Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue

The numbers and types of cells in an area of cortex define its function. Therefore it is essential to characterize the numbers and distributions of total cells in areas of the cortex, as well as to identify numbers of subclasses of neurons and glial cells. To date, the large size of the primate brai...

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Main Authors: Nicole A Young, David K Flaherty, David C. Airey, Feyi eAworunse, Peter A. Varlan, Jon H Kaas, Christine E Collins
Format: Article
Language:English
Published: Frontiers Media S.A. 2012-07-01
Series:Frontiers in Neuroanatomy
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnana.2012.00027/full
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author Nicole A Young
David K Flaherty
David C. Airey
Feyi eAworunse
Peter A. Varlan
Jon H Kaas
Christine E Collins
author_facet Nicole A Young
David K Flaherty
David C. Airey
Feyi eAworunse
Peter A. Varlan
Jon H Kaas
Christine E Collins
author_sort Nicole A Young
collection DOAJ
description The numbers and types of cells in an area of cortex define its function. Therefore it is essential to characterize the numbers and distributions of total cells in areas of the cortex, as well as to identify numbers of subclasses of neurons and glial cells. To date, the large size of the primate brain and the lack of innovation in cell counting methods have been a roadblock to obtaining high-resolution maps of cell and neuron density across the cortex in humans and non-human primates. Stereological counting methods and the isotropic fractionator are valuable tools for estimating cell numbers, but are better suited to smaller, well-defined brain structures or to cortex as a whole. In the present study, we have extended our flow-cytometry based counting method, the flow fractionator (Collins et al., 2010a), to include high-throughput total cell population estimates in homogenized cortical samples. We demonstrate that our method produces consistent, accurate and repeatable cell estimates quickly. The estimates we report are in excellent agreement with estimates for the same samples obtained using a Neubauer chamber and a fluorescence microscope. We show that our flow cytometry-based method for total cell estimation in homogenized brain tissue is more efficient and more precise than manual counting methods. The addition of automated nuclei counting to our flow fractionator method allows for a fully automated, rapid characterization of total cells and neuronal and non-neuronal populations in human and non-human primate brains, providing valuable data to further our understanding of the functional organization of normal, aging and diseased brains.
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spelling doaj.art-457be67418ff49a086877ddba427dcff2022-12-22T02:57:22ZengFrontiers Media S.A.Frontiers in Neuroanatomy1662-51292012-07-01610.3389/fnana.2012.0002728145Use of flow cytometry for high-throughput cell population estimates in fixed brain tissueNicole A Young0David K Flaherty1David C. Airey2Feyi eAworunse3Peter A. Varlan4Jon H Kaas5Christine E Collins6Vanderbilt UniversityVanderbilt University Medical CenterVanderbilt University Medical CenterVanderbilt UniversityVanderbilt UniversityVanderbilt UniversityVanderbilt UniversityThe numbers and types of cells in an area of cortex define its function. Therefore it is essential to characterize the numbers and distributions of total cells in areas of the cortex, as well as to identify numbers of subclasses of neurons and glial cells. To date, the large size of the primate brain and the lack of innovation in cell counting methods have been a roadblock to obtaining high-resolution maps of cell and neuron density across the cortex in humans and non-human primates. Stereological counting methods and the isotropic fractionator are valuable tools for estimating cell numbers, but are better suited to smaller, well-defined brain structures or to cortex as a whole. In the present study, we have extended our flow-cytometry based counting method, the flow fractionator (Collins et al., 2010a), to include high-throughput total cell population estimates in homogenized cortical samples. We demonstrate that our method produces consistent, accurate and repeatable cell estimates quickly. The estimates we report are in excellent agreement with estimates for the same samples obtained using a Neubauer chamber and a fluorescence microscope. We show that our flow cytometry-based method for total cell estimation in homogenized brain tissue is more efficient and more precise than manual counting methods. The addition of automated nuclei counting to our flow fractionator method allows for a fully automated, rapid characterization of total cells and neuronal and non-neuronal populations in human and non-human primate brains, providing valuable data to further our understanding of the functional organization of normal, aging and diseased brains.http://journal.frontiersin.org/Journal/10.3389/fnana.2012.00027/fullFlow Cytometrycell countingNeubauer chamberNuclear suspensionflow fractionator
spellingShingle Nicole A Young
David K Flaherty
David C. Airey
Feyi eAworunse
Peter A. Varlan
Jon H Kaas
Christine E Collins
Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue
Frontiers in Neuroanatomy
Flow Cytometry
cell counting
Neubauer chamber
Nuclear suspension
flow fractionator
title Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue
title_full Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue
title_fullStr Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue
title_full_unstemmed Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue
title_short Use of flow cytometry for high-throughput cell population estimates in fixed brain tissue
title_sort use of flow cytometry for high throughput cell population estimates in fixed brain tissue
topic Flow Cytometry
cell counting
Neubauer chamber
Nuclear suspension
flow fractionator
url http://journal.frontiersin.org/Journal/10.3389/fnana.2012.00027/full
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