Organoids: What are They and Why They’re Amazing

I’m willing to bet you’ve never heard of Organoids. No worries if so, they’re a pretty niche part of biomedical sciences. This is such a shame however as they’re having a great impact on research and our world. Organoids are often defined as tissue samples embedded into 3D extracellular matrix (ECM) gels or clonal derivatives of cells grown without mesenchyme and derived from stem cells. Don’t worry if your eyes have already glazed over! Mine did too the first time I read that.

In simpler terms, they are the growing of cells, taken from a patient, in a nutrient rich gel. While the field might seem niche at first, it has a long history and the potential to impact millions.

Before Organoids, most human physiological research relied on in vitro 2D cell culture and animal models (O’Connell & Winter, 2019). These often fail at clinical trials due to oversimplification of or incompatibility with human systems. At the time, cell cultures were limited to growing single layers of cells in a petri dish with none of the 3D complexities of real biological systems. This all changed with Ross Harrison’s “Hanging drop Cell Cultures” in 1906 (Simian and Bissell, 2016). By creating a 2D cell culture and turning the petri dish upside down, gravity would cause the cells to clump together and form a cohesive 3D spheroid of cells that could intermingle and interact like normal. Think about that! 3D structures could form by simply turning them upside down with no other interference. This was the start of research into organoids which offered a new way to look directly at human biology without risking patient or animal lives.

One key breakthrough was the discovery of 3D extracellular matrices first used in 1963 (Simian and Bissell, 2016). The gel provided a medium for natural scaffolding of cells rather than relying on gravity. This allowed for more complex 3D shapes and allowed for more types of organoids that more closely resembled complex real life systems. The gel also aided in providing the cells with nutrients required to keep them alive. Hence the “nutrient rich gel” from the definition above.

Exploration into this new field was rather slow however. The human body is composed of roughly 200 distinct types of cells, each of which specialize in a different function such as blood or nerve cells. Despite this, organoids mainly rely on only two types of cells, pluripotent stem cells and organ-restricted adult stem cells (Bredenoord et al., 2017). Stem cells are unspecified types of cells, yet to differentiate, with the ability to become any type of specified cell. They often serve to repair the human body as they can replace any damaged cells no matter the type. This ability makes them highly valuable to organoids trying to model systems that rely on the cooperation of multiple cell types.

Before the discovery of induced stem cells in 2006 by Dr. Shinya Yamanaka at Kyoto University, organoids were made from pluripotent stem cells as organ-restricted adult stem cells can be hard to access and are restricted to becoming cell types of their tissue of origin most commonly found in embryos (Agarwal et al., 2024). The line of when a zygote becomes a person, however, is blurry, raising many ethical concerns about the desecration of a person’s body. Induced stem cells allow for the change of specialized cells back into stem cells. Organoids can now be created from any tissue sample, entirely limiting the need for embryonic stem cells outside of studying early organ development (Frans Schutgens & Clevers, 2019).

With better ways to develop organoids, scientists quickly realized their potential. Organoids offer unparalleled advancement in disease research and treatment, personalized medicine, and so much more that we simply don’t have time for here.

Image by Alexandra Marshall

Disease Research and Treatment
One pivotal area of applications is understanding the development of diseases and disease treatment in humans. The lack of 3D structure in in vitro models lead to limited complexity and physiological relevance when studying diseases. Faulty predictions based on these studies can lead to treatments failing, with approximately 90% of clinical study drug candidates failing clinical trials and drug approval (Sun et al., 2022).

Organoid models have none of these limitations. In 2013, the first human condition modeled using organoids, cystic fibrosis, was published. The model had “excellent predictive value” and became the first successful personalized treatment method for cystic fibrosis patients (Lancaster and Huch, 2019). The modeling of other diseases using organoids quickly followed from genetic diseases like Polycystic kidney disease and Alagille syndrome, to infectious diseases like Covid-19 and the Zika virus, to neurological diseases like Alzheimer’s and Parkinson’s, to multitudes of cancers, and more (Xu et al., 2018). The ability for organoids to be made of any human system means they can model any disease regardless of which systems they afflict.

Organoids can be also grown co-culturally with bacteria allowing for bacterial infections and the diseases that arise from them to also be modeled and researched with organoids (Schutgens and Clevers, 2020). This has allowed for better understanding of bacteria like Helicobacter Pylori and Salmonella, aiding in detection and cures. As mentioned above, treatments can then be tested on these organoid systems without risking human lives or animal cruelty.

Respiratory Syncytial Virus (RSV), a disease that is being modeled in airway organoids, causes around 66,000–199,000 deaths among children annually (Schutgens & Clevers, 2020). This is just one of the many diseases for which scientists are now better equipped to find viable treatments. Organoids have already saved millions of lives and their potential for future disease treatment has the potential to millions more.

Personalized Medicine
While these treatments work for many people, genetic and biological differences can impact treatment effectiveness and tolerance with some even leading to negative side effects (Breen, 2021). In such situations, personalized medicine that is specialized to fit these differences might be necessary. Personalized medicine, sometimes called precision medicine, challenges the idea that treatments that pass general trials will work for everyone, instead working to construct a treatment plan with a specific patient in mind best suited to their physiology.

The idea of personalized medicine has been around for thousands of years, using one’s family’s history of diseases to diagnose the likelihood of that person having said disease. In the late 1900s the idea of evidence based medicine, the implementation of explicit reasoning based on a specific patient’s data, started being common practice when prescribing a patient (Visvikis-Siest et al., 2020). This opened the door for personalized medicine in modern science (Visvikis-Siest et al., 2020). This data was largely based on patients’ reactions to other treatments and family history however limited its effectiveness. The development of molecular biology and human genome mapping allows for a direct look at a patient’s physiology, aiding medical professionals in determining the biological reasons behind potential reactions to treatments before administration limiting adverse reactions.

Organoids have greatly furthered this field. As mentioned above, organoids are made from a patient’s induced stem cells. This means that the organoid culture into a structure based on the patient’s DNA, containing all the same biomarkers and physiology that the patient would have at that level. Where other treatments could have varying effects from the tested population, organoids allow medical professionals to see the exact effects a treatment plan would have on the patient. The use of stem cells also allows organoids to grow much quicker than other models. This allows for quick and accurate testing of the effects of medication on a patient. One study published in Science involving gastrointestinal cancer therapies found that organoids would correctly detect when drugs would fail 100% of the time, and when the drug would succeed 88% percent of the time (Vlachogiannis et al., 2018). Once again, because of the diversity of systems that organoids can model, organoids can be used to model many different personalized medicine plans allowing for the variety of treatment testing needed to find the most optimal plan.

Conclusion
Organoids have had a monumental, yet widely unknown, impact on a wide variety of disease advancement and treatment developments and personalized medicine. These structures can then be used to model human tissues and organs with much higher accuracy and complexity than previously used non-human or in vitro models.

Scientists use them to model disease development in humans and treatments designed for humans. This allows for a better understanding of the diseases and more effective treatments with fewer errors. They are also used to limit the risk of negative side effects from non-personalized medicine by furthering personalized medicine through the accuracy and speedy testing of many different treatment options.
With continued and careful development of organoids, disease understanding and treatments, personalized medicine and so much more can continue to be improved, saving millions of lives.

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Work Cited:
Agarwal, S., Saha, L., Sohani, A., Karri, U., Gor, R., & Ramalingam, S. (2024). The importance of niche in therapeutic strategies targeting normal and cancer stem cells. Cancer Stem Cells and Signaling Pathways, 15–33. https://doi.org/10.1016/b978-0-443-13212-4.00031-3

Bredenoord, A. L., Clevers, H., & Knoblich, J. A. (2017). Human tissues in a dish: The research and ethical implications of organoid technology. Science, 355(6322). https://doi.org/10.1126/science.aaf9414

Frans Schutgens, & Clevers, H. (2019). Human Organoids: Tools for Understanding Biology and Treating Diseases. Annual Review of Pathology Mechanisms of Disease, 15(1), 211–234. https://doi.org/10.1146/annurev-pathmechdis-012419-032611

Generation and Cultivation of 3D Cell Models | ibidi. (2024). Ibidi. https://ibidi.com/content/1037-generation-and-cultivation-of-3d-cell-models

Lancaster, M. A., & Huch, M. (2019). Disease modelling in human organoids. Disease Models & Mechanisms, 12(7). https://doi.org/10.1242/dmm.039347

O’Connell, L., & Winter, D. C. (2019). Organoids: Past Learning and Future Directions. Stem Cells and Development, 29(5), 281–289.https://doi.org/10.1089/scd.2019.0227

Schutgens, F., & Clevers, H. (2020). Human Organoids: Tools for Understanding Biology and Treating Diseases. Annual Review of Pathology: Mechanisms of Disease, 15(1), 211–234. https://doi.org/10.1146/annurev-pathmechdis-012419-032611

‌Simian, M., & Bissell, M. J. (2016). Organoids: A historical perspective of thinking in three dimensions. The Journal of Cell Biology, 216(1), 31–40. https://doi.org/10.1083/jcb.201610056

Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica B, 12(7), 3049–3062. https://doi.org/10.1016/j.apsb.2022.02.002

Visvikis-Siest, S., Theodoridou, D., Kontoe, M.-S., Kumar, S., & Marschler, M. (2020). Milestones in Personalized Medicine: From the Ancient Time to Nowadays—the Provocation of COVID-19. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.569175

Vlachogiannis, G., Hedayat, S., Vatsiou, A., Jamin, Y., Fernández-Mateos, J., Khan, K., Lampis, A., Eason, K., Huntingford, I., Burke, R., Rata, M., Koh, D.-M., Tunariu, N., Collins, D., Hulkki-Wilson, S., Ragulan, C., Spiteri, I., Moorcraft, S. Y., Chau, I., & Rao, S. (2018). Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science, 359(6378), 920–926. https://doi.org/10.1126/science.aao2774

Xu, H., Jiao, Y., Qin, S., Zhao, W., Chu, Q., & Wu, K. (2018). Organoid technology in disease modelling, drug development, personalized treatment and regeneration medicine. Experimental Hematology and Oncology, 7(1). https://doi.org/10.1186/s40164-018-0122-9

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.