For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae , that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type–related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

The centrality-lethality rule, which notes that high-degree nodes in a protein interaction network tend to correspond to proteins that are essential, suggests that the topological prominence of a protein in a protein interaction network may be a good predictor of its biological importance. Even though the correlation between degree and essentiality was confirmed by many independent studies, the reason for this correlation remains illusive. Several hypotheses about putative connections between essentiality of hubs and the topology of protein–protein interaction networks have been proposed, but as we demonstrate, these explanations are not supported by the properties of protein interaction networks. To identify the main topological determinant of essentiality and to provide a biological explanation for the connection between the network topology and essentiality, we performed a rigorous analysis of six variants of the genomewide protein interaction network for Saccharomyces cerevisiae obtained using different techniques. We demonstrated that the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. Moreover, we rejected two previously proposed explanations for the centrality-lethality rule, one relating the essentiality of hubs to their role in the overall network connectivity and another relying on the recently published essential protein interactions model. Analysis of protein interaction networks in the budding yeast Saccharomyces cerevisiae has revealed that a small number of proteins, the so-called hubs, interact with a disproportionately large number of other proteins. Furthermore, many hub proteins have been shown to be essential for survival of the cell—that is, in optimal conditions, yeast cannot grow and multiply without them. This relation between essentiality and the number of neighbors in the protein–protein interaction network has been termed the centrality-lethality rule. However, why are such hubs essential? Jeong and colleagues [1] suggested that overrepresentation of essential proteins among high-degree nodes can be attributed to the central role that hubs play in mediating interactions among numerous, less connected proteins. Another view, proposed by He and Zhang, suggested that that the majority of proteins are essential due to their involvement in one or more essential protein–protein interactions that are distributed uniformly at random along the network edges [2]. We find that none of the above reasons determines essentiality. Instead, the majority of hubs are essential due to their involvement in Essential Complex Biological Modules, a group of densely connected proteins with shared biological function that are enriched in essential proteins. This study sheds new light on the topological complexity of protein interaction networks.

Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

A DNase sequencing method termed scDNase-seq detects DNase I hypersensitive sites genome-wide in single cells and pools of cells dissected from cancer biopsies. Regulatory elements in the genome are located in chromatin regions that are accessible to DNase I digestion, and these so-called DNase I hypersensitive sites (DHSs) have been mapped across genomes in populations of cells. Keji Zhao and colleagues have now developed a DNase sequencing method termed single-cell DNase sequencing (scDNase-seq) which allows detection of these sites genome-wide in single cells. They also apply the method to pools of cells dissected from formalin-fixed paraffin-embedded biopsies from cancer patients and can compare DNase I hypersensitive sites from small numbers of tumour cells and adjacent normal cells. DNase I hypersensitive sites (DHSs) provide important information on the presence of transcriptional regulatory elements and the state of chromatin in mammalian cells1,2,3. Conventional DNase sequencing (DNase-seq) for genome-wide DHSs profiling is limited by the requirement of millions of cells4,5. Here we report an ultrasensitive strategy, called single-cell DNase sequencing (scDNase-seq) for detection of genome-wide DHSs in single cells. We show that DHS patterns at the single-cell level are highly reproducible among individual cells. Among different single cells, highly expressed gene promoters and enhancers associated with multiple active histone modifications display constitutive DHS whereas chromatin regions with fewer histone modifications exhibit high variation of DHS. Furthermore, the single-cell DHSs predict enhancers that regulate cell-specific gene expression programs and the cell-to-cell variations of DHS are predictive of gene expression. Finally, we apply scDNase-seq to pools of tumour cells and pools of normal cells, dissected from formalin-fixed paraffin-embedded tissue slides from patients with thyroid cancer, and detect thousands of tumour-specific DHSs. Many of these DHSs are associated with promoters and enhancers critically involved in cancer development. Analysis of the DHS sequences uncovers one mutation (chr18: 52417839G>C) in the tumour cells of a patient with follicular thyroid carcinoma, which affects the binding of the tumour suppressor protein p53 and correlates with decreased expression of its target gene TXNL1. In conclusion, scDNase-seq can reliably detect DHSs in single cells, greatly extending the range of applications of DHS analysis both for basic and for translational research, and may provide critical information for personalized medicine.

The connection between dynamic DNA supercoiling and transcription is not well understood. High-resolution mapping of in vivo DNA supercoiling at transcription start sites (TSSs) now reveals that supercoils spread about 1.5 kb upstream of the TSSs of active genes. Highly expressed genes rely on topoisomerase II to dissipate dynamic supercoiling, whereas moderately expressed genes depend on topoisomerase I. Transcription has the capacity to mechanically modify DNA topology, DNA structure and nucleosome arrangement. Resulting from ongoing transcription, these modifications in turn may provide instant feedback to the transcription machinery. To substantiate the connection between transcription and DNA dynamics, we charted an ENCODE map of transcription-dependent dynamic supercoiling in human Burkitt's lymphoma cells by using psoralen photobinding to probe DNA topology in vivo. Dynamic supercoils spread ~1.5 kilobases upstream of the start sites of active genes. Low- and high-output promoters handled this torsional stress differently, as shown by using inhibitors of transcription and topoisomerases and by chromatin immunoprecipation of RNA polymerase and topoisomerases I and II. Whereas lower outputs are managed adequately by topoisomerase I, high-output promoters additionally require topoisomerase II. The genome-wide coupling between transcription and DNA topology emphasizes the importance of dynamic supercoiling for gene regulation.