DeepFLEX

About

Deep learning-based single-cell analysis pipeline for FLuorescence multiplEX imaging via MELC (Multi-Epitope Ligand Cartography [1]).

Daria Lazic, Florian Kromp et al.
Single-cell landscape of bone marrow metastases in human neuroblastoma unraveled by deep multiplex imaging

Contact: Daria Lazic (daria.lazic@ccri.at)

Content

The pipeline is based on methods for:
-image processing (registration, flat-field correction, retrospective multi-image illumination correction by CIDRE [2])
-cell and nucleus segmentation by Mask R-CNN [3], [4]
-feature extraction
-normalization by negative control secondary antibodies and RESTORE [5]
-single-cell analysis (Cytosplore [6], seaborn)

A compiled release with all necessary dependencies pre-installed is available from dockerhub. Nvidia-docker is required to run the image (for tensorflow-gpu support).

Requirements

All requirements can be found here.

Installation

For interactive and quantitative analysis of single-cell data generated by DeepFLEX, we used:

• Cytosplore: an interactive tool for single-cell analysis (download here)
• Seaborn: a python data visualization library

Start the pipeline

Navigate to the code folder and run the pipeline.sh script.

Data availability

Download the MELC imaging data of our 8 samples here.

References

[1] Schubert, W. et al. (2006). Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nature Biotechnology.
[2] Smith, K. et al. (2015). CIDRE: An illumination-correction method for optical microscopy. Nat. Methods, 12, 404-406.
[3] Kromp, F. et al. (2019). Evaluation of Deep Learning Architectures for Complex Immunofluorescence Nuclear Image Segmentation. IEEE.
[4] Kromp, F. et al. (2020). An annotated fluorescence image dataset for training nuclear segmentation methods. Scientific Data, 7, 262.
[5] Chang, Y.H. et al. (2020). RESTORE: Robust intEnSiTy nORmalization mEthod for multiplexed imaging. Commun. Biol., 3, 1-9.
[6] Höllt, T. et al. (2016). Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets. Comput. Graph., 35, 171-180.