Publications by authors named "Julia Verga"

Gliomas are incurable malignancies notable for an immunosuppressive microenvironment with abundant myeloid cells whose immunomodulatory properties remain poorly defined. Here, utilizing scRNA-seq data for 183,062 myeloid cells from 85 human tumors, we discover that nearly all glioma-associated myeloid cells express at least one of four immunomodulatory activity programs: Scavenger Immunosuppressive, C1Q Immunosuppressive, CXCR4 Inflammatory, and IL1B Inflammatory. All four programs are present in IDH1 mutant and wild-type gliomas and are expressed in macrophages, monocytes, and microglia whether of blood or resident myeloid cell origins.

View Article and Find Full Text PDF
Article Synopsis
  • Tumours typically develop from harmful changes in specific cell groups within the same area of the body, with this study focusing on a rare type of acute leukaemia called blastic plasmacytoid dendritic cell neoplasm (BPDCN) that often begins in the skin.
  • Researchers utilized advanced techniques to show that BPDCN originates from mutated blood cell precursors in the bone marrow, which have been affected by UV radiation exposure in sunlit areas of the skin.
  • The study highlights that UV damage can precede further mutations that lead to cancer, pointing to a role for the TET2 gene in resisting UV-induced cell death, thus highlighting how environmental factors influence the progression of cancer.
View Article and Find Full Text PDF

The combination of single-cell transcriptomics with mitochondrial DNA variant detection can be used to establish lineage relationships in primary human cells, but current methods are not scalable to interrogate complex tissues. Here, we combine common 3' single-cell RNA-sequencing protocols with mitochondrial transcriptome enrichment to increase coverage by more than 50-fold, enabling high-confidence mutation detection. The method successfully identifies skewed immune-cell expansions in primary human clonal hematopoiesis.

View Article and Find Full Text PDF

Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor.

View Article and Find Full Text PDF