FISSA: A neuropil decontamination toolbox for calcium imaging signals

Scientific Reports
Sander W KeeminkNathalie L Rochefort

Abstract

In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.

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Citations

Apr 24, 2020·Nature·Hwei-Ee TanCharles S Zuker
Jul 28, 2020·BMC Bioinformatics·Marcelo Zoccoler, Pedro X de Oliveira
Mar 2, 2021·Frontiers in Cellular Neuroscience·Mary Ann GoSimon R Schultz
Apr 13, 2021·Frontiers in Neuroscience·Austin NeugornetPavel Ivanovich Ortinski
Jun 6, 2021·Proceedings of the National Academy of Sciences of the United States of America·S Andrew ShusterLiqun Luo
Jun 24, 2020·Neuron·Yiming ChenJennifer L Garrison
Nov 7, 2021·Neuron·Zahid PadamseyNathalie L Rochefort

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Key Resources (RRID) Mentioned

IMSR_JAX

Software Mentioned

Python
MATLAB
ImageJ
Fast Image Signal Separation Analysis ( FISSA )
suite2P
Matplotlib
SciPy
FISSA
cNMF
scikit

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