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Automated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Data

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dc.contributor.authorWang, Yinxueen
dc.contributor.authorShi, Guilaien
dc.contributor.authorMiller, David J.en
dc.contributor.authorWang, Yizhien
dc.contributor.authorWang, Congchaoen
dc.contributor.authorBroussard, Gerard J.en
dc.contributor.authorWang, Yueen
dc.contributor.authorTian, Linen
dc.contributor.authorYu, Goquiangen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2018-07-13T13:36:20Zen
dc.date.available2018-07-13T13:36:20Zen
dc.date.issued2017-07-14en
dc.description.abstractRecent discoveries that astrocytes exert proactive regulatory effects on neural information processing and that they are deeply involved in normal brain development and disease pathology have stimulated broad interest in understanding astrocyte functional roles in brain circuit. Measuring astrocyte functional status is now technically feasible, due to recent advances in modern microscopy and ultrasensitive cell-type specific genetically encoded Ca²⁺ indicators for chronic imaging. However, there is a big gap between the capability of generating large dataset via calcium imaging and the availability of sophisticated analytical tools for decoding the astrocyte function. Current practice is essentially manual, which not only limits analysis throughput but also risks introducing bias and missing important information latent in complex, dynamic big data. Here, we report a suite of computational tools, called Functional AStrocyte Phenotyping (FASP), for automatically quantifying the functional status of astrocytes. Considering the complex nature of Ca²⁺ signaling in astrocytes and low signal to noise ratio, FASP is designed with data-driven and probabilistic principles, to flexibly account for various patterns and to perform robustly with noisy data. In particular, FASP explicitly models signal propagation, which rules out the applicability of tools designed for other types of data. We demonstrate the effectiveness of FASP using extensive synthetic and real data sets. The findings by FASP were verified by manual inspection. FASP also detected signals that were missed by purely manual analysis but could be confirmed by more careful manual examination under the guidance of automatic analysis. All algorithms and the analysis pipeline are packaged into a plugin for Fiji (ImageJ), with the source code freely available online at https://github.com/VTcbil/FASP.en
dc.description.sponsorshipResearch reported in this publication was partly supported by the Hartwell foundation (LT), NIH DP2 MH107059 (LT), NIH R21NS095325 (LT) and NIH R01MH110504 (GY).en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fninf.2017.00048en
dc.identifier.urihttp://hdl.handle.net/10919/83949en
dc.identifier.volume11en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectastrocyteen
dc.subjectastrocyte activityen
dc.subjectfunctional phenotypeen
dc.subjectcalcium dynamicsen
dc.subjecttime-lapse calcium imageen
dc.subjectsignal propagationen
dc.titleAutomated Functional Analysis of Astrocytes from Chronic Time-Lapse Calcium Imaging Dataen
dc.title.serialFrontiers in Neuroinformaticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Destination Area: Adaptive Brain and Behavior (ABB)
Scholarly Works, Electrical and Computer Engineering