Article Figures & Data
Figures
- Figure 1.
ML facilitates high-throughput image puncta analysis. A, Example synaptic puncta (synapsin in red) juxtaposed to a dendrite (MAP2 in green) and B, a trace provided by thresholding. C, First and second gradient (green and purple, respectively) provide a template. D, Trace boundary is improved by structure-guided ML (an optimized trace in green) overlaid on the second gradient (purple). E, The improved puncta trace as a result of ML. F, Nested folder input for image pre-processing sorted by data round (i.e., condition). G, Structure-guided ML algorithm runs multiple dilation iterations to optimize traces (green and pink loops indicate processing against the first and second gradients, respectively). H, Puncta properties are extracted and stored in matrix form, allowing flexible post-processing representation and analysis by the user (refer to Fig. 6 for statistical output and data display). Note, this overall paradigm allows user to interact with the program at fixed beginning and end steps (highlighted in green shaded boxes).
- Figure 2.
Comparison of Intellicount’s ML with manual traces and with Fiji. A, Representative traces (red) provided by manual tracing by a human observer, three processing conditions of Intellicount, and user adjusted variable- or fixed-threshold analysis in Fiji. Representative low (image 7) and high (image 4) puncta density fields are shown. White arrows indicate background included as ROI (top row) and merged puncta (bottom row). Note, “no machine learning” and “watershed only” provide traces obtained in Intellicount using default thresholds (without and with selection of a watershed algorithm, respectively) and without application of the ML. B, Comparison of these processing techniques normalized to values obtained by manual tracing. Results are displayed as mean ± SEM from analysis of 10 random fields; *p < 0.05, ***p < 0.001 using ANOVA with post hoc Tukey-Kramer, as compared to manual trace. C, Total analysis time per cropped image performed on 10 fields. Computational analysis time is shown in solid bars and was recorded either from MATLAB (for No Machine Learning, Watershed Only, and Intellicount) or Fiji macro. Diagonally-lined section of bar in Fiji-manual condition highlights the fraction of analysis time used for manual threshold selection.
- Figure 3.
Intellicount recapitulates analysis of synaptogenesis over time in cultured hippocampal neurons. A, Upper panels: representative images taken at increasing numbers of days in culture. Lower panels, corresponding traces provided by Intellicount (red, correlated puncta synapsin traces; green, segmented dendrite traces in MAP2 channel; blue, trace of soma segmented from MAP2 channel). Display of the MAP2 channel was deselected in the lower panels facilitate visualization of puncta. A total of 29–33 images were obtained from randomly sampled fields distributed evenly over two coverslips each obtained from two independent cultures. B–D, Quantification and graphical representation of (B) synapse number, (C) mean fluorescence intensity, and (D) area provided by Intellicount with minor adjustments in Adobe Illustrator (renaming of “rounds” to appropriate conditions and thickening of stroke width for representation of graph and bar lines). Colors displayed are default in the program. Data are displayed as mean ± SEM. Numbers within bars indicate the number of images processed from that time point.
- Figure 4.
Validation of Intellicount under different culture and staining conditions. A, Representative input images of high (left panel) and low (right panel) neuronal densities. B, Corresponding traces provided by Intellicount demonstrating differences in segmented MAP2-positive areas. C, Quantification of puncta number and properties (red graphs) provided by Intellicount under high and low neuronal densities. Total segmented MAP2-positive area shown in blue graph; n = 20. D, Representative fields for lentiviral-infected CaM knock-down (CaM KD) and control (empty viral vector) expressing hippocampal neurons and (E) traces provided by Intellicount. Here, MAP2 “show correlation” is selected, which highlights MAP2 signal at 50% intensity level. F, Quantification as in C displaying no differences in synapse formation as described previously (Pang et al., 2010); n = 52 and 51 for CaM KD and control, respectively. G, vGAT staining (red) and MAP2 staining (green) in inhibitory human iNs. H, Intellicount’s trace of vGAT puncta and MAP2. A total of 60 images were analyzed. White boxes show cropped areas for visualization. Numbers in bars indicate the number of images processed from that condition. Data are displayed as mean ± SEM. Statistical tests were performed using two-tailed Student’s t test, where *p < 0.05 and ***p < 0.001.
- Figure 5.
Intellicount’s GUI. Screenshot of the Intellicount analysis platform displaying results for synapse size from the synapse formation time course (data panel, upper left panels) depicted as dot and cumulative plots and an example analyzed field (figure panel, upper middle pane). For details on operation, please refer to the user guide accompanying the software.
- Figure 6.
Intellicount provides a multifunctional analysis platform with commonly used graphical data representation and statistical methods. A-C, In addition to bar graphs, data distribution can be directly visualized and exported as (A) dot, (B) cumulative fraction (for puncta area and intensity), or (C) box-and-whisker plots. Note, rounds here are reported according to folder nesting order and correspond to increasing time points in the synapse formation analysis presented in bar graph form previously (Fig. 3). D, Histograms for areas of identified puncta are collected for each image for three increasing time points. Here, one image taken from three different rounds are overlaid to demonstrate the shift toward larger puncta during maturation. E, Statistical comparisons can be directly performed in Intellicount’s analysis platform allowing visualization of significantly different groups. Rounds highlighted in red are significantly different from the round selected by the user in blue. F, Computation of statistical test data including p values for one-way ANOVA and post hoc analysis (Tukey-Kramer). Note, box notches (C) and line lengths (E) define the boundary of 95% confidence intervals for the data and the mean for each round, respectively. Thus, in E, rounds colored with red (rounds 1–3) are statistically different (at p < 0.05) from the round selected in blue (round 6), while rounds colored in black (rounds 4–5) are not.
Extended Data
Extended Data 1
File contains two MATLAB files and user guide. Download Extended Data 1, ZIP file.
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