Automated quantification and statistical assessment of proliferating cardiomyocyte rates in embryonic hearts

Abstract

The use of digital image analysis and count regression models contributes to the reproducibility and rigor of histological studies in cardiovascular research. The use of formalized computer-based quantification strategies of histological images essentially removes potential researcher bias, allows for higher analysis throughput, and enables easy sharing of formalized quantification tools, contributing to research transparency, and data transferability. Moreover, the use of count regression models rather than ratios in statistical analysis of cell population data incorporates the extent of sampling into analysis and acknowledges the non-Gaussian nature of count distributions. Using quantification of proliferating cardiomyocytes in embryonic murine hearts as an example, we describe how these improvements can be implemented using open-source artificial intelligence-based image analysis tools and novel count regression models to efficiently analyze real-life data.

Keywords: artificial intelligence; count regression; image analysis; quantitative microscopy; statistical analysis.

Figure 1.

Pipeline for automated quantification of cardiomyocyte proliferation rates. Images of DAPI-stained embryonic heart nuclei are segmented using Stardist with finetuned model. Pretrained Stardist model is finetuned by retraining on manually corrected masks (A). DAPI (nuclei), EdU (proliferation), NKX2-5 (cardiomyocytes) signals, and nuclear borders obtained by segmenting DAPI channel with Stardist are used as inputs for CellProfiler to extract features for each individual nucleus (B). Obtained feature vectors and snapshots of each object are used to build a classifier and to classify every identified nucleus into EdU⁺ (class positive), EdU⁻ cardiomyocytes (class negative), and noncardiomyocytes (class non-CM) in CellProfiler Analyst (C).

Figure 2.

Statistical analysis of cardiomyocyte proliferation data set. Counts of total and proliferating cardiomyocytes were retrieved from 53 microscopy slides from 25 embryonic hearts harvested at 2 embryonic stages (E13.5 and E15.5) belonging to 2 genotypes (control and cKO) (A). Rates of Edu⁺ cardiomyocytes were modeled using COM-Poisson regression as combination of experimental factors, random factor, and offset using glmmTMB package in R (B). The suitability of produced regression model was assessed with DHARMa by evaluating the variance and distribution of residuals (C). Post hoc comparison of proliferating cardiomyocyte rates between genotypes within each embryonic stage was performed using estimated marginal means package emmeans (D) and data visualized using ggplot2 (E). cKO, cardiomyocyte-specific knockout animal; COM, Conway-Maxwell.