In-silico and in-vitro morphometric analysis of intestinal organoids.
In-silico and in-vitro morphometric analysis of intestinal organoids.
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DOI：
10.1371/journal.pcbi.1011386
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发表时间：
2023-08
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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作者：
类器官提供了一个强大的模型来研究细胞自组织，体外特定组织形态的生长，以及评估潜在的医学疗法。然而，这些系统的内在机制尚未完全理解，这可能导致由于培养条件和所用基底膜提取物的差异而导致类器官的变异性。提高类器官培养的标准化对于其在临床方案中的实施至关重要。开发工具来评估和预测这些系统的行为可能会产生一个更强大和标准化的生物模型，以进行准确的临床研究。在这里，我们开发了一种算法，用于在体外和计算机图像中自动计算肠类器官上的隐窝样结构。此外，我们修改了现有的肠道类器官的二维基于代理的数学模型，以更好地描述系统生理学，并与我们生成的新实验数据相比，评估了其复制萌芽结构的能力。隐窝计数算法被证明是有用的，在接近我们的体外肠类器官培养图像中发现的萌芽结构的平均数量在接种后第3天和第7天。我们对计算机模型的改变保持了产生模拟的潜力，该模拟复制了体外数据第5天和第7天发现的出芽结构的数量。本研究旨在帮助量化关键形态结构，并提供一种方法来比较体外和计算机实验。我们的研究结果可以扩展到以后的三维计算机模型。类器官提供了一个强大的生物模型来研究细胞的自组织，特定组织形态的生长，并评估潜在的医学治疗。然而，在实践中可以观察到许多不同的类器官形状，并且它们产生的机制尚未完全理解。这意味着将类器官培养物用于实验操作仍然是一个挑战，因此要获得最大的益处以了解人类健康和疾病。数学模型为这个问题提供了一个潜在的解决方案，尽管问题仍然是如何使用实验室生长的类器官的数据来正确校准和测试模型，考虑到体外和计算机实验生成的图像的数量和复杂性。在本文中，我们旨在解决这个问题。我们开发的方法可以自动提取关键的形态学特征，从肠道类器官图像的集合，特别是计数萌芽结构，从新的体外实验和计算机模拟生成。实验结果进行了比较，模拟的多尺度数学模型，描述了系统的生理学，并可以复制的萌芽结构，看到实验。
Organoids offer a powerful model to study cellular self-organisation, the growth of specific tissue morphologies in-vitro, and to assess potential medical therapies. However, the intrinsic mechanisms of these systems are not entirely understood yet, which can result in variability of organoids due to differences in culture conditions and basement membrane extracts used. Improving the standardisation of organoid cultures is essential for their implementation in clinical protocols. Developing tools to assess and predict the behaviour of these systems may produce a more robust and standardised biological model to perform accurate clinical studies. Here, we developed an algorithm to automate crypt-like structure counting on intestinal organoids in both in-vitro and in-silico images. In addition, we modified an existing two-dimensional agent-based mathematical model of intestinal organoids to better describe the system physiology, and evaluated its ability to replicate budding structures compared to new experimental data we generated. The crypt-counting algorithm proved useful in approximating the average number of budding structures found in our in-vitro intestinal organoid culture images on days 3 and 7 after seeding. Our changes to the in-silico model maintain the potential to produce simulations that replicate the number of budding structures found on days 5 and 7 of in-vitro data. The present study aims to aid in quantifying key morphological structures and provide a method to compare both in-vitro and in-silico experiments. Our results could be extended later to 3D in-silico models. Organoids offer a powerful biological model to study cellular self-organisation, the growth of specific tissue morphologies, and to assess potential medical therapies. However, many different organoid shapes can be observed in practice, and the mechanisms underlying their generation are not yet fully understood. This means that it is still a challenge to standardise organoid cultures for experimental manipulation, and hence to derive the maximum benefit for understanding human health and disease. Mathematical models offer a potential solution to this problem, although the question remains of how to use data of lab-grown organoids to properly calibrate and test the models, given the number and complexity of the images generated by both in-vitro and in-silico experiments. In this paper we aim to address this question. We develop methods that can automatically extract the key morphological features from collections of intestinal organoids images—specifically, to count budding structures—generated from both new in-vitro experiments and in-silico simulations. Experimental results are compared to simulations of a multiscale mathematical model which describes the system physiology and can replicate the budding structures that are seen experimentally.
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影响因子：
5.4
作者：
Hof L;Moreth T;Koch M;Liebisch T;Kurtz M;Tarnick J;Lissek SM;Verstegen MMA;van der Laan LJW;Huch M;Matthäus F;Stelzer EHK;Pampaloni F
通讯作者：
Pampaloni F
DOI：
10.1242/dev.199611
发表时间：
2021-09-15
期刊：
Development (Cambridge, England)
影响因子：
--
作者：
Gritti N;Lim JL;Anlaş K;Pandya M;Aalderink G;Martínez-Ara G;Trivedi V
通讯作者：
Trivedi V
影响因子：
6.1
作者：
Berger, Emanuel;Magliaro, Chiara;Schwamborn, Jens C.
通讯作者：
Schwamborn, Jens C.
影响因子：
9
作者：
Low JT;Ho GY;Scott M;Tan CW;Whitehead L;Barber K;Yip HYK;Dekkers JF;Hirokawa Y;Silke J;Burgess AW;Strasser A;Putoczki TL;O'Reilly LA
通讯作者：
O'Reilly LA
影响因子：
5.4
作者：
Buske, Peter;Przybilla, Jens;Galle, Joerg
通讯作者：
Galle, Joerg