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.

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.

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.

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.

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.

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.

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.