Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes
March 20th, 2018
12.8K views
This protocol targets specific cells in tissue for imaging at nanoscale resolution using a scanning electron microscope (SEM). Large numbers of serial sections from resin-embedded biological material are first imaged in a light microscope to identify the target and then in a hierarchical manner in the SEM.
Overview
This protocol describes a workflow for identifying specific cellular targets within tissue for ultrastructural imaging using light microscopy and scanning electron microscopy (SEM). The method enhances the ability to visualize structures that may be obscured within the original sample volume.
Key Study Components
Area of Science
- Cell Biology
- Developmental Biology
- Neuroscience
- Pathology
Background
- The technique is based on array tomography, which allows for the slicing of tissue into serial sections.
- Originally developed for neuroscience, it can be applied to various biological systems.
- Challenges include the difficulty in collecting and maintaining the integrity of serial sections.
- Proper preparation and handling are crucial to avoid loss or disarrangement of sections.
Purpose of Study
- To provide a detailed imaging workflow for studying cellular and tissue ultrastructure.
- To facilitate the identification of target structures within complex biological samples.
- To improve imaging resolution and accuracy in various research fields.
Methods Used
- Light microscopy for initial target identification.
- Serial sectioning of resin-embedded samples.
- Preparation of substrates for SEM imaging.
- Hierarchical imaging using specific protocols for different imaging resolutions.
Main Results
- Successful identification of target cells within tissue sections.
- High-resolution imaging of cellular structures achieved through SEM.
- Demonstrated effectiveness of array tomography in various biological contexts.
- Provided detailed protocols for sample preparation and imaging procedures.
Conclusions
- The imaging workflow enhances the ability to study ultrastructural details in tissues.
- Array tomography is a versatile technique applicable across multiple biological fields.
- Careful execution of the protocol is essential for obtaining reliable imaging results.
Frequently Asked Questions
What is array tomography?
Array tomography is a technique that involves slicing tissue into serial sections to facilitate the identification of target structures for imaging.
What are the main challenges of this protocol?
The main challenges include collecting many serial sections without loss or disarrangement and ensuring proper sample preparation.
How does this method benefit neuroscience research?
This method allows for high-resolution imaging of cellular structures, aiding in the understanding of complex neural tissues.
What types of samples can be used with this technique?
The technique can be applied to various samples, including bacteria, plants, animals, and human tissue samples in pathology.
What imaging resolutions can be achieved?
The protocol allows for high-resolution imaging at various scales, depending on the specific imaging parameters used.
Is prior experience required to use this method?
Yes, individuals new to this method may find it challenging due to the complexity of collecting and handling serial sections.
Executive Industry Relevance
Hierarchical multimodal imaging of serial sections enables precise identification and ultrastructural analysis of specific cellular targets within large, heterogeneous tissue volumes. This workflow integrates light and electron microscopy, supporting high-confidence target selection and detailed 3D reconstruction, which is critical for de-risking early discovery and translational research. The approach enhances predictive confidence and continuity across discovery, screening, and preclinical inflection points in biopharma R&D portfolios.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables spatially resolved identification of rare or functionally relevant cell types within complex tissues.
- Supports mechanistic de-risking by correlating molecular markers with ultrastructural features.
- Facilitates functional target validation through 3D mapping of cellular microenvironments.
- Improves predictive confidence for downstream biological hypotheses and portfolio triage.
Screening & Assay Development
- Prepares validated section arrays for reproducible imaging and quantitative analysis.
- Standardizes imaging protocols across light and electron microscopy for robust assay development.
- Enables scalable, automated imaging workflows for high-content screening of tissue arrays.
- Supports reliable evaluation of compound effects at subcellular resolution.
Translational & Preclinical Research
- Aligns imaging outputs with disease-relevant tissue architecture and biomarker localization.
- Provides continuity from discovery through preclinical validation by enabling retrospective analysis of archived or biopsy samples.
- Reduces translational risk by supporting 3D ultrastructural correlation with functional endpoints.
- Enhances mechanistic insight into tissue pathology and therapeutic response.
Pipeline & Workflow Integration
This multimodal imaging workflow bridges early discovery, target validation, and preclinical research by enabling high-resolution, quantitative mapping of cellular targets within large tissue volumes.
- Discovery Biology: Supports hypothesis testing and pathway clarification by linking molecular labeling with ultrastructural context.
- Screening: Delivers reproducible, quantitative imaging outputs for assay standardization and compound evaluation.
- Analytics: Provides 3D reconstructions and quantitative measurements for comparative analysis across experimental conditions.
- Translational Research: Facilitates biomarker alignment and retrospective analysis of clinically relevant samples.
- Enterprise Reuse: Offers a flexible, reusable platform adaptable to diverse biological systems and research questions.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in target selection.
- Operational Value: Standardizes imaging workflows and enables scalable, automated data acquisition.
- Strategic Value: Improves go/no-go decision-making and capital efficiency by providing robust, high-content data.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of discovery and translational programs.
Implementation Considerations
- Requires expertise in both light and electron microscopy and serial section handling.
- Demands access to ultramicrotomy, fluorescence labeling, and SEM instrumentation.
- Benefits from cross-team standardization of imaging protocols and data management.
- Adaptable to a wide range of biological samples, including archived and prefixed tissues.
- Section collection and alignment can be technically challenging and may require specialized training.
Biopharma FAQs
Why does null hypothesis testing matter for target cell identification?
Null hypothesis testing ensures that observed ultrastructural differences between cell populations are statistically significant, supporting robust target validation and reducing false positives in early discovery.
How does independent variable isolation fit hierarchical imaging workflows?
Isolating variables such as staining protocols or imaging parameters allows teams to attribute observed structural changes to specific experimental conditions, enhancing mechanistic clarity and reproducibility.
What do quantitative dependent variable measurements enable in SEM imaging?
Quantitative measurements of features like organelle size or spatial distribution enable objective comparison across samples, supporting data-driven decisions in screening and translational research.
Why are replication requirements critical for cross-functional imaging studies?
Replication ensures that imaging results are consistent and reproducible across different operators and instruments, facilitating collaboration and data integration between discovery and translational teams.
Which statistical analysis capabilities are required before 3D reconstruction implementation?
Robust statistical tools are needed to assess alignment accuracy, quantify imaging variability, and validate the significance of observed structural features prior to integrating 3D reconstructions into R&D workflows.
