A community computational challenge generates algorithms to predict activity of drug combinations. Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation–maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

Zoning in on liver growth: For organ homeostasis or regrowth after injury or disease, one or more stem cell populations is needed to rebuild lost tissue. There is considerable debate about the source of new cells in the liver. Two groups now identify the source of new hepatocytes (see the Perspective by Andersson). Although the liver may seem to lack major variation across its structure, its lobule is organized into concentric zones where hepatocytes express different metabolic enzymes. Wei et al. sought to systematically define the source of new liver cells by comparing 14 fate-mapping mice that label different liver cell types. They found that different regions of the liver lobule exhibit differences in hepatocyte turnover, with zone 2 representing a primary source of new hepatocytes during homeostasis and regeneration. Similarly, He et al. designed a genetic approach to record cell proliferation in vivo with high spatial and temporal resolution to enable continuous recording of proliferative events of any specific cell type at the whole-cell population level. Using this method, they identified zone 2 as having the highest proliferative activity and contributing the most to liver regrowth. These findings have implications for the cellular basis of chronic disease pathogenesis, cancer development, and regenerative medicine strategies. Science , this issue p. eabb1625 , p. eabc4346 ; see also p. 887