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.

We sequenced 122 miRNAs in 10 primate species to reveal conservation characteristics of miRNA genes. Strong conservation is observed in stems of miRNA hairpins and increased variation in loop sequences. Interestingly, a striking drop in conservation was found for sequences immediately flanking the miRNA hairpins. This characteristic profile was employed to predict novel miRNAs using cross-species comparisons. Nine hundred and seventy-six candidate miRNAs were identified by scanning whole-genome human/mouse and human/rat alignments. Most of the novel candidates are conserved also in other vertebrates (dog, cow, chicken, opossum, zebrafish). Northern blot analysis confirmed the expression of mature miRNAs for 16 out of 69 representative candidates. Additional support for the expression of 179 novel candidates can be found in public databases, their presence in gene clusters, and literature that appeared after these predictions were made. Taken together, these results suggest the presence of significantly higher numbers of miRNAs in the human genome than previously estimated.

SUMMARY Cellular senescence is a state of irreversibly arrested proliferation, often induced by genotoxic stress [1]. Senescent cells participate in a variety of physiological and pathological conditions, including tumor suppression [2], embryonic development [3, 4], tissue repair [5–8], and organismal aging [9]. The senescence program is variably characterized by several non-exclusive markers, including constitutive DNA damage response (DDR) signaling, senescence-associated β-galactosidase (SA-βgal) activity, increased expression of the cyclin-dependent kinase (CDK) inhibitors p16INK4A (CDKN2A) and p21CIP1 (CDKN1A), increased secretion of many bio-active factors (the senescence-associated secretory phenotype, or SASP), and reduced expression of the nuclear lamina protein LaminB1 (LMNB1) [1]. Many senescence-associated markers result from altered transcription, but the senescent phenotype is variable, and methods for clearly identifying senescent cells are lacking [10]. Here, we characterize the heterogeneity of the senescence program using numerous whole-transcriptome datasets generated by us or publicly available. We identify transcriptome signatures associated with specific senescence-inducing stresses or senescent cell types and identify and validate genes that are commonly differentially regulated. We also show that the senescent phenotype is dynamic, changing at varying intervals after senescence induction. Identifying novel transcriptome signatures to detect any type of senescent cell or to discriminate among diverse senescence programs is an attractive strategy for determining the diverse biological roles of senescent cells and developing specific drug targets.