Glucagon-like peptide-1 analogs activate AMP kinase leading to reversal of the Warburg metabolic switch in breast cancer cells.

Breast cancer (BC) is associated with type 2 diabetes mellitus (T2DM) and obesity. Glucagon-like peptide (GLP)-1 regulates post-prandial insulin secretion, satiety, and gastric emptying. Several GLP-1 analogs have been FDA-approved for the treatment of T2DM and obesity. Moreover, GLP-1 regulates various metabolic activities across different tissues by activating metabolic signaling pathways like adenosine monophosphate (AMP) activated protein kinase (AMPK), and AKT. Rewiring metabolic pathways is a recognized hallmark of cancer, regulated by several cancer-related pathways, including AKT and AMPK. As GLP-1 regulates AKT and AMPK, we hypothesized that it alters BC cells' metabolism, thus inhibiting proliferation. The effect of the GLP-1 analogs exendin-4 (Ex4) and liraglutide on viability, AMPK signaling and metabolism of BC cell lines were assessed. Viability of BC cells was evaluated using colony formation and MTT/XTT assays. Activation of AMPK and related signaling effects were evaluated using western blot. Metabolism effects were measured for glucose, lactate and ATP. Exendin-4 and liraglutide activated AMPK in a cAMP-dependent manner. Blocking Ex4-induced activation of AMPK by inhibition of AMPK restored cell viability. Interestingly, Ex4 and liraglutide reduced the levels of glycolytic metabolites and decreased ATP production, suggesting that GLP-1 analogs impair glycolysis. Notably, inhibiting AMPK reversed the decline in ATP levels, highlighting the role of AMPK in this process. These results establish a novel signaling pathway for GLP-1 in BC cells through cAMP and AMPK modulation affecting proliferation and metabolism. This study suggests that GLP-1 analogs should be considered for diabetic patients with BC.

Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease.

Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNAseq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Single cell mapping of the spatial transcriptome using paired snRNAseq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNAseq data poorly correlated with cellular relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.

Technical optimization of spatially resolved single-cell transcriptomic datasets to study clinical liver disease.

Single cell and spatially resolved 'omic' techniques have enabled deep characterization of clinical pathologies that remain poorly understood, providing unprecedented insights into molecular mechanisms of disease. However, transcriptomic platforms are costly, limiting sample size, which increases the possibility of pre-analytical variables such as tissue processing and storage procedures impacting RNA quality and downstream analyses. Furthermore, spatial transcriptomics have not yet reached single cell resolution, leading to the development of multiple deconvolution methods to predict individual cell types within each transcriptome 'spot' on tissue sections. In this study, we performed spatial transcriptomics and single nucleus RNA sequencing (snRNASeq) on matched specimens from patients with either histologically normal or advanced fibrosis to establish important aspects of tissue handling, data processing, and downstream analyses of biobanked liver samples. We observed that tissue preservation technique impacts transcriptomic data, especially in fibrotic liver. Deconvolution of the spatial transcriptome using paired snRNASeq data generated a spatially resolved, single cell dataset with 24 unique liver cell phenotypes. We determined that cell-cell interactions predicted using ligand-receptor analysis of snRNASeq data poorly correlated with celullar relationships identified using spatial transcriptomics. Our study provides a framework for generating spatially resolved, single cell datasets to study gene expression and cell-cell interactions in biobanked clinical samples with advanced liver disease.

Integrating T cell receptor sequences and transcriptional profiles by clonotype neighbor graph analysis (CoNGA).

Links between T cell clonotypes, as defined by T cell receptor (TCR) sequences, and phenotype, as reflected in gene expression (GEX) profiles, surface protein expression and peptide:major histocompatibility complex binding, can reveal functional relationships beyond the features shared by clonally related cells. Here we present clonotype neighbor graph analysis (CoNGA), a graph theoretic approach that identifies correlations between GEX profile and TCR sequence through statistical analysis of GEX and TCR similarity graphs. Using CoNGA, we uncovered associations between TCR sequence and GEX profiles that include a previously undescribed 'natural lymphocyte' population of human circulating CD8+ T cells and a set of TCR sequence determinants of differentiation in thymocytes. These examples show that CoNGA might help elucidate complex relationships between TCR sequence and T cell phenotype in large, heterogeneous, single-cell datasets.