Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq.

Single-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

Using synthetic biology to study gene regulatory evolution.

Transcriptional enhancers specify the precise time, level, and location of gene expression. Disentangling and characterizing the components of enhancer activity in multicellular eukaryotic development has proven challenging because enhancers contain activator and repressor binding sites for multiple factors that each exert nuanced, context-dependent control of enhancer activity. Recent advances in synthetic biology provide an almost unlimited ability to create and modify regulatory elements and networks, offering unprecedented power to study gene regulation. Here we review several studies demonstrating the utility of synthetic biology for studying enhancer function during development and evolution. These studies clearly show that synthetic biology can provide a way to reverse-engineer and reengineer transcriptional regulation in animal genomes with enormous potential for understanding evolution.

Quantitatively predictable control of Drosophila transcriptional enhancers in vivo with engineered transcription factors.

Genes are regulated by transcription factors that bind to regions of genomic DNA called enhancers. Considerable effort is focused on identifying transcription factor binding sites, with the goal of predicting gene expression from DNA sequence. Despite this effort, general, predictive models of enhancer function are currently lacking. Here we combine quantitative models of enhancer function with manipulations using engineered transcription factors to examine the extent to which enhancer function can be controlled in a quantitatively predictable manner. Our models, which incorporate few free parameters, can accurately predict the contributions of ectopic transcription factor inputs. These models allow the predictable 'tuning' of enhancers, providing a framework for the quantitative control of enhancers with engineered transcription factors.

Cellular resolution models for even skipped regulation in the entire Drosophila embryo.

Transcriptional control ensures genes are expressed in the right amounts at the correct times and locations. Understanding quantitatively how regulatory systems convert input signals to appropriate outputs remains a challenge. For the first time, we successfully model even skipped (eve) stripes 2 and 3+7 across the entire fly embryo at cellular resolution. A straightforward statistical relationship explains how transcription factor (TF) concentrations define eve's complex spatial expression, without the need for pairwise interactions or cross-regulatory dynamics. Simulating thousands of TF combinations, we recover known regulators and suggest new candidates. Finally, we accurately predict the intricate effects of perturbations including TF mutations and misexpression. Our approach imposes minimal assumptions about regulatory function; instead we infer underlying mechanisms from models that best fit the data, like the lack of TF-specific thresholds and the positional value of homotypic interactions. Our study provides a general and quantitative method for elucidating the regulation of diverse biological systems. DOI:http://dx.doi.org/10.7554/eLife.00522.001.

Know your limits: assumptions, constraints and interpretation in systems biology.

Much of modern biological research can be organised under unifying concepts such as 'Network Biology' or 'Systems Biology'. These provide frameworks for discussion and evaluation, which is particularly necessary given the large number of interconnected components being measured in the genomic era. Conversely, they embody simplifications and assumptions that place limits on what can be deduced from experimental data. Understanding these constraints is essential not only for scientific interpretation, but also in evaluating new experimental methods and conceptual advances.