MouseConnects Nanoscale Connectomics Workflow
From Mouse Brain to Memory Circuit Maps
Discover the cutting-edge pipeline used in the MouseConnects: HI-MC project to create an unprecedented view of the hippocampus at single synapse resolution.
The MouseConnects Pipeline
MouseConnects represents the most ambitious connectomics effort to date, aiming to reconstruct 10 mm³ of mouse hippocampal formation at nanometer resolution. This $40M NIH BRAIN CONNECTS project demonstrates how modern neuroscience combines advanced imaging, machine learning, and massive computational resources to unlock the secrets of memory circuits.
Complete Workflow Pipeline
Sample Preparation & Staining
Whole C57BL/6 mouse brains are fixed and stained with heavy metals (osmium tetroxide) to create contrast for electron microscopy. The new ODeCO protocol ensures uniform staining across large volumes.
- Transcardiac perfusion with specialized fixatives
- ODeCO staining protocol for whole brains
- MicroCT imaging for quality control
- Registration to Allen Brain Atlas
Key Technologies:
Heavy Metal Staining MicroCT Imaging Atlas RegistrationSerial Sectioning & Mounting
Stained brains are cut into semithin sections (200-1000 nm thick) and mounted on silicon wafers using magnetic collection technology.
- Precision ultramicrotomy for consistent sections
- MagC magnetic collection onto silicon wafers
- Light microscopy mapping of sections
- ROI selection for high-resolution imaging
Key Technologies:
Ultramicrotomy MagC Collection Wafer MappingHigh-Resolution EM Imaging
Novel mSEM-IBEAM systems combine multibeam scanning electron microscopy with ion beam milling to image at 8 nm isotropic resolution across two sites.
- 91-beam scanning electron microscopes at Harvard and Princeton
- Ion beam etching and milling (IBEAM)
- Automated imaging across multiple sites
- Real-time quality monitoring
Key Technologies:
mSEM-IBEAM Multibeam SEM Ion Beam MillingData Processing & Management
Massive datasets are transferred to Google Cloud, aligned, and prepared for automated segmentation using advanced compression techniques.
- Lossless compression and cloud transfer
- 3D alignment and stitching
- Denoising and lossy compression
- Quality monitoring pipeline
Key Technologies:
Google Cloud Image Compression 3D AlignmentAutomated Segmentation
Machine learning algorithms trace every neuron and identify all synapses to create the connectome. Flood-filling networks achieve state-of-the-art accuracy.
- Flood-filling networks for neuron tracing
- Semantic segmentation for tissue types
- Automated synapse detection
- 3D reconstruction and validation
Key Technologies:
Deep Learning Flood-Filling Networks Synapse DetectionProofreading & Validation
Human experts and community contributors validate and correct automated reconstructions using collaborative web-based tools, with training support from Johns Hopkins APL.
- ChunkedGraph collaborative editing
- CAVE proofreading interface
- Community-driven validation through CIRCUIT program
- Version control for reconstructions
Key Technologies:
ChunkedGraph CAVE Interface Web CollaborationIntegration & Cell Typing
Connectomic data is integrated with transcriptomic, physiological, and morphological data to define comprehensive cell types.
- Patch-seq physiological recordings
- fMOST whole-brain morphology
- Cross-modal cell type matching
- Multi-modal data integration
Key Technologies:
Patch-seq fMOST Multi-modal IntegrationCircuit Analysis & Discovery
The complete connectome enables unprecedented analysis of hippocampal circuits, revealing new insights into memory and spatial navigation.
- Local microcircuit motif analysis
- Long-range connectivity patterns
- Circuit mechanism discovery
- Computational model validation
Key Technologies:
Graph Analysis Circuit Modeling Network ScienceOpen Data Sharing
All data, tools, and discoveries are made freely available to the global research community through advanced web interfaces and APIs.
- Neuroglancer visualization platform
- Science API for data access
- Educational outreach programs
- Community tool development
Key Technologies:
Neuroglancer Science API Open ScienceMouseConnects Team
🏛️ Harvard University
Jeff Lichtman - Sample preparation, tissue processing, mSEM imaging
🎓 Princeton University
Sebastian Seung & David Tank - mSEM imaging, machine learning algorithms
☁️ Google Research
Viren Jain - Cloud processing, automated segmentation, flood-filling networks
🧠 MIT
Ila Fiete - Circuit analysis, computational modeling, spatial navigation
🔬 Allen Institute
Hongkui Zeng - Patch-seq recordings, cell typing, fMOST morphology
🏛️ Cambridge University
Gregory Jefferis - Data integration, proofreading, cross-modal analysis
🚀 Johns Hopkins APL
William Gray-Roncal - Training, connectome quality, CIRCUIT outreach program
Learn This Workflow
Ready to dive deeper? Our educational modules guide you through each step of the connectomics pipeline with hands-on exercises and real data.
👁️ Module 2: Sample Preparation
Learn tissue processing and staining techniques for EM
Start Learning