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Martin, C. A.

Publications and source records attributed to Martin, C. A..

2 recordsLinked to original sources

RNA binding kinase UHMK1 regulates therapy induced metabolic adaptation in BRAFV600 melanoma

Despite the success of therapies targeting oncogenes in cancer, clinical outcomes are limited by a residual disease that results in relapse. This residual disease is characterized by drug-induced adaptation, that in melanoma includes altered metabolism. Here, we examined how targeted therapy reprograms metabolism in BRAF-mutant melanoma cells using a genome-wide RNAi screen and global gene expression profiling. This systematic approach revealed post-transcriptional regulation of metabolism following BRAF inhibition, involving selective mRNA transport and translation. As proof of concept we demonstrate the RNA binding kinase UHMK1 interacts with mRNAs that encode metabolic proteins and selectively controls their transport and translation during adaptation to BRAF targeted therapy. Inactivation of UHMK1 improves metabolic response to BRAF targeted therapy and delays resistance to BRAF and MEK combination therapy in vivo. Our data support a model wherein post-transcriptional gene expression pathways regulate metabolic adaptation underpinning targeted therapy response and suggest inactivation of these pathways may delay disease relapse.

cancer biology

Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

The spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 {micro}m spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seqs utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression. One Sentence SummarySlide-seq measures genome-wide expression in complex tissues at 10-micron resolution.

genomics