Mass spectrometry (MS) has become an indispensable tool for transformed metabolomics studies, whereas exploring single-cell metabolomic profiles remains a challenge due to limited techniques and suitable algorithms. This dissertation delves into cell heterogeneity, including method development and applications to infectious disease, with a focus on Chagas disease caused by Trypanosoma cruzi. Using the effective Single-probe SCMS technique combined with a fixation method that can safely decontaminate samples, we examined individual cell responses during parasite infection. Our findings unveiled significant differences in cell metabolism, even in neighboring uninfected cells, shedding light on the broader impact of infection. This pioneering study, utilizing bioanalytical SCMS, offers versatile tools to understand infectious diseases and the complexities of cell behavior in diseases. Additionally, this dissertation tackles these challenges by merging the Single-probe single-cell MS (SCMS) technique with SinCHet-MS, a specialized bioinformatics software package. This combination allowed us to understand cell diversity, quantify cell subgroups, and identify key metabolites representing cells in subpopulations. Testing this approach with melanoma cancer cell lines revealed new subgroups after drug treatment, showcasing the potential for in-depth exploration of cell diversity and marker identification. This label-free method enhances our comprehension of cell metabolism in diseases and therapeutic responses. Furthermore, this research pioneers a novel approach by integrating CRISPR-Cas9 gene editing with the Single-probe SCMS metabolomics, focusing on FASN-knockout cells in the human cell model HEK293T. This innovative strategy provides valuable insights into gene-metabolite interactions at the single-cell level. By combining advanced SCMS techniques with gene editing, this dissertation opens new avenues for understanding gene editing efficiency and the complex relationship between genes and cell metabolomics. These integrated methods advance our understanding of cell diversity in cancer, infectious diseases, and gene therapy, offering a fresh perspective for future research and therapeutic intervention.