Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response.
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.
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.
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.
Although many primary triple negative breast cancers (TNBCs) have enhanced expression of epidermal growth factor receptor (EGFR), EGFR-targeted therapies have shown only variable and unpredictable clinical responses in TNBCs. Here we integrate cellular barcoding and single-cell transcriptomics methods to comprehensively characterize the subclonal dynamics of adaptation of TNBC cells in response to afatinib, tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. Integrated lineage tracing analysis on these cells uncovered a pre-existing subpopulation of cells composed of 192 clones (out of the initial 2,336) with distinct biological features, such as elevated IGFBP2 (Insulin-Like Growth Factor Binding Protein 2) expression levels. We demonstrate that IGFBP2 overexpression is sufficient to make TNBC cells tolerant to afatinib treatment by activating the compensatory IGF1-R signalling pathway. Finally, by using deep learning techniques, we devise an algorithm to predict the afatinib sensitivity of TNBC cells from the transcriptional status of identified marker genes of the afatinib response. Our findings provide a new understanding of EGFR-dependent hierarchy in TNBC.
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/.
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 and functional enrichment analysis to predict single cell drug sensitivity from transcriptomic data. We extensively tested DREEP on 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. 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 accelerate drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP.
Brest Cancer (BC) patient stratification is mainly driven by receptor status and histological grading and subtyping, with about twenty percent of patients for which absence of any actionable biomarkers results in no clear therapeutic intervention to apply. Here, we evaluated the potentiality of single-cell transcriptomics for automated diagnosis and drug treatment of breast cancer. We transcriptionally profiled 35,276 individual cells from 33 BC cell-lines covering all main BC subtypes to yield a Breast Cancer Single Cell Atlas. We show that single cell transcriptomics can successfully detect clinically relevant BC biomarkers and that atlas can be used to automatically predict cancer subtype and composition from a patient's tumour biopsy. We found that BC cell lines arbour a high degree of heterogeneity in the expression of clinically relevant BC biomarkers and that such heterogeneity enables cells with differential drug sensitivity to co-exist even within a genomically stable isogenic cell line. Finally, we developed a novel bioinformatics approach named DREEP (DRug Estimation from Expression Profiles) to automatically predict responses to more than 450 anticancer agents starting from single-cell transcriptional profiles. We validated DREEP both in-silico and in-vitro by selectively inhibiting the growth of the HER2-deficient subpopulation in the MDAMB361 cell line. Our work shows transcriptional heterogeneity is common, dynamic and plays a relevant role in determining drug sensitivity. Moreover, our Breast Cancer Single Cell Atlas and DREEP approach are a unique resource for the BC research community and to advance the use of single-cell sequencing in the clinics.
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