For many severe liver diseases, the only effective treatment is liver transplantation. Unfortunately, due to the shortage of available donor organs, or the patient's age, liver transplantation is not available to all patients. Hepatocyte transplantation (HTx) is an alternative, experimental treatment, with limited long term success in humans. A major unsolved question is how can we dynamically monitor and quantify cell transplantation in HTx to help improve HTx and to closely monitor clinical implementation? To answer this question, we combine an innovative molecular and cellular MRI approach, with machine learning and computer vision, to non-invasively quantify cell engraftment and long term engraftment and repopulation (LTER) in the liver following HTx. Using a mouse model that facilitates LTER following HTx, we will use this combined approach to quantify transplanted cells in the liver at Days 1 and 7 after HTx, reflecting the timings of initial cell delivery (Day 1) and actual cellular engraftment in the tissue (Day 7). Then, we will measure LTER of these cells in the liver 30 - 90 days post-transplant, making innovative use of Eovist, an FDA approved MRI contrast agent specific for healthy hepatocytes. These experiments will evaluate mouse donor cells, as well as pig primary hepatocytes and pig embryonic stem cell-derived hepatocyte cell line. This transformative work will be the first study to achieve this level of quantification wit molecular and cellular MRI of regenerative medicine, in any animal model. Additionally, we will test pattern recognition algorithms aimed at predicting the outcome of LTER, at an early stage. The capability to predict LTER outcome would be paradigm shifting as it would enable physicians to consider additional HTx regimens or second line treatments if HTx fails. This is seldom possible. Though this project will be developed on mice, clinical translation of the imaging protocol would be straightforward because the exact imaging and data analysis scheme that we use to measure HTx in mouse, can be used to measure HTx in humans. Eovist is FDA approved for use in humans with liver disease, and MRI-based cell tracking of iron labeled cells is in clinical trials. Our preliminary data strongly suggests that MRI and data analysis can discriminate single cells at 200 m resolution, meaning the MRI could likely be performed on any high field human MRI system. Additionally, the discovery that a stem cell-derived hepatocyte achieved even partial LTER would be extremely encouraging for exploring human and/or pig stem cell-derived hepatocytes for human use, because these cells can potentially alleviate the crucial issue of poor cell supply, similar to progress seen in pig islet transplant. The proposed research takes a multidisciplinary approach with expertise in hepatocyte transplant and biology, molecular imaging, machine learning/computer vision, and mouse liver disease models. Collaboration among the researchers is ongoing with extensive preliminary data across all aspects of the proposed work.
The development of hepatocyte transplantation as an alternative to whole liver transplant requires non-invasive methodologies for monitoring the fate of transplanted cells. This project proposes new molecular and cellular MRI methods and analysis protocols to quantify hepatocyte transplantation and long term engraftment and repopulation. Successful completion of this project opens up the near term possibility for clinical utility.
|Afridi, Muhammad Jamal; Ross, Arun; Liu, Xiaoming et al. (2017) Intelligent and automatic in vivo detection and quantification of transplanted cells in MRI. Magn Reson Med 78:1991-2002|