Ultra-rapid laser protein micropatterning: screening for directed polarization of single neurons

Protein micropatterning is a powerful tool for studying the effects of extracellular signals on cell development and regeneration. Laser micropatterning of proteins is the most flexible method for patterning many different geometries, protein densities, and concentration gradients. Despite these advantages, laser micropatterning remains prohibitively slow for most applications. Here, we take advantage of the rapid multi-photon induced photobleaching of fluorophores to generate sub-micron resolution patterns of full-length proteins on polymer monolayers, with sub-microsecond exposure times, i.e. one to five orders of magnitude faster than all previous laser micropatterning methods. We screened a range of different PEG monolayer coupling chemistries, chain-lengths and functional caps, and found that long-chain acrylated PEG monolayers are effective at resisting non-specific protein adhesion, while permitting efficient cross-linking of biotin-4-fluorescein to the PEG monolayers upon exposure to femtosecond laser pulses. We find evidence that the dominant photopatterning chemistry switches from a two-photon process to three- and four-photon absorption processes as the laser intensity increases, generating increasingly volatile excited triplet-state fluorophores, leading to faster patterning. Using this technology, we were able to generate over a hundred thousand protein patterns with varying geometries and protein densities to direct the polarization of hippocampal neurons with single-cell precision. We found that certain arrays of patterned triangles as small as neurite growth cones can direct polarization by impeding the elongation of reverse-projecting neurites, while permitting elongation of forward-projecting neurites. The ability to rapidly generate and screen such protein micropatterns can enable discovery of conditions necessary to create in vitro neural networks with single-neuron precision for basic discovery, drug screening, as well as for tissue scaffolding in therapeutics.Hertz Foundation (Fellowship)National Institutes of Health (U.S.) (R01 EUREKA Award 1-R01-NS066352)David & Lucile Packard Foundation (Award in Science and Engineering

Synchronous Symmetry Breaking in Neurons with Different Neurite Counts

As neurons develop, several immature processes (i.e., neurites) grow out of the cell body. Over time, each neuron breaks symmetry when only one of its neurites grows much longer than the rest, becoming an axon. This symmetry breaking is an important step in neurodevelopment, and aberrant symmetry breaking is associated with several neuropsychiatric diseases, including schizophrenia and autism. However, the effects of neurite count in neuronal symmetry breaking have never been studied. Existing models for neuronal polarization disagree: some predict that neurons with more neurites polarize up to several days later than neurons with fewer neurites, while others predict that neurons with different neurite counts polarize synchronously. We experimentally find that neurons with different neurite counts polarize synchronously. We also show that despite the significant differences among the previously proposed models, they all agree with our experimental findings when the expression levels of the proteins responsible for symmetry breaking increase with neurite count. Consistent with these results, we observe that the expression levels of two of these proteins, HRas and shootin1, significantly correlate with neurite count. This coordinated symmetry breaking we observed among neurons with different neurite counts may be important for synchronized polarization of neurons in developing organisms

Ensemble Analysis of Angiogenic Growth in Three-Dimensional Microfluidic Cell Cultures

We demonstrate ensemble three-dimensional cell cultures and quantitative analysis of angiogenic growth from uniform endothelial monolayers. Our approach combines two key elements: a micro-fluidic assay that enables parallelized angiogenic growth instances subject to common extracellular conditions, and an automated image acquisition and processing scheme enabling high-throughput, unbiased quantification of angiogenic growth. Because of the increased throughput of the assay in comparison to existing three-dimensional morphogenic assays, statistical properties of angiogenic growth can be reliably estimated. We used the assay to evaluate the combined effects of vascular endothelial growth factor (VEGF) and the signaling lipid sphingoshine-1-phosphate (S1P). Our results show the importance of S1P in amplifying the angiogenic response in the presence of VEGF gradients. Furthermore, the application of S1P with VEGF gradients resulted in angiogenic sprouts with higher aspect ratio than S1P with background levels of VEGF, despite reduced total migratory activity. This implies a synergistic effect between the growth factors in promoting angiogenic activity. Finally, the variance in the computed angiogenic metrics (as measured by ensemble standard deviation) was found to increase linearly with the ensemble mean. This finding is consistent with stochastic agent-based mathematical models of angiogenesis that represent angiogenic growth as a series of independent stochastic cell-level decisions

Econometric Information Recovery in Behavioral Networks

In this paper, we suggest an approach to recovering behavior-related, preference-choice network information from observational data. We model the process as a self-organized behavior based random exponential network-graph system. To address the unknown nature of the sampling model in recovering behavior related network information, we use the Cressie-Read (CR) family of divergence measures and the corresponding information theoretic entropy basis, for estimation, inference, model evaluation, and prediction. Examples are included to clarify how entropy based information theoretic methods are directly applicable to recovering the behavioral network probabilities in this fundamentally underdetermined ill posed inverse recovery problem

Dynamics of neuron polarization as defined by Eq. (1).

<p><b>A</b>, Polarity versus time. The solid line is a linear fit to the data. <b>B</b>, Polarity versus total neurite length. Total neurite length was binned at intervals of 20 µm. The solid line is a Gaussian fit to the data, with an inflection point at 114 µm suggesting a phase transition between unpolarized and polarized states. The nonlinearity of this data agrees with previous work on the polarization of neurons with exactly two neurites <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054905#pone.0054905-WissnerGross1" target="_blank">[19]</a>.</p

HRas and shootin1 expression in developing hippocampal neurons as a function of neurite count after 40 h in culture.

<p><b>A</b>, Typical immunocytochemical stain for HRas in a neuron with 5 neurites. <b>B</b>, Typical immunocytochemical stain for shootin1 in a neuron with 3 neurites. The brightness in both <b>A</b> and <b>B</b> were saturated to make the neurites more visible. When these images were analyzed to determine relative HRas and shootin1 expression levels, image brightness was kept unsaturated. <b>C</b>–<b>D</b>, HRas/shootin1 expression as a function of neurite count. Individual neurons are indicated by plus signs, while the solid line indicates linear fits to the data. In both trend lines, the slope was significantly positive (<i>p</i> < 10⁻⁴ for both fits).</p

Bright-field micrographs of four different neurons.

<p>Neurite count (<i>N</i>) and polarity (<i>P</i>) using Eq. (1) are also indicated for each neuron. <b>A</b> and <b>B</b> show examples of neurons with two neurites that are relatively more (<b>A</b>) or less (<b>B</b>) polarized. <b>C</b> and <b>D</b> similarly show neurons with many neurites that are relatively more (<b>C</b>) or less (<b>D</b>) polarized. The image in <b>A</b> was taken 28.5 h after plating, and <b>B</b>–<b>D</b> were taken at 52.5 h after plating. All scale bars are 25 µm.</p

Experimentally and theoretically predicted symmetry breaking in neurons with different neurite counts.

<p>Each color series indicates a different neurite count, as listed in the legend. <b>A</b>, Experimental measurements of neuronal polarity as a function of time and neurite count. Dashed lines separate data from different discrete time points. Bars indicate mean plus/minus SE, and neurite counts at each time point are only shown if at least 3 neurons had that neurite count at that time point. <b>B</b>–<b>D</b>, Computationally predicted polarity vs. time curves for neurons with different neurite counts using the Samuels (<b>B</b>), Fivaz (<b>C</b>), and Toriyama (<b>D</b>) models. <b>E</b>, Computationally predicted polarity vs. time curves for neurons with different neurite counts using a modified version of the Fivaz model in which HRas expression is independent of neurite count. The original Fivaz model, in which HRas expression increases with neurite count, is shown in <b>C</b>.</p

Polarity as a function of time and neurite count as predicted by the three models of neuronal symmetry breaking modified with dynamic neurite counts.

<p>Continuous curves were generated by connecting data for each neurite count at different time points. <b>A–C</b>, Polarity vs. time curves for different neurite counts in the Samuels, Fivaz, and Toriyama models with dynamic neurite counts. In the Fivaz model, HRas expression was normalized so that it was independent of neurite count, as in Fig. 3E. <b>D–F</b>, Expression levels of the protein underlying symmetry breaking now increases linearly with neurite count in all three models. In the Fivaz model, this protein is HRas; in the Toriyama model, it is shootin1; and in the Samuels model, “protein” refers to the rate-limiting chemical for neurite growth. Further details on how the Samuels and Toriyama models were modified can be found in the Materials and Methods section.</p

Causal Entropic Forces

Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems