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Rosales, W. K.

Publications and source records attributed to Rosales, W. K..

2 recordsLinked to original sources

A tailored in vivo CRISPR screen identifies BAP1 as a potent tumor suppressor of soft tissue sarcoma

Undifferentiated pleomorphic sarcoma (UPS) is one of the most common soft tissue sarcomas (STS) in adults. Despite decades of research, therapeutic advancements for STS, including UPS, have remained limited. The genetic complexity of UPS, characterized by the absence of recurrent driver oncogene mutations, has hindered the development of effective targeted therapies beyond conventional chemotherapy and immunotherapy. To address this challenge, we conducted a customized in vivo CRISPR/Cas9 screen in mice to systematically identify potential tumor suppressors involved in UPS development. Our screen revealed BRCA1-associated protein 1 (Bap1) as a potent tumor suppressor in STS. Using total RNA sequencing, multiplex immunohistochemistry, and flow cytometry, we found that Bap1-deficient mouse sarcomas exhibit significant immune suppression. Further analysis indicated that polo-like kinase 1 (Plk1) is essential for the survival of Bap1-deficient sarcomas. Treatment with volasertib, a Plk1 inhibitor, markedly inhibited tumor growth in both syngeneic and spontaneous mouse models of Bap1-loss sarcoma. In conclusion, our findings suggest that PLK1 inhibition, or combined with immunotherapy, may represent a promising targeted therapeutic strategy for tumors lacking BAP1.

cancer biology↗

CITE-Viz: Replicating the Interactive Flow Cytometry Workflow in CITE-Seq

SummaryThe rapid advancement of new genomic sequencing technology has enabled the development of multi-omic single-cell sequencing assays. These assays profile multiple modalities in the same cell and can often yield new insights not revealed with a single modality. For example, CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) simultaneously profiles the single-cell RNA transcriptome and the surface protein expression. The extra dimension of surface protein markers can be used to further identify cell clusters - an essential step for downstream analyses and interpretation. Additionally, multi-dimensional datasets like CITE-Seq require nuanced visualization methods to accurately assess the data. To facilitate cell cluster classification and visualization in CITE-Seq, we developed CITE-Viz. CITE-Viz is a single-cell visualization platform with a custom module that replicates the interactive flow-cytometry gating workflow. With CITE-Viz, users can investigate CITE-Seq specific quality control (QC) metrics, view multi-omic co-expression feature plots, and classify cell clusters by iteratively gating on the abundance of cell surface markers. CITE-Viz was developed to make multi-modal single-cell analysis accessible to a wide variety of biologists, with the aim to discover new insights into their data and to facilitate novel hypothesis generation. Availability and ImplementationCITE-Viz installation and usage instructions can be found in the GitHub repository https://github.com/maxsonBraunLab/CITE-Viz Contactmaxsonj@ohsu.edu Supplementary InformationDown-sampled peripheral blood mononuclear dataset (Hao et al. 2021): https://bit.ly/3vxbhfW

bioinformatics↗