Context-explorer: Analysis of spatially organized protein expression in high-throughput screens

Joel Ostblom, Emanuel J P Nazareth, Mukul Tewary and Peter W Zandstra

PLOS Computational Biology, 2019, vol. 15, issue 1, 1-13

Abstract: A growing body of evidence highlights the importance of the cellular microenvironment as a regulator of phenotypic and functional cellular responses to perturbations. We have previously developed cell patterning techniques to control population context parameters, and here we demonstrate context-explorer (CE), a software tool to improve investigation cell fate acquisitions through community level analyses. We demonstrate the capabilities of CE in the analysis of human and mouse pluripotent stem cells (hPSCs, mPSCs) patterned in colonies of defined geometries in multi-well plates. CE employs a density-based clustering algorithm to identify cell colonies. Using this automatic colony classification methodology, we reach accuracies comparable to manual colony counts in a fraction of the time, both in micropatterned and unpatterned wells. Classifying cells according to their relative position within a colony enables statistical analysis of spatial organization in protein expression within colonies. When applied to colonies of hPSCs, our analysis reveals a radial gradient in the expression of the transcription factors SOX2 and OCT4. We extend these analyses to colonies of different sizes and shapes and demonstrate how the metrics derived by CE can be used to asses the patterning fidelity of micropatterned plates. We have incorporated a number of features to enhance the usability and utility of CE. To appeal to a broad scientific community, all of the software’s functionality is accessible from a graphical user interface, and convenience functions for several common data operations are included. CE is compatible with existing image analysis programs such as CellProfiler and extends the analytical capabilities already provided by these tools. Taken together, CE facilitates investigation of spatially heterogeneous cell populations for fundamental research and drug development validation programs.Author summary: Cell behavior is influenced by cues that cells receive from their surrounding environment such as signals secreted from other cells and cell-to-cell contact. These factors are spatially heterogeneous and cells at different positions within a colony will experience varying degrees of influence from such environmental cues. In vitro assays often do not allow control over environmental variables and there is a lack of easy to use software to investigate the effect of spatial variation in these factors. We have developed a software package to address this gap and facilitate the quantification of spatially heterogeneous cell responses. Our software accurately identifies colonies of cells within a well and individual cells can be grouped according to their position within these colonies, which enables quantification of cell response as a function of cellular location. To support broad scientific accessibility, the full functionality of the software is available through a graphical user interface. Using this software to analyze data from a screening-optimized micropatterning platform, we show that human pluripotent stem cell-derived colonies grown either under pluripotency maintenance or differentiation-inducing conditions exhibit cell responses that are dependent on spatial organization. This technology should enable more accurate and predictive context-dependent drug screening and cell-fate investigation.

Date: 2019
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006384 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 06384&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006384

DOI: 10.1371/journal.pcbi.1006384

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().