Abstract
Current methods to characterize cell–biomaterial interactions are population-based and rely on imaging or biochemical analysis of end-point biological markers. The analysis of stem cells in cultures is further challenged by the heterogeneous nature and divergent fates of stem cells, especially in complex, engineered microenvironments. Here, we describe a high content imaging-based platform capable of identifying cell subpopulations based on cell phenotype-specific morphological descriptors. This method can be utilized to identify microenvironment-responsive morphological descriptors, which can be used to parse cells from a heterogeneous cell population based on emergent phenotypes at the single-cell level and has been successfully deployed to forecast long-term cell lineage fates and screen regenerative phenotype-prescriptive biomaterials.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
References
Causa F, Netti PA, Ambrosio L (2007) A multi-functional scaffold for tissue regeneration: the need to engineer a tissue analogue. Biomaterials 28(34):5093–5099
Guarino V, Causa F, Ambrosio L (2007) Bioactive scaffolds for bone and ligament tissue. Expert Rev Med Devices 4(3):405–418
Pasquinelli G et al (2008) Mesenchymal stem cell interaction with a non-woven hyaluronan-based scaffold suitable for tissue repair. J Anat 213(5):520–530
Reed CR et al (2009) Composite tissue engineering on polycaprolactone nanofiber scaffolds. Ann Plast Surg 62(5):505–512
Shea LD et al (2000) Engineered bone development from a pre-osteoblast cell line on three-dimensional scaffolds. Tissue Eng 6(6):605–617
Weber N et al (2004) Small changes in the polymer structure influence the adsorption behavior of fibrinogen on polymer surfaces: validation of a new rapid screening technique. J Biomed Mater Res 68(3):496–503
Flaim CJ, Chien S, Bhatia SN (2005) An extracellular matrix microarray for probing cellular differentiation. Nat Methods 2(2):119–125
Dittrich PS, Manz A (2006) Lab-on-a-chip: microfluidics in drug discovery. Nat Rev Drug Discov 5(3):210–218
Levsky JM, Singer RH (2003) Gene expression and the myth of the average cell. Trends Cell Biol 13(1):4–6
Vega SL et al (2012) High-content imaging-based screening of microenvironment-induced changes to stem cells. J Biomol Screen 17(9):1151–1162
Treiser MD et al (2010) Cytoskeleton-based forecasting of stem cell lineage fates. Proc Natl Acad Sci U S A 107(2):610–615
Liu E et al (2010) Parsing the early cytoskeletal and nuclear organizational cues that demarcate stem cell lineages. Cell Cycle 9(11):2108–2117
Vidi PA et al (2012) Interconnected contribution of tissue morphogenesis and the nuclear protein NuMA to the DNA damage response. J Cell Sci 125(Pt 2):350–361
McNamara LE et al (2011) Skeletal stem cell physiology on functionally distinct titania nanotopographies. Biomaterials 32(30):7403–7410
Acknowledgments
This study was partially supported by NIH P41 EB001046 (RESBIO, Integrated Resources for Polymeric Biomaterials), Rutgers University Academic Excellence Fund, and NSF Stem Cell IGERT 0801620.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this protocol
Cite this protocol
Kim, J.J., Vega, S.L., Moghe, P.V. (2013). A High Content Imaging-Based Approach for Classifying Cellular Phenotypes. In: Turksen, K. (eds) Imaging and Tracking Stem Cells. Methods in Molecular Biology, vol 1052. Humana Press, Totowa, NJ. https://doi.org/10.1007/7651_2013_29
Download citation
DOI: https://doi.org/10.1007/7651_2013_29
Published:
Publisher Name: Humana Press, Totowa, NJ
Print ISBN: 978-1-62703-558-3
Online ISBN: 978-1-62703-559-0
eBook Packages: Springer Protocols