Identification of potential markers of a physiological stage (e.g., pathology) discovered using MALDI-MSI is an important step in the understanding of signaling pathways or for providing sets of diagnosis and prognosis markers for clinical applications. Classically, identification can be achieved by extraction from a piece of tissue and proteomics strategies. However, this induces loss of information especially for low-abundance proteins or proteins localized to a specific region of the tissue. In this respect, identification directly at the tissue level is an attractive alternative. Because the molecular charge states in MALDI are low, on tissue identification is possible using bottom-up MALDI-MSI strategies. Enzymatic digestion using an enzyme such as trypsin can be performed at the micro-scale level to generate peptide collections while avoiding these peptides to be delocalized. It is, therefore, possible to image proteins through the molecular images of their digested peptides. These peptides can also be used to retrieve information on protein sequences by performing MS/MS, although databank interrogation or de novo sequencing using MS/MS spectra does not always lead to a successful or confident identification because on tissue complexities render PMF data problematic. Identification can be improved by increasing MS/MS spectra quality and simplifying their interpretation. This can be achieved by derivatization of peptides. In fact, derivatization of peptides leads to increases in fragmentation yields and orients fragmentations toward a specific series of fragment ions. In this respect, N-terminal chemical derivatization has proven to be particularly efficient. N-terminal chemical derivatization of tryptic peptides has been developed to be performed at the tissue level after on tissue digestion. Specific focus is given to 4-sulfophenyl isothiocyanate (4-SPITC), 3-sulfobenzoic acid NHS ester (3-SBASE), and (N -succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) derivatizations. This provides a complete strategy for protein identification in a bottom-up MALDI-MSI approach and opens the way for novel biomarker identification.