plants[END_REF], these molecules, known as defense compounds, may allow to protect the merismatic cells against fouling organisms [START_REF] Paix | A multi-omics analysis suggests links between the differentiated surface metabolome and epiphytic microbiota along the thallus of a Mediterranean seaweed holobiont[END_REF].

The biosynthetic pathway and mechanisms allowing the expression of these compounds at the algal surface remain uncertain and not documented, implying the need to improve the annotation of its metabolome.

In this study, we also annotated two new lipid classes, GGG and FG derivatives, which are likely to play an important role in the biogenesis and storage of GGG and sesquiterpenes, respectively, since glycerides are generally known for their fundamental storage role. More precisely, GGG derivatives could act as a lipid form of storage for GGG; while mono-and di-FG, harboring a farnesyl chain, could be used by the alga as storage compounds for biosynthetic precursors of sesquiterpenes.

Concerning the fulvellic acid derivatives, their description remains uncertain because they have only been described from the Japanese alga Sargassum fulvellum [START_REF] Kusumi | A novel type of glycerides bearing a methacrylic acid moiety from the brown alga, Sargassum fulvellum[END_REF], but their presence, in addition to those of all the other lipid families described here, shows that T. atomaria, and more broadly marine macroalgae, constitute an immeasurable source of still untapped lipid derivatives.

Integration of phytochemical approaches and the use of less restrictive parameters in the building of MNs can improve the annotation of relevant metabolites

Several studies have already successfully combined phytochemical approaches and the building of MNs (see [START_REF] Ramos | Natural products targeting strategies involving molecular networking: different manners, one goal[END_REF] for review) but the main part of them were implemented with purposes different than the one driving the present work. Thus, a first set of such studies have employed MNs as a dereplication tool, using in-house or online DBs fed with experimental or in silico MS/MS data, to focus isolation efforts on putatively new compounds (e.g. [START_REF] Alcover | Molecular networking reveals serpentininerelated bisindole alkaloids from Picralima nitida, a previously well-investigated species[END_REF][START_REF] Ren | Targeted isolation of two disesquiterpenoid macrocephadiolides A and B from Ainsliaea macrocephala using a molecular networking-based dereplication strategy[END_REF]). A second set of studies using MNs have been implemented in order to target specific families of chemical compounds (e.g. [START_REF] Ding | MS/MS-based molecular networking approach for the detection of aplysiatoxin-related compounds in environmental marine cyanobacteria[END_REF][START_REF] Li | Application of untargeted tandem mass spectrometry with molecular networking for detection of enniatins and beauvericins from complex samples[END_REF]). Finally, a large third set of studies have described the use of MNs for the identification or the targeted isolation of bioactive compounds (e.g. [START_REF] Buedenbender | Bioactive molecular networking for mapping the antimicrobial constituents of the baltic brown alga Fucus vesiculosus[END_REF][START_REF] Olivon | Bioactive natural products prioritization using massive multi-informational molecular networks[END_REF]). In our case, starting from a non-model species, the aim was to isolate the main chemical components of the extracts of T. atomaria and to use them, in combination with MNs, to annotate as much as possible the metabolome of this alga without any condition of novelty or bioactivity of the characterized metabolites.

The first particularity of the present study was to include a fractionation step of the crude extracts of T. atomaria and the subsequent MS/MS analysis of the resulting fractions in order to maximize the annotation of the metabolome of this alga. By way of comparison, it can be noted that most of the previous studies dealing with MNs have only analyzed crude extracts that can hamper the detection of numerous compounds, probably due to lower detection limits and competition between molecules during the ionization process.

The second particularity of the present analytical workflow consisted in the use of less and less restrictive parameters for the construction of more and more complex MNs. As most studies to date use the default set of values proposed on the GNPS platform (CS: 0.70; Minimum matched fragments ions: 6; Network TopK: 10), only a limited number of them have discussed the impact of such parameters on the structure of the MNs and on the annotation process. However, these parameters need to be appropriate for each analysis according to the expected results [START_REF] Aron | Reproducible molecular networking of untargeted mass spectrometry data using GNPS[END_REF].

Consistently, in several previous studies the use of default parameters gives complex MNs with large clusters composed by mostly unknown metabolites. In such cases, the use of less stringent conditions for the building of MNs could have allowed an easiest determination of common MS/MS fragmentation patterns between nodes which composed a same cluster and thus could have led to a better annotation.

Conclusion

An efficient method dedicated to the annotation of the metabolome of a non-model organism, the brown alga T. atomaria, has been developed by coupling two types of approaches, one based on a classical study of the organic extracts of this alga by natural products chemistry and the other, more recent, highlighting the power of annotation of MNs built with fractions of these extracts. Following the elaboration of a first very restricted MN (i.e. implying a very high similarity between the MS/MS data of the metabolites within a same cluster), the annotation was carried out using either seed nodes (i.e. metabolite identified through the interrogation of online DBs or isolated standards during the phytochemical analysis of the alga) or by analyzing the similarities and/or interpreting the MS/MS data of the nodes belonging to a same cluster.

This first step allowed to annotate 147 compounds in the ten main clusters of the restricted MN (MN-1) and to putatively characterize two new lipid classes including a geranylgeranyl or a farnesyl part together with a rare class of acylglycerols bearing a methacrylic acid moiety (fulvellic acid derivatives). The implementation, in a second phase, of less constrained MNs allowed to enlarge the size of the clusters and to annotate 65 additional metabolites.

The use of such a method, which is simple to implement, could allow to broaden our knowledge of the metabolome of species, particularly marine ones, which are still little studied to date.

This method could thus be an essential tool for the study of such organisms by environmental metabolomics. Node colors were chosen according to their chemical classes (see color code below the network). Nodes with a diamond shape correspond to fragments ions, while those with a triangle shape represent compounds with a distinct adduct from the rest of the cluster. Nodes with a thick border correspond to newly annotated compounds via the MN enlargement. Thickness of an edge between two nodes is proportional to the cosine score (CS, from 0.701 to 0.995). Only clusters with at least three nodes were represented. The whole network also includes 37 clusters of two nodes and 326 unbound nodes.

Tables and Figures

Putative annotation of related nodes

  ), which could be divided in two main groups, namely monoacyl and monoalkyl derivatives of GGG. Eight monoacyl derivatives were then identified by first using the high similarity (CS = 0.90) between the MS/MS data of GGG and those of the node at m/z 640.5293 ([C41H70NO4] + , calc. m/z 640.5299,  1.0 ppm; [M + NH4] + ) (Fig.3). For this particular node, in addition to the characteristic ion fragment of the GG moiety at m/z 273.2577 ([C20H33] + , calc. m/z 273.2577,  -0.2 ppm), a fragment ion at m/z 351.2534 ([C21H35O4] + calc. m/z 351.2530,  -1.1 ppm; [M -GG + H] + ) resulting from the loss of the GG part and an acylium ion at m/z 259.2056 ([C18H27O] + , calc. m/z 259.2056,  0.0 ppm) together with its dehydrated counterpart at m/z 241.1950 ([C18H25] + , calc. m/z 241.1951,  0.4 ppm)

m/z 283

 283 .2632,  0.0 ppm) and its dehydrated counterpart at m/z 265.2529 ([C18H33O] + , calc. m/z 265.2526,  -1.0 ppm) (See Supporting information for MS/MS data and proposed fragmentation pattern, Fig. S13). A similar MS fragmentation was also observed for five other nodes while two other nodes showed the occurrence of oxygenated acyl chains. In the same cluster, 14 other nodes showing higher molecular masses and retention times were annotated as DFGs. For example, DFG (18:1/16:1) was putatively identified on the basis of the adduct ion at m/z 814.7280 ([C52H96NO5] + , calc. m/z 814.7283,  0.3 ppm; [M + NH4] + ) and the characteristic fragment ions at m/z 575.5034 ([C37H67O4] + , calc. m/z 575.5034,  0.0 ppm; [M -FG -H2O + H] + ), m/z 339.2889 ([C21H39O3] + , calc. m/z 339.2894,  1.4 ppm; [M -FG -H2O -C16:1 +H] + ) and m/z 311.2579 ([C19H35O3] + , calc. m/z 311.2581,  0.5 ppm; [M -FG -H2O -C18:1 + H] + ) due to the respective losses of the farnesyl chain, a molecule of water and one acyl chain.

  derivatives (MFuAs) based on their MS/MS fragmentation. For example, MFuA (20:4) was characterized through the adduct ion at m/z 480.3329 ([C27H46NO6] + , calc. m/z 480.3320,  -1.8 ppm; [M + NH4] + ) and the following ion fragments found on its MS/MS spectrum such as: (i) the ion at m/z 445.2963 ([C27H41O5] + , calc. m/z 445.2949,  -3.2 ppm, [M -H2O + H] + ) resulting of a loss of water, (ii) the ion at m/z 361.2746 ([C23H37O3] + , calc. m/z 361.2737,  -2.4 ppm; [M -H2O -C4H5O2 + H] + ) due to the subsequent loss of the methacrylic acid part, (iii) the acylium ion at m/z 287.2376 ([C20H31O] + , calc. m/z 287.2369,  -2.4 ppm) and (iv) the characteristic fragment ion of the methacrylic acid moiety at m/z 85.0288 ([C4H5O2] + , calc. m/z 85.0284,  -4.6 ppm) (Fig. 4). Concerning the six nodes of cluster 1J, their RT, molecular formulae and MS/MS data were in accordance with their putative annotation as diacyl fulvellic acid derivatives (DFuAs). As an example, DFuA (20:4/20:5) was characterized on the basis of the adduct ion at m/z 764.5455 ([C47H74NO7] + , calc. m/z 764.5460,  0.7 ppm; [M + NH4] + ), the fragment ions characteristic of the presence of the fulvellic acid part at m/z 645.4867 ([C43H65O4] + , calc. m/z 645.4877,  1.5 ppm, [M -H2O -C4H5O2 + H] + ) and at m/z 85.0282 ([C4H5O2] + , calc. m/z 85.0284,  2.6 ppm), the fragment ions due to the loss of an acyl chain at m/z 445.2943 ([C27H41O5] + , calc. m/z 445.2949,  1.1 ppm, [M -H2O -C20:5 + H] + ) and m/z 443.2788 ([C27H39O5] + , calc. m/z 443.2792,  0.9 ppm; [M -H2O -C20:4 + H] + ), and the acylium ions at m/z 287.2368 ([C20H31O] + , calc. m/z 287.2369,  0.5 ppm) and m/z 285.2214 ([C20H29O] + , calc. m/z 285.2213,  -0.2 ppm) (See Supporting information for MS/MS data and proposed fragmentation pattern, Fig. S14).

Figure 3 .Figure 4 .Figure 5 .

 345 Figure 3. Annotation of geranylgeranylglycerol (GGG) derivatives in cluster 1F and proposed MS/MS fragmentation pattern for GGG, MGGG (18:4) and MGGG (O-17:2) (Abbreviations: MGGG: Monoradylgeranylgeranylglycerol) (Circles: metabolites; Diamonds: fragments)Figure 4. Annotation of cluster 1I and proposed fragmentation pattern for the compound MFuA (20:4) (Abbreviation: MFuA: Monoacylfulvellic acid) (Circles: metabolites; Triangles: adducts)

Fractions

  Figure 1

  Figure 3

  Thalli of the brown alga Taonia atomaria (Woodward) J. Agardh (family Dictyotaceae, class Phaeophyceae, phylum Ochrophyta) were collected by hand (0.5m depth) on the south eastern

	French Mediterranean coast (Tamaris; N 43°5'35.56, E 5°54'31.81) in February 2017. Algal
	samples were placed in plastic bags filled with surrounding seawater and transported within an
	hour to the laboratory in a cool box maintaining the seawater temperature.

2.4. Annotation of the metabolome of T. atomaria using molecular networking (MN) 2.4.1. UHPLC-ESI-QToF-MS/MS analyses

	For this analysis, the sixteen fractions selected in Part 2.3.2. were solubilized in MeOH
	(5 mg/mL) and injected on a Dionex Ultimate 3000 Rapid Separation chromatographic system
	(Thermo Fisher Scientific, Sunnyvale, CA, USA) coupled via an ESI interface to a QToF
	Impact II mass spectrometer (Bruker Daltonics, Champs-sur-Marne, France). Chromatographic
	separations were performed on an analytical core-shell reversed-phase column (150 × 2.1 mm,
	1.7 μm, Kinetex Phenyl-Hexyl; Phenomenex) with a flow rate fixed at 0.5 mL/min. The column
	was maintained at 40°C, the injected sample volume was 5 µL and the autosampler temperature
	was kept at 4°C. An elution gradient involving a binary programming of acidified H2O and
	ACN (0.1% of formic acid, v/v), was applied. The elution gradient started with 5% ACN and
	was kept for 2 min, then reached 100% ACN (linear ramp) in 8 min and was kept for 4 min;
	Purifications were done by iterative injections using various isocratic eluent conditions
	(mixtures of H2O/ACN) at a flow rate of 3 mL/min on a reversed-phase C18 semi-preparative
	column (Purospher Star RP-18e, 5 μm, 10×250 mm; Merck) maintained at room temperature.
	This purification step yielded sub-fractions which were checked by 1 H NMR and allowed the
	obtention of eight pure compounds.
	The chemical structure of these pure compounds was determined through extensive 1D and 2D-
	NMR analysis ( 13 C, DEPT experiments, 1 H-1 H COSY, 1 H-1 H NOESY, HSQC and HMBC) and
	by comparison with data previously reported in the literature. The chemical shifts of the
	different spectra were fixed with respect to the residual signals of the solvent used: H 7.26 and
	C 77.16 for CDCl3 and H 3.31 and C 49.00 for CD3OD. All the purified compounds were
	analyzed by UHPLC-ESI-QToF-MS/MS and these data [accurate mass, fragmentation pattern
	and retention time (RT)] were implemented in our in-house DB.

H NMR (400 MHz Avance NMR; Bruker BioSpin, Wissembourg, France) at room temperature. Six fractions were selected based on the available quantities (m > 150 mg) and the presence of NMR signals due to algal compounds other than common fatty acid derivatives. These fractions were then fractionated using a semi-preparative HPLC system (Prostar 210; Varian) equipped with a refractive index detector (Varian Prostar Model 350 RI) and a 50 μL injection loop.

  fragment ions of the GG chain, MGGG (O-17:2) was characterized by an adduct

	ion at m/z 616.5652 ([C40H74NO3] + , calc. m/z 616.5663,  1.9 ppm; [M + NH4] + ), a fragment
	ion at m/z 327.2892 ([C20H39O3] + , calc. m/z 327.2894,  0.4 ppm; [M -GG + H] + ) resulting of
	the loss of the GG moiety, and two fragment ions specific to the alkyl chain at m/z 252.2680
	([C17H34N]

+ , calc. m/z 252.2686,  2.3 ppm; [C17H30 + NH4] + ) and m/z 235.2418 ([C17H31] + , calc. m/z 235.2420,  1.2 ppm) (Fig.

  Thisputative assumption was strengthened by the strong similarity of the MS/MS fragmentation pattern of this ion with that of farnesol found in online DBs (e.g. Metlin).

	A detailed analysis of the MS/MS data of the nodes of cluster 1H allowed to putatively annotate
	them as mono-and diacyl derivatives of farnesylglycerol (MFGs and DFGs, respectively). For
	example, the node at m/z 578.5146 ([C36H68NO4] + , calc. m/z 578.5143,  -0.5 ppm;
	m/z 271.2419 ([C20H31] + , calc. m/z 271.2419,  0.6 ppm). An in-depth analysis of the MS/MS (M + NH4] + ) was characterized as MFG (18:1) through: (i) a fragment ion at m/z 357.3010
	spectra of these nodes allowed to putatively annotate them as GGG derivatives bearing an ([C21H41O4] + , calc. m/z 357.2999,  -2.8 ppm, [M -FG + H] + ) resulting from the loss of the
	oxidized GG side chain. More particularly, the node at m/z 398.3273 ([C23H44NO4] + , calc. farnesyl part and the corresponding dehydrated ion at m/z 339.2891 ([C21H39O3] + , calc.
	m/z 271.2419 ([C20H31] + , calc. m/z 271.2424,  0.6 ppm), and the acylium ion at m/z 259.2057
	([C18H27O] + , calc. m/z 259.2056,  0.4 ppm) (See Supporting information for MS/MS data and
	proposed fragmentation pattern, Fig. S12).

m/z 398.3265,  -2.1 ppm; [M + NH4] + ) was characterized using the fragment ions at m/z 289.2529 ([C20H33O] + , calc. m/z 289.2529,  -1.1 ppm; [Mglycerol + H] + ) and m/z 271.2427 ([C20H31] + , calc. m/z 271.2420,  -2.5 ppm; [Mglycerol -H2O + H] + ) as an analog of GGG with one hydroxyl group on the GG chain (named here GGGOH). Through their characteristic MS/MS fragmentation pattern, six other nodes of cluster 1G were easily identified as monoacyl derivatives of GGGOH. For example, among these derivatives MGGGOH (18:4) was characterized through the adduct ion at m/z 656.5245 ([C41H70NO5] + calc. m/z 656.5249,  0.5 ppm; [M + NH4] + ), the characteristic fragment ions at m/z 351.2527 ([C21H35O4] + , calc. m/z 351.2530,  0.8 ppm; [M -GGGOH + H] + ) and m/z 289.2526 [C20H33O] + , calc. m/z 289.2526,  -0.0 ppm; [M -Glycerol -C18:4 + H] + ) and their respective dehydrated counterparts at m/z 333.2426 ([C21H33O3] + calc. m/z 333.2424,  -0.4 ppm) and b) Farnesylglycerol derivatives (Cluster 1H) Cluster (1H) was composed by nodes showing a characteristic ion fragment at m/z 205.1951 [C15H25] + which matched with the occurrence of a farnesyl (i.e. C15-terpenoid) side chain. m/z 339.2894,  0.9 ppm; [M -FG -H2O + H] + ), (ii) the characteristic ion fragment of the farnesyl chain at m/z 205.1950 ([C15H25] + , calc. m/z 205.1951,  0.6 ppm; [M -Glycerol -C18:1 + H] + ) and (iii) an acylium ion at m/z 283.2632 ([C18H35O2] + , calc.

Table 1 .

 1 Selected building parameters and results for the MNs constructed with the MS/MS data of fractions obtained from the crude extracts of the brown alga Taonia atomaria

Table 2 :

 2 Summary of the annotation of the metabolome of the brown alga Taonia atomaria Restrained molecular network (MN-1) built with MS/MS data of fractions obtained from the crude extracts of the brown alga Taonia atomaria Node colors were chosen according to the chemical classes (see color code below the network). Nodes with a diamond shape correspond to fragments ions while those with a triangle shape represent compounds with a distinct adduct from the rest of the cluster. Thickness of an edge between two nodes is proportional to the cosine score (CS, from 0.851 to 0.995). Only clusters with at least three nodes are represented. The whole network also includes 39 clusters of two nodes and 512 unbound nodes.

	Figure 1. Analytical workflow used in this study
	Figure 2.

Table 1 .

 1 Selected building parameters and results for the MNs constructed with the MS/MS data of fractions obtained from the crude extracts of the brown alga T. atomaria

	Molecular	Minimum	Minimum Matched	Network	Number of	Number of
	network	cosine score	fragment ions	TopK	annotated	clusters with 5
					metabolites	or more nodes
	MN-1	0.85	10	7	147	15
	MN-2	0.80	8	7	198	19
	MN-3	0.70	6	10	212	21

Table 1 Chemical family Abbreviation Color code Characteristic ion fragment (Bold: most intense fragment ions) Characteristic neutral mass loss Other MS/MS fragmentation pattern

 1

	Annotated nodes (MN-1)	Annotated nodes (MN-1 to MN-3)

Table 2

 2

Acknowledgments

This work was financially supported by the French "Sud Provence-Alpes-Côte d'Azur (Sud PACA)" regional council (PhD grant of Benoît Paix) and the MAPIEM laboratory of the University of Toulon (Master internship grant of Nathan Carriot). LC-MS experiments were conducted on the regional platform MALLABAR funded by the Institute of Ecology and Environment (INEE) of the French National Centre for Scientific Research (CNRS), the Total Foundation and the French Sud PACA regional council. The authors are grateful to François Audrezet and Damien Devilly (MAPIEM laboratory, University of Toulon, France) for their help during the isolation and the structural characterization of the chemical standards.

Notes

The authors declare no competing financial interest.

Declaration of competing interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://. Credit Author Statement

Data availability