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Computational Analysis of Cellular Environment During Neural Progenitor Cell Differentiation
Author(s) -
Mahadevan Arun,
Robinson Jacob,
Qutub Amina
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.657.6
Subject(s) - thresholding , progenitor cell , biological system , computer science , topology (electrical circuits) , pattern recognition (psychology) , biology , artificial intelligence , microbiology and biotechnology , image (mathematics) , stem cell , mathematics , combinatorics
Objective The objective of this study is to quantify the influence of multi‐cellular environment in Neural Progenitor Cell (NPC) differentiation using time‐lapse phase contrast microscopy. Methods Differentiating human NPCs were tracked over 14 days using time‐lapse phase contrast microscopy.An intensity‐gradient thresholding method was used to extract cell locations and morphological features such as neurite length. A graph theory‐based approach was then employed to quantify cellular neighborhood, with vertices defined as cell centroids and spatial proximity between cells used to define edges.Global network properties such as characteristic path length and global clustering coefficient were evaluated at each time point to quantify the spatial topology of differentiating cells. Parallel assays in patch‐clamp electrophysiology were performed to compare functional maturation with network properties. Results Our image‐processing algorithm rapidly and robustly extracts relevant cell morphological features and locations. Time‐lapse image sequence analysis showed that cultures at different stages of electrical maturation (assayed through patch clamp electrophysiology) exhibit unique network topology. Conclusion We have developed a method to analyze NPC differentiation based on time‐lapse microscopy, which can be used to non‐invasively investigate the role of multi‐cellular neighborhood at different stages of NPC differentiation. Support This work is supported in part by NSF CAREER Award 1150645 and an NSF IGERT Training Grant.