The ability to adjust behavior to sudden changes in the environment develops gradually in childhood and adolescence. For example, in the Dimensional Change Card Sort task, participants switch from sorting cards one way, such as shape, to sorting them a different way, such as color. Adjusting behavior in this way exacts a small performance cost, or switch cost, such that responses are typically slower and more error-prone on switch trials in which the sorting rule changes as compared to repeat trials in which the sorting rule remains the same. The ability to flexibly adjust behavior is often said to develop gradually, in part because behavioral costs such as switch costs typically decrease with increasing age. Why aspects of higher-order cognition, such as behavioral flexibility, develop so gradually remains an open question. One hypothesis is that these changes occur in association with functional changes in broad-scale cognitive control networks. On this view, complex mental operations, such as switching, involve rapid interactions between several distributed brain regions, including those that update and maintain task rules, re-orient attention, and select behaviors. With development, functional connections between these regions strengthen, leading to faster and more efficient switching operations. The current video describes a method of testing this hypothesis through the collection and multivariate analysis of fMRI data from participants of different ages.
24 Related JoVE Articles!
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
Institutions: University of Pennsylvania.
It is now appreciated that condition-relevant information can be present within distributed patterns of functional magnetic resonance imaging (fMRI) brain activity, even for conditions with similar levels of univariate activation. Multi-voxel pattern (MVP) analysis has been used to decode this information with great success. FMRI investigators also often seek to understand how brain regions interact in interconnected networks, and use functional connectivity (FC) to identify regions that have correlated responses over time. Just as univariate analyses can be insensitive to information in MVPs, FC may not fully characterize the brain networks that process conditions with characteristic MVP signatures. The method described here, informational connectivity (IC), can identify regions with correlated changes in MVP-discriminability across time, revealing connectivity that is not accessible to FC. The method can be exploratory, using searchlights to identify seed-connected areas, or planned, between pre-selected regions-of-interest. The results can elucidate networks of regions that process MVP-related conditions, can breakdown MVPA searchlight maps into separate networks, or can be compared across tasks and patient groups.
Neuroscience, Issue 89, fMRI, MVPA, connectivity, informational connectivity, functional connectivity, networks, multi-voxel pattern analysis, decoding, classification, method, multivariate
Planar and Three-Dimensional Printing of Conductive Inks
Institutions: University of Illinois at Urbana-Champaign, Lawrence Livermore National Laboratory, University Of California Santa Barbara .
Printed electronics rely on low-cost, large-area fabrication routes to create flexible or multidimensional electronic, optoelectronic, and biomedical devices1-3
. In this paper, we focus on one- (1D), two- (2D), and three-dimensional (3D) printing of conductive metallic inks in the form of flexible, stretchable, and spanning microelectrodes.
is a 1-to-3D printing technique that enables the fabrication of features ranging from simple lines to complex structures by the deposition of concentrated inks through fine nozzles (~0.1 - 250 μm). This printing method consists of a computer-controlled 3-axis translation stage, an ink reservoir and nozzle, and 10x telescopic lens for visualization. Unlike inkjet printing, a droplet-based process, direct-write assembly involves the extrusion of ink filaments either in- or out-of-plane. The printed filaments typically conform to the nozzle size. Hence, microscale features (< 1 μm) can be patterned and assembled into larger arrays and multidimensional architectures.
In this paper, we first synthesize a highly concentrated silver nanoparticle ink for planar and 3D printing via direct-write assembly. Next, a standard protocol for printing microelectrodes in multidimensional motifs is demonstrated. Finally, applications of printed microelectrodes for electrically small antennas, solar cells, and light-emitting diodes are highlighted.
Bioengineering, Issue 58, Direct-write assembly, silver ink, 3D printing, planar, three-dimensional, microelectrodes, flexible electronics, printed electronics
Generation of Shear Adhesion Map Using SynVivo Synthetic Microvascular Networks
Institutions: CFD Research Corporation.
Cell/particle adhesion assays are critical to understanding the biochemical interactions involved in disease pathophysiology and have important applications in the quest for the development of novel therapeutics. Assays using static conditions fail to capture the dependence of adhesion on shear, limiting their correlation with in vivo
environment. Parallel plate flow chambers that quantify adhesion under physiological fluid flow need multiple experiments for the generation of a shear adhesion map. In addition, they do not represent the in vivo
scale and morphology and require large volumes (~ml) of reagents for experiments. In this study, we demonstrate the generation of shear adhesion map from a single experiment using a microvascular network based microfluidic device, SynVivo-SMN. This device recreates the complex in vivo
vasculature including geometric scale, morphological elements, flow features and cellular interactions in an in vitro
format, thereby providing a biologically realistic environment for basic and applied research in cellular behavior, drug delivery, and drug discovery. The assay was demonstrated by studying the interaction of the 2 µm biotin-coated particles with avidin-coated surfaces of the microchip. The entire range of shear observed in the microvasculature is obtained in a single assay enabling adhesion vs. shear map for the particles under physiological conditions.
Bioengineering, Issue 87, particle, adhesion, shear, microfluidics, vasculature, networks
Identification of Protein Complexes in Escherichia coli using Sequential Peptide Affinity Purification in Combination with Tandem Mass Spectrometry
Institutions: University of Toronto, University of Regina, University of Toronto.
Since most cellular processes are mediated by macromolecular assemblies, the systematic identification of protein-protein interactions (PPI) and the identification of the subunit composition of multi-protein complexes can provide insight into gene function and enhance understanding of biological systems1, 2
. Physical interactions can be mapped with high confidence vialarge-scale isolation and characterization of endogenous protein complexes under near-physiological conditions based on affinity purification of chromosomally-tagged proteins in combination with mass spectrometry (APMS). This approach has been successfully applied in evolutionarily diverse organisms, including yeast, flies, worms, mammalian cells, and bacteria1-6
. In particular, we have generated a carboxy-terminal Sequential Peptide Affinity (SPA) dual tagging system for affinity-purifying native protein complexes from cultured gram-negative Escherichia coli
, using genetically-tractable host laboratory strains that are well-suited for genome-wide investigations of the fundamental biology and conserved processes of prokaryotes1, 2, 7
. Our SPA-tagging system is analogous to the tandem affinity purification method developed originally for yeast8, 9
, and consists of a calmodulin binding peptide (CBP) followed by the cleavage site for the highly specific tobacco etch virus
(TEV) protease and three copies of the FLAG epitope (3X FLAG), allowing for two consecutive rounds of affinity enrichment. After cassette amplification, sequence-specific linear PCR products encoding the SPA-tag and a selectable marker are integrated and expressed in frame as carboxy-terminal fusions in a DY330 background that is induced to transiently express a highly efficient heterologous bacteriophage lambda recombination system10
. Subsequent dual-step purification using calmodulin and anti-FLAG affinity beads enables the highly selective and efficient recovery of even low abundance protein complexes from large-scale cultures. Tandem mass spectrometry is then used to identify the stably co-purifying proteins with high sensitivity (low nanogram detection limits).
Here, we describe detailed step-by-step procedures we commonly use for systematic protein tagging, purification and mass spectrometry-based analysis of soluble protein complexes from E. coli
, which can be scaled up and potentially tailored to other bacterial species, including certain opportunistic pathogens that are amenable to recombineering. The resulting physical interactions can often reveal interesting unexpected components and connections suggesting novel mechanistic links. Integration of the PPI data with alternate molecular association data such as genetic (gene-gene) interactions and genomic-context (GC) predictions can facilitate elucidation of the global molecular organization of multi-protein complexes within biological pathways. The networks generated for E. coli
can be used to gain insight into the functional architecture of orthologous gene products in other microbes for which functional annotations are currently lacking.
Genetics, Issue 69, Molecular Biology, Medicine, Biochemistry, Microbiology, affinity purification, Escherichia coli, gram-negative bacteria, cytosolic proteins, SPA-tagging, homologous recombination, mass spectrometry, protein interaction, protein complex
Environmentally-controlled Microtensile Testing of Mechanically-adaptive Polymer Nanocomposites for ex vivo Characterization
Institutions: Louis Stokes Cleveland Department of Veterans Affairs Medical Center, Case Western Reserve University, Case Western Reserve University.
Implantable microdevices are gaining significant attention for several biomedical applications1-4
. Such devices have been made from a range of materials, each offering its own advantages and shortcomings5,6
. Most prominently, due to the microscale device dimensions, a high modulus is required to facilitate implantation into living tissue. Conversely, the stiffness of the device should match the surrounding tissue to minimize induced local strain7-9
. Therefore, we recently developed a new class of bio-inspired materials to meet these requirements by responding to environmental stimuli with a change in mechanical properties10-14
. Specifically, our poly(vinyl acetate)-based nanocomposite (PVAc-NC) displays a reduction in stiffness when exposed to water and elevated temperatures (e.g.
body temperature). Unfortunately, few methods exist to quantify the stiffness of materials in vivo15
, and mechanical testing outside of the physiological environment often requires large samples inappropriate for implantation. Further, stimuli-responsive materials may quickly recover their initial stiffness after explantation. Therefore, we have developed a method by which the mechanical properties of implanted microsamples can be measured ex vivo
, with simulated physiological conditions maintained using moisture and temperature control13,16,17
To this end, a custom microtensile tester was designed to accommodate microscale samples13,17
with widely-varying Young's moduli (range of 10 MPa to 5 GPa). As our interests are in the application of PVAc-NC as a biologically-adaptable neural probe substrate, a tool capable of mechanical characterization of samples at the microscale was necessary. This tool was adapted to provide humidity and temperature control, which minimized sample drying and cooling17
. As a result, the mechanical characteristics of the explanted sample closely reflect those of the sample just prior to explantation.
The overall goal of this method is to quantitatively assess the in vivo
mechanical properties, specifically the Young's modulus, of stimuli-responsive, mechanically-adaptive polymer-based materials. This is accomplished by first establishing the environmental conditions that will minimize a change in sample mechanical properties after explantation without contributing to a reduction in stiffness independent of that resulting from implantation. Samples are then prepared for implantation, handling, and testing (Figure 1A
). Each sample is implanted into the cerebral cortex of rats, which is represented here as an explanted rat brain, for a specified duration (Figure 1B
). At this point, the sample is explanted and immediately loaded into the microtensile tester, and then subjected to tensile testing (Figure 1C
). Subsequent data analysis provides insight into the mechanical behavior of these innovative materials in the environment of the cerebral cortex.
Bioengineering, Issue 78, Biophysics, Biomedical Engineering, Molecular Biology, Cellular Biology, Electrical Engineering, Materials Science, Nanotechnology, Nanocomposites, Electrodes, Implanted, Neural Prostheses, Micro-Electrical-Mechanical Systems, Implants, Experimental, mechanical properties (composite materials), Dynamic materials, polymer nanocomposite, Young's modulus, modulus of elasticity, intracortical microelectrode, polymers, biomaterials
High Content Screening in Neurodegenerative Diseases
Institutions: VU University Medical Center, Neuroscience Campus Amsterdam.
The functional annotation of genomes, construction of molecular networks and novel drug target identification, are important challenges that need to be addressed as a matter of great urgency1-4
. Multiple complementary 'omics' approaches have provided clues as to the genetic risk factors and pathogenic mechanisms underlying numerous neurodegenerative diseases, but most findings still require functional validation5
. For example, a recent genome wide association study for Parkinson's Disease (PD), identified many new loci as risk factors for the disease, but the underlying causative variant(s) or pathogenic mechanism is not known6, 7
. As each associated region can contain several genes, the functional evaluation of each of the genes on phenotypes associated with the disease, using traditional cell biology techniques would take too long.
There is also a need to understand the molecular networks that link genetic mutations to the phenotypes they cause. It is expected that disease phenotypes are the result of multiple interactions that have been disrupted. Reconstruction of these networks using traditional molecular methods would be time consuming. Moreover, network predictions from independent studies of individual components, the reductionism approach, will probably underestimate the network complexity8
. This underestimation could, in part, explain the low success rate of drug approval due to undesirable or toxic side effects. Gaining a network perspective of disease related pathways using HT/HC cellular screening approaches, and identifying key nodes within these pathways, could lead to the identification of targets that are more suited for therapeutic intervention.
High-throughput screening (HTS) is an ideal methodology to address these issues9-12
. but traditional methods were one dimensional whole-well cell assays, that used simplistic readouts for complex biological processes. They were unable to simultaneously quantify the many phenotypes observed in neurodegenerative diseases such as axonal transport deficits or alterations in morphology properties13, 14
. This approach could not be used to investigate the dynamic nature of cellular processes or pathogenic events that occur in a subset of cells. To quantify such features one has to move to multi-dimensional phenotypes termed high-content screening (HCS)4, 15-17
. HCS is the cell-based quantification of several processes simultaneously, which provides a more detailed representation of the cellular response to various perturbations compared to HTS.
HCS has many advantages over HTS18, 19
, but conducting a high-throughput (HT)-high-content (HC) screen in neuronal models is problematic due to high cost, environmental variation and human error. In order to detect cellular responses on a 'phenomics' scale using HC imaging one has to reduce variation and error, while increasing sensitivity and reproducibility.
Herein we describe a method to accurately and reliably conduct shRNA screens using automated cell culturing20
and HC imaging in neuronal cellular models. We describe how we have used this methodology to identify modulators for one particular protein, DJ1, which when mutated causes autosomal recessive parkinsonism21
Combining the versatility of HC imaging with HT methods, it is possible to accurately quantify a plethora of phenotypes. This could subsequently be utilized to advance our understanding of the genome, the pathways involved in disease pathogenesis as well as identify potential therapeutic targets.
Medicine, Issue 59, High-throughput screening, high-content screening, neurodegeneration, automated cell culturing, Parkinson’s disease
Isolation of Murine Lymph Node Stromal Cells
Institutions: University of Basel and University Hospital Basel.
Secondary lymphoid organs including lymph nodes are composed of stromal cells that provide a structural environment for homeostasis, activation and differentiation of lymphocytes. Various stromal cell subsets have been identified by the expression of the adhesion molecule CD31 and glycoprotein podoplanin (gp38), T zone reticular cells or fibroblastic reticular cells, lymphatic endothelial cells, blood endothelial cells and FRC-like pericytes within the double negative cell population. For all populations different functions are described including, separation and lining of different compartments, attraction of and interaction with different cell types, filtration of the draining fluidics and contraction of the lymphatic vessels. In the last years, different groups have described an additional role of stromal cells in orchestrating and regulating cytotoxic T cell responses potentially dangerous for the host.
Lymph nodes are complex structures with many different cell types and therefore require a appropriate procedure for isolation of the desired cell populations. Currently, protocols for the isolation of lymph node stromal cells rely on enzymatic digestion with varying incubation times; however, stromal cells and their surface molecules are sensitive to these enzymes, which results in loss of surface marker expression and cell death. Here a short enzymatic digestion protocol combined with automated mechanical disruption to obtain viable single cells suspension of lymph node stromal cells maintaining their surface molecule expression is proposed.
Immunology, Issue 90, lymph node, lymph node stromal cells, digestion, isolation, enzymes, fibroblastic reticular cell, lymphatic endothelial cell, blood endothelial cell
Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification
Institutions: Seattle University, Seattle University.
In contrast to other chromatographic methods for purifying proteins (e.g. gel filtration, affinity, and ion exchange), hydrophobic interaction chromatography (HIC) commonly requires experimental determination (referred to as screening or "scouting") in order to select the most suitable chromatographic medium for purifying a given protein 1
. The method presented here describes an automated approach to scouting for an optimal HIC media to be used in protein purification.
HIC separates proteins and other biomolecules from a crude lysate based on differences in hydrophobicity. Similar to affinity chromatography (AC) and ion exchange chromatography (IEX), HIC is capable of concentrating the protein of interest as it progresses through the chromatographic process. Proteins best suited for purification by HIC include those with hydrophobic surface regions and able to withstand exposure to salt concentrations in excess of 2 M ammonium sulfate ((NH4
). HIC is often chosen as a purification method for proteins lacking an affinity tag, and thus unsuitable for AC, and when IEX fails to provide adequate purification. Hydrophobic moieties on the protein surface temporarily bind to a nonpolar ligand coupled to an inert, immobile matrix. The interaction between protein and ligand are highly dependent on the salt concentration of the buffer flowing through the chromatography column, with high ionic concentrations strengthening the protein-ligand interaction and making the protein immobile (i.e. bound inside the column) 2
. As salt concentrations decrease, the protein-ligand interaction dissipates, the protein again becomes mobile and elutes from the column. Several HIC media are commercially available in pre-packed columns, each containing one of several hydrophobic ligands (e.g. S-butyl, butyl, octyl, and phenyl) cross-linked at varying densities to agarose beads of a specific diameter 3
. Automated column scouting allows for an efficient approach for determining which HIC media should be employed for future, more exhaustive optimization experiments and protein purification runs 4
The specific protein being purified here is recombinant green fluorescent protein (GFP); however, the approach may be adapted for purifying other proteins with one or more hydrophobic surface regions. GFP serves as a useful model protein, due to its stability, unique light absorbance peak at 397 nm, and fluorescence when exposed to UV light 5
. Bacterial lysate containing wild type GFP was prepared in a high-salt buffer, loaded into a Bio-Rad DuoFlow medium pressure liquid chromatography system, and adsorbed to HiTrap HIC columns containing different HIC media. The protein was eluted from the columns and analyzed by in-line and post-run detection methods. Buffer blending, dynamic sample loop injection, sequential column selection, multi-wavelength analysis, and split fraction eluate collection increased the functionality of the system and reproducibility of the experimental approach.
Biochemistry, Issue 55, hydrophobic interaction chromatography, liquid chromatography, green fluorescent protein, GFP, scouting, protein purification, Bio-Rad DuoFlow, FPLC
Rat Mesentery Exteriorization: A Model for Investigating the Cellular Dynamics Involved in Angiogenesis
Institutions: Tulane University, University of Virginia , Tulane University.
Microvacular network growth and remodeling are critical aspects of wound healing, inflammation, diabetic retinopathy, tumor growth and other disease conditions1, 2
. Network growth is commonly attributed to angiogenesis, defined as the growth of new vessels from pre-existing vessels. The angiogenic process is also directly linked to arteriogenesis, defined as the capillary acquisition of a perivascular cell coating and vessel enlargement. Needless to say, angiogenesis is complex and involves multiple players at the cellular and molecular level3
. Understanding how a microvascular network grows requires identifying the spatial and temporal dynamics along the hierarchy of a network over the time course of angiogenesis. This information is critical for the development of therapies aimed at manipulating vessel growth.
The exteriorization model described in this article represents a simple, reproducible model for stimulating angiogenesis in the rat mesentery. It was adapted from wound-healing models in the rat mesentery4-7
, and is an alternative to stimulate angiogenesis in the mesentery via i.p. injections of pro-angiogenic agents8, 9
. The exteriorization model is attractive because it requires minimal surgical intervention and produces dramatic, reproducible increases in capillary sprouts, vascular area and vascular density over a relatively short time course in a tissue that allows for the two-dimensional visualization of entire microvascular networks down to single cell level. The stimulated growth reflects natural angiogenic responses in a physiological environment without interference of foreign angiogenic molecules. Using immunohistochemical labeling methods, this model has been proven extremely useful in identifying novel cellular events involved in angiogenesis. Investigators can readily correlate the angiogenic metrics during the time course of remodeling with time specific dynamics, such as cellular phenotypic changes or cellular interactions4, 5, 7, 10, 11
Cellular Biology, Issue 63, mesentery, rat, angiogenesis, microcirculation, microvascular, remodeling
Combining Transcranial Magnetic Stimulation and fMRI to Examine the Default Mode Network
Institutions: Beth Israel Deaconess Medical Center.
The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest."1,2,3
It is thought the default mode network corresponds to self-referential or "internal mentation".2,3
It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia 4,5,6
and autism spectrum disorders 7
whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases 8
). As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses.9
In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.
Neuroscience, Issue 46, Transcranial Magnetic Stimulation, rTMS, fMRI, Default Mode Network, functional connectivity, resting state
Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
Institutions: Heart Research Center Goettingen, University Medical Center Goettingen, German Center for Cardiovascular Research (DZHK) partner site Goettingen, University of Maryland School of Medicine.
In cardiac myocytes a complex network of membrane tubules - the transverse-axial tubule system (TATS) - controls deep intracellular signaling functions. While the outer surface membrane and associated TATS membrane components appear to be continuous, there are substantial differences in lipid and protein content. In ventricular myocytes (VMs), certain TATS components are highly abundant contributing to rectilinear tubule networks and regular branching 3D architectures. It is thought that peripheral TATS components propagate action potentials from the cell surface to thousands of remote intracellular sarcoendoplasmic reticulum (SER) membrane contact domains, thereby activating intracellular Ca2+
release units (CRUs). In contrast to VMs, the organization and functional role of TATS membranes in atrial myocytes (AMs) is significantly different and much less understood. Taken together, quantitative structural characterization of TATS membrane networks in healthy and diseased myocytes is an essential prerequisite towards better understanding of functional plasticity and pathophysiological reorganization. Here, we present a strategic combination of protocols for direct quantitative analysis of TATS membrane networks in living VMs and AMs. For this, we accompany primary cell isolations of mouse VMs and/or AMs with critical quality control steps and direct membrane staining protocols for fluorescence imaging of TATS membranes. Using an optimized workflow for confocal or superresolution TATS image processing, binarized and skeletonized data are generated for quantitative analysis of the TATS network and its components. Unlike previously published indirect regional aggregate image analysis strategies, our protocols enable direct characterization of specific components and derive complex physiological properties of TATS membrane networks in living myocytes with high throughput and open access software tools. In summary, the combined protocol strategy can be readily applied for quantitative TATS network studies during physiological myocyte adaptation or disease changes, comparison of different cardiac or skeletal muscle cell types, phenotyping of transgenic models, and pharmacological or therapeutic interventions.
Bioengineering, Issue 92, cardiac myocyte, atria, ventricle, heart, primary cell isolation, fluorescence microscopy, membrane tubule, transverse-axial tubule system, image analysis, image processing, T-tubule, collagenase
Microwave-assisted Functionalization of Poly(ethylene glycol) and On-resin Peptides for Use in Chain Polymerizations and Hydrogel Formation
Institutions: University of Rochester, University of Rochester, University of Rochester Medical Center.
One of the main benefits to using poly(ethylene glycol) (PEG) macromers in hydrogel formation is synthetic versatility. The ability to draw from a large variety of PEG molecular weights and configurations (arm number, arm length, and branching pattern) affords researchers tight control over resulting hydrogel structures and properties, including Young’s modulus and mesh size. This video will illustrate a rapid, efficient, solvent-free, microwave-assisted method to methacrylate PEG precursors into poly(ethylene glycol) dimethacrylate (PEGDM). This synthetic method provides much-needed starting materials for applications in drug delivery and regenerative medicine. The demonstrated method is superior to traditional methacrylation methods as it is significantly faster and simpler, as well as more economical and environmentally friendly, using smaller amounts of reagents and solvents. We will also demonstrate an adaptation of this technique for on-resin methacrylamide functionalization of peptides. This on-resin method allows the N-terminus of peptides to be functionalized with methacrylamide groups prior to deprotection and cleavage from resin. This allows for selective addition of methacrylamide groups to the N-termini of the peptides while amino acids with reactive side groups (e.g.
primary amine of lysine, primary alcohol of serine, secondary alcohols of threonine, and phenol of tyrosine) remain protected, preventing functionalization at multiple sites. This article will detail common analytical methods (proton Nuclear Magnetic Resonance spectroscopy (;
H-NMR) and Matrix Assisted Laser Desorption Ionization Time of Flight mass spectrometry (MALDI-ToF)) to assess the efficiency of the functionalizations. Common pitfalls and suggested troubleshooting methods will be addressed, as will modifications of the technique which can be used to further tune macromer functionality and resulting hydrogel physical and chemical properties. Use of synthesized products for the formation of hydrogels for drug delivery and cell-material interaction studies will be demonstrated, with particular attention paid to modifying hydrogel composition to affect mesh size, controlling hydrogel stiffness and drug release.
Chemistry, Issue 80, Poly(ethylene glycol), peptides, polymerization, polymers, methacrylation, peptide functionalization, 1H-NMR, MALDI-ToF, hydrogels, macromer synthesis
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Institutions: Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory, Lawrence Berkeley National Laboratory.
Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g.
, signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.
The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.
Bioengineering, Issue 90, 3D electron microscopy, feature extraction, segmentation, image analysis, reconstruction, manual tracing, thresholding
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Institutions: The Feinstein Institute for Medical Research.
The scaled subprofile model (SSM)1-4
is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1
). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data2,5,6
. Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors7,8
. Using logistic regression analysis of subject scores (i.e.
pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e.
composite networks with improved discrimination of patients from healthy control subjects5,6
. Cross-validation within the derivation set can be performed using bootstrap resampling techniques9
. Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets10
. Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation11
. These standardized values can in turn be used to assist in differential diagnosis12,13
and to assess disease progression and treatment effects at the network level7,14-16
. We present an example of the application of this methodology to FDG PET data of Parkinson's Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.
Medicine, Issue 76, Neurobiology, Neuroscience, Anatomy, Physiology, Molecular Biology, Basal Ganglia Diseases, Parkinsonian Disorders, Parkinson Disease, Movement Disorders, Neurodegenerative Diseases, PCA, SSM, PET, imaging biomarkers, functional brain imaging, multivariate spatial covariance analysis, global normalization, differential diagnosis, PD, brain, imaging, clinical techniques
Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
Institutions: University of Toronto, University of Toronto, University of Regina.
Phenotypes are determined by a complex series of physical (e.g.
protein-protein) and functional (e.g.
gene-gene or genetic) interactions (GI)1
. While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships2
. Large-scale GI maps have been reported for eukaryotic organisms like yeast3-7
, but GI information remains sparse for prokaryotes8
, which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods9, 10
Here, we present the key steps required to perform quantitative E. coli
Synthetic Genetic Array (eSGA) screening procedure on a genome-scale9
, using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format.
Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g.
the 'Keio' collection11
) and essential gene hypomorphic mutations (i.e.
alleles conferring reduced protein expression, stability, or activity9, 12, 13
) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system14
. GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected9
. Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process2
. Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e.
slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex2
as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred9
Genetics, Issue 69, Molecular Biology, Medicine, Biochemistry, Microbiology, Aggravating, alleviating, conjugation, double mutant, Escherichia coli, genetic interaction, Gram-negative bacteria, homologous recombination, network, synthetic lethality or sickness, suppression
Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
Institutions: Alpert Medical School, Brown University, University of Georgia.
Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).
Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g.
, working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.
Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.
Medicine, Issue 89, default mode network, neuroimaging, functional magnetic resonance imaging, diffusion tensor imaging, structural connectivity, functional connectivity, posttraumatic stress disorder
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Institutions: Princeton University.
The aim of de novo
protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo
protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (https://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
Genetics, Issue 77, Molecular Biology, Bioengineering, Biochemistry, Biomedical Engineering, Chemical Engineering, Computational Biology, Genomics, Proteomics, Protein, Protein Binding, Computational Biology, Drug Design, optimization (mathematics), Amino Acids, Peptides, and Proteins, De novo protein and peptide design, Drug design, In silico sequence selection, Optimization, Fold specificity, Binding affinity, sequencing
Cortical Source Analysis of High-Density EEG Recordings in Children
Institutions: UCL Institute of Child Health, University College London.
EEG is traditionally described as a neuroimaging technique with high temporal and low spatial resolution. Recent advances in biophysical modelling and signal processing make it possible to exploit information from other imaging modalities like structural MRI that provide high spatial resolution to overcome this constraint1
. This is especially useful for investigations that require high resolution in the temporal as well as spatial domain. In addition, due to the easy application and low cost of EEG recordings, EEG is often the method of choice when working with populations, such as young children, that do not tolerate functional MRI scans well. However, in order to investigate which neural substrates are involved, anatomical information from structural MRI is still needed. Most EEG analysis packages work with standard head models that are based on adult anatomy. The accuracy of these models when used for children is limited2
, because the composition and spatial configuration of head tissues changes dramatically over development3
In the present paper, we provide an overview of our recent work in utilizing head models based on individual structural MRI scans or age specific head models to reconstruct the cortical generators of high density EEG. This article describes how EEG recordings are acquired, processed, and analyzed with pediatric populations at the London Baby Lab, including laboratory setup, task design, EEG preprocessing, MRI processing, and EEG channel level and source analysis.
Behavior, Issue 88, EEG, electroencephalogram, development, source analysis, pediatric, minimum-norm estimation, cognitive neuroscience, event-related potentials
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Institutions: University of Ulm.
Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo
. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls.
DTI data analysis is performed in a variate fashion, i.e.
voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e.
differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.
Medicine, Issue 77, Neuroscience, Neurobiology, Molecular Biology, Biomedical Engineering, Anatomy, Physiology, Neurodegenerative Diseases, nuclear magnetic resonance, NMR, MR, MRI, diffusion tensor imaging, fiber tracking, group level comparison, neurodegenerative diseases, brain, imaging, clinical techniques
Dissection and 2-Photon Imaging of Peripheral Lymph Nodes in Mice
Institutions: University of California, Irvine (UCI), University of California, Irvine (UCI).
Two-photon imaging has revealed an elegant choreography of motility and cellular interactions within the lymph node under basal conditions and at the initiation of an immune response 1
. Here, we present methods for adoptive transfer of labeled T cells, isolation of lymph nodes, and imaging motility of CD4+ T cells in the explanted lymph node as first described in 2002 2
. Two-photon imaging of immune cells requires that the cells are fluorescently labeled, either by staining with a cell tracker dye or by expressing a fluorescent protein. We demonstrate the adoptive transfer procedure of injecting cells derived from donor mice into the tail vein of a recipient animal, where they home to lymphoid organs within approximately 15-30 min. We illustrate the isolation of a lymph node and describe methods to ensure proper mounting of the excised lymph node. Other considerations such as proper oxygenation of perfused media, temperature, and laser power are discussed. Finally, we present 3D video images of naive CD4+ T cells exhibiting steady state motility at 37°C.
Issue 7, Immunology, T Lymphocytes, Lymph Node, 2-photon Imaging, Tail Vein Injections
Reaggregate Thymus Cultures
Institutions: University of Birmingham .
Stromal cells within lymphoid tissues are organized into three-dimensional structures that provide a scaffold that is thought to control the migration and development of haemopoeitic cells. Importantly, the maintenance of this three-dimensional organization appears to be critical for normal stromal cell function, with two-dimensional monolayer cultures often being shown to be capable of supporting only individual fragments of lymphoid tissue function. In the thymus, complex networks of cortical and medullary epithelial cells act as a framework that controls the recruitment, proliferation, differentiation and survival of lymphoid progenitors as they undergo the multi-stage process of intrathymic T-cell development. Understanding the functional role of individual stromal compartments in the thymus is essential in determining how the thymus imposes self/non-self discrimination. Here we describe a technique in which we exploit the plasticity of fetal tissues to re-associate into intact three-dimensional structures in vitro
, following their enzymatic disaggregation. The dissociation of fetal thymus lobes into heterogeneous cellular mixtures, followed by their separation into individual cellular components, is then combined with the in vitro
re-association of these desired cell types into three-dimensional reaggregate structures at defined ratios, thereby providing an opportunity to investigate particular aspects of T-cell development under defined cellular conditions. (This article is based on work first reported Methods in Molecular Biology 2007, Vol. 380 pages 185-196).
Immunology, Issue 18, Springer Protocols, Thymus, 2-dGuo, Thymus Organ Cultures, Immune Tolerance, Positive and Negative Selection, Lymphoid Development
Isolation of CD4+ T cells from Mouse Lymph Nodes Using Miltenyi MACS Purification
Institutions: University of California, Irvine (UCI).
Isolation of cells from the primary source is a necessary step in many more complex protocols. Miltenyi offers kits to isolate cells from several organisms including humans, non-human primates, rat and, as we describe here, mice. Magnetic bead-based cell separation allows for either positive selection (or cell depletion) as well as negative selection. Here, we demonstrate negative selection of untouched or na ve CD4+ helper T cells. Using this standard protocol we typically purify cells that are ≥ 96% pure CD4+/CD3+. This protocol is used in conjunction with the protocol Dissection and 2-Photon Imaging of Peripheral Lymph Nodes in Mice published in issue 7 of JoVE, for purification of T cells and other cell types to adoptively transfer for imaging purposes. Although we did not demonstrate FACS analysis in this protocol video, it is highly recommended to check the overall purity of isolated cells using the appropriate antibodies via FACS. In addition, we demonstrate the non-sterile method of T cell isolation. If sterile cells are needed for your particular end-user application, be sure to do all of the demonstrated procedures in the tissue culture hood under standard sterile conditions. Thank you for watching and good luck with your own experiments!
Immunology, Issue 9, Cell isolation, Cell separation, T cells, Purification, Mouse, Lymphocyte, Purification, Miltenyi, MACS kit,
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Institutions: University of Alabama at Birmingham.
Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events1. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems2,3,4,5,6
. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory,2,7
provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas 3,6
, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology.
A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research.
Medicine, Issue 55, Functional magnetic resonance imaging (fMRI), MRI, Diffusion tensor imaging (DTI), Functional Connectivity, Neuroscience, Developmental disorders, Autism, Fractional Anisotropy
Basics of Multivariate Analysis in Neuroimaging Data
Institutions: Columbia University.
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques1,5,6,7,8,9
. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.
JoVE Neuroscience, Issue 41, fMRI, PET, multivariate analysis, cognitive neuroscience, clinical neuroscience