Literature DB >> 23017980

Automatic isotropic fractionation for large-scale quantitative cell analysis of nervous tissue.

Frederico A C Azevedo1, Carlos H Andrade-Moraes, Marco R Curado, Ana V Oliveira-Pinto, Daniel M Guimarães, Diego Szczupak, Bruna V Gomes, Ana T L Alho, Livia Polichiso, Edilaine Tampellini, Luzia Lima, Daniel Oliveira de Lima, Hudson Alves da Silva, Roberto Lent.   

Abstract

Isotropic fractionation is a quantitative technique that allows reliable estimates of absolute numbers of neuronal and non-neuronal brain cells. However, being fast for single small brains, it requires a long time for processing large brains or many small ones, if done manually. To solve this problem, we developed a machine to automate the method, and tested its efficiency, consistency, and reliability as compared with manual processing. The machine consists of a set of electronically controlled rotation and translation motors coupled to tissue grinders, which automatically transform fixed tissue into homogeneous nuclei suspensions. Speed and torque of the motors can be independently regulated by electronic circuits, according to the volume of tissue being processed and its mechanical resistance to fractionation. To test the machine, twelve paraformaldehyde-fixed rat brains and eight human cerebella were separated into two groups, respectively: one processed automatically and the other, manually. Both pairs of groups (rat and human tissue) followed the same, published protocol of the method. We compared the groups according to nuclei morphology, degree of clustering and number of cells. The machine proved superior for yielding faster results due to simultaneous processing in multiple grinders. Quantitative analysis of machine-processed tissue resulted in similar average numbers of total brain cells, neurons, and non-neuronal cells, statistically similar to the manually processed tissue and equivalent to previously published data. We concluded that the machine is more efficient because it utilizes many homogenizers simultaneously, equally consistent in producing high quality material for counting, and quantitatively reliable as compared to manual processing.
Copyright © 2012 Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 23017980     DOI: 10.1016/j.jneumeth.2012.09.015

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  6 in total

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Journal:  Microbiol Mol Biol Rev       Date:  2017-11-08       Impact factor: 11.056

Review 2.  The search for true numbers of neurons and glial cells in the human brain: A review of 150 years of cell counting.

Authors:  Christopher S von Bartheld; Jami Bahney; Suzana Herculano-Houzel
Journal:  J Comp Neurol       Date:  2016-06-16       Impact factor: 3.215

3.  Cell number changes in Alzheimer's disease relate to dementia, not to plaques and tangles.

Authors:  Carlos Humberto Andrade-Moraes; Ana V Oliveira-Pinto; Emily Castro-Fonseca; Camila G da Silva; Daniel M Guimarães; Diego Szczupak; Danielle R Parente-Bruno; Ludmila R B Carvalho; Lívia Polichiso; Bruna V Gomes; Lays M Oliveira; Roberta D Rodriguez; Renata E P Leite; Renata E L Ferretti-Rebustini; Wilson Jacob-Filho; Carlos A Pasqualucci; Lea T Grinberg; Roberto Lent
Journal:  Brain       Date:  2013-10-17       Impact factor: 13.501

4.  The Isotropic Fractionator as a Tool for Quantitative Analysis in Central Nervous System Diseases.

Authors:  Ivan E Repetto; Riccardo Monti; Marta Tropiano; Simone Tomasi; Alessia Arbini; Carlos-Humberto Andrade-Moraes; Roberto Lent; Alessandro Vercelli
Journal:  Front Cell Neurosci       Date:  2016-08-05       Impact factor: 5.505

5.  The Absolute Number of Oligodendrocytes in the Adult Mouse Brain.

Authors:  Bruna Valério-Gomes; Daniel M Guimarães; Diego Szczupak; Roberto Lent
Journal:  Front Neuroanat       Date:  2018-10-30       Impact factor: 3.856

6.  BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.

Authors:  Elior Rahmani; Regev Schweiger; Liat Shenhav; Theodora Wingert; Ira Hofer; Eilon Gabel; Eleazar Eskin; Eran Halperin
Journal:  Genome Biol       Date:  2018-09-21       Impact factor: 13.583

  6 in total

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