We developed robust analysis methods and platforms to facilitate scRNA-seq. These platforms include SingCellaR (Roy et al., Cell Rep. 2021; Wang et al., STAR Protoc. 2022), a comprehensive analytical tool to integrate, interrogate, and visualize scRNA-seq. VALERIE (Wen et al., PLoS Comput. Biol. 2020) supports visualization of alternative splicing events at single-cell resolution. MARVEL (Wen et al., Nucleic Acids Res. 2023) supports single-cell splicing analysis of scRNA-seq generated from the plate- and droplet-based methods. MARVEL enables systematic and integrated splicing and gene expression to characterize the splicing landscape.

 

 

We have worked with experimental collaborators to develop a novel method to integrate scRNA-seq and high-sensitivity mutation detection from the same single cell to study cancer stem cells in chronic myeloid leukemia (Giustacchini & Thongjuea et al., Nat. Med. 2017). We improved the high-throughput capacity to detect multiple targeted mutations (TARGET-seq) (Rodriguez-Meira et al., Mol Cell. 2019).  We analyzed 10x genomics data to demonstrate dynamic changes in cellular compositions and lineage-biased and identified site and developmental stage-specific transcription and gene regulatory networks across human developmental stages (Roy et al., Cell Rep. 2021). We worked on large single-cell data from myelofibrosis patients and highlighted the strong lineage bias toward megakaryocyte differentiation in the stem cell/progenitor compartment, leading to the discovery of G6B as a potential immunotherapy target (Psaila et al., Mol Cell. 2020).

 

 

The team works closely with KiTZ’s PIs on multiple projects for medulloblastoma single-cell/spatial analyses (Okonechnikov et al., BioRxiv 2021; Ghasemi et al., BioRxiv 2022; Okonechnikov et al., Acta Neuropathol. Commun. 2023) and high-grade glioma. We lead subproject 1 for the HEROES-AYA project to work on applying cutting-edge single-cell technologies to characterize sarcoma heterogeneity from single-cell genomes and epigenomes. We also work with subproject 3 for the non-invasive technologies to detect and monitor sarcoma. We have focused more on integrating single-cell and spatial approaches for cell-cell communication analysis across cell types in brain tumors and also focusing on the alternative splicing analysis for multiple cancer types.

 

This team is not limited to single-cell analysis but also works on the cell-type deconvolution and classification of bulk DNA methylation for precision cancer diagnosis (Maros et al., Nat. Protoc. 2020; Capper et al., Nature 2018).