Advances in Spheroid and Organoid Engineering and Applications
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Advances in three-dimensional (3D) tissue culture are allowing biologists to study cell growth and interactions in a way that has never been possible with conventional two-dimensional (2D) tissue culture. Cell lines grown as 2D sheets have laid the foundation for much of our current knowledge about different cells and tissues and are used for a wide range of applications – from disease modeling to drug screening. Yet, there are limits to what cells growing in 2D can reveal, and researchers are now looking to push the boundaries of bioengineering to create spheroids and organoids that more robustly reproduce the complexity of healthy and diseased tissue. We look at some recent advances and applications and where the field is heading.
Advantages and limitations of organoids
“There are three key things that define an organoid,” explains Katarina Klett, a PhD student developing intestinal organoids in the laboratory of Professor Sarah Heilshorn at Stanford University. “The first is that it contains cell types found in the native organ, the second is that it mimics the native architecture of the organ in some way, and third is functionality – which for intestinal organoids, might mean maintaining the intestinal barrier integrity.”
By contrast, a spheroid – which is a cluster of cells grown in 3D – is likely to contain a single cell type, often a more immature cell type of a tissue, which once differentiated to a more diverse set of cell types, can be considered an organoid.
There are numerous advantages of using spheroids and organoids over 2D tissue culture. They allow us to study cell-cell interactions and the interplay between cells and their environment. They also enable researchers to reproduce aspects of physiology that cannot be studied in cell lines – such as how cells respond to changes in their environment, like stiffness, to alter their multicellular geometry.
In Klett’s work, they are particularly interested in using organoids to study the different cells of the intestinal architecture – from the stem cell crypt out to the villi where there are terminally differentiating cells. “If I want to study how those cells are interacting with each other in specific locations, this is really challenging to do in a 2D model, we lose that spatial organization that we have in organoids,” says Klett.
Another advantage is that 3D cultures allow the study of what’s happening outside the organoid – how is the tissue interacting with the environment it is growing in? Increasingly, they are also being used to study the effects of treatments by testing them on organoids or spheroids grown from patients’ cells. These are both areas of interest for Heilshorn's lab, but there are limitations to overcome.
One of the biggest limitations is that one of the most used substrates for organoids contains many different proteins, which can make it difficult to tease out what mechanical or signaling factors are driving the biology of organoids. In addition, organoids grown in substrates derived from an animal source, such as from mouse tumors, are unlikely to be approved for clinical applications.
To address this limitation, in Heilshorn’s lab they have developed biomaterials that allow them to carefully tune parameters such as stiffness, stress relaxation rate and concentration of different components, such as the cell-adhesion peptide, arginine-glycine-aspartic acid (RGD). This means they can optimize matrices to best support the growth of the organoids they are interested in. One of these biomaterials is called HELP – or hyaluronic acid and elastin-like protein (ELP).1 “The ELP mimics the native elastin we have in our own bodies, but has been engineered to contain an RGD peptide sequence, allowing it to interact with cells. The sequence also contains lysine groups which are chemically modified to bind to the hyaluronic acid,” explains Klett. The result is an optimized gel that can be used to grow intestinal organoids by finely tuning the gel to best support budding stem cells. As the cells grow, they can remodel the matrix by pulling on the gel’s dynamic covalent bonds and releasing enzymes to degrade the surrounding matrix. This also supports the cells’ ability to differentiate.
Nonetheless, the organoids are still some way from being a fully functioning intestine. “Sometimes we refer to intestinal organoids as a ‘mini-gut’,” says Klett, “and in some ways they are, but I’m not able to feed these organoids a burger and watch them go through the digestive process – it would be beautiful at some point to have some sort of tissue that can process food matter. I think about these limitations every day; it’s also what motivates us.”
Despite their limitations, these intestinal organoids are already being used in a project to help understand how intestinal tissue responds to radiotherapy treatment. “Here at Stanford, I’m fortunate to collaborate with a group who is studying FLASH radiation, where radiation doses are given at faster rates compared with what’s currently used clinically. I’m interested in studying how the intestines regenerate, and organoids provide a really nice lens to be able to study how radiation is impacting human tissue.” By irradiating the organoids and then isolating them into single cells, they hope to determine whether the cells remain viable enough to still form organoids. In future work, using the defined matrix they’ve developed, Klett also hopes to understand how different signaling or mechanical cues influence this regeneration.
Label-Free, Real-Time Live Cell Assays for 3D Organoids
Organoid technologies are increasingly being used as in vitro models of human development and disease as they exhibit structural, morphogenetic and functional properties that recapitulate in vivo pathophysiology. However, techniques to robustly characterize and visualize these models may be limited by time-consuming and laborious processes and third-party software. Download this app note to discover a solution. View App Note
Label-Free, Real-Time Live Cell Assays for 3D Organoids
Organoid technologies are increasingly being used as in vitro models of human development and disease as they exhibit structural, morphogenetic and functional properties that recapitulate in vivo pathophysiology. However, techniques to robustly characterize and visualize these models may be limited by time-consuming and laborious processes and third-party software. Download this app note to discover a solution.View App Note
A set of rules for developing spheroids and organoids
Organoids begin life as spheroids – 3D clusters of cells – before differentiating further. Indeed, 3D spheroids have many similar advantages to organoids in terms of reflecting the complex interaction between cells and their environment and can recapitulate features such as hypoxic regions or necrotic cores within tumors. For this reason, they are increasingly popular models for studying biology and for drug screening.
“Spheroids have been described in the literature for over 50 years,” says Matthew Simpson, Professor of Mathematics at Queensland University. “But a lot of new technology has emerged which means we can look back at existing spheroid methodologies and extract new information.”
Simpson is using novel imaging techniques and labeling methods alongside mathematical modeling to understand more about the behavior of cells within spheroids and how this varies depending on availability of nutrients.
They use a platform called fluorescent ubiquitination cell cycle indicator (FUCCI), which labels where individual cells are in the cell cycle. Using this platform, they can track both individual cells and cell populations over time. “One of the most complicated mathematical modelling approaches we use is stochastic simulation where we keep track of individual cells within the population,” explains Simpson. “We start off with 30,000 cells, about the same number in a spheroid, and we simulate them for 10 days. You end up with a very large population, and we can produce an image from that simulation that looks exactly like a real spheroid image you would see with a microscope.”
In one such study, currently published as a preprint,2 they compared their simulation of the growth of a 4D tumor spheroid with experimental melanoma spheroids, tracked using the FUCCI platform. They found that the resulting data compared well with the experimental data and provided quantitative information about nutrient availability within the spheroid, which is difficult to measure data in standard tumor spheroid experiments.
In other recent work,3,4 they modeled cell populations rather than individual cells, to test whether spheroid growth characteristics matched predictions from classical theoretical models. “Rather than model individual cells, we represent the density of cells depending on where they are in the spheroid, because we know cells at the surface with plenty of oxygen will behave differently to cells at the centre of the spheroid where oxygen is limited,” says Simpson. In one study, they tested whether tumor spheroids seeded with different initial numbers of cells grow to the same size.3 “It’s an extremely simple question, but it had never been tested experimentally. Yet many mathematical models written down decades ago imply that spheroids end up the same regardless of initial cell density.”
They seeded different numbers of melanoma cells – ranging from 2,500 to 10,000 – and found that the spheroids grew to similar sizes, in agreement with previously untested predictions. “We were very happy to see that some really basic assumptions that theoreticians might imagine to be the case actually turn out to be true in the laboratory.
Another study set out to address a major limitation in spheroid biology, which is reproducibility.4 “It’s well known that experiments performed in biology are hard to reproduce, whereas in other areas such as engineering and physics this is not the case because mathematics is used to design experiments,” says Simpson. “If you look in the spheroid literature, the experimental design is erratic – spheroids are grown for different lengths of time, they start with different cell numbers, some measure only the outer radius whereas others also measure the necrotic core.” Simpson and colleagues set out to determine how much data you need to record to get a reliable, reproducible result from a spheroid experiment. “To do this experimentally would be very expensive and time-consuming, but with a mathematical model we know that when someone else solves our mathematical equations, they get precisely the same answer.”
The models can be applied both to academic spheroid research, but also to bring new insights and consistency to spheroid biology being used in clinical and translational settings. “One of the key targets for anticancer drugs is the cell cycle, so the idea that you can watch individual cells go through the cell cycle and then hit them with a drug is very important,” says Simpson. “Then there is individualized treatment, where clinicians take a patient tumor sample, grow the cells into spheroids or organoids and screen different drugs to find the ideal option. If no one knows how long the optimum period is to grow these spheroids, and you have a limited window to grow these models, you don’t want to waste that opportunity, you want to get the right measurements.”
The next step is to start changing parameters in their model and seeing what happens to the spheroid’s growth patterns. This includes factors like oxygen levels, or adding in a drug.
The work has at this stage been limited to spheroids, but modeling organoids is definitely an ambition. “Of course, we’d love to do that, but a mathematician operates a little differently to many scientists. Most scientists want to have everything in their experimental model – all the cells, nutrients and so on. We come at it from a different way and say, let’s try to understand the simplest thing first.”
1. Hunt DR, Klett KC, Mascharak S, et al. Engineered matrices enable the culture of human patient-derived intestinal organoids. Adv Sci. 2021;8(10):2004705. doi: 10.1002/advs.202004705
2. Klowss JJ, Browning AP, Murphy RJ, et al. A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling. bioRxiv. Preprint posted online November 29, 2021. doi: 10.1101/2021.11.28.470300
3. Browning AP, Sharp JA, Murphy RJ, et al. Quantitative analysis of tumour spheroid structure. Elife. 2021;10:e73020. doi: 10.7554/eLife.73020
4. Murphy RJ, Browning AP, Gunasingh G, Haass NK, Simpson MJ. Designing and interpreting 4D tumour spheroid experiments. Commun Biol. 2022;5(1):91. doi: 10.1038/s42003-022-03018-3