Abstract
The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales.
Highlights
Monitoring fluorescence changes of indicator molecules over time is one of the primary tools by which neuroscientists try to understand the function of neurons and neuronal networks
We will illustrate the functionality of SamuROI by describing the general workflow of data processing
After explaining data import and pre-processing, we describe the different widgets of the graphical user interface (GUI) and explain data export
Summary
Monitoring fluorescence changes of indicator molecules over time is one of the primary tools by which neuroscientists try to understand the function of neurons and neuronal networks. Batch processing and automation have enabled time-effective data analysis for large populations of cells, but similar advances have not been made in terms of data exploration and visualization of spatiotemporal structure Both quality control (Harris et al, 2016) and manual identification of patterns in imaging data require intuitive and effective visualization. The built-in graphical user interface (GUI) displays data in the space, time and amplitude domains in a way that allows the user to connect fluorescence changes with their morphological location of origin and vice versa This makes data inspection and manual curating of automated ROI generation easier and facilitates the rapid identification of data patterns during exploratory analysis. We provide examples of its application at the micro-, meso-, and macro-scale using Ca2+ imaging data obtained in acute slices
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