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.

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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.

The degree of intrinsic and interpatient phenotypic heterogeneity and its role in tumor evolution is poorly understood. Phenotypic drifts can be transmitted via inheritable transcriptional programs. Cell-type specific transcription is maintained through the activation of epigenetically defined regulatory regions including promoters and enhancers. Here we have annotated the epigenome of 47 primary and metastatic estrogen-receptor (ERα)-positive breast cancer clinical specimens and inferred phenotypic heterogeneity from the regulatory landscape, identifying key regulatory elements commonly shared across patients. Shared regions contain a unique set of regulatory information including the motif for transcription factor YY1. We identify YY1 as a critical determinant of ERα transcriptional activity promoting tumor growth in most luminal patients. YY1 also contributes to the expression of genes mediating resistance to endocrine treatment. Finally, we used H3K27ac levels at active enhancer elements as a surrogate of intra-tumor phenotypic heterogeneity to track the expansion and contraction of phenotypic subpopulations throughout breast cancer progression. By tracking the clonality of SLC9A3R1-positive cells, a bona fide YY1-ERα-regulated gene, we show that endocrine therapies select for phenotypic clones under-represented at diagnosis. Collectively, our data show that epigenetic mechanisms significantly contribute to phenotypic heterogeneity and evolution in systemically treated breast cancer patients.

Tumor evolution is shaped by many variables, potentially involving external selective pressures induced by therapies1. After surgery, estrogen receptor (ERα) positive breast cancer (BCa) patients are treated with adjuvant endocrine therapy2 including selective estrogen receptor modulators (SERMs) and/or aromatase inhibitors (AIs)3. However, over 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: in this study, 21.5% of AI-treated, relapsed patients had acquired CYP19A1 gene (aromatase) amplification (CYP19A1amp). Relapsed patients also developed numerous mutations targeting key breast cancer genes including ESR1 and CYP19A1. Strikingly, CYP19A1amp cells also emerge in vitro but only in AI resistant models. CYP19A1 amplification causes increased aromatase activity and estrogen-independent ERα binding to target genes resulting in CYP19A1amp cells displaying decreased sensitivity to AI treatment. Collectively these data suggest that AI treatment itself selects for acquired CYP19A1 amplification and promotes local autocrine estrogen signalling in AI resistant metastatic patients.