γ-2 and GSG1L bind with comparable affinities to the tetrameric GluA1 core.

BACKGROUND: The AMPA-type ionotropic glutamate receptor mediates fast excitatory neurotransmission in the brain. A variety of auxiliary subunits regulate its gating properties, assembly, and trafficking, but it is unknown if the binding of these auxiliary subunits to the receptor core is dynamically regulated. Here we investigate the interplay of the two auxiliary subunits γ-2 and GSG1L when binding to the AMPA receptor composed of four GluA1 subunits. METHODS: We use a three-color single-molecule imaging approach in living cells, which allows the direct observation of the receptors and both auxiliary subunits. Colocalization of different colors can be interpreted as interaction of the respective receptor subunits. RESULTS: Depending on the relative expression levels of γ-2 and GSG1L, the occupancy of binding sites shifts from one auxiliary subunit to the other, supporting the idea that they compete for binding to the receptor. Based on a model where each of the four binding sites at the receptor core can be either occupied by γ-2 or GSG1L, our experiments yield apparent dissociation constants for γ-2 and GSG1L in the range of 2.0-2.5/µm2. CONCLUSIONS: The result that both binding affinities are in the same range is a prerequisite for dynamic changes of receptor composition under native conditions.

Patterns of Oncogene Coexpression at Single-Cell Resolution Influence Survival in Lymphoma.

Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6- (M+2+6-) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6- percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6- unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance. SIGNIFICANCE: Using single-cell-resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer. This article is highlighted in the In This Issue feature, p. 1027.

Genetic modification of primary human B cells to model high-grade lymphoma.

Sequencing studies of diffuse large B cell lymphoma (DLBCL) have identified hundreds of recurrently altered genes. However, it remains largely unknown whether and how these mutations may contribute to lymphomagenesis, either individually or in combination. Existing strategies to address this problem predominantly utilize cell lines, which are limited by their initial characteristics and subsequent adaptions to prolonged in vitro culture. Here, we describe a co-culture system that enables the ex vivo expansion and viral transduction of primary human germinal center B cells. Incorporation of CRISPR/Cas9 technology enables high-throughput functional interrogation of genes recurrently mutated in DLBCL. Using a backbone of BCL2 with either BCL6 or MYC, we identify co-operating genetic alterations that promote growth or even full transformation into synthetically engineered DLBCL models. The resulting tumors can be expanded and sequentially transplanted in vivo, providing a scalable platform to test putative cancer genes and to create mutation-directed, bespoke lymphoma models.