Browsing by Author "Zhang, Xinlian"

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    Expression profiling of the retina of pde6c, a zebrafish model of retinal degeneration
    (Nature Publishing group, 2017-12-12) Zhang, Liyun; Zhang, Xinlian; Zhang, Gaonan; Pang, Chi Pui; Leung, Yuk Fai; Zhang, Mingzhi; Zhong, Wenxuan; Biochemistry and Molecular Biology, School of Medicine
    Retinal degeneration often affects the whole retina even though the disease-causing gene is specifically expressed in the light-sensitive photoreceptors. The molecular basis of the retinal defect can potentially be determined by gene-expression profiling of the whole retina. In this study, we measured the gene-expression profile of retinas microdissected from a zebrafish pde6cw59 (pde6c) mutant. This retinal-degeneration model not only displays cone degeneration caused by a cone-specific mutation, but also other secondary cellular changes starting from 4 days postfertilization (dpf). To capture the underlying molecular changes, we subjected pde6c and wild-type (WT) retinas at 5 dpf/ 120 h postfertilization (hpf) to RNA sequencing (RNA-Seq) on the Illumina HiSeq 2,000 platform. We also validated the RNA-Seq results by Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) of seven phototransduction genes. Our analyses indicate that the RNA-Seq dataset was of high quality, and effectively captured the molecular changes in the whole pde6c retina. This dataset will facilitate the characterization of the molecular defects in the pde6c retina at the initial stage of retinal degeneration.
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    Normalization of large-scale behavioural data collected from zebrafish
    (Public Library of Science, 2019-02-15) Xie, Rui; Zhang, Mengrui; Venkatraman, Prahatha; Zhang, Xinlian; Zhang, Gaonan; Carmer, Robert; Kantola, Skylar A.; Pang, Chi Pui; Ma, Ping; Zhang, Mingzhi; Zhong, Wenxuan; Leung, Yuk Fai; Department of Biochemistry and Molecular Biology, Indiana University School of Medicine
    Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often complex in structure and are subjected to systematic biases, which require new approaches for analysis and normalization. This study addressed the normalization need by establishing an approach based on linear-regression modeling. The model was established using a dataset of visual motor response (VMR) obtained from several strains of wild-type (WT) zebrafish collected at multiple stages of development. The VMR is a locomotor response triggered by drastic light change, and is commonly measured repeatedly from multiple larvae arrayed in 96-well plates. This assay is subjected to several systematic variations. For example, the light emitted by the machine varies slightly from well to well. In addition to the light-intensity variation, biological replication also created batch-batch variation. These systematic variations may result in differences in the VMR and must be normalized. Our normalization approach explicitly modeled the effect of these systematic variations on VMR. It also normalized the activity profiles of different conditions to a common baseline. Our approach is versatile, as it can incorporate different normalization needs as separate factors. The versatility was demonstrated by an integrated normalization of three factors: light-intensity variation, batch-batch variation and baseline. After normalization, new biological insights were revealed from the data. For example, we found larvae of TL strain at 6 days post-fertilization (dpf) responded to light onset much stronger than the 9-dpf larvae, whereas previous analysis without normalization shows that their responses were relatively comparable. By removing systematic variations, our model-based normalization can facilitate downstream statistical comparisons and aid detecting true biological differences in high-throughput studies of neurobehaviour.