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Prokaryotic and eukaryotic promoters identification based on residual network transfer learning. Bioprocess Biosyst Eng 2022; 45:955-967. [DOI: 10.1007/s00449-022-02716-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/27/2022] [Indexed: 11/26/2022]
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Johnson TS, Xiang S, Helm BR, Abrams ZB, Neidecker P, Machiraju R, Zhang Y, Huang K, Zhang J. Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing. Sci Rep 2020; 10:18014. [PMID: 33093481 PMCID: PMC7582925 DOI: 10.1038/s41598-020-74917-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 09/16/2020] [Indexed: 12/20/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer's brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.
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Affiliation(s)
- Travis S Johnson
- Department of Biomedical Informatics, The Ohio State University, Lincoln Tower 250, 1800 Cannon Dr., Columbus, OH, 43210, USA
- Department of Medicine, Indiana University School of Medicine, Emerson Hall 305, 545 Barnhill Dr., Indianapolis, IN, 46202, USA
- Department of Biostatistics, Indiana University School of Medicine, HITS 3000, 410 W. 10th St., Indianapolis, IN, 46202, USA
| | - Shunian Xiang
- Department of Medicine, Indiana University School of Medicine, Emerson Hall 305, 545 Barnhill Dr., Indianapolis, IN, 46202, USA
| | - Bryan R Helm
- Department of Medicine, Indiana University School of Medicine, Emerson Hall 305, 545 Barnhill Dr., Indianapolis, IN, 46202, USA
| | - Zachary B Abrams
- Department of Biomedical Informatics, The Ohio State University, Lincoln Tower 250, 1800 Cannon Dr., Columbus, OH, 43210, USA
| | - Peter Neidecker
- Department of Mathematics, The Ohio State University, Math Tower 100, 231 West 18th Ave., Columbus, OH, 43210, USA
| | - Raghu Machiraju
- Department of Computer Science and Engineering, The Ohio State University, Dreese Laboratories 779, 2015 Neil Ave., Columbus, OH, 43210, USA
| | - Yan Zhang
- Department of Biomedical Informatics, The Ohio State University, Lincoln Tower 250, 1800 Cannon Dr., Columbus, OH, 43210, USA
| | - Kun Huang
- Department of Medicine, Indiana University School of Medicine, Emerson Hall 305, 545 Barnhill Dr., Indianapolis, IN, 46202, USA.
- Regenstrief Institute, 335, 1101 W. 10th St., Indianapolis, IN, 46202, USA.
- Medical and Molecular Genetics, Indiana University Purdue University Indianapolis, HITS 5015, 410 W. 10th St., Indianapolis, IN, 46202, USA.
| | - Jie Zhang
- Medical and Molecular Genetics, Indiana University Purdue University Indianapolis, HITS 5015, 410 W. 10th St., Indianapolis, IN, 46202, USA.
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