Charles A Whittaker | PubMed Health

PubMed2026

Uncovering the signaling networks of disseminated glioblastoma cells in vivo with INSIGHT.

Dysregulation of intracellular signaling networks underpins cancer. Yet, resolving signaling networks within distinct or rare cell types in cancer in vivo has been unattainable. Here we develop INSIGHT by integrating cell sorting with mass spectrometry to enable quantitative phosphoproteomics and proteomics of discrete cell types from fixed tissues. Using INSIGHT, we map the signaling network within disseminating glioblastoma cells from patient-derived xenografts implanted in mice. Disseminating tumor cells undergo a proteome-wide shift from proliferative to mesenchymal, neural progenitor-like cell states. In parallel, signaling network and global kinase activity are rewired, transitioning from cell cycle-associated circuitries to those governing synaptic function, neuronal migration, and ion channel activity. Changes begin at the tumor margin and persist in distant brain parenchyma. Hornerin and phosphorylation of Ca²⁺-permeable GluA2 at Y876 were identified as mediators of glioblastoma progression. INSIGHT enables systems-level dissection of cell-type-specific signaling circuitries in vivo across wide range of biological systems.

Glioblastoma↗

PubMed2023

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Computational Biology↗