In many cell types, migration can be oriented towards a chemical stimulus. In mammals, for example, embryonic cells migrate to follow developmental cues, immune cells migrate toward sites of inflammation, and cancer cells migrate away from the primary tumour and toward blood vessels during metastasis. Understanding how cells migrate in 3D environments in response to chemical cues is thus crucial to understanding directed migration in normal and disease states. To date, chemotaxis in mammalian cells has been primarily studied using 2D migration models. However, it is becoming increasingly clear that the mechanisms by which cells migrate in 2D and 3D environments dramatically differ, and cells in their native environments are confronted with a complex chemical milieu. To address these issues, we developed a microfluidic device to monitor the behaviour of cells embedded in a 3D collagen matrix in the presence of complex concentration fields of chemoattractants. This tuneable microsystem enables the generation of (1) homogeneous, stationary gradients set by a purely diffusive mechanism, or (2) spatially evolving, stationary gradients, set by a convection-diffusion mechanism. The device allows for stable gradients over several days and is large enough to study the behaviour of large cell aggregates. We observe that primary mature dendritic cells respond uniformly to homogeneous diffusion gradients, while cell behaviour is highly position-dependent in spatially variable convection-diffusion gradients. In addition, we demonstrate a directed response of cancer cells migrating away from tumour-like aggregates in the presence of soluble chemokine gradients. Together, this microfluidic device is a powerful system to observe the response of different cells and aggregates to tuneable chemical gradients.

Glutamate is a major excitatory neurotransmitter, and impaired glutamate clearance following synaptic release promotes spillover, inducing extra-synaptic signaling. The effects of glutamate spillover on animal behavior and its neural correlates are poorly understood. We developed a glutamate spillover model in Caenorhabditis elegans by inactivating the conserved glial glutamate transporter GLT-1. GLT-1 loss drives aberrant repetitive locomotory reversal behavior through uncontrolled oscillatory release of glutamate onto AVA, a major interneuron governing reversals. Repetitive glutamate release and reversal behavior require the glutamate receptor MGL-2/mGluR5, expressed in RIM and other interneurons presynaptic to AVA. mgl-2 loss blocks oscillations and repetitive behavior; while RIM activation is sufficient to induce repetitive reversals in glt-1 mutants. Repetitive AVA firing and reversals require EGL-30/Gαq, an mGluR5 effector. Our studies reveal that cyclic autocrine presynaptic activation drives repetitive reversals following glutamate spillover. That mammalian GLT1 and mGluR5 are implicated in pathological motor repetition suggests a common mechanism controlling repetitive behaviors.

The relation between entropy and information dates back to the classical Maxwell demon paradox¹, a thought experiment proposed in 1867 by James Clerk Maxwell to violate the second law of thermodynamics. A variant of the classical Maxwell demon is the Szilard engine, proposed by Leo Szilard in 1929¹. In it, at a given time, the demon observes the compartment occupied by a single molecule in a vessel and extracts work by operating a pulley device. Here, we introduce the continuous Maxwell demon, a device capable of extracting arbitrarily large amounts of work per cycle by repeated measurements of the state of a system, and experimentally test it in single DNA hairpin pulling experiments. In the continuous Maxwell demon, the demon monitors the state of the DNA hairpin (folded or unfolded) by observing it at equally spaced time intervals, but it extracts work only when the molecule changes state. We demonstrate that the average maximum work per cycle that can be extracted by the continuous Maxwell demon is limited by the information content of the stored sequences, in agreement with the second law. Work extraction efficiency is found to be maximal in the large information-content limit where work extraction is fuelled by rare events.

Modulation of chromatin structure via histone modification is a major epigenetic mechanism and regulator of gene expression. However, the contribution of chromatin features to tumor heterogeneity and evolution remains unknown. Here we describe a high-throughput droplet microfluidics platform to profile chromatin landscapes of thousands of cells at single-cell resolution. Using patient-derived xenograft models of acquired resistance to chemotherapy and targeted therapy in breast cancer, we found that a subset of cells within untreated drug-sensitive tumors share a common chromatin signature with resistant cells, undetectable using bulk approaches. These cells, and cells from the resistant tumors, have lost chromatin marks-H3K27me3, which is associated with stable transcriptional repression-for genes known to promote resistance to treatment. This single-cell chromatin immunoprecipitation followed by sequencing approach paves the way to study the role of chromatin heterogeneity, not just in cancer but in other diseases and healthy systems, notably during cellular differentiation and development.

While thermal rates of state transitions in classical systems have been studied for almost a century, associated transition-path times have only recently received attention. Uphill and downhill transition paths between states at different free energies should be statistically indistinguishable. Here, we systematically investigate transition-path-time symmetry and report evidence of its breakdown on the molecular- and meso-scale out of equilibrium. In automated Brownian dynamics experiments, we establish first-passage-time symmetries of colloids driven by femtoNewton forces in holographically-created optical landscapes confined within microchannels. Conversely, we show that transitions which couple in a path-dependent manner to fluctuating forces exhibit asymmetry. We reproduce this asymmetry in folding transitions of DNA-hairpins driven out of equilibrium and suggest a topological mechanism of symmetry breakdown. Our results are relevant to measurements that capture a single coordinate in a multidimensional free energy landscape, as encountered in electrophysiology and single-molecule fluorescence experiments.

With the molecular revolution in Biology, a mechanistic understanding of the genotype–phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype–fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype–phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

The control of gene expression in cells and organisms allows to unveil gene to function relationships and to reprogram biological responses. Several systems, such as Zinc fingers, TALE (Transcription activator-like effectors), and siRNAs (small-interfering RNAs), have been exploited to achieve this. However, recent advances in Clustered Regularly Interspaced Short Palindromic Repeats and Cas9 (CRISPR-Cas9) have overshadowed them due to high specificity, compatibility with many different organisms, and design flexibility. In this review we summarize state-of-the art for CRISPR-Cas9 technology for large scale gene perturbation studies, including single gene and multiple genes knock-out, knock-down, knock-up libraries, and their associated screening assays. We feature in particular the combination of these methods with single-cell transcriptomics approaches. Finally, we highlight the application of CRISPR-Cas9 systems in building synthetic circuits that can be interfaced with gene networks to control cellular states.

The control of gene expression in cells and organisms allows to unveil gene to function relationships and to reprogram biological responses. Several systems, such as Zinc fingers, TALE (Transcription activator-like effectors), and siRNAs (small-interfering RNAs), have been exploited to achieve this. However, recent advances in Clustered Regularly Interspaced Short Palindromic Repeats and Cas9 (CRISPR-Cas9) have overshadowed them due to high specificity, compatibility with many different organisms, and design flexibility. In this review we summarize state-of-the art for CRISPR-Cas9 technology for large scale gene perturbation studies, including single gene and multiple genes knock-out, knock-down, knock-up libraries, and their associated screening assays. We feature in particular the combination of these methods with single-cell transcriptomics approaches. Finally, we highlight the application of CRISPR-Cas9 systems in building synthetic circuits that can be interfaced with gene networks to control cellular states.

We study experimentally and theoretically the thickness of the coating obtained by pulling out a rod from a reservoir of yield-stress fluid. Opposite to Newtonian fluids, the coating thickness for a fluid of large enough yield stress is determined solely by the flow inside the reservoir and not by the flow inside the meniscus. The stress field inside the reservoir determines the thickness of the coating layer. The thickness is observed to increase nonlinearly with the sizes of the rod and of the reservoir. We develop a theoretical framework that describes this behavior and allows us to precisely predict the coating thickness.

Flexible low-pressure sensors (< 10 kPa) are required in areas as diverse as blood-pressure monitoring, human–computer interactions, robotics, and object detection. For applications, it is essential that these sensors combine flexibility, high sensitivity, robustness, and low production costs. Previous works involve surface micro-patterning, electronic amplification (OFET), and hydrogels. However, these solutions are limited as they involve complex processes, large bias voltages, large energy consumption, or are sensitive to evaporation. Here, we report a major advance to solve the challenge of scalable, efficient and robust e-skin. We present an unconventional capacitive sensor based on composite foam materials filled with conductive carbon black particles. Owing to the elastic buckling of the foam pores, the sensitivity exceeds 35 kPa− 1 for pressure< 0.2 kPa. These performances are one order of magnitude …