Systematically quantifying morphological features reveals constraints on organoid phenotypes

Beck, Lauren E.; Lee, Jasmine; Coté, Christopher; Dunagin, Margaret C.; Lukonin, Ilya; Salla, Nikkita; Chang, Marcello; Hughes, Alex J.; Mornin, Joseph D.; Gartner, Zev J.; Liberali, Prisca; Raj, Arjun

doi:10.1016/j.cels.2022.05.008

Cited by 10 publications

(18 citation statements)

References 26 publications

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“…At the tissue level, we show that cysts can adopt a variable range of shapes beyond being symmetric spheres (Beck et al, 2022; Cerruti et al, 2013) ( Fig. 2A-B , Fig.…”

Section: Discussionmentioning

confidence: 95%

“…In a biomedical context, the possibility of analyzing a very large number of samples is ideal for testing the reproducibility of epithelial organoids cultures and performing detailed comparisons between physiological and pathological conditions. Furthermore, high-throughput drug testing on animal or human epithelial organoids could take advantage of our approach to automatically analyze the effect of every drug against a target disease at the cellular level (Beck et al, 2022; Drost & Clevers, 2018; Lo et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…In this article, we image, process, and analyze whole Madin-Darby Canine Kidney (MDCK) 3D epithelial cell cultures. Despite their simplicity, these cysts have previously been used as a suitable model system to study the establishment of cell polarity and cell junctions (Bryant & Mostov, 2008; Dukes et al, 2011; Martín-Belmonte et al, 2008; Vidal-Quadras et al, 2017; Yonemura, 2014), epithelial morphogenesis and physiology (Alfonso-Pérez et al, 2022; Guo et al, 2008; Herranz & Martín-Belmonte, 2022; Imai et al, 2015; O’Brien et al, 2002; Wells et al, 2013; Yu et al, 2007), tumor progression (Fessenden et al, 2018; Sakurai et al, 2012; Schmeichel & Bissell, 2003; L. Yan et al, 2021), and for exploring the constraints on epithelial tissue morphogenesis (Beck et al, 2022). MDCK cysts have provided a lot of useful information to study more complex systems helping to understand the self-organization in organoids, embryoids and the early stages of mammal development (Beck et al, 2022; Carleton et al, 2022; Metzger et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Román

Gordillo-Vázquez

Franco-Barranco

et al. 2023

Preprint

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Decades of research have not yet fully explained the mechanisms of epithelial self organization and 3D packing. Single-cell analysis of large 3D epithelial libraries is crucial for understanding the assembly and function of whole tissues. Combining 3D epithelial imaging with advanced deep learning segmentation methods is essential for enabling this high-throughput analysis. We introduce CartoCell, a deep learning-based pipeline that uses small datasets to generate accurate labels for hundreds of whole 3D epithelial cysts. Our method detects the realistic morphology of epithelial cells and their contacts in the 3D structure of the tissue. CartoCell enables the quantification of geometric and packing features at the cellular level. Our Single-cell Cartography approach then maps the distribution of these features on 2D plots and 3D surface maps, revealing cell morphology patterns in epithelial cysts.

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“…At the tissue level, we show that cysts can adopt a variable range of shapes beyond being symmetric spheres (Beck et al, 2022; Cerruti et al, 2013) ( Fig. 2A-B , Fig.…”

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Román

Gordillo-Vázquez

Franco-Barranco

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Whereas the CellTiterGlo assay measures net cell viability based on ATP content, increased size of individual organoids could indicate increased proliferation/reduced cell death rates, while a greater number of organoids in response to a treatment may suggest a higher stem cell fraction. However, only a few studies have addressed which phenotypic/morphological parameters of organoids best correlate with the impact of, e.g., drug treatments (Beck et al, 2022; Kim et al, 2020). For each condition, we therefore determined (i) total cell viability using the CellTiterGlo viability assay, and (ii) organoid surface areas using Orgaquant analysis (Kassis et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance in a p53-dependent manner

Stigliani

Ialchina

Yao

et al. 2023

Preprint

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The harsh environments in poorly perfused tumor regions have been proposed to select for traits that may drive cancer aggressiveness. Here, we tested the hypothesis that tumor acidosis interacts with driver mutations to exacerbate cancer hallmarks, including drug resistance, in pancreatic cancer. We gradually adapted mouse organoids from normal pancreatic duct (mN) and early PDAC (mP, with KRAS G12V mutation and +/- p53 knockout ), from pH 7.4 (physiological level) to 6.7, representing acidic tumor niches. Acid adaptation rewired organoid transcriptional activity, increased viability and, strikingly, increased Gemcitabine- and Erlotinib resistance. Importantly, this response only occurred in organoids expressing wild-type p53 and was most pronounced when acid-adapted cells were returned to physiological pH (mimicking increased perfusion or invasion). While the acid adaptation transcriptional change was overall not highly similar to that induced by drug adaptation of the organoids, acid adaptation induced expression of cytidine deaminase (Cda) and ribonucleotide reductase regulatory subunit M2 (Rrm2), both associated with Gemcitabine resistance, and inhibition of these proteins partially restored Gemcitabine sensitivity. Thus, adaptation to the acidic tumor microenvironment increases drug resistance even after cells leave this niche, and this is in part dependent on acid-adaptation-induced transcriptional upregulation of Cda and Rrm2.

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“…Indeed, many breakthroughs over the past decade came from successful differentiations of various organ-specific, multicellular tissues called organoids 2,3 . Nonetheless, despite their consistent cellular profiles, many of these 3D tissue models have random unspecific structures, mostly in a globular shape 4,5 .…”

Section: Introductionmentioning

confidence: 99%

Localization of multiple hydrogels with MultiCUBE platform spatially guides 3D tissue morphogenesis in vitro

Suthiwanich

Hagiwara

2022

Preprint

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Localization of multiple hydrogels is expected to develop the structure of 3D tissue models in a location specific manner. Here, we successfully localize morphogenesis within individual tissues by exposing different hydrogel conditions to different parts of the tissues. We develop a unit-based scaffold with a unique frame design to trap hydrogel solutions inside their designated units. Interestingly, this unit-based scaffold within an optimal range of dimensional size and surface wettability can trap several cubic millimeters of hydrogels. This localization capability enables the spatial organization of hydrogel compositions, growth factors and physical conditions, as well as the position of biological samples (cells, spheroids, reconstituted tissues) relative to each hydrogel compartment. We succeed to localize the branching development of reconstituted human epithelial tissues according to the localized biomolecular and physical cues from hydrogels, regardless of the initial tissue configurations. Unlike 3D-bioprinting or microfluidics, the localization with this unit-based scaffold requires only manual pipetting and handling without any specialized equipment or skills, thus ready to use by researchers from any field. This scaffold-based localization provides a new promising route to spatially control morphogenesis, differentiation, and other developmental processes within organoids or other 3D tissues, resulting in 3D functional models for practical biomedical applications.

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Systematically quantifying morphological features reveals constraints on organoid phenotypes

Cited by 10 publications

References 26 publications

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

CartoCell, a high-content pipeline for 3D image analysis, unveils cell morphology patterns in epithelia

Adaptation to an acid microenvironment promotes pancreatic cancer organoid growth and drug resistance in a p53-dependent manner

Localization of multiple hydrogels with MultiCUBE platform spatially guides 3D tissue morphogenesis in vitro

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