We are recruiting: Postdoc scholars who love science and dare to invent

We welcome bold minds who will innovate in genomic technologies, including spatial, single-cell, and extracellular transcriptomics. We are interested in revealing novel RNA types and functions. Our favorite application areas include blood vasculature in diverse tissues and diseases, liquid biopsy-based diagnoses, and mechanisms of Alzheimer's disease and metabolic diseases. Ph.D.s in genomics, bioinformatics, molecular biology, and neuroscience are most welcome.

About the PI

Sheng Zhong is a Professor of Bioengineering at UC San Diego. He received an NIH Director's Pioneer Award, an NIH Catalyst Award in Diabetes, Endocrinology and Metabolic Diseases, an NIH Director's New Innovator Award, an NSF CAREER Award, and an Alfred Sloan Fellowship. He serves as the director for UCSD Center for Liquid Biopsy Research and the organizational hub of the NIH funded 4D Nucleome (4DN) Network. He leads a Human Cell Atlas (HCA) seed network and a Transformative Technology Development team for the Human BioMolecular Atlas Program (HuBMAP). Eight of his previous trainees are contributing to science on tenure-track faculty positions.

About the lab

Our lab invented PROPER-seq (Protein-protein interaction by sequencing) to massively reveal protein-protein interactions (Molecular Cell, 2021). We also invented the MARIO (Mapping RNA interactome in vivo) technology to massively reveal RNA-RNA interactions from human tissue (Nat Comm, 2016) and the MARGI (Mapping RNA-Genome Interactions) technology for revealing thousands of chromatin-associated RNAs (caRNA) and their respective genomic interaction sites (Current Biology, 2017; Nature Protocols, 2019). Leveraging MARGI, we and our collaborators characterized caRNA’s roles in 3D organization of the nucleus (bioRxiv, 2021), modulation of gene expression during progression of diabetes mellitus in blood vessel endothelium (Nat Comm, 2020), and the biogenesis of fusion RNAs (PNAS 2019a). These results inspired the idea that caRNA is a layer of the epigenome (Trends Genetics, 2018).

We contributed to discovering the nuclear-encoded RNAs that are stably attached to the cell surface and exposed to the extracellular space, called membrane-associated extracellular RNAs (maxRNAs). maxRNAs are functional components of the cell surface and mediate cell-cell interactions (Genome Biology, 2020).

We developed SILVER-seq for extracellular RNA (exRNA) sequencing from ultra-small volumes of liquid biopsy, solidifying a basis for future in vitro diagnostic trials using finger-prick blood (PNAS, 2019b; Current Biology, 2020).

We contributed to discovering that the earliest cell fate decision in mouse is made sooner than the commonly thought 8-cell stage (Genome Res, 2014). Our Rainbow-seq technology combined tracing of cell division history and single-cell RNA sequencing into one experiment (iScience 2018b).

We contributed to revealing that transposons are indispensable regulatory sequences in the mammalian genomes. Species-specific transposons are required for preimplantation embryonic development in humans and other mammals (Genome Res, 2010). Nature highlighted this discovery as "Hidden Differences," reporting that "transposons or 'jumping genes' had hopped in front of the genes, changing their regulation" (Nature, 2010). We contributed to establishing the proof-of-principle that cis-regulatory sequences can be annotated by cross-species epigenomic comparison (Cell, 2012).

See more from our YouTube channel.

Read about Aitana Manuela Castro Colabianchi who recently joined us as a postdoc scholar on Biology Open.

Projects for graduate rotations and extremely motivated undergraduates

Our projects contribute to the Human Cell Atlas (HCA), the Human BioMolecular Altas (HuBMAP), the 4D Nucleome (4DN) program, diabetes research, and Alzheimer's research.

Spatial transcriptomics with ultra-high spatial resolution

Spatial transcriptomics methods allow for sequencing RNA from human tissue while registering the spatial locations of every sequenced RNA in the original tissue. This project will develop next generation spatial transcriptomics tools that will significantly improve the spatial resolution and the size of the tissue that can be analyzed. We will develop extremely high-resolution, high-density, high-fidelity and reproducible spatial mapping slides (HiFi slides) for spatial transcriptome analysis.

Revealing protein-protein interactions at the genomic scale

Genome-scale mapping of protein-protein interactions (PPI) remains laborious and resource-intensive. This project will develop an extremely high-throughput genomic-based technology for mapping the human PPI network at the genomic scale. This genomic technology and its coupled genomic informatics tools will generate a reference map of the human PPI network.

Revealing drug-protein interactions between a drug library and all human proteins by one experiment

This project will develop a genomic technology that can reveal all drug-protein interactions between a small molecule library and a protein library. This technology is expected to transform the current practices of identification of drug screening by 1000-fold increase of efficiency.

Revealing RNA-chromatin interactions in single cells

This project will develop technologies that can reveal RNA-chromatin interactions in single cells.

The relationship between genome-wide RNA-chromatin interactions and 3D genome organization

It remains unclear to what extent chromatin-associated RNAs can reflect the 3D organization of the genome. To this end, we used iMARGI (a sequencing technology) to map genome-wide RNA-chromatin interactions in human embryonic stem cells, foreskin cells, and leukemia cells. This project will compare these iMARGI data with genome interaction data including Hi-C and PLAC-seq on three different scales. At the compartment scale, we will test whether the A compartment chromatin is associated with large amounts of RNAs, involving both intrachromosomal and interchromosomal RNA-chromatin interactions. At the TAD scale, we will test whether the RNA ends of nearly all RNA-chromatin interactions are confined to within the boundaries of one or of a few consecutive TADs. At the loop scale, we will test whether RNA-chromatin interactions are enriched with PLAC-seq derived enhancer-promoter interactions.


Complete list of publications on Google Scholar, NCBI

Selected papers (please choose a category)

  • Three-dimensional organization of chromatin associated RNAs and their role in chromatin architecture in human cells. Riccardo Calandrelli, Xingzhao Wen, Tri C. Nguyen, Chien-Ju Chen, Zhijie Qi, Weizhong Chen, Zhangming Yan, Weixin Wu, Kathia Zaleta-Rivera, Rong Hu, Miao Yu, Yuchuan Wang, Jian Ma, Bing Ren, Sheng Zhong.
    bioRxiv, 2021, Text, 4DN web portal
  • Revealing protein-protein interactions at the transcriptome scale by sequencing. Kara L. Johnson, Zhijie Qi, Zhangming Yan, Xingzhao Wen, Tri C.Nguyen, Kathia Zaleta-Rivera, Chien-JuChen, Xiaochen Fan, Kiran Sriram, Xueyi Wan, Zhen Bouman Chen, Sheng Zhong.
    Molecular Cell, 2021, online preprint, Text, PROPER database, PROPERTools software. Blog: Proteins Network Too!, UCSD news.
  • Stress-induced RNA-chromatin interactions promote endothelial dysfunction. Riccardo Calandrelli, Lixia Xu, Yingjun Luo, Weixin Wu, Xiaochen Fan, Tri Nguyen, Chien-Ju Chen, Kiran Sriram, Xiaofang Tang, Andrew Burns, Rama Natarajan, Zhen Chen, Sheng Zhong.
    Nature Communications, 2020, 11:5211. Text.
  • Natural display of nuclear-encoded RNA on the cell surface and its impact on cell interaction. Norman Huang, Xiaochen Fan, Kathia Zaleta-Rivera, Tri C. Nguyen, Jiarong Zhou, Yingjun Luo, Jie Gao, Ronnie H. Fang, Zhangming Yan, Zhen Bouman Chen, Liangfang Zhang, Sheng Zhong.
    Genome Biology, 2020, 21:225. Text, A 2-minute introduction on YouTube.
  • Presymptomatic Increase of an Extracellular RNA in Blood Plasma Associates with the Development of Alzheimer’s Disease. Zhangming Yan, Zixu Zhou, Qiuyang Wu, Zhen Bouman Chen, Edward H. Koo, Sheng Zhong.
    Current Biology, 2020, 30:1771–1782. Text, Seminar for the Extracellular RNA Communication (ERCC) consortium, SILVER-seq flowchart, exRNA sequencing service at Genemo.
  • Extracellular RNA in a single droplet of human serum reflects physiologic and disease states. Zixu Zhou, Qiuyang Wu, Zhangming Yan, Haizi Zheng, Chien-Ju Chen, Yuan Liu, Zhijie Qi, Riccardo Calandrelli, Zhen Chen, Shu Chien, H. Irene Su, Sheng Zhong.
    PNAS, 2019, 116:19200–19208. Text, SILVER-seq flowchart, Mapping by exceRpt software, SILVER-seq service at Genemo, The POISE trial @
  • Mapping RNA-chromatin interactions by sequencing with iMARGI. Weixin Wu, Zhangming Yan, Tri C. Nguyen, Zhen Chen, Shu Chien, Sheng Zhong.
    Nature Protocols, 2019, 14:3243–3272. Text, Protocol, Software, 4DN web portal
  • Genome-wide co-localization of RNA-DNA interactions and fusion RNA pairs. Zhangming Yan, Norman Huang, Weixin Wu, Weizhogn Chen, Yiqun Jiang, Jingyao Chen, Xuerui Huang, Xingzhao Wen, Jie Xu, Qiushi Jin, Kang Zhang, Zhen Chen, Shu Chien, Sheng Zhong.
    PNAS, 2019, 116 (8) 3328-3337. Text 4DN web portal
  • RNA, action through interactions. Tri C. Nguyen, Kathia Zaleta-Rivera, Xuerui Huang, Xiaofeng Dai, Sheng Zhong.
    Trends in Genetics, 2018, 34:867-882. Text
  • RNAs as proximity labeling media for identifying nuclear speckle positions relative to the genome. Weizhong Chen, Zhangming Yan, Simin Li, Norman Huang, Xuerui Huang, Jin Zhang, Sheng Zhong.
    iScience, 2018, 4:204-215. Text, Cover proposal
  • Systematic mapping of RNA-chromatin interactions in vivo. Bharat Sridhar, Marcelo Rivas-Astroza, Tri C. Nguyen, Weizhong Chen, Zhangming Yan, Xiaoyi Cao, Lucie Hebert, Sheng Zhong.
    Current Biology, 2017, 27(4): 602–609. Text, Data, Protocols, Bioinformatic pipeline, F1000Prime
  • Mapping RNA-RNA interactome and RNA structure in vivo by MARIO. Tri C. Nguyen, Xiaoyi Cao, Pengfei Yu, Shu Xiao, Jia Lu, Fernando H. Biase, Bharat Sridhar, Norman Huang, Kang Zhang, Sheng Zhong.
    Nature Communications, 2016, 7:12023. Text, Software, Data
  • Rainbow-seq: combining cell lineage tracing with single-cell RNA sequencing in preimplantation embryos. Fernando H. Biase, Qiuyang Wu, Riccardo Calandrelli, Marcelo Rivas-Astroza, Shuigeng Zhou, Zhen Chen, Sheng Zhong.
    iScience, 2018, 7:16-29. Text
  • Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing. Fernando H. Biase, Xiaoyi Cao, Sheng Zhong.
    Genome Research, 2014, 24:1787-1796. Cover Article, Abstract, single-cell RNA-seq data, single-cell genome browser, single-cell Fluidigm qPCR data, in situ images
  • Rewirable gene regulatory networks in the preimplantation embryonic development of three mammalian species. Dan Xie, Chieh-Chun Chen, Leon M Ptaszek, Shu Xiao, Xiaoyi Cao, Fang Fang, Huck H Ng, Harris A Lewin, Chad Cowan, Sheng Zhong.
    Genome Research, 2010 20:804-815. Cover Article, Abstract, Embryo data, Mtf2 knockdown data.
    Research Highlight: Hidden differences. Nature 464: 1248.
  • A core Klf circuitry regulates self-renewal of embryonic stem cells. DJianming Jiang, Yun-Shen Chan, Yuin-Han Loh, Jun Cai, Guo-Qing Tong, Ching-Aeng Lim, Paul Robson, Sheng Zhong, Huck-Hui Ng.
    Nature cell biology, 2008, 10:353-360. Abstract.
  • Suppression of endothelial AGO1 promotes adipose tissue browning and improves metabolic dysfunction. Xiaofang Tang, Yifei Miao, Yingjun Luo, Kiran Sriram, Zhijie Qi, Feng-Mao Lin, Kendall Van Keuren-Jensen, Patrick Fueger, Gene W. Yeo, Rama Natarajan, Sheng Zhong, Zhen Bouman Chen
    Circulation, 2020, 142:365–379.Article, Editorial: Endotheliopathy of Obesity.
  • Genomic analysis of hepatic farnesoid X receptor binding sites reveals altered binding in obesity and direct gene repression by farnesoid X receptor in mice. Jiyoung Lee, Sunmi Seok, Pengfei Yu, Kyungsu Kim, Zachary Smith, Marcelo Rivas‐Astroza, Sheng Zhong, Jongsook Kim Kemper
    Hepatology, 2012, 56(1):108-117. Text.
  • The 4D nucleome project. Job Dekker, Andrew S. Belmont, Mitchell Guttman, Victor O. Leshyk, John T. Lis, Stavros Lomvardas, Leonid A. Mirny, Clodagh C. O’Shea, Peter J. Park, Bing Ren, Joan C. Ritland Politz, Jay Shendure, Sheng Zhong & the 4D Nucleome Network.
    Nature, 2017, 549:219–226. Text, Artwork, 4DN web portal
  • SMARCAD1 contributes to regulation of naïve pluripotency by interacting with histone citrullination. Shu Xiao, Jia Lu, Bharat Sridhar, Xiaoyi Cao, Pengfei Yu, Chieh-Chun Chen, Darina McDee, Laura Sloofman, Yang Wang, Marcelo Rivas-Astroza, Bhanu Prakash V.L. Telugu, Dana Levasseur, Kang Zhang, Han Liang, Jing Crystal Zhao, Tetsuya S. Tanaka, Gary Stormo, Sheng Zhong.
    Cell Reports, 2017, 18:3117-3128. Text, Raw images, Cover proposal
  • Spatiotemporal clustering of epigenome reveals rules of dynamic gene regulation. Pengfei Yu, Shu Xiao, Xiaoyun Xin, Chun-Xiao Song, Wei Huang, Darina McDee, Tetsuya Tanaka, Ting Wang, Chuan He, Sheng Zhong.
    Genome Research, 2013, 23:352-384. Cover article, Abstract, Software, Data; Review
  • Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions. Chieh-Chun Chen, Shu Xiao, Dan Xie, Xiaoyi Cao, Chun-Xiao Song, Ting Wang, Chuan He, Sheng Zhong.
    PLoS Computational Biology, 2013, 9(12): e1003367. Text, Software, Supplementary Figures
  • Comparative epigenomic annotation of regulatory DNA. Shu Xiao, Dan Xie, Xiaoyi Cao, Pengfei Yu, Xiaoyun Xing, Chieh-Chun Chen, Meagan Musselman, Mingchao Xie, Franklin D. West, Harris A. Lewin, Ting Wang, Sheng Zhong.
    Cell, 2012, 49: 1381-1391. Abstract, Data, Comparative Epigenome Browser.
    Reviewed by: J Stem Cell Res Ther, 2012, S10:007. SCIENCE CHINA Life Sciences, 2013, 56(3): 213-219. WIREs Systems Biol Med, 2012, 4(6): 525-545.
  • The p23 molecular chaperone and GCN5 acetylase jointly modulate protein-DNA dynamics and open chromatin status. Elena Zelin, Yang Zhang, Oyetunji A Toogun, Sheng Zhong, Brian C Freeman.
    Molecular Cell, 2012, 48(3):459-470. Text.
  • A likelihood approach to testing hypotheses on the co-evolution of epigenome and genome. Jia Lu, Xiaoyi Cao, Sheng Zhong.
    PLoS Computational Biology, 2018, 14(12):e1006673. Text
  • Towards an evolutionary model of transcription networks. Dan Xie, Chieh-Chun Chen, Xin He, Xiaoyi Cao, Sheng Zhong.
    PLoS Computational Biology, 2011, 7(6): e1002064. Text. Website.
  • Modeling co-expression across species for complex traits: insights to the difference of human and mouse embryonic stem cells. Jun Cai, Dan Xie, Zhewen Fan, John Marden, Wing H. Wong, Sheng Zhong.
    PLoS Computational Biology, 2010, 6(3): e1000707. Text, Data, Software
  • Cross-species de novo identification of cis-regulatory modules with GibbsModule: application to gene regulation in embryonic stem cells. Dan Xie, Jun Cai, Na-Yu Chia, Huck H. Ng, Sheng Zhong.
    Genome Research, 2008, 18:1325-1335. Text. Software
  • Cross-species microarray analysis with the OSCAR system suggests an INSR-Pax6-NQO1 neuro-protective pathway in ageing and Alzheimer's disease. Yue Lu, Xin He, Sheng Zhong.
    Nucleic Acids Research, 2007, 35: W105-W114. TEXT.
  • Time-variant clustering model for understanding cell fate decisions. Wei Huang, Xiaoyi Cao, Fernando H. Biase, Pengfei Yu, Sheng Zhong.
    PNAS, 2014, 111(44):E4797-E4806. Abstract
  • Network based comparison of temporal gene expression patterns. Wei Huang, Xiaoyi Cao, Sheng Zhong.
    Bioinformatics, 2010, 26(23): 2944-2951. Abstract, Software
  • Dissecting early differentially expressed genes in a mixture of differentiating embryonic stem cells. Feng Hong, Fang Fang, Xuming He, Xiaoyi Cao, Hiram Chipperfield, Dan Xie, Wing H. Wong, Huck H. Ng, Sheng Zhong.
    PLoS Computational Biology, 2009, 5(12): e1000607. Text, Data
  • Reproducibility Probability Score - incorporating measurement variability across laboratories for gene selection. Guixian Lin, Xuming He, Hanlee Ji, Leming Shi, Ronald Davis, Sheng Zhong.
    Nature Biotechnology, 2007, 41:105-115. 24(12): 6-7. Text, Software, Supplementary Material. The article has been reviewed by: Pharmacogenomics, 2007, 8(8): 1037-1049. European Journal of Cancer, 2007, 5(5): 97-104. Current Opinion in Biotechnology Systems Biomedicine: Concepts and Perspectives, Edison Liu, Douglas Lauffenburger (editors), Elsevier, 2009, p.172. WIREs Systems Biol Med, 2012, 4(1): 39-49. WIREs Systems Biol Med, 2012, 4(6): 525-545.
  • EpiAlignment: alignment with both DNA sequence and epigenomic data. Jia Lu, Xiaoyi Cao, Sheng Zhong.
    Nucleic Acids Research, 2019, 47(W1):W11-W19. Text, Software.
  • GITAR: An Open Source Tool for Analysis and Visualization of Hi-C Data. Riccardo Calandrelli, Qiuyang Wu, Jihong Guan, Sheng Zhong.
    Genomics, Proteomics & Bioinformatics, 2018, 16(5):365-372. Text, Software.
  • GIVE: portable genome browsers for personal websites. Xiaoyi Cao, Zhangming Yan, Qiuyang Wu, Alvin Zheng, Sheng Zhong.
    Genome Biology, 2018, 19:92. Text, Software, News & Comments: Nature 549:117, Research Highlight: Genome Biology 19:93 , Technical feature: Nature 576:171-172.
  • GeNemo: a search engine for web-based functional genomic data. Yongqing Zhang, Xiaoyi Cao, Sheng Zhong.
    Nucleic Acids Research, 2016, 44: W122-W127. Text, Software
    News coverage: HIT Consultant, Science Daily, MediaPost, HealthDataManagement
  • Enabling interspecies epigenomic comparison with CEpBrowser. Xiaoyi Cao, Sheng Zhong.
    Bioinformatics, 2013, 29(9):1223–1225. Text. Software
  • Mapping personal functional data to personal genomes. Marcelo Rivas-Astroza, Dan Xie, Xiaoyi Cao, Sheng Zhong.
    Bioinformatics, 2011, 27(24):3427-3429. Text. Software

Text book

3D Genome: from technologies to visualization (draft). [ISBN: 987-1-17325643-0-5]. Textbook for BENG183. Suggestions and content contributions are welcome!


Build your own genome browser website.


Internet search for genomic big data.


Analyze RNA interaction data.


Comparative Epigenome Browser.


Sequence mapping on personal genome.


Genome annotation using temporal epigenomic data.

4D Nucleome web portal

4D Nucleome Portal

Entry to NIH 4D Nucleome network.

Get in Touch

Powell-Focht Bioengineering Hall 371, University of California San Diego, 9500 Gilman Drive, MC 0412, La Jolla, CA 92093-0412

Lab Phone: (858) 822-5649