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ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamic

bioRxiv · 15 Jul 2021 · 10.1101/2021.07.15.452462

Abstract

Plants respond to the surrounding environment in countless ways. One of these responses is their ability to sense and orient their root growth toward the gravity vector. Root gravitropism is studied in many laboratories as a hallmark of auxin-related phenotypes. However, manual analysis of images and microscopy data is known to be subjected to human bias. This is particularly the case for manual measurements of root bending as the selection lines to calculate the angle are set subjectively. Therefore, it is essential to develop and use automated or semi-automated image analysis to produce reproducible and unbiased data. Moreover, the increasing usage of vertical-stage microscopy in plant root biology yields gravitropic experiments with an unprecedented spatiotemporal resolution. To this day, there is no available solution to measure root bending angle over time for vertical-stage microscopy. To address these problems, we developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images. Moreover, the software can be used semi-automated for camera, mobile phone or stereomicroscope images. ACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. By its automated nature, the workflow is limiting human interactions and has high reproducibility. ACORBA will support the plant biologist community by reducing time and labor and by producing quality results from various kinds of inputs. Significance statementACORBA is implementing an automated and semi-automated workflow to quantify root bending and waving angles from images acquired with a microscope, a scanner, a stereomicroscope or a camera. It will support the plant biology community by reducing time and labor and by producing trustworthy and reproducible quantitative data.

Plant phenotyping relevance

根の屈曲角度を画像から自動抽出するソフトウェアとワークフローの開発が研究の中心であり、植物形態表現型の定量手法に該当する。

abstractwe developed ACORBA (Automatic Calculation Of Root Bending Angles), a fully automated software to measure root bending angle over time from vertical-stage microscope and flatbed scanner images.
abstractACORBA represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time.

Code and data availability

The paper explicitly releases the ACORBA software (source code, trained models, annotated training libraries, notebooks, user manual) on SourceForge and the raw microscopy/scanner image stacks used for the root-angle measurements on Zenodo (DOI 10.5281/zenodo.5105719). Both are paper-specific, public, and actionable.

Codepublic

and online Python image analysis and machine learning tutorials. Availability of data and materials The latest versions of ACORBA software training annotated libraries, source code, examples, image pre-processing scripts, deep machine learning model training Jupyter notebooks and user manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw microscopy and scanner stacks used in this paper are available at ZENODO (https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental data). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the European Research Council (G

Open resource ↗sourceforge.net/projects/acorba · pdf-raw-page:17 lines:1-45
Datasetpublic

ACORBA software training annotated libraries, source code, examples, image pre-processing scripts, deep machine learning model training Jupyter notebooks and user manual maintained by NBCS are available at https://sourceforge.net/projects/acorba/. The raw microscopy and scanner stacks used in this paper are available at ZENODO (https://doi.org/10.5281/zenodo.5105719). The analyzed results are supplemented (Supplemental data). Competing interests The authors declare that they have no competing interests. Funding This work was supported by the European Research Council (Grant No. 803048), Charles University Primus (Grant No. PRIMUS/19/SCI/09). Author contributions NBCS and MF conceived the p

Open resource ↗ZENODO · 10.5281/zenodo.5105719 · pdf-raw-page:17 lines:1-45

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