SCSAP SPEAKER SPOTLIGHT
Jean Fan, Ph.D.
Assistant Professor/ Biomedical Engineering
Center for Computational Biology/
Johns Hopkins University
Abstract
Recent advances in high-throughput spatially resolved transcriptomics technologies now enable high-throughput molecular profiling of cells while maintaining their spatial organization within tissues. Application of these technologies provides the opportunity to contribute to a more complete understanding of how cellular spatial organization relates to tissue function and how cellular spatial organization is altered in disease. New statistical approaches and scalable computational tools are needed to connect these molecular states and spatial-contextual differences.
In this talk, I will provide an overview of spatially resolved transcriptomics technologies and associated computational analysis methods developed by my lab. Specifically, to facilitate spatial molecular comparisons across structurally matched tissue sections from replicates, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. Likewise, to facilitate comparison cell-type spatial organizational patterns, we developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, to quantify pair-wise cell-type spatial relationships across length scales. We demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples to identify consistent as well as patient and sample-specific cell-type spatial relationships.
We anticipate that such statistical approaches and computational methods for analyzing spatially resolved transcriptomic data will offer the potential to identify and characterize the heterogeneity of cells within their spatial contexts and contribute to important fundamental biological insights regarding how tissues are organized in both the healthy and diseased settings.
Short Bio
Jean Fan is a member of the faculty of Biomedical Engineering in the Center for Computational Biology at Johns Hopkins University. Her research team, the JEFworks lab, is interested in understanding the molecular and spatial-contextual factors shaping cellular identity and heterogeneity. She develops new open-source computational software for analyzing single-cell multi-omic and imaging data that can be tailored and applied to diverse cancer types and biological systems. Prof. Fan is the founder, director, and lead software developer for the non-profit organization CuSTEMized, which provides personalized STEM picture storybooks to encourage young girls to see themselves as scientists. She currently serves as an editor for PLoS Computational Biology. The impact of Prof. Fan’s work has been recognized by several awards and honors, including the Forbes 30 Under 30, the Nature Research Award for Inspiring Science, the NSF CAREER Award, and the Presidential Early Career Award for Scientists and Engineers (PECASE).
Publications
Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.Dos Santos Peixoto R, Miller BF, Brusko MA, Aihara G, Atta L, Anant M, Atkinson MA, Brusko TM, Wasserfall CH, Fan J.Nat Commun. 2025 Jan 3;16(1):350. doi: 10.1038/s41467-024-55700-1.PMID: 39753600 Free PMC article.
Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities.Miller BF, Bambah-Mukku D, Dulac C, Zhuang X, Fan J.Genome Res. 2021 Oct;31(10):1843-1855. doi: 10.1101/gr.271288.120. Epub 2021 May 25.PMID: 34035045 Free PMC article.
Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastoma.Hara T, Chanoch-Myers R, Mathewson ND, Myskiw C, Atta L, Bussema L, Eichhorn SW, Greenwald AC, Kinker GS, Rodman C, Gonzalez Castro LN, Wakimoto H, Rozenblatt-Rosen O, Zhuang X, Fan J, Hunter T, Verma IM, Wucherpfennig KW, Regev A, Suvà ML, Tirosh I.Cancer Cell. 2021 Jun 14;39(6):779-792.e11. doi: 10.1016/j.ccell.2021.05.002. Epub 2021 Jun 3.PMID: 34087162 Free PMC article.
Computational challenges and opportunities in spatially resolved transcriptomic data analysis.Atta L, Fan J.Nat Commun. 2021 Sep 6;12(1):5283. doi: 10.1038/s41467-021-25557-9.PMID: 34489425 Free PMC article.
VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories.Atta L, Sahoo A, Fan J.Bioinformatics. 2022 Jan 3;38(2):391-396. doi: 10.1093/bioinformatics/btab653.PMID: 34500455 Free PMC article.
Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics dataMiller BF, Huang F, Atta L, Sahoo A, Fan J.Nat Commun. 2022 Apr 29;13(1):2339. doi: 10.1038/s41467-022-30033-z.PMID: 35487922 Free PMC article.
Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP).Jain S, Pei L, Spraggins JM, Angelo M, Carson JP, Gehlenborg N, Ginty F, Gonçalves JP, Hagood JS, Hickey JW, Kelleher NL, Laurent LC, Lin S, Lin Y, Liu H, Naba A, Nakayasu ES, Qian WJ, Radtke A, Robson P, Stockwell BR, Van de Plas R, Vlachos IS, Zhou M; HuBMAP Consortium; Börner K, Snyder MP.Nat Cell Biol. 2023 Aug;25(8):1089-1100. doi: 10.1038/s41556-023-01194-w. Epub 2023 Jul 19.PMID: 37468756 Free PMC article. Review.
Single cell and spatial transcriptomics analysis of kidney double negative T lymphocytes in normal and ischemic mouse kidneys.Gharaie S, Lee K, Noller K, Lo EK, Miller B, Jung HJ, Newman-Rivera AM, Kurzhagen JT, Singla N, Welling PA, Fan J, Cahan P, Noel S, Rabb H.Sci Rep. 2023 Nov 28;13(1):20888. doi: 10.1038/s41598-023-48213-2.PMID: 38017015 Free PMC article.
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping.Clifton K, Anant M, Aihara G, Atta L, Aimiuwu OK, Kebschull JM, Miller MI, Tward D, Fan J.Nat Commun. 2023 Dec 8;14(1):8123. doi: 10.1038/s41467-023-43915-7.PMID: 38065970 Free PMC article.