Periodic Reporting for period 1 - TRaMaSI (Tracer Raman-Driven Mass Spectrometry Imaging (TRaMaSI): a new platform for on-tissue Tracing of Nutritional and Biochemical Trajectories)
Periodo di rendicontazione: 2024-05-01 al 2026-04-30
Sintesi del contesto e degli obiettivi generali del progetto
The TraMaSi project was conceived within the rapidly evolving field of spatial metabolomics, which seeks to map metabolic processes directly within biological tissues. This approach addresses a critical need in biomedical research: understanding how metabolic pathways are spatially organized and altered in physiological and pathological conditions. In particular, spatial tracer metabolomics using stable isotopes such as ^13C offers the potential to provide unprecedented insights into nutrient utilization and metabolic fluxes at the tissue level. Such knowledge is highly relevant in a broader scientific and strategic context, including disease research (e.g. cancer, hypoxia-related disorders) and the development of precision medicine approaches.
Against this background, the primary objective of TraMaSi was to develop a robust multi-imaging workflow for spatial tracer metabolomics by integrating Scattered Raman spectroscopy/microscopy with desorption electrospray ionization mass spectrometry imaging (DESI MSI). The original concept aimed to use Raman microscopy as a non-destructive, label-specific pre-screening tool to identify ^13C-labelled regions within tissues, thereby guiding subsequent high-resolution mass spectrometry imaging of nutrients and metabolites. This combination was expected to improve both the efficiency and spatial specificity of tracer metabolomics workflows.
To evaluate this concept, we conducted a series of experiments focusing on the detection of 13C-labelled glucose in different biological matrices, including intact tissues, tissue homogenates, and cultured cells following 13C glucose labelling. However, these studies consistently demonstrated that current Raman spectroscopy technologies lack the sensitivity required for detecting metabolites at physiologically relevant concentrations, with detection limits in the millimolar range. This limitation prevents its effective use for tracing nutrient incorporation in complex biological systems.
As a result, the project strategy was adapted to focus on DESI MSI as the core analytical platform. This led to the development of an optimized DESI MSI workflow, culminating in a benchmark study comparing solvent systems for imaging murine organs and demonstrating application in a hypoxic rat kidney model. These results represent a significant methodological contribution, as they establish best practices for DESI MSI in spatial metabolomics.
A further objective of the project was to establish reliable protocols for administering 13C-labelled nutrients in animal models. This proved more challenging than anticipated. While sufficient labelling could be achieved in kidney tissue, it required the use of high concentrations of 13C glucose. Although these experiments validated the feasibility of high-resolution DESI MSI for spatial tracer metabolomics—demonstrating that isotopic distributions can be resolved and spatial fractional contribution analysis can be performed—they also highlighted a key limitation. The high tracer doses necessary for detection can perturb normal physiology and metabolism, meaning that the observed spatial metabolic patterns may not fully reflect the native pre-labelling state of the tissue. This finding is important for the field, as it underscores the need to balance analytical sensitivity with biological relevance.
To further strengthen the spatial metabolomics workflow, the project incorporated laser microdissection (LMD), enabled through access to new instrumentation obtained during the Marie Curie fellowship. LMD provided a complementary approach for validating and refining spatial information obtained by DESI MSI. By isolating specific regions of interest identified through mass spectrometry imaging, LMD enabled follow-up bulk LC/MS-based metabolomics and proteomics analyses. A strong concordance between DESI MSI and LMD–LC/MS results was observed, confirming the reliability of this integrated approach.
This combined DESI MSI–LMD workflow represents a key outcome of the project and offers a scalable and broadly applicable strategy for spatial omics studies. By linking spatially resolved metabolite distributions with downstream molecular analyses, the workflow enhances the interpretability and biological relevance of spatial metabolomics data. In terms of impact, the project contributes to advancing analytical methodologies that are essential for understanding tissue heterogeneity, with potential applications across biomedical research, pharmacology, and systems biology. Ultimately, these developments support ongoing efforts at the European and global level to improve disease characterization and enable more targeted therapeutic interventions.
Against this background, the primary objective of TraMaSi was to develop a robust multi-imaging workflow for spatial tracer metabolomics by integrating Scattered Raman spectroscopy/microscopy with desorption electrospray ionization mass spectrometry imaging (DESI MSI). The original concept aimed to use Raman microscopy as a non-destructive, label-specific pre-screening tool to identify ^13C-labelled regions within tissues, thereby guiding subsequent high-resolution mass spectrometry imaging of nutrients and metabolites. This combination was expected to improve both the efficiency and spatial specificity of tracer metabolomics workflows.
To evaluate this concept, we conducted a series of experiments focusing on the detection of 13C-labelled glucose in different biological matrices, including intact tissues, tissue homogenates, and cultured cells following 13C glucose labelling. However, these studies consistently demonstrated that current Raman spectroscopy technologies lack the sensitivity required for detecting metabolites at physiologically relevant concentrations, with detection limits in the millimolar range. This limitation prevents its effective use for tracing nutrient incorporation in complex biological systems.
As a result, the project strategy was adapted to focus on DESI MSI as the core analytical platform. This led to the development of an optimized DESI MSI workflow, culminating in a benchmark study comparing solvent systems for imaging murine organs and demonstrating application in a hypoxic rat kidney model. These results represent a significant methodological contribution, as they establish best practices for DESI MSI in spatial metabolomics.
A further objective of the project was to establish reliable protocols for administering 13C-labelled nutrients in animal models. This proved more challenging than anticipated. While sufficient labelling could be achieved in kidney tissue, it required the use of high concentrations of 13C glucose. Although these experiments validated the feasibility of high-resolution DESI MSI for spatial tracer metabolomics—demonstrating that isotopic distributions can be resolved and spatial fractional contribution analysis can be performed—they also highlighted a key limitation. The high tracer doses necessary for detection can perturb normal physiology and metabolism, meaning that the observed spatial metabolic patterns may not fully reflect the native pre-labelling state of the tissue. This finding is important for the field, as it underscores the need to balance analytical sensitivity with biological relevance.
To further strengthen the spatial metabolomics workflow, the project incorporated laser microdissection (LMD), enabled through access to new instrumentation obtained during the Marie Curie fellowship. LMD provided a complementary approach for validating and refining spatial information obtained by DESI MSI. By isolating specific regions of interest identified through mass spectrometry imaging, LMD enabled follow-up bulk LC/MS-based metabolomics and proteomics analyses. A strong concordance between DESI MSI and LMD–LC/MS results was observed, confirming the reliability of this integrated approach.
This combined DESI MSI–LMD workflow represents a key outcome of the project and offers a scalable and broadly applicable strategy for spatial omics studies. By linking spatially resolved metabolite distributions with downstream molecular analyses, the workflow enhances the interpretability and biological relevance of spatial metabolomics data. In terms of impact, the project contributes to advancing analytical methodologies that are essential for understanding tissue heterogeneity, with potential applications across biomedical research, pharmacology, and systems biology. Ultimately, these developments support ongoing efforts at the European and global level to improve disease characterization and enable more targeted therapeutic interventions.
Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti
WP 1: Infusion of stable isotope labelled nutrients into an AKI rat model: The acute kidney injury (AKI) induction protocol and collected blood samples at multiple time points to determine the optimal timing for kidney collection in rats based on glucose pharmacokinetics were perfomed. However, administration of the nutrient was only partially successful, as effective delivery required a high concentration (0.8 M) when administered as a bolus injection. In parallel, ocular injections in mice were also evaluated. Rat kidney tissues from hypoxic and control groups were successfully obtained, but the resulting labeling data were not physiologically interpretable. In addition, the nutrient could not be detected by stimulated Raman scattering (SRS) imaging because of insufficient sensitivity. Pharmacokinetic analysis revealed that glucose concentrations decreased rapidly, with approximately 80% of the injected glucose cleared within 6 minutes, indicating fast biodistribution. Based on these findings, kidney collection was performed 6 minutes after injection, corresponding to the time of highest glucose concentration in the tissue. Overall, the planned scientific deliverables and milestones were only partially achieved.
WP 2 Setting up of a Stable Isotope Probing experiment: This work package focused on establishing and optimizing stimulated Raman scattering (SRS) microscopy for the detection of isotope-labeled nutrients in biological samples. SRS measurements were successfully set up and performed using standard compounds, tissues, and cells, with D7-glucose selected as the nutrient tracer because it produces signals in the Raman silent region. After gaining experience with the technology and experimental workflow, it was found that the isotope-labeled nutrient could not be reliably detected by SRS due to the technique’s limited sensitivity. In particular, D7-glucose was not detectable in either tissue or cell samples. However, complementary analysis of the same tissues using desorption electrospray ionization (DESI) mass spectrometry successfully detected D7-glucose, demonstrating that the nutrient was present and indicating that the main limitation was the sensitivity of Raman-based detection rather than nutrient uptake. Although valuable expertise was gained in setting up SRS microscopy experiments and generating chemical images of kidney tissues, the intended visualization of nutrient distribution could not be achieved. Consequently, while the methodological objectives related to SRS operation and tissue imaging were accomplished, the scientific deliverables were only partially achieved. Specifically, WP 3 (discovery of isotope-specific spectral features/peaks) and WP 4 (localization of nutrients and selection of regions of interest) could not be completed because the isotope-labeled nutrient could not be detected with sufficient sensitivity by SRS microscopy.
WP 5 Setting up of mass spectrometry imaging (MSI) workflow: This work package focused on the development, optimization, and validation of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) workflows for spatial metabolomics and tracer metabolomics applications. Experimental protocols were systematically optimized by comparing different solvent systems and assessing the sensitivity, reproducibility, and robustness of DESI-MSI measurements. The study included analyses of rat kidneys with and without stable isotope-labeled nutrients to evaluate isotopologue detection, as well as mouse kidneys following ocular nutrient injection. DESI-MSI results were benchmarked against LC–MS data to assess analytical reliability. In parallel, a comprehensive quality control and data assurance workflow was established using homogenate-based controls, MOZAIC software for mass accuracy and total ion current (TIC) assessments, and the Cardinal R package for data preprocessing and analysis. This work also led to collaborations with Spectroswiss for software development and with the Cardinal development team. The results demonstrated successful detection of administered isotope-labeled nutrients and confirmed the ability of DESI-MSI to identify nutrient-derived isotopologues in biological tissues. Although metabolite labeling levels were relatively low and limited biological interpretation, tracer-administered samples consistently showed a higher fractional contribution of labeled species than controls, demonstrating the feasibility of spatial tracer metabolomics with DESI-MSI. The benchmarking study further revealed that DESI solvent composition is a critical factor affecting metabolite coverage, with 90% acetonitrile providing superior sensitivity for polar metabolites and 90% methanol favoring fatty acid detection. Consequently, 90% acetonitrile was selected for spatial metabolomics analyses of rat kidney tissue. Overall, the developed workflow showed excellent technical reproducibility, sensitivity, and reliability.
WP 6 Bulk tracer metabolomics: This work package focused on validating spatial metabolomics results through complementary bulk tracer metabolomics analyses. Consecutive tissue sections were analyzed to detect the administered nutrient and its downstream metabolites, while blood samples were examined to characterize nutrient pharmacokinetics. Spatial metabolomics results obtained by DESI-MSI were compared with bulk analyses to confirm metabolite identities and increase confidence in the findings. In addition, laser microdissection (LMD) was introduced to enable region-specific sampling and analysis of selected areas of interest within the tissue. The results confirmed the presence of the administered nutrient in rat kidney tissues following bolus injection. However, the high concentration required for administration resulted in metabolic perturbations that limited the physiological interpretability of the tracer metabolomics data. Despite this limitation, the LMD-based analyses produced promising results, revealing differential abundances of metabolites such as lactate in specific tissue regions. These findings were consistent with and provided additional validation for the spatial distributions observed by DESI-MSI.
WP 7 Spatial isotopologue analysis from MSI data: This work package focused on integrating histological information with spatial tracer metabolomics data following DESI-MSI analysis. After mass spectrometry imaging, hematoxylin and eosin (H&E) staining was performed on the same tissue sections to enable histological co-registration and facilitate the interpretation of spatial metabolic patterns. In addition, IsoScope software was implemented for the analysis of spatial tracer metabolomics datasets, allowing visualization and mapping of isotopologue distributions and calculation of fractional contributions across tissue regions. Although the workflow for histological co-registration and spatial tracer metabolomics data processing was successfully established, the level of isotope labeling detected in the tissues was insufficient to clearly map metabolic pathways or distinguish between ischemic and control regions. As a result, the spatial distributions of labeled metabolites did not provide adequate biological contrast to identify tissue-specific metabolic alterations associated with ischemia. Consequently, the planned scientific deliverables and milestones were partially achieved. For these reasons, the research project could not proceed with the WP 8 that was focused on identification of the metabolic routes of healthy and ischemic kidneys.
WP 2 Setting up of a Stable Isotope Probing experiment: This work package focused on establishing and optimizing stimulated Raman scattering (SRS) microscopy for the detection of isotope-labeled nutrients in biological samples. SRS measurements were successfully set up and performed using standard compounds, tissues, and cells, with D7-glucose selected as the nutrient tracer because it produces signals in the Raman silent region. After gaining experience with the technology and experimental workflow, it was found that the isotope-labeled nutrient could not be reliably detected by SRS due to the technique’s limited sensitivity. In particular, D7-glucose was not detectable in either tissue or cell samples. However, complementary analysis of the same tissues using desorption electrospray ionization (DESI) mass spectrometry successfully detected D7-glucose, demonstrating that the nutrient was present and indicating that the main limitation was the sensitivity of Raman-based detection rather than nutrient uptake. Although valuable expertise was gained in setting up SRS microscopy experiments and generating chemical images of kidney tissues, the intended visualization of nutrient distribution could not be achieved. Consequently, while the methodological objectives related to SRS operation and tissue imaging were accomplished, the scientific deliverables were only partially achieved. Specifically, WP 3 (discovery of isotope-specific spectral features/peaks) and WP 4 (localization of nutrients and selection of regions of interest) could not be completed because the isotope-labeled nutrient could not be detected with sufficient sensitivity by SRS microscopy.
WP 5 Setting up of mass spectrometry imaging (MSI) workflow: This work package focused on the development, optimization, and validation of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) workflows for spatial metabolomics and tracer metabolomics applications. Experimental protocols were systematically optimized by comparing different solvent systems and assessing the sensitivity, reproducibility, and robustness of DESI-MSI measurements. The study included analyses of rat kidneys with and without stable isotope-labeled nutrients to evaluate isotopologue detection, as well as mouse kidneys following ocular nutrient injection. DESI-MSI results were benchmarked against LC–MS data to assess analytical reliability. In parallel, a comprehensive quality control and data assurance workflow was established using homogenate-based controls, MOZAIC software for mass accuracy and total ion current (TIC) assessments, and the Cardinal R package for data preprocessing and analysis. This work also led to collaborations with Spectroswiss for software development and with the Cardinal development team. The results demonstrated successful detection of administered isotope-labeled nutrients and confirmed the ability of DESI-MSI to identify nutrient-derived isotopologues in biological tissues. Although metabolite labeling levels were relatively low and limited biological interpretation, tracer-administered samples consistently showed a higher fractional contribution of labeled species than controls, demonstrating the feasibility of spatial tracer metabolomics with DESI-MSI. The benchmarking study further revealed that DESI solvent composition is a critical factor affecting metabolite coverage, with 90% acetonitrile providing superior sensitivity for polar metabolites and 90% methanol favoring fatty acid detection. Consequently, 90% acetonitrile was selected for spatial metabolomics analyses of rat kidney tissue. Overall, the developed workflow showed excellent technical reproducibility, sensitivity, and reliability.
WP 6 Bulk tracer metabolomics: This work package focused on validating spatial metabolomics results through complementary bulk tracer metabolomics analyses. Consecutive tissue sections were analyzed to detect the administered nutrient and its downstream metabolites, while blood samples were examined to characterize nutrient pharmacokinetics. Spatial metabolomics results obtained by DESI-MSI were compared with bulk analyses to confirm metabolite identities and increase confidence in the findings. In addition, laser microdissection (LMD) was introduced to enable region-specific sampling and analysis of selected areas of interest within the tissue. The results confirmed the presence of the administered nutrient in rat kidney tissues following bolus injection. However, the high concentration required for administration resulted in metabolic perturbations that limited the physiological interpretability of the tracer metabolomics data. Despite this limitation, the LMD-based analyses produced promising results, revealing differential abundances of metabolites such as lactate in specific tissue regions. These findings were consistent with and provided additional validation for the spatial distributions observed by DESI-MSI.
WP 7 Spatial isotopologue analysis from MSI data: This work package focused on integrating histological information with spatial tracer metabolomics data following DESI-MSI analysis. After mass spectrometry imaging, hematoxylin and eosin (H&E) staining was performed on the same tissue sections to enable histological co-registration and facilitate the interpretation of spatial metabolic patterns. In addition, IsoScope software was implemented for the analysis of spatial tracer metabolomics datasets, allowing visualization and mapping of isotopologue distributions and calculation of fractional contributions across tissue regions. Although the workflow for histological co-registration and spatial tracer metabolomics data processing was successfully established, the level of isotope labeling detected in the tissues was insufficient to clearly map metabolic pathways or distinguish between ischemic and control regions. As a result, the spatial distributions of labeled metabolites did not provide adequate biological contrast to identify tissue-specific metabolic alterations associated with ischemia. Consequently, the planned scientific deliverables and milestones were partially achieved. For these reasons, the research project could not proceed with the WP 8 that was focused on identification of the metabolic routes of healthy and ischemic kidneys.
Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)
The project opened a new technological avenue in which DESI-MSI can be used as a metabolic guide for other technologies such as laser microdissection. The workflow developed enables the direct visualization of nutrients within tissue, particularly in the context of acute kidney injury. This approach is expected to have significant scientific impact for researchers studying the effects of hypoxia on metabolism and is therefore highly relevant to the field of organ transplantation.
The project demonstrated the capability to detect specific stable isotope-labelled nutrients and their metabolites in kidney tissue. With further optimization—particularly regarding tracer administration to better reflect physiological metabolism—this approach has strong potential to impact transplantation research, especially in improving organ preservation during transport.
The study of nutrient metabolism can also be extended to other organs where hypoxia and ischemia influence transplantation outcomes, such as the heart, liver, and lungs. In these contexts, understanding ongoing metabolic processes and nutrient requirements is critical. Therefore, continuing this project and expanding the application of DESI-MSI for spatial tracer metabolomics, in collaboration with clinicians and surgeons, has the potential to improve transplantation success rates, with clear economic and societal benefits.
A key outcome of the project is the publication of technical aspects of DESI-MSI, particularly addressing the effect of solvent composition across different tissue types. Such work strengthens the reproducibility and robustness of MSI methodologies, which is essential for the analysis of clinical samples where standardized procedures are required for biomarker discovery and quantification.
More broadly, this project is expected to impact multiple areas of biology and pathology. DESI-MSI provides spatially resolved metabolic information, offering new criteria for disease characterization and supporting pathologists in achieving more accurate diagnoses.
The project demonstrated the capability to detect specific stable isotope-labelled nutrients and their metabolites in kidney tissue. With further optimization—particularly regarding tracer administration to better reflect physiological metabolism—this approach has strong potential to impact transplantation research, especially in improving organ preservation during transport.
The study of nutrient metabolism can also be extended to other organs where hypoxia and ischemia influence transplantation outcomes, such as the heart, liver, and lungs. In these contexts, understanding ongoing metabolic processes and nutrient requirements is critical. Therefore, continuing this project and expanding the application of DESI-MSI for spatial tracer metabolomics, in collaboration with clinicians and surgeons, has the potential to improve transplantation success rates, with clear economic and societal benefits.
A key outcome of the project is the publication of technical aspects of DESI-MSI, particularly addressing the effect of solvent composition across different tissue types. Such work strengthens the reproducibility and robustness of MSI methodologies, which is essential for the analysis of clinical samples where standardized procedures are required for biomarker discovery and quantification.
More broadly, this project is expected to impact multiple areas of biology and pathology. DESI-MSI provides spatially resolved metabolic information, offering new criteria for disease characterization and supporting pathologists in achieving more accurate diagnoses.