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.

Dendritic cells (DCs) are critical regulators of immune responses. Under noninflammatory conditions, several human DC subsets have been identified. Little is known, however, about the human DC compartment under inflammatory conditions. Here, we characterize a DC population found in human inflammatory fluids that displayed a phenotype distinct from macrophages from the same fluids and from steady-state lymphoid organ and blood DCs. Transcriptome analysis showed that they correspond to a distinct DC subset and share gene signatures with in vitro monocyte-derived DCs. Moreover, human inflammatory DCs, but not inflammatory macrophages, stimulated autologous memory CD4(+) T cells to produce interleukin-17 and induce T helper 17 (Th17) cell differentiation from naive CD4(+) T cells through the selective secretion of Th17 cell-polarizing cytokines. We conclude that inflammatory DCs represent a distinct human DC subset and propose that they are derived from monocytes and are involved in the induction and maintenance of Th17 cell responses.

Cell-to-cell communication can be inferred from ligand–receptor expression in cell transcriptomic datasets. However, important challenges remain: global integration of cell-to-cell communication; biological interpretation; and application to individual cell population transcriptomic profiles. We develop ICELLNET, a transcriptomic-based framework integrating: 1) an original expert-curated database of ligand–receptor interactions accounting for multiple subunits expression; 2) quantification of communication scores; 3) the possibility to connect a cell population of interest with 31 reference human cell types; and 4) three visualization modes to facilitate biological interpretation. We apply ICELLNET to three datasets generated through RNA-seq, single-cell RNA-seq, and microarray. ICELLNET reveals autocrine IL-10 control of human dendritic cell communication with up to 12 cell types. Four of them (T cells, keratinocytes, neutrophils, pDC) are further tested and experimentally validated. In summary, ICELLNET is a global, versatile, biologically validated, and easy-to-use framework to dissect cell communication from individual or multiple cell-based transcriptomic profiles.