MCA: A Multicellular analysis Calcium Imaging toolbox for ImageJ

Abstract

Functional imaging using genetically encoded indicators, such as GCaMP, has become a foundational tool for in vivo experiments and allows for the analysis of cellular dynamics, sensory processing, and cellular communication. However, large scale or complex functional imaging experiments pose analytical challenges. Many programs have worked to create pipelines to address these challenges, however, most platforms require proprietary software, impose operational restrictions, offer limited outputs, or require significant knowledge of various programming languages, which collectively can limit utility. To address this, we designed MCA (a Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software which has been the standard image analysis platform for the last 30 years. We developed MCA to be visually intuitive, utilizing ImageJ's platform to generate new images based on completed tasks so users can visually see each step in the analysis pipeline. In addition, MCA implements a user-friendly GUI providing a simple interface which resembles other native ImageJ plugins. We incorporated functionality for rigid registration to correct motion artifacts, algorithms for cell body prediction, and methods for annotating cells and exporting data. For cell prediction, we trained a custom model in Cellpose 2.0 for segmentation of nuclei expressing pan-neuronal nuclear localized GCaMP in zebrafish. We validated the accuracy of MCA output to previously published zebrafish calcium imaging data which elicited visually evoked neuronal responses. To show the versatility of MCA, we also show that our software can be utilized for multiple sensory modalities, brain regions, and multiple model organisms including Drosophila and mouse. Together these data show that MCA is viable for extracting calcium dynamics in a user-friendly environment for multiple forms of functional imaging.

Keywords: calcium imaging; functional imaging; model systems; open source; software; zebrafish.

Figure 1:. MCA is a calcium imaging toolbox integrated with ImageJ.

A. Screenshot of the Cell Manager GUI and functional layout. B. Functions available in the Cell Manager and representative functions that can integrate with the Cell Manager from Fiji/ImageJ. C. Representative import of most imaging formats to the ImageJ ImagePlus format which is used for all analysis in MCA. D. Representative of motion correction used by MCA integrating the OpenCV template matching algorithm. E. Representative cell segmentation of a sum projection of an imaging series using a trained H2B-GCaMP model for cell detection in Cellpose. F. Representatives for polygonal grouping of cells or point grouping. G. Illustrative example of imaging series converted from an acquired 8-bit gray value image into a 32-bit image series that represents the ΔF/F values at each pixel.

Figure 2:. Workflow for using MCA.

A. Representative workflow following the import of an imaging series into ImageJ. There are two main paths for working in the Cell Manager. An analytical path and an annotation path. The annotation path consists of using external functions, like creating a composite image or max projections to create a map which can be annotated. From here the user can set standard naming to ROIs and then group ROIs with two methods, polygonal grouping or point based grouping. The analysis path is required to generate a dataset from the imaging series. Initially begin with the motion correction step, continue to the cell detection step by using either Cellpose or Stardist2d to predict cellular ROIs, or manually label them. Next, the imaging series is converted to raw data and finally that data is exported into spreadsheets.

Figure 3:. MCA maintains previous trends in calcium imaging data.

A. Representative calcium imaging assay and representative OFF (magenta) or ON (cyan) response. Larvae are imaged for 3 minutes with the light on for 1 minute, light OFF for 1 minute, and light back on for 1 minute. This series is repeated twice, and data are averaged together. B. Representative sectioning of the Thalamus into the Lateral Thalamus (Lat Th., orange) which contains the AMNs (magenta), the Greater Thalamus (Great Th., green) which is every cell medial and anterior to the Lateral Thalamus, and the Posterior Tuberculum (PT, purple) scale bar 20μm. C Averaged output for OFF-responsive neurons in the AMNs between matched (cyan) and opposed (red) hemispheres for published data (Prior data: matched: N=79; opposed, N=106). * Indicates p < 0.05 between matched and opposed for at least 10 consecutive timepoints. D. Same as C yet using MCA (matched, N=50, opposed: N=60). * Indicates p<0.05 two tailed t-test between matched and opposed where p<0.05 for at least 20 consecutive timepoints. E. Relative frequency of response types among regions between analysis methods (Prior data: Great Th.: None (Black), N=1799 (83.2%); OFF (purple), N=143 (6.1%); OFF/ON (teal), N=13 (0.5%); ON (yellow), N=72 (3.2%); Lat Th.: None, N=939 (79.9%); OFF N=252 (20.5%); OFF/ON, N=5 (0.4%); ON, N=35 (2.8%); PT: None, N=1706 (79.2%); OFF N=318 (14.7%); OFF/ON, N=16 (0.7%); ON, N=115 (5.3%); MCA: Great Th.: None, N=1799 (86.1%); OFF, N=164 (7.9%); OFF/ON, N=57 (2.7%); ON, N=69 (3.3%); Lat Th.: None, N=606 (79.9%); OFF N=89 (12.2%); OFF/ON, N=30 (4.1%); ON, N=5 (0.7%); PT: None, N=2421 (86.8%); OFF N=250 (9.0%); OFF/ON, N=45 (1.6%); ON, N=74 (2.7%)). F. Average peak off response between matched (cyan) and opposed (red) OFF responsive neurons in the Greater Thalamus (green; Prior data: matched N=63, opposed N=80; MCA: matched N=84, opposed N=80), Lateral Thalamus AMN+ (orange; Prior data: matched N=79, opposed N=106; MCA: matched N=36, opposed N=44), and Posterior Tuberculum (purple; Prior data: matched N=170, opposed N=148) * indicates p<0.05 two-tailed t-test between matched and opposed within methods.

Figure 4:. MCA is versatile for a variety of stimulus delivery techniques.

A. Representative diagram of acoustic calcium imaging series where larvae are exposed to an acoustic stimulus of increasing amplitude every 30 seconds. B. Representative imaging plane indicating SAG cells (red) and pan-neuronal GCaMP6f (white). Scale bar (20 μm). C. Raster plot where every row is a different cell that responds to at least one of the acoustic stimuli (denoted by red arrows). Color indicates ΔF/F value. D. Average of all responsive cells through the imaging series. Line and ribbon indicate mean ± SEM. Dotted red lines indicate stimulus points. E. Pie chart indicating the percentage of SAG cells that responded to at least one of the acoustic stimuli (Response: gold, N=70; No Response: blue, N=192). F. Same as in E Indicating the percentage of responsive cells to each amplitude (1V, N=48; 2V, N=36; 3V, N=31; 4V, N=8; 5V, N=15)

Figure 5:. MCA is viable for multiple model organisms.

A. Representative of imaging plane for recording odor evoked responses from local interneurons in the antennal lobe of Drosophila (scale bar=20μm). Yellow circles indicate locations of individual glomeruli. B. Raster plot where every row is a different glomerulus (N=16) and response to ACV stimulation at frame 23. Color indicates ΔF/F value. C. Average glomeruli response. D. Representative of mouse calcium imaging plane in V1 of the superficial visual cortex (scale bar=20μm). E. Activity map generated from recording use in D. Green outline highlights an example cell where cell signal was significantly increasing, while blue highlights significantly decreasing. F. Number of significant decreases (top, blue) or increases (bottom, green) in signal activity among all neurons measured (N=98). Grey bars indicate visual stimulus is being presented to the mouse. G-H. Representative image of a zebrafish tail G. prior to spontaneous movement or H. during spontaneous movement. Yellow outline indicates cell being plotted in I. I. Calcium signal for single muscle cell outline in yellow in G-H. Red arrow denotes timepoint shown in H.