Deep Learning Resolves Myovascular Dynamics in the Failing Human Heart

Abstract

The adult mammalian heart harbors minute levels of cycling cardiomyocytes (CMs). Large numbers of images are needed to accurately quantify cycling events using microscopy-based methods. CardioCount is a new deep learning-based pipeline to rigorously score nuclei in microscopic images. When applied to a repository of 368,434 human microscopic images, we found evidence of coupled growth between CMs and cardiac endothelial cells in the adult human heart. Additionally, we found that vascular rarefaction and CM hypertrophy are interrelated in end-stage heart failure. CardioCount is available for use via GitHub and via Google Colab for users with minimal machine learning experience.

Keywords: LVAD; UNets; cardiomyocyte cell cycle; heart failure; vascular rarefaction.

Graphical abstract

Figure 1

Generation and Validation of CardioCount (A) Diagram of CardioCount’s machine learning pipeline for instance segmentation of RGB microscopy images. RGB images were converted to RB (red+blue channel) images as input to the Erg or PCM1 cell-identity models and GB (green+blue channel) images as input to the Ki67 cycling model. Probability map outputs were then converted to object maps using a postprocessing script. Lastly, cycling information was overlayed with cell-identity information by colocalizing nuclear objects. Ki67⁺ nuclei were filtered by the presence of a centroid of an Erg or PCM1 nucleus within the borders of the Ki67 nucleus. (B) Precision-recall (PR) curves for Erg, PCM1, and Ki67 models using test data (Erg, PCM1, Ki67). The red data point indicates the optimal machine learning model parameters, as determined by the best F1 score on the validation image set. (C) Representative application CardioCount to identify cycling cardiomyocytes from human cardiac sections immunostained for PCM1 and Ki67. Object instances of PCM1⁺ nuclei are outlined in the second subpanel; object instances of Ki67⁺ cells are outlined in the third subpanel, and a PCM1⁺Ki67⁺ double-positive object is outlined in the fourth subpanel. Scale bars are 50 μm. (D) Representative application CardioCount to identify cycling cardiac endothelial cells from human cardiac sections immunostained for Erg and Ki67. Object instances of Erg⁺ nuclei are outlined in the second subpanel; object instances of Ki67⁺ cells are outlined in the third subpanel, and an Erg⁺Ki67⁺ double-positive object is outlined in the fourth subpanel. Scale bars are 50 μm. CNN = convolutional neural network.

Figure 2

Myovascular Dynamics With HF (A and B) Representative images of human cardiac sections from a control patient and a patient with heart failure (HF). Sections in (A) are immunostained for PCM1 to mark cardiomyocyte (CM) nuclei (yellow), WGA to mark cell membranes (magenta), and nuclei (cyan). Sections in (B) are immunostained for Erg to mark cardiac endothelial cell (CEC) nuclei (yellow), WGA to mark cell membranes (purple) and nuclei (turquoise). Insets show a magnified image of a PCM1⁺ CM nucleus and an Erg⁺ CEC nucleus. Scale bars are 50 μm. Arrowheads indicate double-positive nuclei. (C) Violin plot of CM nuclear density in patients without heart failure (control patients) and with heart failure (HF/pre-LVAD). ∗∗∗P < 0.001, Wilcoxon rank sum test. (D) Violin plot of CEC nuclear density in patients without heart failure (control patients) and with heart failure (pre-LVAD). ∗∗∗P < 0.001 2-sided t-test. (E) Violin plot of the CEC:CM ratio in patients without heart failure (control patients) and with heart failure (pre-LVAD). P = 0.13, n.s. = not significant, 2-sided t-test. (F) Correlation of CEC:CM to NT-proBNP levels in patients with HF. Blue line is best-fit regression line. P = 0.007, Pearson correlation test. LVAD = left ventricular assist device.

Figure 3

Myovascular Changes After LVAD Support (A and B) Representative images of human cardiac sections from a control patient and a patient with heart failure. (A) Sections are immunostained for PCM1 to mark CM nuclei (yellow), WGA to mark cell membranes (purple) and nuclei (turquoise). (B) Sections are immunostained for Erg to mark CEC nuclei (yellow), WGA to mark cell membranes (purple) and nuclei (turquoise). Insets show a magnified image of a PCM1⁺ CM nucleus and Erg⁺ CEC nuclei. Scale bars are 50 μm. Arrowheads indicate PCM1⁺ and Erg⁺ nuclei. (C) Violin plot of CM nuclear density in patients pre-LVAD and post-LVAD. ∗∗∗P < 0.001, generalized additive models with random effects with Wald P. (D) Violin plot of CEC nuclear density in patients pre-LVAD and post-LVAD. Lines connect paired samples from the same patient. ∗∗P = 0.002, generalized additive models with random effects with Wald P. (E) Violin plot of CEC:CM ratio in patients pre-LVAD and post-LVAD. P = 0.44, n.s.= not significant, generalized additive models with random effects with Wald P. For C to F, lines connect paired samples from the same patient. Abbreviations as in Figure 2.

Figure 4

CM and CEC Cycling With LVAD Support (A and B) Representative images of human cardiac sections from the same patient before and after LVAD support. (A) Sections are immunostained for PCM1 to mark CM nuclei (yellow), Ki67 to mark cycling nuclei (purple) and nuclei (turquoise). (B) Sections are immunostained for Erg to mark CEC nuclei (yellow), Ki67 to mark cycling nuclei (purple) and nuclei (turquoise). Insets show magnified images of Ki67⁺PCM1⁺ CM nuclei (A) and Ki67⁺Erg⁺ CEC nuclei (B). Scale bars are 50 μm. Arrowheads indicate Ki67⁺ nuclei. (C) Differences in % of Ki67⁺_, PCM1⁺ CM nuclei before and after LVAD support. P = 0.102, n.s. = not significant, generalized additive models with random effects with Wald P. (D) Differences in % of Ki67⁺_, Erg⁺ CEC nuclei before and after LVAD support. P = 0.46, n.s. = not significant, generalized additive models with random effects with Wald P. (E) Correlation of % of Ki67⁺_, Erg⁺ CEC nuclei with % of Ki67⁺_, PCM1⁺ CM nuclei. Note that a constant of 0.001 was added to log-scale zero-valued data. Blue line is best-fit regression line. P = 0.002, Pearson correlation test. Abbreviations as in Figure 2.

Figure 5

Application of CardioCount to Murine Hearts (A) Schematic of transfer learning to assay murine data. Optimal model parameters for the human cardiac data sets were used as a starting point to train models for microscopy images of mouse hearts. (B) Final F1 scores for murine images as a function of different training and validation (Train/Val) set sizes. (C) Representative application of CardioCount to identify PCM1⁺ cardiomyocytes, Erg⁺ cardiac endothelial cells, and EdU⁺ cycling cells from a mouse cardiac section. The Erg⁺ cardiac endothelial cell image is a 512 × 512 crop of a full image. Identified objects are outlined in green for each marker. (D) Representative image from a neonatal mouse heart after injection with AAV-cTnT-Cre. Sections were stained for Cre (green), PCM1 (red), and DAPI (blue). The circles indicate Cre⁺ cells that were counted as PCM1⁺, and arrowheads point to Cre⁺ cells missed by CardioCount. Magnified images point to a correctly scored Cre⁺ nucleus and a missed Cre⁺ nucleus. (E) Comparison of scores from CardioCount to prior published work, when applied to the same set of images. Scale bars are 50 μm. CNN = convolutional neural network.