Processing and Analysis Pipeline for
Fluorescence Microscopy Imaging Data
> Single particle tracking (mRNA) and translocation through NPCs in S. cerevisiae
Production-quality pipeline for automated nuclear envelope morphology analysis from dual-channel fluorescence microscopy with statistical rigor and FAIR compliance.
This pipeline processes and analyzes imaging data collected using fluorescence microscopy into quantitative measurements of the nuclear pore complex (NPC) via labeling of pairs of nucleoporins (Nups) with sub-pixel precision. Originally developed for studying translocation of mRNA through NPC in yeast, the modular architecture is designed to support diverse membrane analysis applications.
Key Innovation: Systematic transformation from research prototype to production-ready, FAIR-compliant software demonstrating research software engineering best practices.
- ✅ Automated NE Detection - U-Net++ neural network segmentation with adaptive thresholding
- ✅ Sub-pixel Refinement - spline fitting via intensity profile modeled using a Richards-Gaussian model fit through non-linear optimization (Levenberg-Marquardt algorithm)
- ✅ Multi-channel Registration - Phase correlation with sub-pixel accuracy
- ✅ Statistical Rigor - Likelihood ratio testing, AIC model selection, robust outlier filtering
- ✅ Quality Control - Comprehensive validation with automated PDF reports
- ✅ Reproducible Analysis - Complete parameter justification with citations
- ✅ Modular Architecture - Object-oriented design with clear separation of concerns
- ✅ Checkpoint/Resume - FOV-level checkpoints allow for distributed HPC computing
- ✅ FAIR Compliance - Findable, Accessible, Interoperable, Reusable
- ✅ Production Quality - Adaptive algorithms reduced optimization failures
{
"pipe globals": {
"strains": ["experiment_001"],
"directories": {
"imaging root": "/path/to/your/data",
"camera root": "/path/to/calibration/images",
"output root": "/path/to/output",
"model root": "./trained_models"
},
"pixelsize": 128,
"frame_duration": 0.02
},
"image processor": {
"model_NE": "Modelweights_NE_segmentation.pt",
"responsivity": {
"ch1": {
"bright": "bright_Ch1.tif",
"dark": "dark_Ch1.tif"
},
"ch2": {
"bright": "bright_Ch2.tif",
"dark": "dark_Ch2.tif"
}
}
}
}Output structure:
output_root/
├── responsivity/ # Camera calibration results
├── initial_fit/ # U-Net++ detection and initial splines
├── registration/ # Multi-channel alignment + PDF reports
├── refined_fit/ # Sub-pixel refined splines
└── distances/ # Dual-label distance measurements
- Installation guide
- Configuration Reference
- Bibliography of statistical method justifications
- User guide (data preparation, running pipeline, troubleshooting)
- Methodology documentation (detailed algorithm descriptions)
- Developer guide (architecture, contributing, testing)
- API reference
Still undergoing testing on multiple systems. The singularity recipe is provided.
- Local: macOS (Apple Silicon M4 Max)
- HPC: LSF cluster
- Camera Calibration
- Derives gain, offset, read noise per channel
- Enables accurate photon counting
- Initial NE Detection
- U-Net++ neural network segmentation (Zhou et al. 2018)
- Initial B-spline fitting to boundaries
- Segment merging for continuous envelopes
- Multi-channel Registration
- Phase correlation with sub-pixel precision (Guizar-Sicairos et al. 2008)
- Quality control filtering (MAD-based robust statistics)
- Drift correction and stability analysis
- Spline Refinement
- Richards-Gaussian profile fitting (Richards 1959; Zhang et al. 2007)
- Levenberg-Marquardt optimization with adaptive step sizing
- Likelihood ratio test for outlier detection (Smith et al. 2010)
- AIC-based model selection (Akaike 1974)
- Bezier Bridging (Optional)
- Interpolates gaps in segmented data
- Creates continuous, periodic splines
- Maintains biological curvature constraints (Zimmerberg & Kozlov 2006)
- Distance Calculation
- IoU-based label pairing between channels
- Perpendicular distance sampling (1000 points per nucleus)
- Statistical distance metrics (mean, std, distribution)
Raw Images → Camera Calibration → NE Detection → Registration
↓ ↓
Quantified Aligned Channels
Photons ↓
Spline Refinement
↓
Distance Calculation
This pipeline enables quantitative analysis of: - Nuclear pore complex (NPC) spatial organization - Protein co-localization at nuclear membranes - Membrane morphology in disease models - mRNA transport dynamics through NPCs
- Sub-pixel Localization: protein positioning relative to membrane
- Statistical Rigor: LRT-based quality control ensures data validity
- Reproducibility: Complete parameter justification enables method validation
- Scalability: Checkpoint/resume enables analysis of 100+ nuclei per experiment
- Proof-of-concept demonstrating algorithmic feasibility
- Validated approach on biological data
- Generated initial publication results
Systematic transformation underway for open-source release:
Architecture: - Monolithic scripts → Modular object-oriented design - Hard-coded parameters → JSON configuration system - Minimal docs → Comprehensive user/developer guides
Statistical Rigor: - Arbitrary thresholds → Citation-backed methods - No justifications → peer-reviewed citations - Fixed parameters → Adaptive optimization
Reproducibility: - "Works on my machine" → FAIR-compliant - No checkpointing → FOV-level resume capability - Manual validation → Automated quality control - Scattered code → DRY principles
This evolution demonstrates research software engineering expertise: transforming exploratory code into sustainable, community-ready tools.
This project is licensed under the MIT License - see LICENSE file for details.
- ✅ Free for academic and commercial use
- ✅ Modification and distribution permitted
- ✅ Attribution required
- ✅ No warranty (use at your own risk)
Maintainer: Jocelyn Petitto (formerly Tourtellotte)
Email: jocelyn.petitto@gmail.com
Institution: RNA Therapeutics Institute, UMass Chan Medical School\
Last Updated: January 2, 2026
Pipeline Version: 2.0.0
Python Version: 3.11+