Person: Fan, Jean
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Fan, Jean
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Publication Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain(2017) Lake, Blue B.; Chen, Song; Sos, Brandon C.; Fan, Jean; Kaeser, Gwendolyn E.; Yung, Yun C.; Duong, Thu E.; Gao, Derek; Chun, Jerold; Kharchenko, Peter; Zhang, KunDetailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, and computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus Droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive-site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from the human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for studying complex processes in the brain, such as genetic programs coordinating adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, providing insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.Publication RNA Velocity of Single Cells(Springer Science and Business Media LLC, 2018-08) La Manno, Gioele; Soldatov, Ruslan; Zeisel, Amit; Braun, Emelie; Hochgerner, Hannah; Petukhov, Viktor; Lidschreiber, Katja; Kastriti, Maria E.; Lönnerberg, Peter; Furlan, Alessandro; Fan, Jean; Borm, Lars E.; Liu, Zehua; van Bruggen, David; Guo, Jimin; He, Xiaoling; Barker, Roger; Sundström, Erik; Castelo-Branco, Gonçalo; Cramer, Patrick; Adameyko, Igor; Linnarsson, Sten; Kharchenko, PeterRNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena, such as embryogenesis or tissue regeneration. Here we show that RNA velocity—the time derivative of the gene expression state—can be directly estimated by distinguishing unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.Publication Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis(2016) Fan, Jean; Salathia, Neeraj; Liu, Rui; Kaeser, Gwendolyn E.; Yung, Yun C.; Herman, Joseph L.; Kaper, Fiona; Fan, Jian-Bing; Zhang, Kun; Chun, Jerold; Kharchenko, PeterThe transcriptional state of a cell reflects a variety of biological factors, from persistent cell-type specific features to transient processes such as cell cycle. Depending on biological context, all such aspects of transcriptional heterogeneity may be of interest, but detecting them from noisy single-cell RNA-seq data remains challenging. We developed PAGODA to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability amongst measured cells.Publication Clonal evolution in patients with chronic lymphocytic leukaemia developing resistance to BTK inhibition(Nature Publishing Group, 2016) Burger, Jan A.; Landau, Dan A.; Taylor-Weiner, Amaro; Bozic, Ivana; Zhang, Huidan; Sarosiek, Kristopher; Wang, Lili; Stewart, Chip; Fan, Jean; Hoellenriegel, Julia; Sivina, Mariela; Dubuc, Adrian; Fraser, Cameron; Han, Yulong; Li, Shuqiang; Livak, Kenneth J.; Zou, Lihua; Wan, Youzhong; Konoplev, Sergej; Sougnez, Carrie; Brown, Jennifer R.; Abruzzo, Lynne V.; Carter, Scott L.; Keating, Michael J.; Davids, Matthew S.; Wierda, William G.; Cibulskis, Kristian; Zenz, Thorsten; Werner, Lillian; Cin, Paola Dal; Kharchencko, Peter; Neuberg, Donna; Kantarjian, Hagop; Lander, Eric; Gabriel, Stacey; O'Brien, Susan; Letai, Anthony; Weitz, David; Nowak, Martin; Getz, Gad; Wu, CatherineResistance to the Bruton's tyrosine kinase (BTK) inhibitor ibrutinib has been attributed solely to mutations in BTK and related pathway molecules. Using whole-exome and deep-targeted sequencing, we dissect evolution of ibrutinib resistance in serial samples from five chronic lymphocytic leukaemia patients. In two patients, we detect BTK-C481S mutation or multiple PLCG2 mutations. The other three patients exhibit an expansion of clones harbouring del(8p) with additional driver mutations (EP300, MLL2 and EIF2A), with one patient developing trans-differentiation into CD19-negative histiocytic sarcoma. Using droplet-microfluidic technology and growth kinetic analyses, we demonstrate the presence of ibrutinib-resistant subclones and estimate subclone size before treatment initiation. Haploinsufficiency of TRAIL-R, a consequence of del(8p), results in TRAIL insensitivity, which may contribute to ibrutinib resistance. These findings demonstrate that the ibrutinib therapy favours selection and expansion of rare subclones already present before ibrutinib treatment, and provide insight into the heterogeneity of genetic changes associated with ibrutinib resistance.