## 3 Results and Discussion
### Principal Components Analysis  
A new _E. coli_ cell was selected from the air-dried samples (Figure 3A). As described in Section 2.1, bacteria were let grow to reach their mid-exponential growth phase. In this stage of growth, bacteria are replicating exponentially, thus the probability to find bacterial cells that are duplicating is maximized. Figure 3B shows the Raman chemical map of the sample taken into consideration, it is easy to notice that the dimensions of this cell are bigger than those of the cells observed before, in particular, its length is nearly double with respect to the cells shown in Figure 2. Furthermore, the intensity of the Raman signal at 2940 cm\({}^{-1}\), which corresponds to CH stretching modes of proteins, nucleic acids, carbohydrates, and lipids, highlights an accumulation of biomaterials at the two poles of the cell with a very small amount of signal in the center. All this information supports the hypothesis that we are focusing on an actually duplicating bacterial cell. The principal component analysis (PCA) of the Raman spectra of the map was performed in order to visualize similarities between spectra and to identify unsupervised grouping of image pixels on the basis of their Raman signature, which reflects the chemical composition in that location. PCA allows an easier visualization of the variance of spectral profile in different regions of the bacterial cell and helps to focus the attention in the spectral regions containing nonrandom variation. The maps of the scores revealing the spatial distribution of the two first components are shown in Figure 3C,D. The significant variance of the Raman spectra that compose the chemical images was modelled by the first two principal components (PCs), which explain 86.2% of total variance (see the plot of the explained variance by each PC in Figure 4A). PC1 and PC2 loadings allowed to interpret the chemical meaning of compositional differences within the image (Figure 4B). Pixels from yellow to red represent regions where the scores of the selected PC have the higher value, meaning that in that region, the collected Raman spectra mainly show the spectral features captured by that PC. The loadings profiles in Figure 4B allow to determine the Raman signals represented by each component (PC1 in blue and PC2 in orange), revealing which chemical species are more represented in each PC (see Table 1 as reference for the chemical meaning assignment of the main Raman signals) and more abundant in the regions where the PC has higher scores (red regions in Figure 4B,C), providing the chemical information associated to the maps of the scores. In particular, it can be noticed that the main feature of both PCs is represented by the C-H stretching signal at 2940 cm\({}^{-1}\), reflecting the nonhomogeneous distribution of biomaterial within the considered cell. Indeed, PC1 and PC2 scores maps show two different distributions based on the intensity of the scores, which are not homogeneously spread in the whole cell but seem to concentrate in different regions of the bacterium. The chemical map in Figure 4A, built on the Raman signal at 2940 cm\({}^{-1}\), which corresponds to C-H stretching modes, shows a distribution along the cell similar to that revealed by PC1. Since C-H bonds are the most frequent in each biomolecule present in the cell (e.g., lipids, protein, and DNA), this distribution indicates that there is a higher density of biomaterial at the cell edges, suggesting that the cell is undergoing replication. However, it can be noticed that DNA related bands (780, 1000-1100, and 1243 cm\({}^{-1}\)), already commented on in the discussion of the Raman maps in a univariate way, are captured by the first two PCs, attesting to their importance in the spectral variance and therefore for the description of the biological system under analysis.