Scintillate: An open-source graphical viewer for time-series calcium imaging evaluation and pre-processing
Research output: Contribution to journal › Article
Background Calcium imaging is based on the detection of minute signal changes in an image time-series encompassing pre- and post-stimuli. Depending on the function of the elicited response, change may be pronounced, as in the case of a genetically encoded calcium-reporter protein, or subtle, as is the case in a bath-applied dye system. Large datasets are thus often acquired and appraised only during post-processing where specific Regions of Interest (ROIs) are examined. New method The scintillate software provides a platform allowing for near instantaneous viewing of time-sequenced tiffs within a discrete GUI environment. Whole sequences may be evaluated. In its simplest form scintillate provides change in florescence (ΔF) across the entire tiff image matrix. Evaluating image intensity level differences across the whole image allows the user to rapidly establish the value of the preparation, without a priori ROI-selection. Additionally, an implementation of Independent Component Analysis (ICA) provides additional rapid insights into areas of signal change. Results We imaged transgenic flies expressing Calcium-sensitive reporter proteins within projection neurons and moth mushroom bodies stained with a Ca2+ sensitive bath-applied dye. Instantaneous pre-stimulation background subtraction allowed us to appraise strong genetically encoded neuronal Ca2+ responses in flies and weaker, less apparent, responses within moth mushroom bodies. Comparison with existing methods At the time of acquisition, whole matrix ΔF analysis alongside ICA is ordinarily not performed. We found it invaluable, minimising time spent with unresponsive samples, and assisting in optimisation of subsequent acquisitions. Conclusions We provide a multi-platform open-source system to evaluate time-series images.
|Research areas and keywords||
Subject classification (UKÄ) – MANDATORY
|Number of pages||8|
|Journal||Journal of Neuroscience Methods|
|Publication status||Published - 2016 Nov 1|