Scientists are designing new methods in their microfluidic technology to produce optimized platforms for tissue and disease modeling

A team of scientists, led by Xiling Shen, Ph.D., scientific director and professor at the Terasaki Institute for Biomedical Innovation (TIBI), has reached new levels in the development of patient models. They developed improved methods for generating micro-organospheres (MOS) and showed that these MOS have superior capabilities for a variety of clinical uses. As documented in a recent post 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, which is not possible with conventional patient-derived models.

Dr. Shen’s team has developed emulsion microfluidic technology to create MOS, tiny, nanoliter-sized basement membrane extract (BME) droplets composed of mixtures of tissue cells that can be generated at a rate quickly from an automated device. Once the droplets are created, the excess oil is removed by an innovative membrane demulsification process, leaving behind thousands of uniformly sized, viscous droplets containing tiny 3D tissue structures.

The team then demonstrated unique MOS capabilities and functionality in several novel experiments. They were able to show that MOS could be created from a variety of different tissue sources and that the resulting MOS retained histopathological morphology, the ability for differentiation and gene expression, and the ability to be frozen and under -grown, 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 removal or suspension of cells from its surrounding BME scaffold, thus recapitulating the process of viral infection of the host tissue. Dr. Shen’s team was able to create a MOS atlas of human respiratory and digestive tissue from autopsies of patients and infect them with SARS-COV-2 viruses, followed by drug screening to identify drugs that block viral infection and replication in these tissues.

MOS also provides a unique platform to study and develop immune cell therapy. Within the natural diffusion limit of vascularized tissues, tumor-derived MOS allowed sufficient penetration of therapeutic immune T cells such as CAR-Ts, allowing a novel T cell potency assay to assess tumor destruction by modified T cells. Such a model would be very useful for studying tumor reactivity and developing anti-tumor immune cell therapies.

MOS could be further integrated with deep learning imaging analysis for rapid drug screening of small and heterogeneous clinical tumor biopsies. In addition, the algorithm was able to distinguish the effects of cytotoxic drugs from cytostatic effects and drug-resistant clones that will give rise to a later relapse. This breakthrough capability will pave the way for the use of MOS in the clinic to inform treatment decisions.

“Dr. Shen and his team continue to refine and improve MOS technology and highlight its versatility, not only as a physiological model for screening potential personalized treatments, but also for studies of diseases and various other apps,” Ali said. Khademhosseini, Ph.D., Director and CEO of TIBI. “This seems to be the wave of the future for precision medicine. »

The 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, Grecia O. Rivera, Ravi 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.

Leave a Comment