In public health contexts, where results of conventional imaging methodologies are often disappointing, molecular multimodal imaging is within a realm of possibility. Various imaging techniques are informative for functional groups, molecular weights, or special recognition sites, but no individual technique provides optimal answers to all questions. Thus, combining information from two or more measurement platforms is highly attractive. Such an approach is required to elucidate the complex spatial distribution of biomolecules in tissues, opening the way for a qualitative and quantitative multi-omics overview, both targeted and unbiased, of lipids, proteins, peptides, antibiotics, nucleic acids as well as glycans. Herein, we propose to combine vibrational spectroscopy, namely Raman Scattering (RS) and Mass Spectrometry Imaging (MSI) in a single workflow. Strengths and weaknesses of these technologies make them highly complementary. Key objectives of MultiRaMaS are both methodological and biological. On the one hand, as an emerging and challenging strategy multimodal imaging combining RS and Matrix-Assisted Laser Desorption/Ionization (MALDI) MSI needs further improvements: 1) optimization of a stringent and common sample preparation including cryo-sectioning and accurate identification of anatomical structures, 2) optimization of settings for fast multi-omics screening by RS imaging, 3) optimisation of settings to access the high chemical specificities and structure details by MALDI-MSI, 4) quantification of biomolecules of interest by MALDI-MSI, and 5) data processing including visualization, quantification, co-registration as well as fusion of hyperspectral imaging data. On the other hand, this original and remarkable approach, not fully explored, will be applied throughout a single tissue section and methodological outputs will be applied for the first time to the characterization of tuberculosis (TB), an infectious disease which affects each year 10 million humans and causes 1.7 million deaths. Mycobacterium tuberculosis (Mtb), the etiological agent of TB, establishes a durable lung infection in complex lesions where it is found intracellularly in various immune cell types and extracellularly in the central necrotic core of these lesions. Our current efforts are focused on increasing spatial resolution to visualize and identify the structures of molecules of interest at the cellular and subcellular level, i.e. lifting a scientific barrier. Of particular interest is the mapping and quantification of anti-TB drugs and biomarkers in the phagolysosome of macrophages where M. tuberculosis bacilli reside. Thus, the major biological objectives are: 1) to decipher the architecture and the microenvironment of tuberculous lesions, 2) to map, identify and quantify anti-TB drugs and biomarkers in the phagolysosome of macrophages, and finally 3) to evaluate the effect on Mtb of anti-TB drug concentration and duration of treatment. As an ultimate objective, all imaging data will be used to apply artificial intelligence and machine learning to facilitate the automation of the superimposition of imaging data onto digitized and fully annotated histological images. To meet the strategic objectives, MultiRaMaS is organized into 5 tasks. MALDI-MSI experiments shall rely on a highly performant combination of a source with high spatial resolution and high resolution mass spectrometer combining high accuracy and efficient MS/MS capability. This project gathers the complementary expertise of the project coordinator and his renowned collaborators to ensure the development of a multimodal imaging workflow to investigate tuberculosis. Complementary resources and expertise are assembled to ensure the feasibility of the proposed scientific program. This multidisciplinary and interdisciplinary project is original because of the technologies implemented which have rarely been combined, as well as by its applications to investigate tuberculosis.