Chemical Cartography Approaches to Study Trypanosomatid Infection
January 21st, 2022
This protocol describes the steps to generate a 3D model of metabolite distribution during trypanosomatid infection, including sample collection, metabolite extraction, an overview of liquid chromatography-tandem mass spectrometry data acquisition, 3D model generation, and finally, data visualization.
This protocol can be used to build 3D models of metabolite and parasite distribution. This can help in understanding the relationship between local tissue metabolic perturbation, parasite distribution, and clinical disease symptoms. The main advantage of this method is that it enables live scale cross-organ mapping of metabolite distribution versus pathogen distribution and its statistical analysis.
Theoretically, chemical cartography can explore the development and impact of the disease on the host by directly examining the metabolites responsible for the pathogenesis and the effects of treatment. Begin by weighing and labeling the tubes. Section the tissues systematically with one section per tube.
Weigh the tubes containing tissue samples to determine sample weight and record the weight. To perform water-based homogenization of the tissue samples, add one five millimeter stainless steel bead to the two milliliter microcentrifuge tubes containing tissue samples. Make one blank tube containing LCMS grade water.
Make up the volume to 500 microliters per 50 milligrams of the sample by adding chilled LCMS grade water, then homogenize samples at 25 hertz for three minutes. Samples should be thoroughly homogenized. Collect 1/10th of the homogenization volume for DNA extraction and other analyses.
Add ice cold LCMS grade methanol spiked with four micromolar of sulfachloropyridazine to the homogenate. Homogenize samples in a tissue homogenizer at 25 hertz for three minutes, followed by centrifugation for 10 minutes. Collect an equal volume of supernatant into a 96 well plate and keep solid residue on ice while collecting the supernatants.
Dry the aqueous extraction supernatant completely using maximum speed and no heating. Perform organic extraction by adding 1, 000 microliters of pre-chilled dichloromethane methanol spiked with two micromolar sulfachloropyridazine per 50 milligram of solid residue sample. Homogenize samples at 25 hertz for five minutes, followed by centrifugation for 10 minutes.
Collect an equal volume of supernatant into a 96 well plate. Take a picture of the organ of interest. Click on File, then Import in the SketchUp software to import a picture of the organs of interest.
Click on the lines tool and select the freehand option. Use the pencil tool to trace and draw the outlines of the organs of interest. Select the push/pull tool and pull up on the shaded area to convert the drawing from 2D to 3D.
Export the file in dae format by clicking on File Export and then 3D model. To improve the model's realism, import the model into MeshLab software by selecting File, then clicking on Import Mesh. Select Wireframe on the top menu, then select Filters, click on Remeshing Simplification and Reconstruction, and then on Subdivision Surfaces Midpoint.
Leave all values as default and select Apply twice. Export the model in sdl format by clicking on File, then on Export Mesh As, and then selecting SDL File Format in the files of type dropdown menu. Save this and select OK in the next popup menu.
Open the Meshmixer software and import the sdl file generated at the previous step. Use the sculpt, brushes, dragon sculpt, brushes, inflate tools to pull out the model surfaces that need to be rounded out. Save it in sdl format once the model has the desired appearance by clicking on File and Export.
Name the file as desired, select the SDL Binary Format and save. Open the 3D model in the MeshLab software. Click on OK in the post open processing popup window.
Obtain X, Y, and Z coordinates for each sampling spot by selecting the pick points tool and then right-click at regularly spaced intervals across the 3D model surface. Once all the desired coordinates have been selected, click on the topmost Save button in the form popup window. In spreadsheet software, open the pp file generated in the previous step.
Adjust data display by clicking on Data Text to Columns, then Delimited. Click on Next and then select Space and click on Finish. Reformat so that only numerical values remain in the spreadsheet cells by selecting Home Find and Select Replace.
In the find what box, enter Y equal coat and leave the replace box empty. Click on Replace All and then on OK.Repeat for X equal coat and for Z equal coat and for coat slash greater than. In the spreadsheet software table, rows correspond to each position and columns to data.
Paste the appropriate metadata and metabolite feature abundance in the subsequent spreadsheet columns. In the radius column, enter the desired size of the sampling spots to be visualized on the model. Determine the values for the radius empirically and save the file in CSV format.
Navigate to ELI Plot software, select Surface, then drag and drop the created 3D model into the browser window. Drag and drop the created feature table into the same browser window. Use the legend at the bottom right corner to project the desired data column on the 3D model.
Consecutively select each data column to assess the distribution of this metabolite feature on the 3D model. This analysis led to a table of 5, 502 features and their visualization in 3D. This approach enables the visualization of metabolite features in individual animals that are high at the site of high parasite load, metabolites with differential distribution across tissue regions and metabolite features that are found at comparable levels across small and large intestines.
It is important to remember to use only LCMS solvents and to collect all the adjacent tissue and extract the metabolites for all collected samples to avoid gaps in spatial maps. This technique can be used to explore nutrient requirements for tissue colonization by Trypanosomatid parasites to understand disease pathogenesis through comparing 3D metabolite distribution maps upon infection.
Overview
This protocol outlines the generation of a 3D model of metabolite distribution during trypanosomatid infection, detailing sample collection, metabolite extraction, and data visualization.
Key Study Components
Area of Science
- Neuroscience
- Biology
- Metabolomics
Background
- Understanding the relationship between tissue metabolic changes and parasite distribution is crucial for disease research.
- This method allows for live-scale mapping of metabolite and pathogen distribution.
- It facilitates the examination of metabolites involved in pathogenesis and treatment effects.
- 3D modeling enhances the visualization of complex biological interactions.
Purpose of Study
- To build 3D models that illustrate the distribution of metabolites and parasites.
- To analyze the correlation between metabolic perturbations and clinical symptoms of disease.
- To explore nutrient requirements for tissue colonization by Trypanosomatid parasites.
Methods Used
- Sample collection and weighing of tissue samples.
- Homogenization and extraction of metabolites using LCMS grade solvents.
- 3D model generation using software like SketchUp and MeshLab.
- Statistical analysis of metabolite distribution across different tissues.
Main Results
- Successful visualization of metabolite features in relation to parasite load.
- Identification of metabolites with differential distribution across tissue regions.
- Creation of a comprehensive dataset with over 5,500 metabolite features.
- Insights into the metabolic landscape during trypanosomatid infection.
Conclusions
- This technique provides a powerful tool for studying disease mechanisms.
- It enhances our understanding of host-pathogen interactions at a metabolic level.
- Future applications may include exploring treatment effects and disease progression.
Frequently Asked Questions
Executive Industry Relevance
Chemical cartography enables spatial mapping of metabolite and pathogen distribution, providing a mechanistic lens to interrogate tissue-specific host-pathogen interactions in trypanosomatid infections. This approach supports target validation by linking local metabolic perturbations to parasite tropism and clinical symptomatology, informing preclinical de-risking strategies. The method enhances predictive confidence in early discovery by visualizing nutrient requirements and immunometabolic shifts at infection sites.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Interrogates therapeutic hypotheses by mapping metabolite abundance relative to parasite load in specific tissues.
- Operational Value: Enables functional target validation through spatial correlation of metabolic pathways with tissue colonization patterns.
- Predictive Value: Supports portfolio triage by identifying metabolites differentially distributed across tissue regions as potential biomarkers or targets.
Screening & Assay Development
- Assay Readiness: Prepares validated biological systems for downstream workflows by generating quantitative 3D metabolite distribution maps.
- Reproducibility: Standardizes sample preparation and extraction using LCMS-grade solvents to ensure consistent spatial mapping across tissues.
- Scalability: Facilitates platform reuse for screening compound effects on metabolite-parasite co-localization in disease-relevant systems.
Translational & Preclinical Research
- Translational Continuity: Connects discovery findings to preclinical validation by visualizing metabolic perturbations aligned with tissue-specific disease manifestations.
- Risk-Adjusted Advancement: Informs go/no-go decisions by revealing relationships between local tissue metabolic changes and clinical symptoms.
- Mechanistic De-risking: Focuses on predictive de-risking by elucidating nutrient requirements for tissue colonization across trypanosomatid species.
Pipeline & Workflow Integration
This method integrates into the discovery continuum from hypothesis testing through lead identification, enabling spatial metabolomics to guide target selection and mechanistic follow-up in infectious disease research.
- Discovery Biology: Supports hypothesis testing and pathway clarification by correlating metabolite features with parasite distribution in individual animals.
- Screening: Describes assay readiness through quantitative outputs that enable comparison of metabolite distribution across tissue regions and infection states.
- Analytics: Highlights statistical analysis of metabolite feature abundance across 5,502 detected features to identify spatially resolved metabolic signatures.
- Translational Research: Connects to preclinical continuity by linking local tissue metabolic perturbation to clinical disease symptoms via 3D visualization.
- Enterprise Reuse: Frames the method as a reusable capability for cross-organ mapping applicable to multiple trypanosomatid models and therapeutic areas.
Operational & Enterprise Impact
- Scientific Value: Predictive confidence, target validation, reduction of mechanistic ambiguity in host-pathogen metabolic interactions.
- Operational Value: Standardization, reproducibility, and scalability of spatial metabolomics workflows across tissue types.
- Strategic Value: Better go/no-go decisions, capital efficiency, and reduced late-stage biological risk through early metabolic de-risking.
- Portfolio Impact: Risk-adjusted prioritization and advancement decisions based on spatially resolved metabolite-parasite relationships.
Implementation Considerations
- Required expertise in metabolomics, mass spectrometry, and 3D modeling software such as SketchUp, MeshLab, and Meshmixer.
- Instrumentation needs include LCMS-compatible homogenization tools, centrifuges, and metabolite profiling platforms.
- Cross-team standardization requires harmonized tissue sampling, extraction protocols, and metadata annotation for reproducible spatial mapping.
- Adaptation considerations involve adjusting sampling density and radius parameters across varying organ sizes and tissue architectures.
- Practical limitations include the requirement for LCMS-grade solvents and comprehensive adjacent tissue collection to avoid spatial map gaps.
