Use of artificial neural networks to identify and analyze polymerized actin-based cytoskeletal structures in 3D confocal images.

Abstract

We propose an artificial neural network (ANN) as a kernel function of the recognizer of legitimate CABs from candidate CABs, that does not need human interventions. The performance of the recognizer shows noticeable recognition accuracy and addresses shortcomings of previous methods, including the need for human visual validation to recognize CABs from candidate CABs. Further, it helps to find and reduce errors resulting from human visual validation, which in turn would provide biologists/biophysicists a more comprehensive. understanding of a CAB.

Background: Living cells need to undergo subtle shape adaptations in response to the topography of their substrates. These shape changes are mainly determined by reorganization of their internal cytoskeleton, with a major contribution from filamentous (F) actin. Bundles of F-actin play a major role in determining cell shape and their interaction with substrates, either as "stress fibers," or as our newly discovered "Concave Actin Bundles" (CABs), which mainly occur while endothelial cells wrap micro-fibers in culture.

Methods: To better understand the morphology and functions of these CABs, it is necessary to recognize and analyze as many of them as possible in complex cellular ensembles, which is a demanding and time-consuming task. In this study, we present a novel algorithm to automatically recognize CABs without further human intervention. We developed and employed a multilayer perceptron artificial neural network ("the recognizer"), which was trained to identify CABs.

Results: The recognizer demonstrated high overall recognition rate and reliability in both randomized training, and in subsequent testing experiments.

Conclusion: It would be an effective replacement for validation by visual detection which is both tedious and inherently prone to errors.