AutoSmarTrace: Automated chain tracing and flexibility analysis of biological filaments
| Autor: | Mathew Schneider, Alaa Al-Shaer, Nancy R. Forde |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Fluorescence-lifetime imaging microscopy
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biophysics 02 engineering and technology Tracing 010402 general chemistry Microscopy Atomic Force 01 natural sciences 03 medical and health sciences Chain (algebraic topology) Computational Tool Cytoskeleton 030304 developmental biology TRACE (psycholinguistics) Persistence length Ground truth 0303 health sciences Artificial neural network business.industry Pattern recognition DNA 021001 nanoscience & nanotechnology 0104 chemical sciences Microscopy Electron Probability distribution Noise (video) Artificial intelligence 0210 nano-technology business Algorithms |
| Zdroj: | Biophys J |
| ISSN: | 0006-3495 |
| Popis: | Single-molecule imaging is widely used to determine statistical distributions of molecular properties. One such characteristic is the bending flexibility of biological filaments, which can be parameterized via the persistence length. Quantitative extraction of persistence length from images of individual filaments requires both the ability to trace the backbone of the chains in the images and sufficient chain statistics to accurately assess the persistence length. Chain tracing can be a tedious task, performed manually or using algorithms that require user input and/or supervision. Such interventions have the potential to introduce user-dependent bias into the chain selection and tracing. Here, we introduce a fully automated algorithm for chain tracing and determination of persistence lengths. Dubbed “AutoSmarTrace”, the algorithm is built off a neural network, trained via machine learning to identify filaments within images recorded using atomic force microscopy (AFM). We validate the performance of AutoSmarTrace on simulated images with widely varying levels of noise, demonstrating its ability to return persistence lengths in agreement with the ground truth. Persistence lengths returned from analysis of experimental images of collagen and DNA agree with previous values obtained from these images with different chain-tracing approaches. While trained on AFM-like images, the algorithm also shows promise to identify chains in other single-molecule imaging approaches, such as rotary shadowing electron microscopy and fluorescence imaging.Statement of SignificanceMachine learning presents powerful capabilities to the analysis of large data sets. Here, we apply this approach to the determination of bending flexibility – described through persistence length – from single-molecule images of biological filaments. We present AutoSmarTrace, a tool for automated tracing and analysis of chain flexibility. Built on a neural network trained via machine learning, we show that AutoSmarTrace can determine persistence lengths from AFM images of a variety of biological macromolecules including collagen and DNA. While trained on AFM-like images, the algorithm works well to identify filaments in other types of images. This technique can free researchers from tedious tracing of chains in images, removing user bias and standardizing determination of chain mechanical parameters from single-molecule conformational images. |
| Databáze: | OpenAIRE |
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