OrgDyn: feature- and model-based characterization of spatial and temporal organoid dynamics

Abstract

Summary Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature- and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease. Availability and Implementation https://github.com/zakih/organoidDynamics (BSD 3-Clause License). Supplementary information Supplementary data are available at Bioinformatics online.

Recent Trends and Perspectives in Cerebral Organoids Imaging and Analysis

“…Bright-field microscopy is used to observe shape (Monzel et al, 2017) and surface parameters (Iefremova et al, 2017) of 3D brain cultures. On other 3D organ cultures, these images are also used to measure the overall size with automatic methods (Borten et al, 2018;Kassis et al, 2019;Hasnain et al, 2020). In such cases, samples do not require any particular preparation.…”

“…A scale bar is used as a reference to correctly assess the distance (Bergmann et al, 2018). In mammalian, colon and intestinal organoids, the whole area of the entire organoid digitized after bright-field imaging is calculated (Borten et al, 2018;Ren et al, 2018;Hasnain et al, 2020). As an example, for Borten et al (2018), after a segmentation of colon organoids (by a conversion, opening-closure, thresholding, filtering to denoise, filling holes, denoising, and removing debris), the surface area of identified region of interest is measured.…”

“…Others methods have been validated for the study of noncerebral organoids: Cytocensus for retinal organoids (Hailstone et al, 2020); OrgDyn for widefield images of mammalian organoids (Hasnain et al, 2020); OrganoSeg for 3D bright-field images of colon organoids (Borten et al, 2018). Most of these tools are based on image filtering and segmentation.…”

“…Almost half of the related articles and reviews included in this review are about pathological organoids (Figure 2 and Table 1). In others organ models, automatic tools have emerged to measure the size or to classify the morphology of others organ models (Borten et al, 2018;Kassis et al, 2019;Hasnain et al, 2020). We think a similar tool for cerebral organoids could help to measure and identify the growing step of cerebral organoids.…”

“…In others organ models, i.e., mammary organoids (Hasnain et al, 2020), automated tools allowed the discovery of various groups of morphology. Such classification would be interesting to highlight in cerebral organoids.…”