Retinoic acid enhances expression of neural specific genes in Sca-1^+ cells of mouse fetal liver through activating protein kinase C

BACKGROUND： Interstitial stem cell is charactenzed by multiple differentiations, and retinoic acid （RA） can induce differentiation of stromal cells into nerve tissue cells in fetal liver of mice, so, its signal transduction pathway should be discussed to trigger differentiation. OBJECTIVE ： To study the effect of RA on expression of neural specific gene and its signal transduction in fetal liver of mice.DESIGN ： Paired controlled study on the basis of cell.SETTING ： Institute of Hematology, Medical College of Jinan University.MATERIALS： The experiment was completed in the Institute of Hematology, Medical College of Jinan University from April to December 2005. C57BL/6 mice, of clean grade, aged 8-10 weeks, weighting 20-35 g, 10 females and 4 males, were selected in this study.METHODS： Sca-1^＋ cells in fetal liver were prepared with MACS kit and cultured with DMEM ＋ 10% fetal bovine serum （FBS）. On the fourth day, it was added with or without protein kinase C （PKC） inhibitor chelerythrine chloride （3μmol/L） and 5×10^-7 mol/L RA for 24 hours, and then incubated in serum-free medium for 5 days. Expressions of genes were assayed by Westem blotting and semi-quantitative RT-PCR.MAIN OUTCOME MEASURES ： Expression of neural specific gene NF-L, NF-H, BF-1 and TH.RESULTS： Expression of neural specific gene NF-L, NF-H, BF-1 and TH was significantly increased after treatment with RA and they were increased 5.06, 5.15, 4.63 and 3.33 times, respectively. However, chelerythrine chloride could inhibit expression of neural specific gene NF-L, NF-H, BF-1 and TH induced by RA.CONCLUSION ： RA can promote the expression of neural specific genes in Sca-1^＋ cells of fetal liver, and its pathway may be related to PKC.

Reduction of OsFLW7 expression enhanced leaf area and grain production in rice

Increasing rice production is important to ensure food security in China. Exploring yield potential and identifying genes beneficial to yield are important goals in the modern rice breeding. Generally, controlling leaf morphology, increasing photosynthesis efficiency and modulating the ＂sink-source＂ relationship can pro- mote the breeding of high-yield rice as well as other cereal crops . The morphology of the leaf includes length, width and degree of curl after its emergence from the meristem and is determined by the establishment of polariW along the adaxial-abaxial, ventral-dorsal and medial-lateral axes and regulated at genetic, hormonal and environmental levels . Rice leaf morphology varies widely among rice subspecies and cultivars. Quantitative trait loci （QTLs） affecting leaf shape commonly have pleiotropic effects on rice yield , which are more useful than mutant genes for studying the molecular mechanism of leaf shape regulation and their application to rice breeding.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics(ST)is an emerging technology to study in situ gene expression patterns at the whole-genome scale.Currently,ST data analysis is still complicated by high technical noises and low resolution.In addition to the transcriptomic data,matched histopathological images are usually generated for the same tissue sample along the ST experiment.The matched high-resolution histopathological images provide complementary cellular phenotypical information,providing an opportunity to mitigate the noises in ST data.We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST(TIST),which enables the identification of spatial clusters(SCs)and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images.TIST devises a histopathological feature extraction method based on Markov random field(MRF)to learn the cellular features from histopathological images,and integrates them with the transcriptomic data and location information as a network,termed TIST-net.Based on TIST-net,SCs are identified by a random walk-based strategy,and gene expression patterns are enhanced by neighborhood smoothing.We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods.Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios.TIST is available at http://lifeome.net/software/tist/and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.