Engineering brain activity patterns by neuromodulator polytherapy for treatment of disorders.

Conventional drug screens and treatments often ignore the underlying complexity of brain network dysfunctions, resulting in suboptimal outcomes. Here we ask whether we can correct abnormal functional connectivity of the entire brain by identifying and combining multiple neuromodulators that perturb connectivity in complementary ways. Our approach avoids the combinatorial complexity of screening all drug combinations. We develop a high-speed platform capable of imaging more than 15000 neurons in 50ms to map the entire brain functional connectivity in large numbers of vertebrates under many conditions. Screening a panel of drugs in a zebrafish model of human Dravet syndrome, we show that even drugs with related mechanisms of action can modulate functional connectivity in significantly different ways. By clustering connectivity fingerprints, we algorithmically select small subsets of complementary drugs and rapidly identify combinations that are significantly more effective at correcting abnormal networks and reducing spontaneous seizures than monotherapies, while minimizing behavioral side effects. Even at low concentrations, our polytherapy performs superior to individual drugs even at highest tolerated concentrations.

Combinatorial programming of human neuronal progenitors using magnetically-guided stoichiometric mRNA delivery.

Identification of optimal transcription factor expression patterns to direct cellular differentiation along a desired pathway presents significant challenges. We demonstrate massively combinatorial screening of temporally-varying mRNA transcription factors to direct differentiation of neural progenitor cells using a dynamically-reconfigurable magnetically-guided spotting technology for localizing mRNA, enabling experiments on millimetre size spots. In addition, we present a time-interleaved delivery method that dramatically reduces fluctuations in the delivered transcription factor copy numbers per cell. We screened combinatorial and temporal delivery of a pool of midbrain-specific transcription factors to augment the generation of dopaminergic neurons. We show that the combinatorial delivery of LMX1A, FOXA2 and PITX3 is highly effective in generating dopaminergic neurons from midbrain progenitors. We show that LMX1A significantly increases TH-expression levels when delivered to neural progenitor cells either during proliferation or after induction of neural differentiation, while FOXA2 and PITX3 increase expression only when delivered prior to induction, demonstrating temporal dependence of factor addition.

Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects.

Neurological drugs are often associated with serious side effects, yet drug screens typically focus only on efficacy. We demonstrate a novel paradigm utilizing high-throughput in vivo electrophysiology and brain activity patterns (BAPs). A platform with high sensitivity records local field potentials (LFPs) simultaneously from many zebrafish larvae over extended periods. We show that BAPs from larvae experiencing epileptic seizures or drug-induced side effects have substantially reduced complexity (entropy), similar to reduced LFP complexity observed in Parkinson's disease. To determine whether drugs that enhance BAP complexity produces positive outcomes, we used light pulses to trigger seizures in a model of Dravet syndrome, an intractable genetic epilepsy. The highest-ranked compounds identified by BAP analysis exhibit far greater anti-seizure efficacy and fewer side effects during subsequent in-depth behavioral assessment. This high correlation with behavioral outcomes illustrates the power of brain activity pattern-based screens and identifies novel therapeutic candidates with minimal side effects.

Automated deep-phenotyping of the vertebrate brain.

Here, we describe an automated platform suitable for large-scale deep-phenotyping of zebrafish mutant lines, which uses optical projection tomography to rapidly image brain-specific gene expression patterns in 3D at cellular resolution. Registration algorithms and correlation analysis are then used to compare 3D expression patterns, to automatically detect all statistically significant alterations in mutants, and to map them onto a brain atlas. Automated deep-phenotyping of a mutation in the master transcriptional regulator fezf2 not only detects all known phenotypes but also uncovers important novel neural deficits that were overlooked in previous studies. In the telencephalon, we show for the first time that fezf2 mutant zebrafish have significant patterning deficits, particularly in glutamatergic populations. Our findings reveal unexpected parallels between fezf2 function in zebrafish and mice, where mutations cause deficits in glutamatergic neurons of the telencephalon-derived neocortex.

Agglomerative joint clustering of metabolic data with spike at zero: A Bayesian perspective.

In many biological applications, for example high-dimensional metabolic data, the measurements consist of several continuous measurements of subjects or tissues over multiple attributes or metabolites. Measurement values are put in a matrix with subjects in rows and attributes in columns. The analysis of such data requires grouping subjects and attributes to provide a primitive guide toward data modeling. A common approach is to group subjects and attributes separately, and construct a two-dimensional dendrogram tree, once on rows and then on columns. This simple approach provides a grouping visualization through two separate trees, which is difficult to interpret jointly. When a joint grouping of rows and columns is of interest, it is more natural to partition the data matrix directly. Our suggestion is to build a dendrogram on the matrix directly, thus generalizing the two-dimensional dendrogram tree to a three-dimensional forest. The contribution of this research to the statistical analysis of metabolic data is threefold. First, a novel spike-and-slab model in various hierarchies is proposed to identify discriminant rows and columns. Second, an agglomerative approach is suggested to organize joint clusters. Third, a new visualization tool is invented to demonstrate the collection of joint clusters. The new method is motivated over gas chromatography mass spectrometry (GCMS) metabolic data, but can be applied to other continuous measurements with spike at zero property.

A radial flow microfluidic device for ultra-high-throughput affinity-based isolation of circulating tumor cells.

Circulating tumor cells (CTCs) are believed to play an important role in metastasis, a process responsible for the majority of cancer-related deaths. But their rarity in the bloodstream makes microfluidic isolation complex and time-consuming. Additionally the low processing speeds can be a hindrance to obtaining higher yields of CTCs, limiting their potential use as biomarkers for early diagnosis. Here, a high throughput microfluidic technology, the OncoBean Chip, is reported. It employs radial flow that introduces a varying shear profile across the device, enabling efficient cell capture by affinity at high flow rates. The recovery from whole blood is validated with cancer cell lines H1650 and MCF7, achieving a mean efficiency >80% at a throughput of 10 mL h(-1) in contrast to a flow rate of 1 mL h(-1) standardly reported with other microfluidic devices. Cells are recovered with a viability rate of 93% at these high speeds, increasing the ability to use captured CTCs for downstream analysis. Broad clinical application is demonstrated using comparable flow rates from blood specimens obtained from breast, pancreatic, and lung cancer patients. Comparable CTC numbers are recovered in all the samples at the two flow rates, demonstrating the ability of the technology to perform at high throughputs.

Using microfluidics chips for live imaging and study of injury responses in Drosophila larvae.

Live imaging is an important technique for studying cell biological processes, however this can be challenging in live animals. The translucent cuticle of the Drosophila larva makes it an attractive model organism for live imaging studies. However, an important challenge for live imaging techniques is to noninvasively immobilize and position an animal on the microscope. This protocol presents a simple and easy to use method for immobilizing and imaging Drosophila larvae on a polydimethylsiloxane (PDMS) microfluidic device, which we call the 'larva chip'. The larva chip is comprised of a snug-fitting PDMS microchamber that is attached to a thin glass coverslip, which, upon application of a vacuum via a syringe, immobilizes the animal and brings ventral structures such as the nerve cord, segmental nerves, and body wall muscles, within close proximity to the coverslip. This allows for high-resolution imaging, and importantly, avoids the use of anesthetics and chemicals, which facilitates the study of a broad range of physiological processes. Since larvae recover easily from the immobilization, they can be readily subjected to multiple imaging sessions. This allows for longitudinal studies over time courses ranging from hours to days. This protocol describes step-by-step how to prepare the chip and how to utilize the chip for live imaging of neuronal events in 3(rd) instar larvae. These events include the rapid transport of organelles in axons, calcium responses to injury, and time-lapse studies of the trafficking of photo-convertible proteins over long distances and time scales. Another application of the chip is to study regenerative and degenerative responses to axonal injury, so the second part of this protocol describes a new and simple procedure for injuring axons within peripheral nerves by a segmental nerve crush.

Microfluidic chips for in vivo imaging of cellular responses to neural injury in Drosophila larvae.

With powerful genetics and a translucent cuticle, the Drosophila larva is an ideal model system for live imaging studies of neuronal cell biology and function. Here, we present an easy-to-use approach for high resolution live imaging in Drosophila using microfluidic chips. Two different designs allow for non-invasive and chemical-free immobilization of 3(rd) instar larvae over short (up to 1 hour) and long (up to 10 hours) time periods. We utilized these 'larva chips' to characterize several sub-cellular responses to axotomy which occur over a range of time scales in intact, unanaesthetized animals. These include waves of calcium which are induced within seconds of axotomy, and the intracellular transport of vesicles whose rate and flux within axons changes dramatically within 3 hours of axotomy. Axonal transport halts throughout the entire distal stump, but increases in the proximal stump. These responses precede the degeneration of the distal stump and regenerative sprouting of the proximal stump, which is initiated after a 7 hour period of dormancy and is associated with a dramatic increase in F-actin dynamics. In addition to allowing for the study of axonal regeneration in vivo, the larva chips can be utilized for a wide variety of in vivo imaging applications in Drosophila.

Selection-Fusion Approach for Classification of Datasets with Missing Values.

This paper proposes a new approach based on missing value pattern discovery for classifying incomplete data. This approach is particularly designed for classification of datasets with a small number of samples and a high percentage of missing values where available missing value treatment approaches do not usually work well. Based on the pattern of the missing values, the proposed approach finds subsets of samples for which most of the features are available and trains a classifier for each subset. Then, it combines the outputs of the classifiers. Subset selection is translated into a clustering problem, allowing derivation of a mathematical framework for it. A trade off is established between the computational complexity (number of subsets) and the accuracy of the overall classifier. To deal with this trade off, a numerical criterion is proposed for the prediction of the overall performance. The proposed method is applied to seven datasets from the popular University of California, Irvine data mining archive and an epilepsy dataset from Henry Ford Hospital, Detroit, Michigan (total of eight datasets). Experimental results show that classification accuracy of the proposed method is superior to those of the widely used multiple imputations method and four other methods. They also show that the level of superiority depends on the pattern and percentage of missing values.