The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

The role of epigenetics in endothelial cell senescence is a cutting‐edge topic in ageing research. However, little is known of the relative contribution to pro‐senescence signal propagation provided by microRNAs shuttled by extracellular vesicles (EVs) released from senescent cells. Analysis of microRNA and DNA methylation profiles in non‐senescent (control) and senescent (SEN) human umbilical vein endothelial cells (HUVECs), and microRNA profiling of their cognate small EVs (sEVs) and large EVs demonstrated that SEN cells released a significantly greater sEV number than control cells. sEVs were enriched in miR‐21‐5p and miR‐217, which target DNMT1 and SIRT1. Treatment of control cells with SEN sEVs induced a miR‐21/miR‐217‐related impairment of DNMT1‐SIRT1 expression, the reduction of proliferation markers, the acquisition of a senescent phenotype and a partial demethylation of the locus encoding for miR‐21. MicroRNA profiling of sEVs from plasma of healthy subjects aged 40–100 years showed an inverse U‐shaped age‐related trend for miR‐21‐5p, consistent with senescence‐associated biomarker profiles. Our findings suggest that miR‐21‐5p/miR‐217 carried by SEN sEVs spread pro‐senescence signals, affecting DNA methylation and cell replication.

Resistant tumours are thought to arise from the action of Darwinian selection on genetically heterogenous cancer cell populations. However, simple clonal selection is inadequate to describe the late relapses often characterising luminal breast cancers treated with endocrine therapy (ET), suggesting a more complex interplay between genetic and non-genetic factors. Here, we dissect the contributions of clonal genetic diversity and transcriptional plasticity during the early and late phases of ET at single-cell resolution. Using single-cell RNA-sequencing and imaging we disentangle the transcriptional variability of plastic cells and define a rare subpopulation of pre-adapted (PA) cells which undergoes further transcriptomic reprogramming and copy number changes to acquire full resistance. We find evidence for sub-clonal expression of a PA signature in primary tumours and for dominant expression in clustered circulating tumour cells. We propose a multi-step model for ET resistance development and advocate the use of stage-specific biomarkers.

Saverio Minucci, Giancarlo Pruneri, Luca Magnani and colleagues identify acquired CYP19A1 amplification as a mechanism of resistance to aromatase inhibitors in ERα metastatic breast cancer. Mechanistically, they show that CYP19A1 amplification results in increased aromatase activity and estrogen-independent ERα binding to target genes. Tumor evolution is shaped by many variables, potentially involving external selective pressures induced by therapies1. After surgery, patients with estrogen receptor (ERα)-positive breast cancer are treated with adjuvant endocrine therapy2, including selective estrogen receptor modulators (SERMs) and/or aromatase inhibitors (AIs)3. However, more than 20% of patients relapse within 10 years and eventually progress to incurable metastatic disease4. Here we demonstrate that the choice of therapy has a fundamental influence on the genetic landscape of relapsed diseases. We found that 21.5% of AI-treated, relapsed patients had acquired CYP19A1 (encoding aromatase) amplification (CYP19A1amp). Relapsed patients also developed numerous mutations targeting key breast cancer–associated genes, including ESR1 and CYP19A1. Notably, CYP19A1amp cells also emerged in vitro, but only in AI-resistant models. CYP19A1 amplification caused increased aromatase activity and estrogen-independent ERα binding to target genes, resulting in CYP19A1amp cells showing decreased sensitivity to AI treatment. These data suggest that AI treatment itself selects for acquired CYP19A1amp and promotes local autocrine estrogen signaling in AI-resistant metastatic patients.

Histone post-translational modifications (PTMs) generate a complex combinatorial code that regulates gene expression and nuclear functions, and whose deregulation has been documented in different types of cancers. Therefore, the availability of relevant culture models that can be manipulated and that retain the epigenetic features of the tissue of origin is absolutely crucial for studying the epigenetic mechanisms underlying cancer and testing epigenetic drugs. In this study, we took advantage of quantitative mass spectrometry to comprehensively profile histone PTMs in patient tumor tissues, primary cultures and cell lines from three representative tumor models, breast cancer, glioblastoma and ovarian cancer, revealing an extensive and systematic rewiring of histone marks in cell culture conditions, which includes a decrease of H3K27me2/me3, H3K79me1/me2 and H3K9ac/K14ac, and an increase of H3K36me1/me2. While some changes occur in short-term primary cultures, most of them are instead time-dependent and appear only in long-term cultures. Remarkably, such changes mostly revert in cell line- and primary cell-derived in vivo xenograft models. Taken together, these results support the use of xenografts as the most representative models of in vivo epigenetic processes, suggesting caution when using cultured cells, in particular cell lines and long-term primary cultures, for epigenetic investigations.