Enterprise AI Analysis
A Flexible Framework for Automated STED Super-Resolution Microscopy
Super-resolution microscopy enables the observation of cells at unprecedented detail but usually entails high light exposure and slow imaging. Thus, often only a few manually selected regions are imaged, limiting the ability to capture the distribution of quantitative features in a population of cells in an unbiased fashion. An exciting strategy to circumvent these limitations are imaging pipelines in which informative regions are detected on-the-fly by software and imaged automatically.
Unlocking Automated Super-Resolution Microscopy
The autoSTED framework provides a robust and flexible solution for automating STED super-resolution microscopy, dramatically accelerating data acquisition, reducing photobleaching, and enabling unbiased, high-throughput studies of cellular dynamics. It integrates advanced image analysis with microscope control to transform laborious manual processes into efficient, autonomous workflows.
0
Speedup in Acquisition Time
0
Enhanced Spatial Resolution
0
Autonomous Operation
0
Manual Bias Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Flexible Pipeline Design
autoSTED uses a dynamic priority queue of acquisition tasks and callback functions to build adaptable imaging pipelines. This modular approach allows for on-the-fly adjustments of parameters, indefinite operation, and integration of custom code like deep learning models for object detection.
Selective Imaging & Speed
By selectively imaging only relevant subcellular regions, autoSTED drastically reduces photobleaching and overall acquisition times. This approach enabled a 7.85-fold speedup for detailed Nup153 imaging, facilitating high-throughput DNA-FISH studies at kilobase resolution without compromising signal intensity.
On-the-Fly Stitching
To address challenges with large objects spanning multiple fields-of-view and sample drift, autoSTED implements an on-the-fly stitching strategy. This allows seamless detection and imaging of cells at overview image borders by dynamically combining overlapping acquisitions, enhancing robust, long-term autonomous imaging.
Complex Pipelines & Reactivity
autoSTED supports multi-step hierarchical imaging pipelines, including pre-scan steps to skip uninformative FOVs and intermediate cell-level imaging for accurate segmentation. It also enables reactive time-series imaging by dynamically switching between slow 'search' and fast 'event' acquisition rates based on real-time data analysis.
Optimized STED Automation Workflow
Initial Task Queue
→
Acquire Image
→
Run Callbacks
→
Add New Tasks to Queue
→
Loop Until Done
Significant Speedup Achieved
7.85x Faster Super-Resolution Acquisition| Feature | Traditional STED | autoSTED |
|---|---|---|
| Acquisition Method | Manual, fixed loops | Dynamic priority queue |
| Efficiency | High light exposure, slow | Selective imaging, reduced photobleaching, faster |
| Adaptability | Pre-defined, rigid | Flexible callbacks, real-time adjustments |
| Throughput | Limited cells/regions | Population-wide, unbiased imaging |
Application in DNA-FISH Studies
autoSTED was successfully applied in studies of spatial arrangement of small genomic loci using DNA-FISH. It enabled systematic single-locus DNA-FISH and resolution of multiple genomic elements labelled in one color, providing unprecedented detail for active and inactive chromatin. The high throughput allowed autonomous acquisition of hundreds of super-resolved images per sample, crucial for quantitative analysis of genomic organization.
Outcome: Enhanced resolution, reduced photobleaching, and accelerated data collection for chromatin biology research.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by automating STED microscopy workflows with autoSTED.
Estimated Annual Savings
$0
Researcher Hours Reclaimed Annually
0
Your Implementation Roadmap
A typical phased approach to integrating autoSTED into your research workflow, ensuring a smooth transition and maximum impact.
Phase 1: Initial Setup & Integration
Integrate autoSTED with existing microscope hardware and software (Imspector/SpecPy). Configure basic imaging parameters and ensure stable communication.
Phase 2: Pipeline Customization & Object Detection
Develop custom callback functions for specific biological questions. Implement object detection algorithms (e.g., Cellpose) to identify regions of interest for automated selective imaging.
Phase 3: Advanced Automation & Adaptive Imaging
Integrate features like on-the-fly stitching for large samples, image-based focus updates, and multi-step hierarchical pipelines. Implement reactive imaging strategies for time-series experiments.
Phase 4: Data Analysis & Workflow Optimization
Streamline data processing pipelines for super-resolved images. Continuously optimize acquisition parameters and callback logic based on experimental feedback to maximize throughput and data quality.
Ready to Automate Your STED Microscopy?
Transform your research with autonomous, high-throughput, super-resolution imaging. Schedule a personalized consultation to see how autoSTED can integrate with your lab's workflow and accelerate your discoveries.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat