Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.

BACKGROUND: Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses. METHODS: To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. RESULTS: Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells. CONCLUSIONS: Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.

Predicting drug response from single-cell expression profiles of tumours.

BACKGROUND: Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention. RESULTS: Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations. CONCLUSIONS: DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.

Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.

The VersaLive platform enables microfluidic mammalian cell culture for versatile applications.

Microfluidic-based cell culture allows for precise spatio-temporal regulation of microenvironment, live cell imaging and better recapitulation of physiological conditions, while minimizing reagents' consumption. Despite their usefulness, most microfluidic systems are designed with one specific application in mind and usually require specialized equipment and expertise for their operation. All these requirements prevent microfluidic-based cell culture to be widely adopted. Here, we designed and implemented a versatile and easy-to-use perfusion cell culture microfluidic platform for multiple applications (VersaLive) requiring only standard pipettes. Here, we showcase the multiple uses of VersaLive (e.g., time-lapse live cell imaging, immunostaining, cell recovery, cell lysis, plasmid transfection) in mammalian cell lines and primary cells. VersaLive could replace standard cell culture formats in several applications, thus decreasing costs and increasing reproducibility across laboratories. The layout, documentation and protocols are open-source and available online at https://versalive.tigem.it/ .

TRPV4 and purinergic receptor signalling pathways are separately linked in airway epithelia to CFTR and TMEM16A chloride channels.

KEY POINTS: Eact is a putative pharmacological activator of TMEM16A. Eact is strongly effective in recombinant Fischer rat thyroid (FRT) cells but not in airway epithelial cells with endogenous TMEM16A expression. Transcriptomic analysis, gene silencing and functional studies in FRT cells reveal that Eact is actually an activator of the Ca2+ -permeable TRPV4 channel. In airway epithelial cells TRPV4 and TMEM16A are expressed in separate cell types. Intracellular Ca2+ elevation by TRPV4 stimulation leads to CFTR channel activation. ABSTRACT: TMEM16A is a Ca2+ -activated Cl- channel expressed in airway epithelial cells, particularly under conditions of mucus hypersecretion. To investigate the role of TMEM16A, we used Eact, a putative TMEM16A pharmacological activator. However, in contrast to purinergic stimulation, we found little effect of Eact on bronchial epithelial cells under conditions of high TMEM16A expression. We hypothesized that Eact is an indirect activator of TMEM16A. By a combination of approaches, including short-circuit current recordings, bulk and single cell RNA sequencing, intracellular Ca2+ imaging and RNA interference, we found that Eact is actually an activator of the Ca2+ -permeable TRPV4 channel and that the modest effect of this compound in bronchial epithelial cells is due to a separate expression of TMEM16A and TRPV4 in different cell types. Importantly, we found that TRPV4 stimulation induced activation of the CFTR Cl- channel. Our study reveals the existence of separate Ca2+ signalling pathways linked to different Cl- secretory processes.

A mutant of phosphomannomutase1 retains full enzymatic activity, but is not activated by IMP: Possible implications for the disease PMM2-CDG.

The most frequent disorder of glycosylation, PMM2-CDG, is caused by a deficiency of phosphomannomutase activity. In humans two paralogous enzymes exist, both of them require mannose 1,6-bis-phosphate or glucose 1,6-bis-phosphate as activators, but only phospho-mannomutase1 hydrolyzes bis-phosphate hexoses. Mutations in the gene encoding phosphomannomutase2 are responsible for PMM2-CDG. Although not directly causative of the disease, the role of the paralogous enzyme in the disease should be clarified. Phosphomannomutase1 could have a beneficial effect, contributing to mannose 6-phosphate isomerization, or a detrimental effect, hydrolyzing the bis-phosphate hexose activator. A pivotal role in regulating mannose-1phosphate production and ultimately protein glycosylation might be played by inosine monophosphate that enhances the phosphatase activity of phosphomannomutase1. In this paper we analyzed human phosphomannomutases by conventional enzymatic assays as well as by novel techniques such as 31P-NMR and thermal shift assay. We characterized a triple mutant of phospomannomutase1 that retains mutase and phosphatase activity, but is unable to bind inosine monophosphate.