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

doi:10.15302/J-QB-022-0325

Quant. Biol.

2023, Vol. 11

Issue (3) : 306-319 https://doi.org/10.15302/J-QB-022-0325

RESEARCH ARTICLE

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

Doyoung Park(

)

Department of Mathematics, Computer & Information Science, State University of New York at Old Westbury, Old Westbury, New York, 11568, USA

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Abstract

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.

Keywords Concave Actin Bundles artificial neural network recognizer planar actin distribution 3D probability density estimation cytoskeletal structures

Corresponding Author(s): Doyoung Park

Just Accepted Date: 21 June 2023 Online First Date: 11 August 2023 Issue Date: 08 October 2023

Cite this article:

Doyoung Park. Use of artificial neural networks to identify and analyze polymerized actin-based cytoskeletal structures in 3D confocal images[J]. Quant. Biol., 2023, 11(3): 306-319.

URL:

https://academic.hep.com.cn/qb/EN/10.15302/J-QB-022-0325
https://academic.hep.com.cn/qb/EN/Y2023/V11/I3/306

Fig.1 F-actin stained by FITC-phalloidin is marked in green.

Fig.2 Surface reconstruction of image stacks acquired with a confocal microscope where fibers are represented in red, actin filaments in green, and cells in blue.

Fig.3 Statistics for 100 experiments.

Fig.4 Overall recognition rates, TP rates, TN rates, and F1 scores on various forms of feature vectors. TP, true positive; TN, true negative.

Fig.5 Validation by the regcognizer.

Fig.6 The procedures for detecting candidate CABs.

Fig.7 The method to planarize actin distribution over a cylindrical fiber.

Fig.8 The cylinder enveloping a fiber and actins, and the computation of the planar actin distribution.

Fig.9 An example of a 3D PDF valued image.

Fig.10 The architecture of the recognizer for CABs from candidate CABs.

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