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.
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