# The single-berry metabolomic clock paradigm reveals new stages and metabolic switches during grapevine berry development

Flora Tavernier, Stefania Savoi, Laurent Torregrosa, Philippe Hugueney, Raymonde Baltenweck, Vincent Segura, Charles Romieu

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Flora Tavernier, Stefania Savoi, Laurent Torregrosa, Philippe Hugueney, Raymonde Baltenweck, et al.. The single-berry metabolomic clock paradigm reveals new stages and metabolic switches during grapevine berry development. 2024. hal-05279589

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# The single-berry metabolomic clock paradigm reveals new stages and metabolic switches during grapevine berry development

2

3
4 Flora Tavernier¹, Stefania Savoi², Laurent Torregrosa³,⁵, Philippe Hugueney⁴, Raymonde

5 Baltenweck⁴*, Vincent Segura¹,⁵* and Charles Romieu¹,⁵*

6

7 ¹UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France.

8 ²Department of Agricultural, Forest and Food Sciences, Università di Torino, 10095 Grugliasco, Italy.

9 ³UMR LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France.

10 ⁴INRAE, University of Strasbourg, UMR SVQV, 68000 Colmar, France.

11 ⁵Geno-Vigne, IIFV-INRAE-Institut Agro, F-34398, Montpellier, France.

12

13 Corresponding authors: raymonde.baltenweck@inrae.fr, vincent.segura@inrae.fr, charles.romieu@inrae.fr

14 Summary

15
16
17 Asynchronous development of berries causes metabolic chimerism in usual samples.
We thus revisited the developmental changes in the metabolome of the *Vitis vinifera* single berries from anthesis to over-ripening.

18
19
20
21 A dataset of 9,256 ions obtained by non-targeted ultra-performance liquid chromatography coupled to high-resolution mass spectrometry was submitted to an analysis workflow combining classification and dimension reduction tools, to reveal the dynamics of metabolite composition without phenological *a priori*.

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33 This approach led to a metabolome-based definition of developmental stages, as well as the clustering of metabolites into 12 specific kinetic patterns. The single berry intrinsic metabolomic clock alleviates constitutive asynchronicity biases in the usual combination of phenological scales and observer clock. Such increase in temporal resolution enabled the identification of metabolite clusters annunciative of the onset of ripening since the herbaceous plateau. In particular, these clusters included transient lipidic changes and the start of ABA accumulation. We also highlighted a cluster of stilbenes that accumulate after sugar loading stops, during fruit shriveling.

* This non-targeted approach enables a more precise and unbiased characterization of grapevine berry development through the metabolomic clock paradigm. The discovery of new metabolic milestones of berry development paves the way towards an unbiased assessment of berry physiological stages.

34 **Keywords:** *Vitis vinifera* L., fruit development, metabolomics, untargeted metabolites, single berry, phenology

36

37 **Word count:**

38 Introduction: 941

39 Material and methods: 1313

40 Results: 1983

41 Discussion: 2219

42 Total: 6456

43
44 **Introduction**

By providing both seed protection and dissemination to Angiosperms (Seymour et al., 2013), the apparition of the fruit stands as a pivotal leap in the evolutionary history of plants and their related animal vectors. Fleshy fruits shift from an immature seed protective organ, in green stage, to a strongly attractive one during ripening, when mature seeds become resistant enough to be disseminated. This key transition includes typical metabolic shifts affecting both primary metabolites serving as major *osmoticum*, from organic acids to energy-rewarding soluble sugars, and secondary metabolites, from astringent and antinutritive tannins to attractive anthocyanins and aromas (Gillaspy et al., 1993). Most fleshy fruits undergo these developmental transformations regardless of their climacteric or non-climacteric status. However, by contrast with climacteric fruit which rapidly ripen when triggered by the ethylene hormone following long periods of post-harvest storage, non-climacteric fruits do not store significant starch reserve during green stage, and are thus forced to ripen on the plant, in real time with phloem unloading of soluble sugars, making them particularly sensitive to environmental conditions during this period (Giovannoni, 2004).

Among non-climacteric fruits, grapevine (*Vitis vinifera*) is one of the world's most important, cultivated to produce table grapes, wine, juice and other products. Grape phenology was described in two main stages, following a double sigmoid growth curve (Coombe and Hale, 1973; CooMbe and McCarthy, 2000). The first one, also called green stage, starts with intense cell divisions and expansion, resulting from the vacuolar accumulation of malic and tartaric acids as major *osmoticum* (Ojeda et al., 1999; Terrier and Romieu, 2001). Condensed tannins are synthesized at the beginning of this stage (Ollé et al., 2011). These phenolic compounds are important for wine organoleptic qualities (Del-Castillo-Alonso et al., 2021). The second stage, also called ripening, starts with an abrupt softening of the berries indicating the sudden induction of sugar accumulation. Such accumulation involves an accelerated phloem discharge that is accompanied by a significant respiration of malic acid (Coombe, 1992; Shahood et al., 2020). By the time sugars reach their peak, berry volume has doubled due to the parallel import of water. The berries then concentrate all solutes as they shrivel, but no longer import sugars (Castellarin et al., 2007a; Daviet et al., 2023; Savoi et al., 2021). The transition between green and ripening stages is commonly called "veraison" and refers to the start of anthocyanidins accumulation, responsible for the color change in red-skinned grape varieties (Lund and Bohlmann, 2006). By extension, this also indicates the softening and the initiation of sugar loading, slightly before color change, as elucidated on single berries (Bigard et al., 2019; Daviet et al., 2023).
Research on grape development is widely documented, but the division into distinct stages varies between studies (Bigard et al., 2019; Castellarin et al., 2007a; Rogiers et al., 2017; Savoi et al., 2021; Shahood et al., 2020). Stage characterisation relies on the measurement of the main primary metabolites (such as sugars, malic and tartaric acids), berry coloration, softness and growth. Omics technologies have enhanced the exhaustivity of developmental characterization at both the transcriptomic (Cabral et al., 2023; Castellarin and Di Gaspero, 2007; Cramer et al., 2014; Goes da Silva et al., 2005; Savoi et al., 2021; Tornielli et al., 2023) and metabolomic (Bonada et al., 2013; Duan et al., 2019; Leng et al., 2021; Nicolas et al., 2024; Ollé et al., 2011) levels. Furthermore, some studies have adopted an integrative approach that combines both transcriptomic and metabolomic analyses, leading to the identification of genes that could be activated at specific developmental stages and of molecules that could signal or mark stages beyond those defined by growth, primary metabolism and anthocyanins (Fasoli et al., 2018, 2012). However, most of these traits vary continuously during development, making the discretization of development beyond the two main stages somewhat arbitrary.

Research on berry development usually focuses on successives sets of mixed berries in order to smooth their marked heterogeneity at the plot level. Indeed, the berries exhibit both intra and inter cluster developmental asynchrony, ripening both at different rates and dates (Daviet et al., 2023; Shahood et al., 2020). Classical grapevine phenological scales explicitly refers to the median of the population (Coombe, 1995; Lorenz et al., 1995), but it was only recently pointed out that smoothing asynchronous berries obviously generates developmental chimera fundamentally incompatible with the identification of physiological stages as pure metabolic units (Bigard et al., 2019; Shahood et al., 2020). Single berry sampling and characterization have enabled to revisit the flow of water and primary metabolites in depth, to gather quantitative and molecular arguments on the origin of the sugar/acid relationship in grapes, and to shed light on the organization and energetics of the unloading of sugars in the berry. Only studies on single berries have made it possible to establish that softening, initiation of sugar storage, growth resumption and coloration mentioned above occur step by step in this order (Bigard et al., 2019; Savoi et al., 2021; Shahood et al., 2020), while they rather seemed to occur simultaneously on sets of mixed berries (Fasoli et al., 2018).

Recently, new methods have been developed to analyze developmental processes using pipelines combining statistical methods to process omics data. For example, the use of unsupervised learning methods, based on dimension reduction and analysis of distances between samples, has already made it possible to define a new grape phenology scale based on transcriptomics (Tornielli et al., 2023). The present study combines multivariate
114 analyses with untargeted metabolomics on single berries to revisit and refine the
115 phenological stages of grape development. We show that metabolome-wide analyses
116 provide a better understanding of berry development, identifying new metabolites, markers
117 of key steps of berry growth and ripening.

118 **Material and Methods**

119 **Experimental design**

120 Single berries were sampled by Savoi et al. (2021) in the years 2018 and 2019 on
121 *Vitis vinifera* cv. Syrah within the experimental vineyard Pierre Galet of Institut Agro
122 Montpellier (France). Plants established in 2000, were grafted onto SO4 rootstock and
123 irrigated to avoid severe water deprivation. Throughout the growth cycle, the plants received
124 regular phytosanitary sprayings to limit fungus diseases.

125 Berries were sampled in accordance with the double sigmoidal growth pattern, as
126 identified through recurring photographic observations and quantification of sugars and
127 organic acids (Savoi et al., 2021). This framework delineated eleven temporal waypoints in
128 the developmental sequence, called « expert stages » for this study and encompassing the
129 green growth phase (G1, G2, and G3), the green lag period (L4 and L5), the onset of
130 ripening (characterized by the softening phase, S6 and S7), the ripening phase (R8, R9, and
131 R10), culminating to the shriveling stage (Sh11) (Savoi et al., 2023). Systematic sampling
132 was undertaken, with the green stage (from G1 to L4) being sampled in 2019, and ripening
133 one (from L5 to Sh11) in 2018. The herbaceous plateau (L4 and L5), allowed to interconnect
134 the two temporal domains.

135 Berries were frozen without pedicel and seeds in liquid nitrogen, before being
136 crushed using a stainless-steel ball mill (Retsch MM400, Verder Scientific, Inc., Newtown,
137 US). Subsequently, the 153 frozen berry powders underwent a lyophilization for 72h
138 (Cryotec pilot freeze-dryer, Cryotec, Lunel, France), before being analyzed by VIS-NIR
139 reflectance spectroscopy using a LabSpec 2500 with an optical probe (Analytical Spectral
140 Devices, Inc., Boulder, CO, US). These spectra allowed the curated selection of 125
141 samples, using the Kennard-Stone algorithm (Kennard and Stone, 1969), to ensure parity in
142 berry quantities among stages (Table 1).

143 **Metabolomic analyses using ultra high-performance liquid chromatography coupled**
144 **to high-resolution mass spectrometry (UHPLC-HRMS)**

145 Metabolites were extracted using 20 µL methanol per mg of lyophilized sample,
146 containing 5 µg/mL of chloramphenicol as an internal standard. The extract was sonicated
147 for 10 minutes, before centrifugation at 13000 g at 10°C for 10 minutes. Quality control
148 sample (QC) was prepared by mixing an equal volume of each sample to evaluate the
149 stability of the equipments (retention time and area) and the repeatability of metabolites
150 detection in all of the samples. Supernatants were analyzed as described previously
151 (Rodrigues et al., 2023), with some modifications. Metabolomic analyses were performed
152 using a Vanquish Flex binary UHPLC system (Thermo Scientific, Waltham, MA) equipped
153 with a diode array detector (DAD). Chromatographic separation was performed on a
154 Nucleodur C18 HTec column (150 x 2 mm, 1.8 µm particle size; Macherey-Nagel, Duren,
155 Germany) maintained at 30°C. The mobile phase consisted of acetonitrile/formic acid (0.1%,
156 v/v) (eluant A), and water/formic acid (0.1%, v/v) (eluant B), at a flow rate of 0.25 mL/min.
157 The gradient elution program was as follows: 0 to 4 min, 80% to 70% B; 4 to 5 min, 70% to
158 50% B; 5 to 6.5 min, 50% B isocratic; 6.5 to 8.5 min, 0% B; and 8.5 to 10 min, 0% B
159 isocratic. The injected volume of sample was 1 µL. The UHPLC was coupled to an Exploris
160 120 Q-Orbitrap MS system (Thermo Scientific, Waltham, MA) operated with a heated
161 electrospray ionization source in positive and negative ion modes. The key parameters were
162 as follows: spray voltage, + 3.5 and – 3.5 kV; sheath-gas flow rate, 40 arbitrary units (arb.
163 unit); auxiliary-gas flow rate, 10 arb. unit; sweep-gas flow rate, 1 arb. unit; capillary
164 temperature, 320°C; and auxiliary-gas-heater temperature, 300°C. The scan modes were full
165 MS with a resolution of 60 000 fwhm (at m/z 200) and ddMS2 with a resolution of 60 000
166 fwhm; the normalized collision energy was 30 V; and the scan range was m/z 85–1200.
167 Internal mass calibration was operated using EASY-IC internal calibration source allowing
168 single mass calibration for full mass range. Data acquisition and processing were carried out
169 with Xcalibur 4.5 and Free Style 1.7 (Thermo Scientific, Waltham, MA), respectively.
170 Analyses were performed in both positive and negative ionization modes, thereby
171 constituting dual data sets for each individual sample.

172 UHPLC-HRMS raw data were processed using the Compound Discoverer 3.3
173 software (Thermo Fisher Scientific, Waltham, MA, USA). Data processing for positive and
174 negative modes were performed separately. QC samples were used to verify the
175 repeatability of retention times and signal intensity throughout the data set. Untargeted
176 metabolomics workflows were used for peaks detection, peaks groupement and alignment,
177 and fill in missing peak data. Background compounds found in the blank samples, as well as
178 known environmental contaminants related to plant protection products were filtered out from
179 the data set. Peaks alignment parameters mainly included mass tolerance and retention time
180 (RT), which were set at 5 ppm and at 0.1 min, respectively. Peaks detection was performed
181 using a signal-to-noise ratio (S/N) of 2 and peak intensity thresholds at 10,000. Poorly
182 repeatable ions were filtered out by keeping those with a peak rating greater or equal to 4 in
183 at least 4 samples and by keeping ions with a coefficient of variation (CV) ≤ 30% in all QC
184 samples. Using these settings, 5123 and 4133 ions were obtained in positive and negative
185 modes, respectively. Metabolomics data have been deposited to the EMBL-EBI
186 MetaboLights database (DOI: 10.1093/nar/gkad1045, PMID:37971328) with the identifier
187 MTBLS10572.

188 **Data analysis and classifications**

189 The high dimensional dataset documenting the evolution of the berry metabolome
190 across the 11 expert documented phenological stages (MS data) was further processed to
191 classify metabolites by similarities in their evolution, using a workflow based on several
192 multivariate analyses (Fig. 1).

193 Each peak surface was normalized by their sum of areas measured throughout berry
194 development. Berry samples were classified using the K-means algorithm on the scaled
195 metabolomic matrix to redefine developmental stages, based on variations in metabolite
196 composition. The statistically relevant cluster number was determined using the average
197 silhouette method (Govender and Sivakumar, 2020; Rousseeuw, 1987). The scaled
198 metabolomic matrix was also submitted to a principal component analysis (PCA). The
199 successive samples, projected onto the first three principal components (PC), were spread
200 on a series of linear trajectories separated by abrupt directional changes. Linear regressions
201 were calculated on all possible planes, before selecting the best ones based on their
202 coefficient of determination. The curvilinear distance was then calculated, following an
203 orthogonal projection of each sample on the respective lines, and tracing the progression of
204 samples in the interconnected regressions. Such distance enabled the definition of a
205 continuous metabolomic clock.

206 Developmentally invariant metabolites were filtered out using the Kruskall-Wallis test
207 with a false discovery rate of 0.01. Subsequently, the metabolites were clustered according
208 to their similarities in developmental profiles, using the K-means algorithm and the average
209 silhouette method. A PCA on the metabolites within each cluster yielded the specific profile
210 of its representative "eigen-metabolite". Their developmental patterns were finally plotted
211 with respect to the newly defined metabolomic clock. Finally, the filtered MS data were
212 processed with the t-distributed stochastic neighbor embedding (t-SNE) method, yielding a
213 three-dimensional projection of the metabolites. t-SNE was performed using a perplexity
214 parameter of 30 over 1,000 iterations. This approach was designed to preserve the local
215 structures of the data, providing a more accurate and faithful representation than that
216 obtained by PCA, and to validate the use of the K-means method against the actual
217 distribution of the data (Hebra et al., 2021; Olivon et al., 2018; Sorkun et al., 2022).
218 **Identification of key metabolites**

219 Metabolites presenting the highest correlation with the eigen-metabolite (correlation
220 > 0.8) were selected for identification in each cluster showing interesting dynamics. Ion and
221 metabolite annotations were based on expert analysis of molecular formulae, mass spectra
222 and MS/MS fragmentation patterns in comparison with authentic standards when available
223 and with data from MassBank (https://massbank.eu/MassBank/), mzCloud
224 (https://www.mzcloud.org/) and Chem-Spider (http://www.chemspider.com/) using the
225 Compound Discoverer 3.3 software. Standards for metabolite identification were purchased
226 from Sigma-Aldrich (Saint-Quentin Fallavier, France) and Extrasynthese (Lyon, France).
227 Identification confidence levels (ICL) established by Schymanski et al. (2014) were applied
228 to score identifications.

229 **Results**

230 **De novo deciphering of grape developmental stages based on single berry**
231 **metabolomics**

232 A total of 9,256 ions were detected in the 125 berry samples. The 11 expert stages
233 relied on observations of growth stages, accumulation of primary metabolites (sugars,
234 tartaric and malic acids and potassium) and the softening date. In contrast, the K-means
235 blind analysis of metabolomic data allowed to distinguish 18 developmental stages (Table 2),
236 henceforth coined "metabolomic stages". The intrinsic chemical profile of the fruits enabled
237 to break down expert stages into sub categories, offering a more accurate view of berry
238 developmental cycle from early to late stages. For example, the expert stage L4, which
239 brings together transitional samples between the first and second part of the green phase
240 (Fig. 2a), was subdivided into 5 distinct metabolomic stages (Table 2), while the L5, G1 and
241 R9 expert stages precisely matched single metabolomic stages (Fig. 2a). Reciprocally,
242 metabolic stages could also include samples from different expert stages, frequently
243 contiguous, but not only (Table 2, Fig. S1).

244 In parallel, a "metabolomic clock" has been established by applying linear
245 regressions on PCA-projected sample trajectories, calculating the distances between
246 samples. The 3D PCA projection, where PC 1 to 3 accounted for respectively 27.9%, 12%
247 and 5.5% of the dataset variance, allowed the assessment of the distribution of berries and
248 its comparison with expert stages annotations (Fig. 2a). The dispersion around the 3D curve
249 decreased from green stage to softening, and became almost non-existent thereafter.
250 According to this projection, the grape development followed 5 main successive trajectories
251 delimited by abrupt changes in direction during L4, S6, R8 and end R9. The "early green
stage" (spanning G1 to G3 and some L4 samples) and the "herbaceous plateau stage", also known as the latent stage (encompassing certain L4 berries and all L5 ones), are distinctly demarcated on the PC2/3 plane. Notably, PC3 clearly distinguished the L4, L5 and S6 expert stages subsequent to sudden changes in berry composition. This segment of the PCA plot is sparsely populated, indicating a potential lack of samples from this stealthy stage. The ripening period itself is subdivided into three trajectories: an "early ripening" group composed of soft and green berries (S6 S7), however these samples harvested a few days apart largely overlap. The subsequent trajectory included R8, R9, and one berry from the Sh11 stage, with a large evolution on PC2. The final trajectory, encompassing R10 and the remaining Sh11 berries, represented the post phloem arrest stage (Savoi et al., 2021) as shown in Fig. 2a. Fig. 2b shows that expert stages are less resolutive than metabolic clock, particularly for stages G2, G3 and L4. In contrast, expert stages G1, L5, S6, S7, R10 and Sh11 group berries in a narrower range of metabolomic time. When comparing days after flowering (DAF) and the metabolomic clock (in Fig. 2c), a fast evolution can be observed inside the L4 expert stage and at the end of maturation. The 50 % flowering date is measured at the scale of the whole cluster and not for each single berry, so it does not take asynchrony into account. It is also important to note that the two sampling seasons (G1 to L4 for 2019 and L5 to Sh11 for 2018, Table 1) do not appear to be sharply separated on the PCA and graphical representations of Fig. 2. Altogether, these results clearly show that the metabolomic synchronization of berries outperforms the most severe sorting procedures based on observer sampling time, relative growth and primary metabolites.

## Clustering of the untargeted metabolites using K-means

Clustering single berry metabolites according to their relative amounts in the extracts of 125 single berries revealed 12 major developmental patterns, labeled A to L. These profiles showed 3 major trends: clusters of metabolites that decreased along development (Fig. 3, profiles A to D), those showing a peak at certain stages (Fig. 4, profiles E to I) and finally, clusters with a metabolite content that increased along berry development (Fig. 5, profiles J to L).

The decreasing profiles A, B and C (Fig. 3) comprised 161, 405 and 1339 metabolites respectively, that could be efficiently summarized with a single PC explaining more than 50% of the intra-cluster variance. In contrast, profile D is distinguished by a larger number of metabolites (1,630), but its PC1 captured a smaller proportion of the total variance (25%). It suggests that this cluster may include more heterogeneous developmental profiles, as underlined by the distribution of correlations between individual metabolite profiles and that of the corresponding eigen-metabolite (Fig. S2). The decline of profile A
287 was particularly fast and occurred in the very early green stage, during the G1 and G2 expert
288 stages. Metabolites in profile B decreased continuously through the green stage, from G1 to
289 L4. Profile C exhibited a more gradual decrease over time, starting from a peak at the
290 beginning of the early green stage and declining until the shriveling stage. In contrast, profile
291 D displayed an unusual pattern with a slight decrease during the entire green phase (from
292 the early green stage to the herbaceous plateau) followed by a sharper decline starting at
293 the softening stage and continuing to the shriveling stage.

294 Figure 4 shows the profiles exhibiting accumulation peaks at different times during
295 berry development. Among these, G and H profiles displayed particularly transient peaks.
296 These profiles contained 146 and 87 metabolites respectively, and could be efficiently
297 summarized with a single PC that explained more than 60% of their variation. Interestingly,
298 the peak in the G profile coincided with the herbaceous plateau phase (L4 and L5),
299 commonly described as a state of metabolic homeostasis. In contrast, profiles E, F and I
300 showed a more gradual accumulation dynamic extending over several stages. These profiles
301 included a higher number of metabolites, 747, 1,296 and 596 respectively, and a lower
302 percentage of variance explained by their first PC by comparison with profiles G and H (Fig.
303 4, Fig. S2). Profile E showed significant accumulation during the early green stage, peaking
304 at the G3 stage and then gradually decreasing. Interestingly, this peak occurred exactly
305 when profile A reached its minimum. Profile F showed a peak during the herbaceous plateau
306 stage (expert stages L4 and L5), marking a transition between the early green stage and the
307 beginning of the ripening. The G profile exhibited a much more abrupt peak compared to
308 profiles E and F, with particular points reaching their maximum at the beginning of the
309 herbaceous plateau stage and then decreasing at the beginning of the softening stage,
310 specifically between stages L4 and S6. The H profile was characterized by an even more
311 pronounced peak, starting at the end of the herbaceous plateau stage (L5) and reaching its
312 maximum during the softening stage (S6 and S7), marking the beginning of grape ripening.
313 At the same time, the I profile also reached its maximum, but with a more gradual increase
314 and decrease, starting at the end of the early green stage (between G3 and L4) and ending
315 at the completion of berry development (Sh11).

316 Clusters profiles with an ascending shape starting at the S6 expert stage
317 encompassed metabolites specifically accumulated during the berry ripening phase (Fig. 5).
318 Profile J began to accumulate from the start of the softening stage (S6), reaching a peak at
319 the end of the ripening stage, before decreasing during the shriveling stage. Profile K started
320 to increase at the end of the softening stage, from expert stage R8, and reached its
321 maximum at the end of shriveling (expert stage Sh11). Profile J stands out for its large
322 number of metabolites, with 1,297 compounds, and a PC1 that explains only 31% of their
323 variance. On the other hand, profiles K and L contained a smaller number of metabolites,
324 827 and 72 respectively, and a larger part of their variability was explained by their first PC.
325 The L profile in particular included the largest proportion of metabolites with a correlation of
326 at least 0.8 with the eigen-metabolite (Fig. S2). This profile showed a late pattern of
327 accumulation, after phloem arrest and during shriveling (expert stages R10 and Sh11).

328 Finally, the t-SNE method provides an alternative to PCA for metabolites visualization
329 (Fig. 6). The metabolites separated into two main branches on the 3D graph, reflecting their
330 evolution over time. Early green stage specific patterns (A, B, C and D in shades of blue) are
331 at the upper end of the t-SNE, while dynamics varying during ripening and shriveling stages
332 (J, K and L in shades of red) are at the lower left end of the graph. The dynamics showing
333 accumulation peaks between the early green stage and the ripening stage are in the central
334 part. Transient dynamics observed in clusters A, E, F, G, H, I, J, and L indicate abrupt
335 variations at specific points in berry development, facilitating accurate estimation of
336 developmental stages. In addition, some of these clusters, such as A, H and I, are
337 particularly distinct and clearly separated from the others.
338

338 **Metabolite annotation in selected clusters**

339 Among the 12 metabolite clusters, those with the most transient dynamics were
340 chosen for annotation of their metabolites. Profiles A, C (Fig. 3), E, F, H, I (Fig. 4), J, K and L
341 (Fig. 5) were therefore selected, focusing on particularly well classified ions, i.e. having a
342 correlation greater or equal to 0.8 with their respective eigen-metabolites. The total number
343 of selected ions was 2,586, and ranged from 60 to 606 for L and C clusters, respectively.
344 Expert analysis of MS and MS/MS data in the 10 selected clusters resulted in the annotation
345 of 483 of the 2,586 ions (Table S1), which could be attributed to a total of 117 metabolites,
346 listed in Table S2, together with their molecular families. Thus, 19 % of all selected ions
347 could be annotated, this proportion ranging from 7 % (cluster E) to 83 % (cluster L) (Fig. S3,
348 Table S1).

349 Cluster A was mainly composed of monomers and small polymers of gallate ester
350 tannins (Fig. S4), including confirmed compounds by molecular standard (ICL 1 on the
351 Schymanski et al. (2014) scale) such as epicatechin gallate (ECG) and epigallocatechin
352 gallate (EGCG). Although condensed tannins were predominant in cluster C, particularly
353 procyanidins of types A, B, and C, a few type A procyanidins were also detected in cluster A.
354 Cluster C also contained various flavonoids, including glycosylated derivatives of chalcone
355 and kaempferol, as well as their dihydro-derivatives (ICL 4). Additionally, quercetin
356 glucuronide (ICL 2) was identified in this cluster, along with phenolic acids coupled with
357 tartaric acid, including caftaric, coutaric, and fertaric acids (ICL 1). Malic and citric acids were
358 detected in cluster F (ICL 1). Cluster H encompassed C₁₈ and C₂₀ lipids, notably linolenic 359 acid (C₁₈), confirmed by molecular standard (ICL 1), and its potential derivatives, noted as 360 ICL 2 and 3. Aspartic acid was identified in cluster I (ICL 1), along with polyol sugars and 361 hexose (ICL 2). Cluster J included an anthocyanin, cyanidin glucoside (ICL 1), flavonoids 362 such as derivatives of isorhamnetin and syringetin (ICL 3), the glycosylated form of abscisic 363 acid (ABA-glucoside), as well as glycosylated and esterified coumaric acid and sugar 364 polymers (ICL 1 and 2). Cluster K contained several flavonoids, including derivatives of 365 isorhamnetin and kaempferol (ICL 1 for glycosylated forms and ICL 3 for other derivatives), 366 as well as anthocyanins like myricetin (ICL 2) and malvidin (ICL 1), and contained ABA- 367 glucoside (ICL 2) and proline (ICL 1). Given that ABA-glucosides were present in both 368 clusters J and K, The profile of free ABA was searched for among metabolites with a 369 correlation lower than 0.8 with the eigen-metabolite. Indeed, the free form of ABA had a 370 correlation of 0.75 with the eigen-metabolite of cluster I (Table S1). Finally, based on the 371 annotation of the vast majority of its ions (83%), the metabolites in group L belonged almost 372 exclusively to the stilbene family, including isomeric forms and derivatives of resveratrol and 373 piceid (ICL 1), as well as viniferin glucoside (ICL 1).

374 Discussion

375 Present work provides a new perspective on grapevine berry development based 376 solely on its solute composition to establish an internal metabolomic clock. By employing a 377 novel strategy that combines single berry sampling, untargeted metabolomics, multivariate 378 analyses and expert annotation, we provide a much detailed description of the phenological 379 sequence that undergoes grapevine fruit. This approach revealed potentially important 380 groups of metabolites in fruit development. Furthermore, the integration of clustering coupled 381 with t-SNE, applied independently from observer time, led to a more precise and incisive 382 understanding of the grape ripening process.

383 Single berry metabolomics reveals an internal clock regulating grape development

384 The blind statistical analysis of metabolites at single berry level highlighted transient 385 metabolic shifts, which would have been smoothed by averaging asynchronous berries as 386 usually carried out (Bigard et al., 2019; Shahood et al., 2020; Zamboni et al., 2010). In 387 addition, the main solutes that exhibit monotonous changes during green stage or ripening, 388 such as malic acid and sugars, do not allow these phases to be divided into transient stages. 389 We have shown that relying only on variations in fruit intrinsic composition led to a more 390 accurate synchronization of berries than sorting by growth or primary metabolites. The use 391 of PCA to get insights on sample distribution during fruit development has already revealed a 392 common "U" shape pattern throughout the literature, but uncontrolled variations in the real
age pyramid of berries inside each sample scatters the data and prevents to detect discontinuities (Dai et al., 2013; Tornielli et al., 2023). Without prior filtering of the data except invariant metabolites, sub periods in the green and ripening stages were clearly resolved on the first 3 PCs, being discriminated by very sudden changes in sample direction vectors, which are necessarily smoothed in average samples. The t-SNE method confirmed the robustness of using metabolites to characterize grape development, organizing them within a developmental continuum. This technique also highlighted isolated groups (clusters A, H, and I) which, although distinct, remained integrated within the developmental flow.

### 401 Profiles are consistent across harvest years

The kinetics of the two sampling years were linked by the lag phase, during which berry growth pauses and which is widely accepted as quite stable during grape development (Coombe, 1976; Matthews et al., 1987; Thomas et al., 2008). Examination of the PCA and representative dynamics revealed a remarkable consistency: the change of vintage did not seem to affect the evolution of the samples. Indeed, the L5 group was not impacted by the sampling year and was equidistant from the other stages (L4 and S6). This continuity was also obvious on representative dynamics, particularly in profiles B, C, D, I, K and L (Fig. S4). These results validated our sampling procedure and underlined a certain robustness over two vintages.

### 411 Metabolic clusters reveal transient shifts in berry physiology

Blind, dynamic-based classification of grape metabolites resulted in 12 distinct developmental clusters. Among these, 10 showed relatively transient dynamics, enabling us to trace the developmental history of grapevine berry and propose a metabolic map, which highlights some metabolites and pathways as milestones of grape development (Fig. 7).

Catechins monomers are phenolic compounds produced via the flavonoid biosynthesis pathway, but their polymerisation in condensed tannins remains elusive (Yu et al., 2023). The conversion of phenylalanine into p-coumaroyl-CoA is considered as the first step in the phenylpropanoid pathway. The latter is then converted into flavonoids, including tannins, by chalcone synthase (CHS), or into stilbenes by stilbene synthase (STS) (Flamini et al., 2013; Rienth et al., 2021, Fig. 7). Profile A and C showed that the monomers and small proanthocyanidin (PA) polymers (EC, EGC, etc., from monomers to hexamers), rapidly decreased after anthesis. This decline coincided precisely with the period when higher molecular weight PAs began to accumulate, suggesting small polymers might be rate-limiting intermediaries (Kennedy et al., 2001, 2000; Ollé et al., 2011). Puzzlingly, profile A, encompassing many galloylated PA derivatives (Fig. S3), decreased before the non-
427 galloylated ones in cluster C. Further work is needed to understand if the faster decrease of
428 galloylated forms reflects (i) an increased in gallate/shikimate competition subsequent to an
429 acceleration of the PA branch, (ii) an improved NADPH reducing power, or (iii) a sequential
430 expression of specialized SDH/ isogenes (Tahara et al., 2020).

431 Linolenic acid, found in the H cluster announcing the onset of ripening at softening, is
432 a precursor of jasmonic acid, a phytohormone involved in various developmental processes
433 (Singh et al., 2022). It also serves as a precursor to diverse volatile compounds determinant
434 for fruit aroma and defense mechanisms (Li et al., 2021; Rienth et al., 2021; Schwab et al.,
435 2008). Its accumulation should mark increased membrane phospholipid turnover
436 immediately before the onset of ripening, where its peroxidation takes place (Pilati et al.,
437 2014).

438 The accumulation profiles of metabolites belonging to clusters J, K and L are
439 consistent with their involvement in successive ripening-associated biological processes.
440 During ripening, a second growth phase is set on due to a significant accumulation of water
441 and solutes, mainly glucose and fructose, originating from sucrose translocated by phloem
442 mass flow (Daviet et al., 2023). When the berries reach their maximum volume, phloem
443 unloading in berries ceases, leading to a loss of water and volume known as the shriveling
444 or over-ripening (Griesser et al., 2024; Rogiers et al., 2017; Savoi et al., 2021). The fruits
445 then undergo constitutive water stress, which influences their metabolic composition.

446 **ABA as a key player of the ripening process**

447 Cluster I started with the herbaceous plateau (L4) and peaked at the softening
448 coloring transition (S7/R8). This cluster is marked by the accumulation of abscisic acid (ABA)
449 in its free form, the level of which began to rise at the end of the herbaceous plateau, before
450 peaking at the softening stage (Fig. S5). ABA is a phytohormone regulating plant growth and
451 development. It plays multiple roles including tolerance to desiccation (Fujii and Zhu, 2009).
452 ABA has been shown to promote color changes in non-climacteric fruits like strawberries
453 (Batista-Silva et al., 2018; Jia et al., 2011; Jiang and Joyce, 2003). Analysis of grapevine
454 berry development has highlighted ABA as an early indicator (if not a trigger) of veraison and
455 berry coloration (Pilati et al., 2017), particularly by positively regulating the phenylpropanoid
456 biosynthesis pathway (Chaves et al., 2010; Giribaldi et al., 2010; Kuhn et al., 2014;
457 Lacampagne et al., 2010; Sun et al., 2010; Wheeler et al., 2009). This regulatory role of ABA
458 is also evident in other non-climacteric fruits, such as strawberries (Jia et al., 2011), where
459 ABA levels continue to increase from the beginning of ripening until the end of fruit
460 development. In contrast, in grapevine berries, ABA peaks during the initial color change of
461 the berries but then declines as the ripening phase progresses (Davies et al., 1997; Fortes et
al., 2015; Pilati et al., 2017; Villalobos-González et al., 2016). Present single berry results largely refined these views, showing that ABA starts to accumulate before softening, acting as a trigger rather than a consequence of ripening, and maintains a high level throughout the entire phase of active phloem unloading, before shriveling (Fig. S5). If ABA belonged to cluster I, its glucose ester derivative (ABA-GE) accumulated later, and was found in cluster K. ABA-GE has been considered as a storage form of ABA or a final inactive product of its catabolism (Zeevaart, 1999). Hydrolysis of ABA-GE by specialized glucosidases has been shown to lead to a rapid increase in free ABA concentrations in response to osmotic stress in *Arabidopsis thaliana* (Xu et al., 2012). Moreover, the ABA catabolite phaseic acid was associated with cluster J (correlation of 0.69), suggesting that active ABA catabolism is responsible for the rapid decrease of free ABA at the very end of ripening (Fig. S5). Cluster J also contained a large number of sugar-derived ions, consistent with the accumulation profile of glucose and fructose as major *osmoticum* in ripening berries, starting at S6. To our knowledge, this is the first evidence that the sugar accumulation process precisely starts at the peak of ABA concentration (cluster I). ABA has been shown to activate the transcription of a cascade of enzyme and transporter genes playing key roles in sugar metabolism and accumulation during berry ripening (Bennett et al., 2023; Pan et al., 2005). Altogether, the rapid shift of the veraison-associated active ABA pool to inactive ABA forms such as ABA-GE and phaseic acid suggests that fine temporal tuning of ABA concentration is critical to a proper coordination of the ripening process.

## 482 Flavonol and proline dynamics during ripening as indicators of constitutive stress

Noticeably, the sequential accumulation of anthocyanins and flavonols such as kaempferol, quercetin, isorhamnetin and myricetin derivatives, as well as malvidin and cyanidin derivatives in clusters J and K, reflects the decline of dihydroflavonols, such as dihydrokaempferol, in cluster C. This developmental shift in flavonol profiles follows the biosynthetic pathway and aligns with the expression profiles of the genes encoding dihydroflavonol-4-reductase and late genes of the anthocyanin regulated by MybA1 (Cutanda-Perez et al., 2009; Massonnet et al., 2017).

Cluster K shows a massive, but late accumulation of proline, already known to accumulate when both berry growth and net protein accumulation have ceased (Stines et al., 1999). The levels of delta-1-pyrroline-5-carboxylate synthetase mRNA and protein, a key regulatory enzyme in proline biosynthesis, do not seem to be affected by ripening (Deluc et al., 2009; Stines et al., 1999). Present results revealed that proline didn't start to accumulate before late ripening (R8), and largely escaped the peak of free ABA, which in addition clearly vanished after phloem arrest, while proline accumulation persisted. This very poor
497 coincidental timing between ABA and proline in single berries apparently contradicts the
498 hypothesis that ABA would simply trigger proline biosynthesis in grapevines submitted to
499 water deficit (Deluc et al., 2009). Finally, most metabolites associated to cluster L, which are
500 characterized by a sharp accumulation at the very end of the ripening process, have been
501 identified as stilbenes, including *trans*-resveratrol, *trans*- and *cis*-piceid and viniferins (Table
502 S2). This cluster appeared therefore remarkably homogeneous, as all its annotated
503 compounds, representing 83% of all ions, were derived from stilbenes (Table S1, Fig. S3).
504 Stilbene biosynthesis has been associated with a wide array of biotic and abiotic stresses in
505 grapevine (Chong et al., 2009). In grapes, stilbene metabolism has been shown to respond
506 to water deficit, albeit with different responses depending on varieties (Deluc et al., 2011). In
507 addition, transcriptomic analysis of berries subjected to post-harvest withering has shown a
508 massive induction of the stilbene synthase gene family (Vannozzi et al., 2012). In the context
509 of berry development, the present work shows that stilbenes can be considered as metabolic
510 markers of the water stress associated to the cessation of phloem sap flow and wilting
511 occuring at over-maturation stage.

512 Dealing with complex accumulation profiles

513 K-means clustering, while useful for grouping data by similarity, may keep complex
514 metabolic profiles hidden, resulting in misclassification of physiologically important
515 metabolites. For example, by contrast with proline which also derives from glutamate,
516 gamma-aminobutyric acid (GABA), the key intermediary of the GABA shunt in plants (Ansari
517 et al., 2021), was found progressively accumulated during the green phase, followed by a
518 sudden disappearance preceding softening, and a sharp increase at the end of maturation
519 (Fig. S6). The recent identification of GABA as an inhibitor of the malate channel ALMT1
520 (Long et al., 2020), may provide a functional link between GABA abrupt decrease and the
521 trigger of the typical reversal of malate accumulation during the green stage/ripening
522 transition. Then, GABA being reversibly accumulated in mature berries submitted to O2
523 deprivation (Tesnière et al., 1994), its terminal accumulation is symptomatic of increased
524 anaerobiosis in the fruit core at this stage (Xiao et al., 2018). Such a particular profile led to
525 its misclassification in cluster E, albeit with a correlation of 0.62 to the cluster eigen-
526 metabolite (Fig. S6). Similarly, tryptophan, a precursor of the phenylpropanoid pathway (Fig.
527 7), had a distinct profile with a moderated peak at veraison but an overall decreasing trend.
528 This led to its placement in cluster D with a correlation of 0.64. To address this issue,
529 increasing the number of clusters in K-means could allow for a finer classification of
530 metabolites with specific trends. Furthermore, the use of more sophisticated grouping
531 methodologies, for example by combining a Gaussian mixture model (Polanski et al., 2015)
532 with the uniform or tree manifold approximation and projection (UMAP, TMAP) dimension
533 reduction methods (Ebbels et al., 2023; Olivon et al., 2018), could allow better adaptation to
534 these atypical profiles. This potential has already been partially explored in this study using t-
535 SNE.

536 **Conclusion**

537 This study provides a detailed analysis of grapevine berry development through an
538 untargeted metabolomic profiling, identifying 12 distinct metabolite clusters that map the
539 fruit's phenological stages. Utilizing single berry sampling and multivariate analyses, we
540 highlighted abrupt metabolic changes and the key roles of many metabolites. These results
541 improve our understanding of the grape's internal metabolic clock, offering the possibility of
542 optimizing the harvesting of berries according to their internal developmental clock, thanks in
543 particular to high-throughput and non-destructive phenotyping methods, opening the way to
544 new applications in breeding. Additionally, this research paves the way for studying the
545 physiology of non-climacteric fruits, allowing for more effective identification of key
546 developmental regulators, such as the gene networks involved in the onset of ripening. This
547 enhanced understanding could provide deeper insights into the molecular mechanisms
548 underlying plant acclimatization to abiotic stresses.

549 **Acknowledgments**

550 This work was supported by the Fondation Poupelain, the Agence Nationale de la
551 Recherche (ANR, G2WAS project, ANR-19-CE20-0024), the Institut National de Recherche
552 pour l'Agriculture, l'alimentation et l'Environnement (INRAe) Biologie et Amélioration des
553 Plantes (BAP) department (Métab'EAU project) and the Région Occitanie (Métab'EAU
554 project). Thanks to the INRAE UMR AGAP for helping with the sampling and to the INRAE
555 UMR SVQV of Colmar for the metabolomics analysis and to Camille Rustenholz and
556 Amandine Velt for the metabolomics pipeline.

557 **Competing interests**

558 The authors declare no competing interest.

559 **Author contributions**

560 F.T. prepared the samples for LCMS analysis, established and implemented the
561 multivariate analysis workflow, interpreted the results, and drafted the manuscript. S.S.
562 monitored and collected the grapevine berry samples, prepared the samples, and
563 contributed to the review and editing of the manuscript. L.T. provided the plant material and
564 contributed to the review and editing of the manuscript. P.H. interpreted the results, and
565 drafted the manuscript. R.B. supervised the metabolomic analyses, generated raw
566 metabolomics data, identified the metabolites, interpreted the results, and drafted the
567 manuscript. V.S. conceived and designed the study, coordinated and supervised the
568 experiments, interpreted the results, and drafted the manuscript. C.R. monitored and
569 collected the grapevine berry samples, conceived and designed the study, coordinated and
570 supervised the experiments, interpreted the results, and drafted the manuscript. All authors
571 have reviewed and approved the final version of the manuscript for publication.

572 **Data availability**

573 Metabolomics data have been deposited to the EMBL-EBI MetaboLights database
574 (DOI: 10.1093/nar/gkad1045, PMID:37971328) with the identifier MTBLS10572.

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888 Tables and Figures

889 Figures

890 **Figure 1.** Metabolomic data analysis workflow. The 11 expert stages were defined before

891 our study by Savoi et al. (2023). The workflow starts with raw metabolomic data, each of the

892 9,256 detected ions surfaces is initially scaled by its sum over berry development. The

893 normalized data underwent a classification of the samples by the K-means algorithm, in

894 order to identify new metabolomic stages independently from expert stages and observer

895 time. In parallel, the normalized data were submitted to a principal component analysis

896 (PCA). Single berry molecular clock was calculated from the curvilinear distance of samples

897 projected on successive linear regressions, selecting the best principal component (PC)

898 plane for each one. These newly identified stages and clock were then compared to the

899 expert stages. 653 invariant ions were then filtered out using a Kruskal-Wallis test with a

900 false discovery rate of 0.01. The 8,603 remaining ions were then grouped together using K-

901 means on the metabolites, followed by a PCA for each metabolite cluster, enabling the

902 representative dynamics (or “eigen-metabolite”) to be shown. Finally, a t-distributed

903 stochastic neighbor embedding (t-SNE) representation allowed more direct insights on the

904 data structure.
Figure 2. Comparative analysis of berry developmental stages: expert classification of single berry metabolic clock and days after flowering (DAF), color-coded by expert stages. (a) 3D principal component analysis (PCA) plot displaying berries colored according to expert-defined phenological stages (G = green, L = lag, S = softening, R = ripening, Sh = shriveling). Each color represents a different stage, illustrating the separation and distribution of berries within the multi-dimensional space defined by the first three principal components (PC) of the dataset. (b) Scatter plot showing a side-by-side comparison of the metabolomic clock (y-axis) against the expert-defined stages (x-axis). Data points are color-coded by the expert stages. The metabolomic clock was determined by calculating the distances between samples across successive linear regressions of PCA-projected samples trajectories in a, providing a continuous scale that contrasts with the discrete categorization of the expert stages. (c) Comparison of the metabolomic clock (x-axis) and days after flowering (DAF) (y-axis) and colored by expert stages.

Figure 3. Representative profiles of metabolite clusters decreasing throughout grapevine berry development. n indicates the number of metabolites within each cluster. Each point is a single berry. The y axis represents the first principal component (PC1) from the cluster principal component analysis (PCA) and the x axis is the metabolomic clock. For expert stages description see Table 1.

Figure 4. Representative profiles of metabolites that accumulate as a peak at specific stages of the berry development. n indicates the number of metabolites within each cluster. Each point is a single berry. The y axis represents the first principal component (PC1) from the cluster principal component analysis (PCA) and the x axis is the metabolomic clock. For expert stages description see Table 1.

Figure 5. Clusters representative profiles of metabolites showing an accumulation trend during the ripening phase. n value indicates the number of metabolites within each cluster. Each point is a single berry. The y axis represents the first principal component (PC1) from the cluster principal component analysis (PCA) and the x axis is the metabolomic clock. For expert stages description see Table 1.

Figure 6. 3D t-distributed stochastic neighbor embedding (t-SNE) representation of the untargeted metabolites. Each point represents a metabolite, colored by its K-means cluster with its representative pattern over the metabolomic clock (see details in Fig. 2, 3 and 4). Patterns A through D include metabolites consumed in the early green stage. Between E and I, there are noticeable peaks in metabolite accumulation, signaling the stepwise transition from green phase to ripening. Finally, patterns J to L depict the accumulation of
939 metabolites toward the end of the berries' development. In the 3D t-SNE visualization, these
940 three cluster groups appear to be organized into three distinct branches.

941 **Figure 7. Biosynthesis pathways of the identified metabolites and their accumulation profile through the grapevine fruit development.** Each point represents a metabolite, 942 colored by its K-means cluster with its representative pattern over the metabolomic clock 943 (see details in Fig. S5). Metabolites in gray correspond to molecular standards; most of 944 these molecules have a correlation with the eigen-metabolite of their cluster of less than 0.8 945 (see dynamics in Fig. S6). Chalcone, epicatechin, gallocatechin, and epigallocatechin have 946 been identified in their derivative, fragment, or polymerized forms. Due to the multiple 947 profiles associated with each fragment/derivative/polymer, we have chosen not to display 948 them in this figure (see details in Fig. S5). (KEGG Pathway database 949 (https://www.genome.jp/kegg/pathway.html), Ali et al., 2010; Castellarin et al., 2007b; Dong 950 and Lin, 2021; Clifford et al., 2017; Petrussa et al., 2013; Tang YuHan et al., 2018; Yang et 951 al., 2020). 952

953

954 Tables

<table><caption>955 Table 1. Repartition of the 125 samples analyzed by UHPLC.</caption><thead><tr><th rowspan="2"></th><th colspan="6">Green</th><th colspan="3">Ripening</th><th>Over-ripening</th></tr><tr><th colspan="3">Growth</th><th colspan="2">Lag</th><th colspan="2">Softening</th><th colspan="2">Colouring</th><th>Shrivelling</th></tr></thead><tbody><tr><th>Expert stages</th><td>G1</td><td>G2</td><td>G3</td><td>L4</td><td>L5</td><td>S6</td><td>S7</td><td>R8</td><td>R9</td><td>R10</td><td>Sh11</td></tr><tr><th>Year of sampling</th><td>2019</td><td>2019</td><td>2019</td><td>2019</td><td>2018</td><td>2018</td><td>2018</td><td>2018</td><td>2018</td><td>2018</td><td>2018</td></tr><tr><th>Harvested samples</th><td>4</td><td>26</td><td>28</td><td>19</td><td>8</td><td>9</td><td>9</td><td>11</td><td>14</td><td>12</td><td>13</td></tr><tr><th>Sample selected</th><td>4</td><td>14</td><td>14</td><td>17</td><td>8</td><td>9</td><td>9</td><td>11</td><td>14</td><td>12</td><td>13</td></tr></tbody></table>

956

957 Table 2. Relationship between expert stages and K-means determined metabolomic
958 stages. Each cluster represents a stage where berries exhibit similar metabolomic
959 characteristics, G = green phase, L = lag phase, S = softening, R = ripening and Sh =
960 shriveling.

960

<table><thead><tr><th>Expert stages</th><th>G1</th><th>G2</th><th>G3</th><th>L4</th><th>L5</th><th>S6</th><th>S7</th><th>R8</th><th>R9</th><th>R10</th><th>Sh11</th></tr></thead><tbody><tr><th>K-means stages</th><td>1</td><td>1,2,3<br>,4</td><td>4,5</td><td>5,6,7,<br>8,9</td><td>10</td><td>11,12<br>,13</td><td>12,13</td><td>14,15</td><td>15</td><td>16,17,<br>18</td><td>15,17,<br>18</td></tr></tbody></table>

961
<figure><img src="image_2.png" /></figure>

Blind stages redefinition

Metabolites clustering
<figure><img src="image_3.png" /><figcaption>(a) PCA representation colored by expert stages</figcaption></figure>

bioRxiv preprint doi: https://doi.org/10.1101/2024.07.06.602344; this version posted July 10, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

<figure><img src="image_4.png" /><figcaption>(c) Comparison between single berry metabolomic clock and DAF</figcaption></figure>

<figure><img src="image_5.png" /></figure>
Profile A

n = 162

<figure><img src="image_6.png" /></figure>

bioRxiv preprint doi: https://doi.org/10.1101/2024.07.06.602344; this version posted July 10, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Profile B

n = 405

<figure><img src="image_7.png" /></figure>

Expert
stages

Profile C

n = 1344

<figure><img src="image_8.png" /></figure>

Profile D

n = 1626

<figure><img src="image_9.png" /></figure>

G1
G2
G3
L4
L5
S6
S7
R8
R9
R10
Sh11
Profile E

n = 750

<figure><img src="image_10.png" /></figure>

Profile F

n = 1293

<figure><img src="image_11.png" /></figure>

Profile G

n = 152

<figure><img src="image_12.png" /></figure>

bioRxiv preprint doi: https://doi.org/10.1101/2024.07.06.602344; this version posted July 10, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Profile H

n = 87

<figure><img src="image_13.png" /></figure>

Profile I

n = 584

<figure><img src="image_14.png" /></figure>
Profile J

n = 1300

<figure><img src="image_15.png" /></figure>

bioRxiv preprint doi: https://doi.org/10.1101/2024.07.06.602344; this version posted July 10, 2024. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Profile K

n = 828

<figure><img src="image_16.png" /></figure>

Profile L

n = 72

<figure><img src="image_17.png" /></figure>
<figure><img src="image_18.png" /></figure>

Clusters of metabolites

A B C D E F G H I J K L
<figure><img src="image_19.png" /><figcaption>Clusters of metabolites</figcaption></figure>

<figure><img src="image_20.png" /></figure>