Quantitative 3-D analysis of GFAP labeled astrocytes from fluorescence confocal images.
BACKGROUND
There is a need for effective computational methods for quantifying the three-dimensional (3-D) spatial distribution, cellular arbor morphologies, and the morphological diversity of brain astrocytes to support quantitative studies of astrocytes in health, injury, and disease.


METHODS
Confocal fluorescence microscopy of multiplex-labeled (GFAP, DAPI) brain tissue is used to perform imaging of astrocytes in their tissue context. The proposed computational method identifies the astrocyte cell nuclei, and reconstructs their arbors using a local priority based parallel (LPP) tracing algorithm. Quantitative arbor measurements are extracted using Scorcioni's L-measure, and profiled by unsupervised harmonic co-clustering to reveal the morphological diversity.


RESULTS
The proposed method identifies astrocyte nuclei, generates 3-D reconstructions of their arbors, and extracts quantitative arbor measurements, enabling a morphological grouping of the cell population.


METHODS
Our method enables comprehensive spatial and morphological profiling of astrocyte populations in brain tissue for the first time, and overcomes limitations of prior methods. Visual proofreading of the results indicate a >95% accuracy in identifying astrocyte nuclei. The arbor reconstructions exhibited 3.2% fewer erroneous jumps in tracing, and 17.7% fewer false segments compared to the widely used fast-marching method that resulted in 9% jumps and 20.8% false segments.


CONCLUSIONS
The proposed method can be used for large-scale quantitative studies of brain astrocyte distribution and morphology.
Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building 1, Houston, TX 77004-4005, United States.
Prathamesh M
PM
Kulkarni
Department of Psychology, University of Houston, 126 Heyne Building, Houston, TX 77204-5022, United States.
Emily
E
Barton
Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building 1, Houston, TX 77004-4005, United States.
Michalis
M
Savelonas
Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building 1, Houston, TX 77004-4005, United States.
Raghav
R
Padmanabhan
Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building 1, Houston, TX 77004-4005, United States.
Yanbin
Y
Lu
Center for Integrative Brain Research, Seattle Children's Research Institute, 1900 Ninth Avenue 10th Floor, Mail Stop JMB-10, Seattle, WA 98101-1309, United States.
Kristen
K
Trett
Center for Integrative Brain Research, Seattle Children's Research Institute, 1900 Ninth Avenue 10th Floor, Mail Stop JMB-10, Seattle, WA 98101-1309, United States.
William
W
Shain
Department of Psychology, University of Houston, 126 Heyne Building, Houston, TX 77204-5022, United States.
J Leigh
JL
Leasure
Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building 1, Houston, TX 77004-4005, United States. Electronic address: broysam@central.uh.edu.
Badrinath
B
Roysam
D000818:Animals; D001253:Astrocytes / Q000378:metabolism* / Q000648:ultrastructure; D005904:Glial Fibrillary Acidic Protein / Q000378:metabolism*; D021621:Imaging, Three-Dimensional; D018613:Microscopy, Confocal; D009419:Nerve Tissue Proteins / Q000378:metabolism; D017397:Prefrontal Cortex / Q000166:cytology*; D051381:Rats
D005904:Glial Fibrillary Acidic Protein; D009419:Nerve Tissue Proteins
Astrocyte arbor reconstruction; Astrocyte quantification; Harmonic co-clustering; L-measure; Machine learning; Quantitative arbor analytics