Increasing evidence suggests the consensus that direct in vivo application of induced pluripotent stem cells (iPSCs) is infeasible may not be true.Methods: Teratoma formation and fate were examined in 53 normal and disease conditions involving brain, lung, liver, kidney, islet, skin, hind limb, and arteries.Results: Using classic teratoma generation assays, which require iPSCs to be congregated and confined, all mouse, human, and individualized autologous monkey iPSCs tested formed teratoma, while iPSC-derived cells did not. Intravenously or topically-disseminated iPSCs did not form teratomas with doses up to 2.5×108 iPSCs/kg and observation times up to 18 months, regardless of host tissue type; autologous, syngeneic, or immune-deficient host animals; presence or absence of disease; disease type; iPSC induction method; commercial or self-induced iPSCs; mouse, human, or monkey iPSCs; frequency of delivery; and sex. Matrigel-confined, but not PBS-suspended, syngeneic iPSCs delivered into the peritoneal cavity or renal capsule formed teratomas. Intravenously administered iPSCs were therapeutic with a dose as low as 5×106/kg and some iPSCs differentiated into somatic cells in injured organs. Disseminated iPSCs trafficked into injured tissue and survived significantly longer in injured than uninjured organs. In disease-free animals, no intravenously administered cell differentiated into an unwanted long-lasting cell or survived as a quiescent stem cell. In coculture, the stem cell medium and dominant cell-type status were critical for iPSCs to form cell masses.Conclusion: Teratoma can be easily and completely avoided by disseminating the cells. Direct in vivo iPSC application is feasible and can be safe.

Endoscopic surgery is increasingly used for early gastric cancer (EGC) treatment worldwide, and lymph node metastasis remains the most important risk factor for endoscopic surgery in EGC patients. Olfactomedin 4 (OLFM4) is mainly expressed in the digestive system and upregulated in several types of tumors. However, the role of OLFM4 in EGC has not been explored. We evaluated OLFM4 expression by immunohistochemical staining in 105 patients with EGC who underwent gastrectomy. The clinicopathological factors and OLFM4 expression were co-analyzed to predict lymph node metastasis in EGC. The metastatic mechanism of OLFM4 in gastric cancer was also investigated. We found that OLFM4 was upregulated in EGC tumor sections, and relatively low expression of OLFM4 was observed in patients with lymph node metastasis. OLFM4 expression as well as tumor size and differentiation were identified as independent factors, which could be co-analyzed to generate a better model for predicting lymph node metastasis in EGC patients. In vitro studies revealed that knockdown of OLFM4 promoted the migration of gastric cancer cells through activating the NF-κB/interleukin-8 axis. Negative correlation between OLFM4 and interleukin-8 expression was also observed in EGC tumor samples. Our study implies that OLFM4 expression is a potential predictor of lymph node metastasis in EGC, and combing OLFM4 with tumor size and differentiation could better stratify EGC patients with different risks of lymph node metastasis.

The dissection of the genetic architecture of quantitative traits, including the number and locations of quantitative trait loci (QTL) and their main and epistatic effects, has been an important topic in current QTL mapping. We extend the Bayesian model selection framework for mapping multiple epistatic QTL affecting continuous traits to dynamic traits in experimental crosses. The extension inherits the efficiency of Bayesian model selection and the flexibility of the Legendre polynomial model fitting to the change in genetic and environmental effects with time. We illustrate the proposed method by simultaneously detecting the main and epistatic QTLs for the growth of leaf age in a doubled-haploid population of rice. The behavior and performance of the method are also shown by computer simulation experiments. The results show that our method can more quickly identify interacting QTLs for dynamic traits in the models with many numbers of genetic effects, enhancing our understanding of genetic architecture for dynamic traits. Our proposed method can be treated as a general form of mapping QTL for continuous quantitative traits, being easier to extend to multiple traits and to a single trait with repeat records.