Literature DB >> 35705097

Systematically quantifying morphological features reveals constraints on organoid phenotypes.

Lauren E Beck1, Jasmine Lee1, Christopher Coté1, Margaret C Dunagin2, Ilya Lukonin3, Nikkita Salla1, Marcello K Chang1, Alex J Hughes4, Joseph D Mornin5, Zev J Gartner6, Prisca Liberali3, Arjun Raj7.   

Abstract

Organoids recapitulate complex 3D organ structures and represent a unique opportunity to probe the principles of self-organization. While we can alter an organoid's morphology by manipulating the culture conditions, the morphology of an organoid often resembles that of its original organ, suggesting that organoid morphologies are governed by a set of tissue-specific constraints. Here, we establish a framework to identify constraints on an organoid's morphological features by quantifying them from microscopy images of organoids exposed to a range of perturbations. We apply this framework to Madin-Darby canine kidney cysts and show that they obey a number of constraints taking the form of scaling relationships or caps on certain parameters. For example, we found that the number, but not size, of cells increases with increasing cyst size. We also find that these constraints vary with cyst age and can be altered by varying the culture conditions. We observed similar sets of constraints in intestinal organoids. This quantitative framework for identifying constraints on organoid morphologies may inform future efforts to engineer organoids.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  design principles; morphology; organoids

Mesh:

Year:  2022        PMID: 35705097      PMCID: PMC9350855          DOI: 10.1016/j.cels.2022.05.008

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   11.091


  26 in total

1.  Induction of epithelial tubular morphogenesis in vitro by fibroblast-derived soluble factors.

Authors:  R Montesano; G Schaller; L Orci
Journal:  Cell       Date:  1991-08-23       Impact factor: 41.582

Review 2.  Rethinking organoid technology through bioengineering.

Authors:  Elena Garreta; Roger D Kamm; Susana M Chuva de Sousa Lopes; Madeline A Lancaster; Ron Weiss; Xavier Trepat; Insoo Hyun; Nuria Montserrat
Journal:  Nat Mater       Date:  2020-11-16       Impact factor: 43.841

3.  Histone-GFP fusion protein enables sensitive analysis of chromosome dynamics in living mammalian cells.

Authors:  T Kanda; K F Sullivan; G M Wahl
Journal:  Curr Biol       Date:  1998-03-26       Impact factor: 10.834

4.  Live cell analysis of G protein beta5 complex formation, function, and targeting.

Authors:  Evan A Yost; Stacy M Mervine; Jonathan L Sabo; Thomas R Hynes; Catherine H Berlot
Journal:  Mol Pharmacol       Date:  2007-06-27       Impact factor: 4.436

Review 5.  Deep learning for cellular image analysis.

Authors:  Erick Moen; Dylan Bannon; Takamasa Kudo; William Graf; Markus Covert; David Van Valen
Journal:  Nat Methods       Date:  2019-05-27       Impact factor: 28.547

6.  Evaluation of variability in human kidney organoids.

Authors:  Belinda Phipson; Pei X Er; Alexander N Combes; Thomas A Forbes; Sara E Howden; Luke Zappia; Hsan-Jan Yen; Kynan T Lawlor; Lorna J Hale; Jane Sun; Ernst Wolvetang; Minoru Takasato; Alicia Oshlack; Melissa H Little
Journal:  Nat Methods       Date:  2018-12-20       Impact factor: 28.547

Review 7.  Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.

Authors:  Filippo Piccinini; Tamas Balassa; Antonella Carbonaro; Akos Diosdi; Timea Toth; Nikita Moshkov; Ervin A Tasnadi; Peter Horvath
Journal:  Comput Struct Biotechnol J       Date:  2020-06-03       Impact factor: 7.271

Review 8.  Past, Present, and Future of Brain Organoid Technology.

Authors:  Bonsang Koo; Baekgyu Choi; Hoewon Park; Ki-Jun Yoon
Journal:  Mol Cells       Date:  2019-09-30       Impact factor: 5.034

9.  OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks.

Authors:  Timothy Kassis; Victor Hernandez-Gordillo; Ronit Langer; Linda G Griffith
Journal:  Sci Rep       Date:  2019-08-28       Impact factor: 4.379

10.  Multiscale 3D phenotyping of human cerebral organoids.

Authors:  Alexandre Albanese; Justin M Swaney; Dae Hee Yun; Nicholas B Evans; Jenna M Antonucci; Silvia Velasco; Chang Ho Sohn; Paola Arlotta; Lee Gehrke; Kwanghun Chung
Journal:  Sci Rep       Date:  2020-12-08       Impact factor: 4.379

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