We are an academic research lab in the Department of Biomedical Engineering at Johns Hopkins University. We are interested in understanding the molecular and spatial-contextual factors shaping cellular identity and heterogeneity, particularly in the context of cancer and how this heterogeneity impacts tumor progression, therapeutic resistance, and ultimately clinical prognosis. We develop machine learning and other statistical approaches as computational software to enable ourselves and the scientific community to harness the power of large-scale multi-omic and imaging data in addressing these basic science and translational research questions.
Research Synopsis* Denotes equal contribution
Statistical methods and software for analyses of single cell data
While heterogeneity within cellular systems has long been widely recognized, only recently have technological advances enabled measurements to be made on a single cell level. Applying traditional bulk analysis methods on single cells has met with varied degrees of success due to the high levels of technical as well as biological stochasticity and noise inherent in single cell measurements. Therefore, we develop novel statistical methods to identify and characterize varying aspects of heterogeneity (genomic, transcriptomic, epigenomic) and their interplay in single cells.
- Jean Fan*, Hae-Ock Lee*, Soohyun Lee, Da-eun Ryu, Semin Lee, et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq. Genome Research. 2018. doi:10.1101/gr.228080.117
- Blue B Lake*, Song Chen*, Brandon C Sos*, Jean Fan*, Gwendolyn E Kaeser, et al. Integrative single-cell analysis by transcriptional and epigenetic states in human adult brain. Nature Biotechnology 2017. doi:10.1038/nbt.4038
- Jean Fan, Neeraj Salathia, Rui Liu, Gwendolyn E Kaeser, Yun C Yung, et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nature Methods. 2016. doi: 10.1038/nmeth.3734
Role of alternative splicing in development and disease
Alternative splicing governs cell-type transitions and cell fate in a variety of normal developmental processes including the mammalian brain. Aberrations in alternative splicing have also been shown to dysregulate cellular functions and drive cancer pathogenesis. We aim to gain a better understanding of the functional consequences of alternative splicing and dysregulation in normal development and in cancer.
- Lili Wang*, Angela N. Brooks*, Jean Fan*, Youzhong Wan*, Rutendo Gambe, et al. Transcriptomic characterization of SF3B1 mutation reveals its pleiotropic effects in chronic lymphocytic leukemia. Cancer Cell. Nov 3, 2016. doi: 10.1016/j.ccell.2016.10.005
- Xiaochang Zhang, Ming Hui Chen, Xuebing Wu, Andrew Kodani, Jean Fan, Ryan Doan, et al. Cell-Type-Specific Alternative Splicing Governs Cell Fate in the Developing Cerebral Cortex. Cell. 2016. doi:10.1016/j.cell.2016.07.025
Molecular and spatial-contextual factors impacting cancer progression
Advancements in high-throughput sequencing and imaging technologies have uncovered tremendous genetic, epigenetic, transcriptional, and even spatial heterogeneity in various cancers but their impact on clinical course is not well understood. We aim to establish close collaborations with clinical collaborators to focus on developing and applying bioinformatics methods that contribute to a more complete understanding of cancer pathogenesis, progression, treatment response, and resistance. We are particularly interested in pediatric gliomas.
- Chenglong Xia*, Jean Fan*, George Emanuel*, Junjie Hao, and Xiaowei Zhuang. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. PNAS. 2019. doi:10.1073/pnas.1912459116
- Lili Wang*, Jean Fan*, Joshua M. Francis, George Georghiou, Sarah Hergert, et al. Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia. Genome Research. 2017. doi:10.1101/gr.217331.116
- Single-cell transcriptomics in cancer - computational challenges and opportunities on 15 September 2020
- Fast, sensitive and accurate integration of single-cell data with Harmony on 19 November 2019
- Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression on 09 September 2019
- Lyla Atta successfully completes her rotations and will official join the lab for her PhD! Glad to have you on the team Lyla! on 19 September 2020
- Our invited review on computational challenges and opportunities in single cell transcriptomics analysis in cancer is published in Nature Experimental and Molecular Medicine as part of their special Single Cell Genomics feature! on 15 September 2020
- We welcome Lyla Atta to the lab! Welcome Lyla! on 10 August 2020
- We welcome Brendan Miller to the lab! Welcome Brendan! on 15 July 2020
- We welcome Catherine Lo to the lab! Welcome Catherine! on 19 June 2020
Latest Blog Posts
- Using scVelo in R using Reticulate on 25 August 2020
- A Guide to Responding to Scientific Peer Review on 17 June 2020
- Quickly Creating Pseudobulks on 06 April 2020
- A Guide to Scientific Peer Review on 23 March 2020
- Ten PhD Transition Tips for the Biological Sciences on 23 January 2020