Abstract: Mass spectrometry imaging (MSI) combined with stable isotope tracing has emerged as a powerful analytical approach for investigating the spatial heterogeneity and dynamic reprogramming of metabolic networks in biological tissues. This approach enables in situ visualization of metabolite distributions together with isotopic labeling patterns, providing invaluable insights into tissue-specific metabolic activities and inter-organ metabolic crosstalk. However, MSI analysis is inherently susceptible to matrix effects, which originate from variations in tissue composition, ionization efficiency, and microenvironmental factors. For instance, differences in the chemical environment of various tissues can influence the ionization process, leading to biased or inconsistent ion intensities. Therefore, it is critical to evaluate how these matrix effects might affect the accuracy and reliability of stable isotope tracing results, especially within complex biological systems involving multiple tissues and organs. In this study, the influence of tissue matrix effects on MSI-based spatial stable isotope tracing analysis was systematically examined by airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI). Three distinct sample preparation methods were employed, including spiked tissue homogenate strips, stable isotope spraying on single-organ tissue sections, and isotope spraying on whole-body animal sections. Uniformly ¹³C-labeled glucose isotopologues (¹³C₂- and ¹³C₆-glucose) were used to evaluate the impact of tissue matrix effects on the ion intensities of target molecules. The isotopic labeling ratios were calculated to assess the matrix effects on the quantification accuracy of stable isotope tracing. The results showed that when the signal intensities of target ions in MSI analysis were directly compared, differences in tissue matrix effects led to significant variations in ion intensities and a poor linear correlation. These discrepancies highlight the challenges associated with comparing metabolite levels across tissues. However, after the application of stable isotope internal standard correction, the linear correlation coefficients and consistency of calibration curves were significantly improved, effectively reducing interference from tissue matrix effects. This enhancement in correlation accuracy allows for more reliable comparisons between tissue types. Further analyses revealed that the calculated labeling fractions across distinct tissues were unaffected by matrix effects, indicating that the determination of labeling fractions in spatial stable isotope tracing ensured cross-tissue comparability. Verification through liquid chromatography-mass spectrometry (LC-MS) confirmed that the calculated labeling fractions were consistent across tissues and not influenced by matrix differences, further supporting the robustness of the developed method. This study provides important technical references and methodological foundations for MSI analysis, spatially resolved metabolomics, and in vivo stable isotope tracing studies, offering a more reliable analytical strategy for investigating complex biological systems and metabolic processes.