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Selected publications
Preclinical genetically engineered mouse models (GEMMs) of lung adenocarcinoma are invaluable for investigating molecular drivers of tumor formation, progression, and therapeutic resistance. However, histological analysis of these GEMMs requires significant time and training to ensure accuracy and consistency. To achieve a more objective and standardized analysis, we used machine learning to create GLASS-AI, a histological image analysis tool that the broader cancer research community can utilize to grade, segment, and analyze tumors in preclinical models of lung adenocarcinoma. GLASS-AI demonstrates strong agreement with expert human raters while uncovering a significant degree of unreported intratumor heterogeneity. Integrating immunohistochemical staining with high-resolution grade analysis by GLASS-AI identified dysregulation of Mapk/Erk signaling in high-grade lung adenocarcinomas and locally advanced tumor regions. Our work demonstrates the benefit of employing GLASS-AI in preclinical lung adenocarcinoma models and the power of integrating machine learning and molecular biology techniques for studying the molecular pathways that underlie cancer progression.
Archived tumor specimens are routinely preserved by formalin fixation and paraffin embedding. Despite the conventional wisdom that proteomics might be ineffective due to the cross-linking and pre-analytical variables, these samples have utility for both discovery and targeted proteomics. Building on this capability, proteomics approaches can be used to maximize our understanding of cancer biology and clinical relevance by studying preserved tumor tissues annotated with the patients' medical histories. Proteomics of formalin-fixed paraffin-embedded (FFPE) tissues also integrates with histological evaluation and molecular pathology strategies, so that additional collection of research biopsies or resected tumor aliquots is not needed. The acquisition of data from the same tumor sample also overcomes concerns about biological variation between samples due to intratumoral heterogeneity. However, the protein extraction and proteomics sample preparation from FFPE samples can be onerous, particularly for small (i.e., limited or precious) samples. Therefore, we provide a protocol for a recently introduced kit-based EasyPep method with benchmarking against a modified version of the well-established filter-aided sample preparation strategy using laser-capture microdissected lung adenocarcinoma tissues from a genetically engineered mouse model. This model system allows control over the tumor preparation and pre-analytical variables while also supporting the development of methods for spatial proteomics to examine intratumoral heterogeneity. Data are posted in ProteomeXchange (PXD045879).
Cardiovascular disease is the leading cause of death and disability worldwide. Effective delivery of cell-selective therapies that target atherosclerotic plaques and neointimal growth while sparing the endothelium remains the Achilles heel of percutaneous interventions. The current study utilizes synthetic microRNA switch therapy that self-assembles to form a compacted, nuclease-resistant nanoparticle <200 nM in size when mixed with cationic amphipathic cell-penetrating peptide (p5RHH). These nanoparticles possess intrinsic endosomolytic activity that requires endosomal acidification. When administered in a femoral artery wire injury mouse model in vivo, the mRNA-p5RHH nanoparticles deliver their payload specifically to the regions of endothelial denudation and not to the lungs, liver, kidney, or spleen. Moreover, repeated administration of nanoparticles containing a microRNA switch, consisting of synthetically modified mRNA encoding for the cyclin-dependent kinase inhibitor p27Kip1 that contains one complementary target sequence of the endothelial cell-specific miR-126 at its 5' UTR, drastically reduced neointima formation after wire injury and allowed for vessel reendothelialization. This cell-selective nanotherapy is a valuable tool that has the potential to advance the fight against neointimal hyperplasia and atherosclerosis.
mRNA therapeutics hold great promise for the treatment of human diseases. While incorporating naturally occurring modified nucleotides during synthesis has greatly increased their potency and safety, challenges in selective expression have hindered clinical applications. MicroRNA (miRNA)-regulated in vitro-transcribed mRNAs, called miRNA switches, have been used to control the expression of exogenous mRNA in a cell-selective manner. However, the effect of nucleotide modifications on miRNA-dependent silencing has not been examined. Here we show that the incorporation of pseudouridine, N1-methylpseudourdine, or pseudouridine and 5-methylcytidine, which increases translation, tends to decrease the regulation of miRNA switches. Moreover, pseudouridine and 5-methylcytidine modification enables one miRNA target site at the 3' UTR to be as effective as four target sites. We also demonstrate that the effects of pseudouridine, pseudouridine and 5-methylcytidine, and N1-methylpseudourdine modification are miRNA switch specific and do not correlate with the proportion of modified nucleotides in the miRNA target site. Furthermore, modified miRNA switches containing seed-complementary target sites are poorly regulated by miRNA. We also show that placing the miRNA target site in the 5' UTR of the miRNA switch abolishes the effect of nucleotide modification on miRNA-dependent silencing. This work provides insights into the influence of nucleotide modifications on miRNA-dependent silencing and informs the design of optimal miRNA switches.
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