Quantifying proliferative and surface marker heterogeneity in colony-founding connective tissue progenitors and their progeny using time-lapse microscopy.
Edward J KweeGerald M SaidelKimerly PowellChristopher HeylmanCynthia BoehmGeorge F MuschlerPublished in: Journal of tissue engineering and regenerative medicine (2019)
Connective tissue progenitors (CTPs) are defined as the heterogeneous population of tissue-resident stem and progenitor cells that are capable of proliferating and differentiating into connective tissue phenotypes. The prevalence and variation in clonal progeny of CTPs can be characterized using a colony formation assay. However, colony assays do not directly assess the characteristics of the colony-founding CTP. We performed large, field-of-view, time-lapse microscopy to manually track colonies back to the founding cells. Image processing and analysis was used to characterize the colonies and their founding cells. We found that the traditional colony-forming unit (CFU) assay underestimates the number of founding cells as colonies can be formed by more than one founding cell. After 6 days in culture, colonies do not completely express CD73, CD90, and CD105. Heterogeneity in colony cells was characterized by two cell populations, proliferative and spread cells. Regression modelling of duration of lag phase and doubling time by cell marker suggests the presence of CD90 and CD105 in CTP subpopulations with different proliferative capabilities. From mathematical modelling of clonal colonies, we quantitatively characterized proliferation, migration, and cell marker expression rates to identify desirable clones for selection. Direct assessment of colony formation parameters led to more accurate assessment of CFU heterogeneity. Furthermore, these parameters can be used to quantify the diversity and hierarchy of stem and progenitor cells from a cell source or tissue for tissue engineering applications.
Keyphrases
- single cell
- induced apoptosis
- cell cycle arrest
- high throughput
- cell therapy
- endoplasmic reticulum stress
- tissue engineering
- machine learning
- cell proliferation
- optical coherence tomography
- magnetic resonance imaging
- mesenchymal stem cells
- high resolution
- poor prognosis
- deep learning
- quality improvement
- magnetic resonance
- patient safety
- long non coding rna
- computed tomography
- bone marrow
- high speed
- binding protein
- data analysis
- light emitting