Prathamesh M Kulkarni1, Emily Barton2, Michalis Savelonas1, Raghav Padmanabhan1, Yanbin Lu1, Kristen Trett3, William Shain3, J Leigh Leasure2, Badrinath Roysam4. 1. Department of Electrical and Computer Engineering, University of Houston, N308 Engineering Building 1, Houston, TX 77004-4005, United States. 2. Department of Psychology, University of Houston, 126 Heyne Building, Houston, TX 77204-5022, United States. 3. 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. 4. 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.
Abstract
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. NEW METHOD: 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. COMPARISON WITH EXISTING 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.
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. NEW METHOD: 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. COMPARISON WITH EXISTING 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.
Authors: Lilia Mesina; Aaron A Wilber; Benjamin J Clark; Sutherland Dube; Alexis J Demecha; Craig E L Stark; Bruce L McNaughton Journal: J Neurosci Methods Date: 2016-03-31 Impact factor: 2.390
Authors: Clara Muñoz-Castro; Ayush Noori; Colin G Magdamo; Zhaozhi Li; Jordan D Marks; Matthew P Frosch; Sudeshna Das; Bradley T Hyman; Alberto Serrano-Pozo Journal: J Neuroinflammation Date: 2022-02-02 Impact factor: 8.322