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Jo, S.-J.

Publications and source records attributed to Jo, S.-J..

3 recordsLinked to original sources

Development of a low-dose PBMC humanized mouse model using CD47;Rag2;IL2rγ triple KO mice: Enhanced leukocyte reconstitution and extended experimental window

Humanized mice (hu-mice), which recapitulate the human immune system, have become increasingly important for preclinical immunotherapy studies. Among these models, the human peripheral blood mononuclear cells (PBMC)-engrafted hu-mice model is the simplest and fastest. However, its utility is hindered by the development of lethal graft-versus-host disease (GvHD) and the insufficient reconstitution of human leukocytes. To address these limitations, we developed PBMC hu-mice models using a novel strain, NOD-CD47nullRag2nullIL-2r{gamma}null (RTKO) focusing on the immunological defects of the NOD strain and the immunotolerance provided by CD47 deficiency. Six-week-old female NOD-Rag2nullIL-2r{gamma}null (RID) and RTKO mice were intravenously injected with three different PBMC doses (3x106, 5x106, and 1x107 cells). At standard doses (5x106 and 1x107 cells), RTKO mice exhibited enhanced engraftment of human leukocytes, though GvHD was more severe compared to the RID strain, resulting in a limited experimental window. However, in a subsequent trial using a lower dose of PBMCs (3 x 106 cells), RTKO mice demonstrated notable advantages, including stable reconstitution of human leukocytes, milder GvHD symptoms without life-threatening lesions, and a markedly prolonged experimental window. Considering the difficulties in generating hematopoietic stem cell (HSC)-engrafted hu-mice, the extended experimental window provided by this model, which is comparable to HSC hu-mice, is a significant improvement. Moreover, the radiation tolerance conferred by the Rag gene mutation in this model offers another advantage for radiotherapy research. Consequently, the low-dose PBMC RTKO model serves as a versatile and valuable platform for a broad spectrum of immunotherapy studies, especially in the field of immuno-oncology.

cancer biology↗

Central role of Prominin-1 in lipid rafts during liver regeneration

Prominin-1 (PROM1), a lipid raft protein, is required for maintaining cancer stem cell properties in hepatocarcinoma cell lines, but its physiological roles in the liver have not been well studied. Here, we investigated the role of PROM1 in lipid rafts with a precise molecular mechanism during liver regeneration. We found that the expression of PROM1 increased during liver regeneration after 2/3 partial hepatectomy (PHx) or CCl4 injection. Interestingly, hepatocyte proliferation and liver regeneration were attenuated in liver-specific Prom1 knockout (Prom1LKO) mice compared to wild-type (Prom1f/f) mice. Detailed mechanistic studies revealed that PROM1 interacted with the interleukin-6 signal transducer glycoprotein 130 (GP130) and confined GP130 to lipid rafts so that STAT3 signaling by IL-6 was effectively activated. Moreover, the overexpression of the glycosylphosphatidylinsositol (GPI)-anchored first extracellular domain of PROM1 (PROM1GPI-EX1), which is a domain that binds to GP130, rescued the proliferation of hepatocytes and liver regeneration in Prom1LKO mice. PROM1 is upregulated in hepatocytes during liver regeneration, and upregulated PROM1 recruits GP130 into lipid rafts and activates the IL6-GP130-STAT3 axis. Thus, we conclude that PROM1 plays an important role in lipid rafts during liver regeneration and might be a promising target for therapeutic applications of liver transplantation.

cell biology↗

Holographic deep learning for rapid optical screening of anthrax spores

Establishing early warning systems for anthrax attacks is crucial in biodefense. Here we present an optical method for rapid screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and sub-genus specificity. The unique representation learning capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate diagnosis of pathogens, and facilitate exciting new applications.

microbiology↗