A team of scientists, led by Xiling Shen, Ph.D., Chief Scientific Officer and Professor at the Terasaki Institute for Biomedical Innovation (TIBI), has reached new heights in patient model development. They have developed improved methods for the generation of micro-organospheres (MOS) and have shown that these MOS have superior capabilities for a variety of clinical uses. As documented in a recent publication in Stem Cell Reports, Their MOS can be used as patient avatars for studies involving direct viral infection, immune cell penetration, and high-throughput therapeutic drug screening, something not achievable with conventional patient-derived models.

The team of Dr. Shen has developed emulsion microfluidic technology for creating MOS, nanoliter-sized droplets of basement membrane extract (BME) composed of tissue cell mixtures, which can be generated at a rapid rate by an automated device. Once the droplets are formed, excess oil is removed by an innovative membrane demulsification process, leaving behind thousands of viscous, uniformly sized droplets that contain tiny 3D tissue structures.

The team went on to demonstrate the unique capabilities and features of MOS in several first-of-its-kind experiments. They were able to show that MOS could be generated from a variety of different tissue sources and the resultant MOS had retention of histopathological morphology, the ability to differentiate and gene expression, and the ability to be frozen and sub-cultured, as in conventional organoids. . .

Experiments were conducted to test the ability to infect MOS with viruses. Unlike conventional organoids, MOS can be directly infected with viruses without removing and suspending cells from its surrounding BME scaffold, thus recapitulating the process of viral infection of the host tissue. The team of Dr. Shen was able to create a MOS atlas of human respiratory and digestive tissues from patient autopsies and infect them with SARS-COV-2 viruses, followed by drug screening to identify drugs that block viral infection and replication within those tissues.

MOS also provides a unique platform for the study and development of immune cell therapy. Within the natural diffusion limit of vascularized tissue, tumor-derived MOS allowed sufficient penetration by immunotherapeutic T cells such as CAR-T, enabling a novel assay of T cell potency to assess tumor killing by T cells. engineered. Such a model would be very useful in the investigation of tumor response and in the development of anti-tumor immune cell therapies.

MOS can be further integrated with deep learning image analysis for rapid drug testing of clinical biopsies of small and heterogeneous tumors. Additionally, the algorithm was able to distinguish between cytotoxic and cytostatic effects of drugs and drug-resistant clones that will cause subsequent relapse. This groundbreaking capability will pave the way for MOS to be used in the clinic to inform therapeutic decisions.

“Dr. Shen and his team continue to refine and improve the MOS technology and demonstrate its versatility, not only as a physiological model for screening potential personalized treatments, but also for disease studies and a variety of applications of others,” said Ali. Khademhosseini, Ph.D., Director and CEO of TIBI. “It appears to be the wave of the future for precision medicine.”

Authors are: Zhaohui Wang, Matteo Boretto2, Rosemary Millen, Naveen Natesh, Elena S. Reckzeh, Carolyn Hsu, Marcos Negrete, Haipei Yao, William Quayle, Brook E. Heaton, Alfred T. Harding, Else Driehuis, Joep Beumer, Gre Rivera , Ravian L van Ineveld, Donald Gex, Jessica DeVilla, Daisong Wang, Jens Puschhof, Maarten H. Geurts, Shree Bose, Athena Yeung, Cait Hamele, Amber Smith, Eric Bankaitis, Kun Xiang, Shengli Ding, Daniel Nelson, Daniel Delubac , Anne Rios, Ralph Abi-Hachem1, David Jang, Bradley J. Goldstein, Carolyn Glass, Nicholas S. Heaton, David Hsu, Hans Clevers, Xiling Shen.

This work was supported by funding from the National Institutes of Health (R35GM122465, U01CA217514, U01CA214300) and the Duke Woo Center for Big Data and Precision Health.

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