Breed-Associated Differences in Differential Gene Expression Following Immunotherapy-Based Treatment of Canine High-Grade Glioma by Susan A. Arnold, Walter C. Low, Grace Elizabeth Pluhar

“…Given this breed-specific observation, we hypothesized that within the French bulldog breed, there are key transcriptomic differences when compared to other breeds, and that their tumors change differently in response to immunotherapy. …”
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Multi-omics analysis of flavor differences in pectoral muscles between Wuqin 10 duck and Cherry valley duck by Ping Gong, Shengqiang Ye, Xing Chen, Lixia Wang, Yunguo Qian, Mingli Zhai, Yu Yang

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Identification of reference genes via real-time quantitative PCR for investigation of the transcriptomic basis of the squalene biosynthesis in different tissues on olives under drought stress by Haiyang Chi, Shasha Bai, Chenxiao Chen, Yuying Bao, Xiaoxin Qu, Shitao Sun, Jiangpeng Pan, Xiushi Yang, Chunsheng Hou, Yanchun Deng

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Analysis of the Differences Among <i>Camellia oleifera</i> Grafting Combinations in Its Healing Process by Zhilong He, Ying Zhang, Chengfeng Xun, Zhen Zhang, Yushen Ma, Xin Wei, Zhentao Wan, Rui Wang

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Intravenous leiomyomatosis is inclined to a solid entity different from uterine leiomyoma based on RNA‐seq analysis with RT‐qPCR validation by Wenze Wang, Yanfeng Wang, Fei Chen, Ming Zhang, Rujing Jia, Xingrong Liu, Chaoji Zhang, Jiang Shao, Ninghai Cheng, Guotao Ma, Zhaohui Zhu, Qi Miao, Zhiyong Liang

“…CXCL8, LIF, CDKN2A, BCL2A1, COL2A1, IGF1, and HMGA2 were also differently expressed between IVL and LM groups, but showed no statistical difference (P = .2409, P = .1773, P = .0596, P = .2737, P = .1553, P = .1045, and P = .1847, respectively) due to the large differences among individuals. …”
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Multi-omics profiling of hepatic macromolecules in laying hens with difference feed intake: Mechanistic insights into antioxidant capacity modulation by Zhouyang Gao, Yuchen Jie, Yuhui Qin, Xianli Liu, Chuanwei Zheng, Guoming Ma, Yan Wu, Songchuan Fu, Xiangrui Meng, Guiyun Xu

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Comprehensive single-cell transcriptomic reveals different destinies of melanocytes and dynamic changes of immune microenvironment in a psychological stress-induced leukoderma and leukotrichia mouse model by Xuechen Cao, Yongkai Yu, Hang Yao, Yujie Zheng, Jiawei Lu, Yifei Feng, Tongxin Pei, Ziyu Li, Ming Lu, Yan Lu

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Senescence-associated alterations in histone H3 modifications, HP1 alpha levels and distribution, and in the transcriptome of vascular smooth muscle cells in different types of senescence by Agnieszka Gadecka, Marta Koblowska, Helena Kossowska, Roksana Iwanicka-Nowicka, Dorota Janiszewska, Grażyna Mosieniak, Krzysztof Bojakowski, Krzysztof Goryca, Anna Bielak-Zmijewska

“…We can conclude that certain similar alterations occur in senescent VSMCs ex vivo, although inter-individual differences usually obscure them. Our results clearly showed that differences existed not only between young and senescent cells but also between SIPS and RS ones. …”
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Comparative transcriptomic analysis of Rosa sterilis inflorescence branches with different trichome types reveals an R3-MYB transcription factor that negatively regulates trichome formation by Wen-tao MA, Min LU, Hua-ming AN, Yin YI

“…Many of R. sterilis’ organs are covered with different types of trichomes or prickles that directly affect fruit appearance and plant management. …”
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Transcriptomics resources of human tissues and organs by Mathias Uhlén, Björn M Hallström, Cecilia Lindskog, Adil Mardinoglu, Fredrik Pontén, Jens Nielsen

“…Here, we review publicly available human transcriptome resources and discuss body‐wide data from independent genome‐wide transcriptome analyses of different tissues. …”
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Transcriptome analysis of the aortic coarctation area by Rada Ellegård, Torsten Malm, Constance G. Weismann, Eva Fernlund, Anneli Nordén Björnlert, Hanna Klang Årstrand, Katarina Ellnebo-Svedlund, Cecilia Gunnarsson

“…Conclusions: The ongoing processes in the coarctation area are influenced by the age and sex of the patient, and possibly by differences in etiology between different patients. …”
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Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma by Kai Zhang, Kai Zhang, Luyao Wang, Huili Chen, Lili Deng, Mengling Hu, Ziqiang Wang, Yiluo Xie, Chaoqun Lian, Xiaojing Wang, Xiaojing Wang, Jing Zhang

“…In addition, patients of different subtypes showed significant differences in genomic mutation and functional enrichment and exhibited different anti-tumor immune profiles. …”
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RNA-sequencing demonstrates transcriptional differences between human vocal fold fibroblasts and myofibroblasts by Michelle Bretl, Lingxin Cheng, Christina Kendziorski, Susan L. Thibeault

“…Conclusions Transcriptomics of normal fibroblasts differ from myofibroblasts, including from those retrieved from scar and those treated with TGF-β1. …”
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Whole-transcriptome analysis reveals the characteristics of intramuscular fat circRNA expression and its associated network in grazing yaks of different months of age under cold stress by Yaqian Liu, Yonggang Sun, Yincang Han, Weiqin Ding, Shengwei Jin, Jianyu Chen, Fajie Gou

“…The results may provide a reference for further investigation of the regulatory network of intramuscular fat deposition in yak.How to cite: Liu Y, Sun Y, Han Y, et al. Whole-transcriptome analysis reveals the characteristics of intramuscular fat circRNA expression and its associated network in grazing yaks of different months of age under cold stress. …”
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Single‐cell transcriptomic atlas of different endometriosis indicating that an interaction between endometriosis‐associated mesothelial cells (EAMCs) and ectopic stromal cells may influence progesterone resistance by Shengdi Hou, Jing Zhang, Zhiqiang Zhang, Hong Qu, Shuhong Li, Ying Jiang, Chongdong Liu

“…However, there is limited literature focusing on the differences among these types. In this study, we employed single‐cell RNA sequencing (scRNA‐seq) to profile the transcriptome of each type using surgical biopsy samples obtained from six patients. …”
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Research on the pathogen causing root rot on Phyllanthus emblica from the perspectives of identification, bionomics, fungicide sensitivity assay, and transcriptome analysis under different pH stress by Yiming Zhang, Minyan Song, Minyan Song, Yonghong He, Yehua Si

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Expression Profiles of Housekeeping Genes and Tissue-Specific Genes in Different Tissues of Chinese Sturgeon (<i>Acipenser sinensis</i>) by Yanping Li, Yunyun Lv, Peilin Cheng, Ying Jiang, Cao Deng, Yongming Wang, Zhengyong Wen, Jiang Xie, Jieming Chen, Qiong Shi, Hao Du

“…To comprehensively characterize the expression profiles of genes and their associated biological functions across different tissues, we performed a transcriptome-scale gene expression analysis, focusing on housekeeping genes (HKGs), tissue-specific genes (TSGs), and co-expressed gene modules in various tissues. …”
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Multi-omics analysis of Taiwanofungus gaoligongensis: effects of different cultivation methods on secondary metabolites by Tingwen He, Tingwen He, Tingwen He, Xiaolong Yuan, Liangjun Xiao, Tanggeran Hu, Yi Wang, Xiaolei Zhao, Xiaolei Zhao, Xiaolei Zhao, Lu Li, Lu Li, Chengbo Peng, Chengbo Peng, Chengbo Peng, Hongling Zhang, Hongling Zhang, Hongling Zhang, Yuan Zheng, Yuan Zheng, Yuan Zheng, Yuan Zheng

“…In addition, the levels of steroids, phenolic compounds, and flavonoids were also significantly increased in NZJB and XZJB. Transcriptome analysis revealed significant differences in the expression of genes involved in the biosynthesis of antcins and antrodin C across the different cultivation methods. …”
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The interictal transcriptomic map of migraine without aura by Peter Petschner, Sahel Kumar, Duc A. Nguyen, Dora Torok, Zsofia Gal, Daniel Baksa, Kinga Gecse, Gyongyi Kokonyei, Hiroshi Mamitsuka, Gabriella Juhasz

“…Subsequent refining and functional analyses were performed, including: 1) testing additional patient factors, 2) running genetic association analysis on migraine in the UK Biobank (UKB) and our cohort, and 3) predicting drug binding with AutoDock Vina and machine learning to proteins of transcriptomic changes. Results Difference in CYP26B1 was identified as key alteration in migraine. …”
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Deterministic patterns in single-cell transcriptomic data by Zhixing Cao, Yiling Wang, Ramon Grima

“…The theory precisely predicts the patterns in data from multiplexed error-robust fluorescence in situ hybridization and five different types of single-cell sequencing platforms.…”
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