Automatic isotropic fractionation for large-scale quantitative cell analysis of nervous tissue
| Autor: | Edilaine Tampellini, Hudson Alves da Silva, Diego Szczupak, Carlos H. Andrade-Moraes, Daniel Oliveira de Lima, Ana V. Oliveira-Pinto, Marco Rocha Curado, Roberto Lent, Luzia Carreira Lima, Ana Tereza Di Lorenzo Alho, Daniel Menezes Guimarães, Frederico A. C. Azevedo, Livia Polichiso, Bruna V. Gomes |
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| Rok vydání: | 2012 |
| Předmět: |
Indoles
Computer science Cell Count In Vitro Techniques Translation (geometry) Consistency (statistics) medicine Torque Homogenizer Animals Humans Cluster analysis Cell Nucleus Neurons Analysis of Variance Electronic Data Processing business.industry General Neuroscience Nervous tissue Brain Rats medicine.anatomical_structure Phosphopyruvate Hydratase Artificial intelligence business Biological system Rotation (mathematics) Neuroglia Volume (compression) |
| Zdroj: | Journal of neuroscience methods. 212(1) |
| ISSN: | 1872-678X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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