Dataset Information

An image analysis method for regionally defined cellular phenotyping of the Drosophila midgut.

ABSTRACT: The intestine is divided into functionally distinct regions along the anteroposterior (A/P) axis. How the regional identity influences the function of intestinal stem cells (ISCs) and their offspring remain largely unresolved. We introduce an imaging-based method, "Linear Analysis of Midgut" (LAM), which allows quantitative, regionally defined cellular phenotyping of the whole Drosophila midgut. LAM transforms image-derived cellular data from three-dimensional midguts into a linearized representation, binning it into segments along the A/P axis. Through automated multivariate determination of regional borders, LAM allows mapping and comparison of cellular features and frequencies with subregional resolution. Through the use of LAM, we quantify the distributions of ISCs, enteroblasts, and enteroendocrine cells in a steady-state midgut, and reveal unprecedented regional heterogeneity in the ISC response to a Drosophila model of colitis. Altogether, LAM is a powerful tool for organ-wide quantitative analysis of the regional heterogeneity of midgut cells.

SUBMITTER: Viitanen A

PROVIDER: S-EPMC9017226 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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An image analysis method for regionally defined cellular phenotyping of the <i>Drosophila</i> midgut.

Viitanen Arto A Gullmets Josef J Morikka Jack J Katajisto Pekka P Mattila Jaakko J Hietakangas Ville V

Cell reports methods 20210730 5

The intestine is divided into functionally distinct regions along the anteroposterior (A/P) axis. How the regional identity influences the function of intestinal stem cells (ISCs) and their offspring remain largely unresolved. We introduce an imaging-based method, "Linear Analysis of Midgut" (LAM), which allows quantitative, regionally defined cellular phenotyping of the whole <i>Drosophila</i> midgut. LAM transforms image-derived cellular data from three-dimensional midguts into a linearized re ...[more]

PMID: 35474669

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Project description:BackgroundRust is a damaging disease affecting vital crops, including pea, and identifying highly resistant genotypes remains a challenge. Accurate measurement of infection levels in large germplasm collections is crucial for finding new resistance sources. Current evaluation methods rely on visual estimation of disease severity and infection type under field or controlled conditions. While they identify some resistance sources, they are error-prone and time-consuming. An image analysis system proves useful, providing an easy-to-use and affordable way to quickly count and measure rust-induced pustules on pea samples. This study aimed to develop an automated image analysis pipeline for accurately calculating rust disease progression parameters under controlled conditions, ensuring reliable data collection.ResultsA highly efficient and automatic image-based method for assessing rust disease in pea leaves was developed using R. The method's optimization and validation involved testing different segmentation indices and image resolutions on 600 pea leaflets with rust symptoms. The approach allows automatic estimation of parameters like pustule number, pustule size, leaf area, and percentage of pustule coverage. It reconstructs time series data for each leaf and integrates daily estimates into disease progression parameters, including latency period and area under the disease progression curve. Significant variation in disease responses was observed between genotypes using both visual ratings and image-based analysis. Among assessed segmentation indices, the Normalized Green Red Difference Index (NGRDI) proved fastest, analysing 600 leaflets at 60% resolution in 62 s with parallel processing. Lin's concordance correlation coefficient between image-based and visual pustule counting showed over 0.98 accuracy at full resolution. While lower resolution slightly reduced accuracy, differences were statistically insignificant for most disease progression parameters, significantly reducing processing time and storage space. NGRDI was optimal at all time points, providing highly accurate estimations with minimal accumulated error.ConclusionsA new image-based method for monitoring pea rust disease in detached leaves, using RGB spectral indices segmentation and pixel value thresholding, improves resolution and precision. It rapidly analyses hundreds of images with accuracy comparable to visual methods and higher than other image-based approaches. This method evaluates rust progression in pea, eliminating rater-induced errors from traditional methods. Implementing this approach to evaluate large germplasm collections will improve our understanding of plant-pathogen interactions and aid future breeding for novel pea cultivars with increased rust resistance.

Dataset Information

An image analysis method for regionally defined cellular phenotyping of the Drosophila midgut.

Publications

An image analysis method for regionally defined cellular phenotyping of the <i>Drosophila</i> midgut.

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