# Spatial Variability of Grape Berry Maturation Program at the Molecular Level
## 3 Results and Discussion
### Spatial Variability in Berry Ripening Transcriptomic Program  
To explore more general differences in the developmental program of berries collected from the different blocks, the whole transcriptomic dataset (14 blocks \(\times\) 5 time points, 70 samples in total; Supplementary Dataset S2) was investigated by PCA (Figure S2). The first component (Dim1) showed a clear separation in accordance with sampling time points, whereas the second compound (Dim2) mainly described transient changes in gene expression and no clear effect attributable to NDVI. Aiming at unraveling transcriptomic changes related to opposite vigor levels, we reduced the dataset to the grape transcriptomes derived from the six vineyard blocks showing the lowest (2, 3, and 5) and highest (6, 11, and 12) NDVI values, which were defined as low and high vigor status (LVS and HVS, respectively). In the resulting PCA (Figure 5a), Dim1 accounted for 30.6% of the variance  
Figure 4: Spatial gene expression variability maps. Gene expression levels (RPKM, reads per kilobase per million mapped reads) measured in berries collected from the vineyard blocks were interpolated at a specific time point, indicated as days after veraison (DAV). Interpolations followed inverse distance weighting and quantile classification methods. Numbers from 1 to 14 indicate the vineyard blocks. and again separated samples according to maturation stages, whereas Dim2 provided a minor contribution to the overall variance (12.6%). Notwithstanding, a distribution along Dim2 by vigor level was observed within the sample group corresponding to 11 DAV. This time point was further examined in an ensuing PCA applied only on 11 DAV samples which results clearly distinguished HVS and LVS samples along Dim1 (Figure 5b). Although at each time point, grape samples were collected the same day across the vineyard, we argue that, in the PCA of samples collected on 11 DAV, Dim1 likely discriminates differences related to berry phenology, which we discussed being affected by variation in vine vigor (Figure S1). Taking advantage of detailed grapevine fruit transcriptomic data, a molecular phenology scale (MPhS) was recently built [32]. This tool allows mapping the progression of fruit development by molecular instead of classical time- or phenotype-based approaches. Here, we used the MPhS to classify the averaged phenological stage of berries from HVS and LVS at all five collection time points. This approach revealed a clear shift in the developmental stage of these berries. Grapes from LVS showed an advancement in the ripening stage starting from 11 DAV that held true until harvest (47 DAV) in comparison with those from HVS vines (Figure 5c). This result adds value to the choice of focusing much of the analysis on the transcriptomic changes occurring 11 DAV. Regardless, a slight difference was also observed for the molecular phenological classification \(-\)30 DAV time point, which overlapped with \(-\)9 DAV time point for HVS. Figure 5: Transcriptome of berries collected from six blocks showing the highest (HVS) and lowest (LVS) vigor status. (**a**) PCA scatterplot of transcriptomic data of each sampling location (block) at five time points during berry development by days after veraison (DAV). Purple circles indicate a single time point. (**b**) PCA scatterplot of transcriptomic data of each sampling location (block) at 11 DAV. (**c**) Schematic showing the mapping of the averaged transcriptomic data from HVS and LVS blocks at the five collection time points onto the MPhS. Further characterization of the molecular events on the basis of the differences observed 11 DAV consisted in the identification of the DEGs between HVS and LVS (Supplementary Dataset S2). A total of 2084 DEGs (\(t\)-test; \(p<0.05\)) found correspondence in the most recent genome annotation (VITVI V3 code), of which 968 passed the filtering by \(\mid\)FC\(\mid\)\(\geq\) 1.5.