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Transforming the spleen into a liver-like organ in vivo.

ABSTRACT: Regenerating human organs remains an unmet medical challenge. Suitable transplants are scarce, while engineered tissues have a long way to go toward clinical use. Here, we demonstrate a different strategy that successfully transformed an existing, functionally dispensable organ to regenerate another functionally vital one in the body. Specifically, we injected a tumor extract into the mouse spleen to remodel its tissue structure into an immunosuppressive and proregenerative microenvironment. We implanted autologous, allogeneic, or xenogeneic liver cells (either primary or immortalized), which survived and proliferated in the remodeled spleen, without exerting adverse responses. Notably, the allografted primary liver cells exerted typical hepatic functions to rescue the host mice from severe liver damages including 90% hepatectomy. Our approach shows its competence in overcoming the key challenges in tissue regeneration, including insufficient transplants, immune rejection, and poor vascularization. It may be ready for translation into new therapies to regenerate large, complex human tissue/organs.

SUBMITTER: Wang L

PROVIDER: S-EPMC7286668 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Organ volume measurements are a key metric for managing ADPKD (the most common inherited renal disease). However, measuring organ volumes is tedious and involves manually contouring organ outlines on multiple cross-sectional MRI or CT images. The automation of kidney contouring using deep learning has been proposed, as it has small errors compared to manual contouring. Here, a deployed open-source deep learning ADPKD kidney segmentation pipeline is extended to also measure liver and spleen volumes, which are also important. This 2D U-net deep learning approach was developed with radiologist labeled T2-weighted images from 215 ADPKD subjects (70% training = 151, 30% validation = 64). Additional ADPKD subjects were utilized for prospective (n = 30) and external (n = 30) validations for a total of 275 subjects. Image cropping previously optimized for kidneys was included in training but removed for the validation and inference to accommodate the liver which is closer to the image border. An effective algorithm was developed to adjudicate overlap voxels that are labeled as more than one organ. Left kidney, right kidney, liver and spleen labels had average errors of 3%, 7%, 3%, and 1%, respectively, on external validation and 5%, 6%, 5%, and 1% on prospective validation. Dice scores also showed that the deep learning model was close to the radiologist contouring, measuring 0.98, 0.96, 0.97 and 0.96 on external validation and 0.96, 0.96, 0.96 and 0.95 on prospective validation for left kidney, right kidney, liver and spleen, respectively. The time required for manual correction of deep learning segmentation errors was only 19:17 min compared to 33:04 min for manual segmentations, a 42% time saving (p = 0.004). Standard deviation of model assisted segmentations was reduced to 7, 5, 11, 5 mL for right kidney, left kidney, liver and spleen respectively from 14, 10, 55 and 14 mL for manual segmentations. Thus, deep learning reduces the radiologist time required to perform multiorgan segmentations in ADPKD and reduces measurement variability.

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Transforming the spleen into a liver-like organ in vivo.

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