Resolving systematic errors in widely used enhancer activity assays in human cells.

The identification of transcriptional enhancers in the human genome is a prime goal in biology. Enhancers are typically predicted via chromatin marks, yet their function is primarily assessed with plasmid-based reporter assays. Here, we show that such assays are rendered unreliable by two previously reported phenomena relating to plasmid transfection into human cells: (i) the bacterial plasmid origin of replication (ORI) functions as a conflicting core promoter and (ii) a type I interferon (IFN-I) response is activated. These cause confounding false positives and negatives in luciferase assays and STARR-seq screens. We overcome both problems by employing the ORI as core promoter and by inhibiting two IFN-I-inducing kinases, enabling genome-wide STARR-seq screens in human cells. In HeLa-S3 cells, we uncover strong enhancers, IFN-I-induced enhancers, and enhancers endogenously silenced at the chromatin level. Our findings apply to all episomal enhancer activity assays in mammalian cells and are key to the characterization of human enhancers.

Transcriptional regulators form diverse groups with context-dependent regulatory functions.

One of the most important questions in biology is how transcription factors (TFs) and cofactors control enhancer function and thus gene expression. Enhancer activation usually requires combinations of several TFs, indicating that TFs function synergistically and combinatorially. However, while TF binding has been extensively studied, little is known about how combinations of TFs and cofactors control enhancer function once they are bound. It is typically unclear which TFs participate in combinatorial enhancer activation, whether different TFs form functionally distinct groups, or if certain TFs might substitute for each other in defined enhancer contexts. Here we assess the potential regulatory contributions of TFs and cofactors to combinatorial enhancer control with enhancer complementation assays. We recruited GAL4-DNA-binding-domain fusions of 812 Drosophila TFs and cofactors to 24 enhancer contexts and measured enhancer activities by 82,752 luciferase assays in S2 cells. Most factors were functional in at least one context, yet their contributions differed between contexts and varied from repression to activation (up to 289-fold) for individual factors. Based on functional similarities across contexts, we define 15 groups of TFs that differ in developmental functions and protein sequence features. Similar TFs can substitute for each other, enabling enhancer re-engineering by exchanging TF motifs, and TF-cofactor pairs cooperate during enhancer control and interact physically. Overall, we show that activators and repressors can have diverse regulatory functions that typically depend on the enhancer context. The systematic functional characterization of TFs and cofactors should further our understanding of combinatorial enhancer control and gene regulation.

Genome-scale functional characterization of Drosophila developmental enhancers in vivo.

Transcriptional enhancers are crucial regulators of gene expression and animal development and the characterization of their genomic organization, spatiotemporal activities and sequence properties is a key goal in modern biology. Here we characterize the in vivo activity of 7,705 Drosophila melanogaster enhancer candidates covering 13.5% of the non-coding non-repetitive genome throughout embryogenesis. 3,557 (46%) candidates are active, suggesting a high density with 50,000 to 100,000 developmental enhancers genome-wide. The vast majority of enhancers display specific spatial patterns that are highly dynamic during development. Most appear to regulate their neighbouring genes, suggesting that the cis-regulatory genome is organized locally into domains, which are supported by chromosomal domains, insulator binding and genome evolution. However, 12 to 21 per cent of enhancers appear to skip non-expressed neighbours and regulate a more distal gene. Finally, we computationally identify cis-regulatory motifs that are predictive and required for enhancer activity, as we validate experimentally. This work provides global insights into the organization of an animal regulatory genome and the make-up of enhancer sequences and confirms and generalizes principles from previous studies. All enhancer patterns are annotated manually with a controlled vocabulary and all results are available through a web interface (http://enhancers.starklab.org), including the raw images of all microscopy slides for manual inspection at arbitrary zoom levels.

A self-generated Toddler gradient guides mesodermal cell migration.

The sculpting of germ layers during gastrulation relies on the coordinated migration of progenitor cells, yet the cues controlling these long-range directed movements remain largely unknown. While directional migration often relies on a chemokine gradient generated from a localized source, we find that zebrafish ventrolateral mesoderm is guided by a self-generated gradient of the initially uniformly expressed and secreted protein Toddler/ELABELA/Apela. We show that the Apelin receptor, which is specifically expressed in mesodermal cells, has a dual role during gastrulation, acting as a scavenger receptor to generate a Toddler gradient, and as a chemokine receptor to sense this guidance cue. Thus, we uncover a single receptor-based self-generated gradient as the enigmatic guidance cue that can robustly steer the directional migration of mesoderm through the complex and continuously changing environment of the gastrulating embryo.

Genome-wide assessment of sequence-intrinsic enhancer responsiveness at single-base-pair resolution.

Gene expression is controlled by enhancers that activate transcription from the core promoters of their target genes. Although a key function of core promoters is to convert enhancer activities into gene transcription, whether and how strongly they activate transcription in response to enhancers has not been systematically assessed on a genome-wide level. Here we describe self-transcribing active core promoter sequencing (STAP-seq), a method to determine the responsiveness of genomic sequences to enhancers, and apply it to the Drosophila melanogaster genome. We cloned candidate fragments at the position of the core promoter (also called minimal promoter) in reporter plasmids with or without a strong enhancer, transfected the resulting library into cells, and quantified the transcripts that initiated from each candidate for each setup by deep sequencing. In the presence of a single strong enhancer, the enhancer responsiveness of different sequences differs by several orders of magnitude, and different levels of responsiveness are associated with genes of different functions. We also identify sequence features that predict enhancer responsiveness and discuss how different core promoters are employed for the regulation of gene expression.

Associating approximate paths and temporal sequences of noisy detections: Application to the recovery of spatio-temporal cancer cell trajectories.

In this paper we address the problem of recovering spatio-temporal trajectories of cancer cells in phase contrast video-microscopy where the user provides the paths on which the cells are moving. The paths are purely spatial, without temporal information. To recover the temporal information associated to a given path we propose an approach based on automatic cell detection and on a graph-based shortest path search. The nodes in the graph consist of the projections of the cell detections onto the geometrical cell path. The edges relate nodes which correspond to different frames of the sequence and potentially to the same cell and trajectory. In this directed graph we search for the shortest path and use it to define a temporal parametrization of the corresponding geometrical cell path. An evaluation based on 286 paths of 7 phase contrast microscopy videos shows that our algorithm allows to recover 92% of trajectory points with respect to the associated ground truth. We compare our method with a state-of-the-art algorithm for semi-automated cell tracking in phase contrast microscopy which requires interactively placed starting points for the cells to track. The comparison shows that supporting geometrical paths in combination with our algorithm allow us to obtain more reliable cell trajectories.

Learning cellular texture features in microscopic cancer cell images for automated cell-detection.

In this paper we present a new approach for automated cell detection in single frames of 2D microscopic phase contrast images of cancer cells which is based on learning cellular texture features. The main challenge addressed in this paper is to deal with clusters of cells where each cell has a rather complex appearance composed of sub-regions with different texture features. Our approach works on two different levels of abstraction. First, we apply statistical learning to learn 6 different types of different local cellular texture features, classify each pixel according to them and we obtain an image partition composed of 6 different pixel categories. Based on this partitioned image we decide in a second step if pre-selected seeds belong to the same cell or not. Experimental results show the high accuracy of the proposed method and especially average precision above 95%.