Heterogeneous response of endothelial cells to insulin-like growth factor 1 treatment is explained by spatially clustered sub-populations.
Christina KimGregory J SeedorfSteven H AbmanDouglas P ShepherdPublished in: Biology open (2019)
A common strategy to measure the efficacy of drug treatment is the in vitro comparison of ensemble readouts with and without treatment, such as proliferation and cell death. A fundamental assumption underlying this approach is that there exists minimal cell-to-cell variability in the response to a drug. Here, we demonstrate that ensemble and non-spatial single-cell readouts applied to primary cells may lead to incomplete conclusions due to cell-to-cell variability. We exposed primary fetal pulmonary artery endothelial cells (PAEC) isolated from healthy newborn sheep and persistent pulmonary hypertension of the newborn (PPHN) sheep to the growth hormone, insulin-like growth factor 1 (IGF-1). We found that IGF-1 increased proliferation and branch points in tube formation assays but not angiogenic signaling proteins at the population level for both cell types. We hypothesized that this molecular ambiguity was due to the presence of cellular sub-populations with variable responses to IGF-1. Using high throughput single-cell imaging, we discovered a spatially localized response to IGF-1. This suggests localized signaling or heritable cell response to external stimuli may ultimately be responsible for our observations. Discovering and further exploring these rare cells is critical to finding new molecular targets to restore cellular function.
Keyphrases
- single cell
- high throughput
- growth hormone
- pulmonary hypertension
- rna seq
- pulmonary artery
- cell therapy
- endothelial cells
- cell death
- signaling pathway
- induced apoptosis
- coronary artery
- emergency department
- cell cycle arrest
- mass spectrometry
- binding protein
- endoplasmic reticulum stress
- mesenchymal stem cells
- cell proliferation
- combination therapy
- vascular endothelial growth factor
- deep learning
- convolutional neural network
- neural network
- replacement therapy