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MRBLEs decoding and analysis package

Project description

This project aims to provide a package to find, decode, extract, analyze and MRBLEs using a set of images.

This package provides the tools to: (1) find the MRBLEs in a monochrome brightfield microscopy image; (2) decode the MRBLEs beads by spectral unmxing, using reference spectra, and then spectrally decode the found beads using Iterative Closest Point Matching and Gaussian Mixture Modeling; (3) extract, statistical values of interest in additional fluorescence channels using the morphology of the MRBLEs, their locations, and respective code; (4) analyze affinity information based on titrations of MRBLEs assays.

MRBLEs project

MRBLEs (Microspheres with Ratiometric Barcode Lanthanide Encoding) rely on spectral multiplexing to track analytes throughout an experiment. In these assays, we can create microspheres containing > 1,000 unique ratios of lanthanide nanophosphors that can be uniquely identified via imaging alone. We are currently developing new assays that use these microspheres to understand how signaling proteins recognize their peptide substrates and to improve our ability to extract information from single cells.

Abstact on MRBLEs and the mrbles package

Multiplexed bioassays, in which multiple analytes of interest are probed in parallel within a single small volume, have greatly accelerated the pace of biological discovery. Bead-based multiplexed bioassays have many technical advantages, including near solution-phase kinetics, small sample volume requirements, many within-assay replicates to reduce measurement error, and, for some bead materials, the ability to synthesize analytes directly on beads via solid-phase synthesis. To allow bead-based multiplexing, analytes can be synthesized on spectrally encoded beads with a 1:1 linkage between analyte identity and embedded codes. Bead-bound analyte libraries can then be pooled and incubated with a fluorescently-labeled macromolecule of interest, allowing downstream quantification of interactions between the macromolecule and all analytes simultaneously via imaging alone. Extracting quantitative binding data from these images poses several computational image processing challenges, requiring the ability to identify all beads in each image, quantify bound fluorescent material associated with each bead, and determine their embedded spectral code to reveal analyte identities. Here, we present a novel open-source Python software package (the mrbles analysis package) that provides the necessary tools to: (1) find encoded beads in a bright-field microscopy image; (2) quantify bound fluorescent material associated with bead perimeters; (3) identify embedded ratiometric spectral codes within beads; and (4) return data aggregated by embedded code and for each individual bead. We demonstrate the utility of this package by applying it towards analyzing data generated via multiplexed measurement of calcineurin protein binding to MRBLEs (Microspheres with Ratiometric Barcode Lanthanide Encoding) containing known and mutant binding peptide motifs. We anticipate that this flexible package should be applicable to a wide variety of assays, including simple bead or droplet finding analysis, quantification of binding to non-encoded beads, and analysis of multiplexed assays that use ratiometric, spectrally encoded beads.


The MRBLEs software package is a Python Package available through the Python Package Index (PyPI): To makes us of mrbles follow the following instructions.

Python installation

Install (preferably) Python >3.5 Windows ( or MacOS ( mrbles is built to be compatible with Pyhton >2.7, but its dependencies (other Python Packages) are getting less and less support for Python 2.7.


This is based on the detailed Windows installation instructions from the official Python page:

  • Install Python by downloading the latest version of Python:
  • Execute the installation file and follow the instructions.
  • On the first window make sure to tick the box “Add Pyhton x.x to PATH”.
  • Preferably, use the “Customize installation”.
  • On the second window use the standard selections, or if other people are using the same machine, also select “Install for all users”.
  • Preferably, use a custumized installation location, e.g. for Python 3.7 “C:Pyhton37”. This way it is easy to access when necessary.
  • Press “Install”.

To make use of Python packages that do not come with a pre-compiled file (which mrbles does), as in they need to be compiled on your computer from the source code. It is required to install a C++ compiler.

Mac OSx

This is based on the detailed Mac OSx installation instructions from the Python-Guide page:

The installation for Mac OSx is slightly more complicated, but will help you in the future with Python dependencies.

  • This is required for the installation of Python packages (C++ compiler) and for the installation of Brew, which is software that makes installing Python and dependencies much easier.
  • Second, install brew using the following command in Mac OSx Terminal:

    ruby -e "$(curl -fsSL

The mrbles package installation

Once Python is installed it can be accessed using your systems’ Command Prompt (Windows) or Terminal (Mac OSx), using: python (if both Python 2 & 3 are installed this is: python3 or python3).

To install packages one can use the following commands, using the package name:

  • pip install package_name or
  • python -m pip install package_name

Visit the Python Package Index (PyPI) for package names:

Now that everything is installed the mrbles package can be installed, using the following commands in the terminal:

  • pip install mrbles (if both Python 2 & 3 are installed this is: pip3 or pip) or
  • python -m pip install mrbles (if both Python 2 & 3 are installed this is: pip3 or pip)

Using the mrbles package

The mrbles package automatically installs the Jupyter Notebook environment: https://

To test the mrbles package download the example Notebook and all the data, reference, library (.xlsx), and flat-field files:

The set used for the publication can be found on a OSF repository. Make sure to download all the data, reference, library (.xlsx), and flat-field files:

To start using the these notebooks:

  • Place the Notebook file in a location at your convenience, e.g. “C:\docs\mrbles_test” or “/users/your_name/docs/mrbles_test”.

  • Place the data files in the sub-folder “data” of the location of the Notebook file.

  • Open a Command Prompt or Terminal and navigate to that folder location.

  • Now start your Jupyter Notebook environment by using the following command:

    jupyter notebook

  • This should open your default browser and display the contents of the folder your started the Jupyter Notebook environment from.

  • Click on the downloaded “example-notebook-Github-data.ipynb”, this will open that file.

  • Following the instruction in the opened Notebook.

  • Fore more information on using Jupyter Notebook:

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