Major support for this work was provided by the NYUAD Center for Genomics and Systems Biology (CGSB), funded by Tamkeen under New York University Abu Dhabi Research Institute grant (73 71210 CGSB9) and NYU Abu Dhabi Faculty Research Funds (AD060). W.F. was additionally supported by the Hundred Talents Program of Zhejiang University. We thank Ashish Jaiswal for help in recording the video. We thank Hong Cai for generating the metabolic phenotype data.

1. Phenotype Microarray Experiments
<ol>
	<li>Obtain C. reinhardtii strain CC-503 from the Chlamydomonas Resource Center at the University of Minnesota, USA (https://www.chlamycollection.org).</li>
	<li>Grow the cells in fresh Tris-Acetate-Phosphate (TAP) media19&#160;with final concentrations of 400 &#181;g/mL timentin, 50 &#181;g/mL ampicillin, and 100 &#181;g/mL kanamycin (to inhibit bacterial growth) under 400 micromol photons/m2s, at 25 &#176;C, for two days to mid-log phase.</li>
	<li>Spin down the culture at 2,000 x g for 10 min at 22 &#176;C and discard the supernatant without disturbing the pellet.</li>
	<li>Prepare fresh TAP media containing 0.1% tetrazolium violet dye &#34;D&#34;. 
	NOTE: Modify TAP media in this step to exclude some nutrients depending on the metabolite category tested in each plate (e.g., exclude ammonium chloride for nitrogen source plates).</li>
	<li>Resuspend the pellet in fresh TAP media prepared (from step 1.2) to a final concentration of 1 x 106 cells/mL.</li>
	<li>Use chemical compound array assay plates (carbon sources, nitrogen sources, phosphorus and sulfur sources plates, and the peptide nitrogen sources).</li>
	<li>Inoculate a 100 &#181;L aliquot of cell-containing media into each well of the assay plates. 
	NOTE: Make sure to duplicate the assays.</li>
	<li>Streak cells on yeast extract/peptone plates and perform gram staining, as in Smith et al.20&#160;before and after the assay to monitor bacterial contamination.</li>
	<li>Insert the chemical compound array assay plates into the microplate reader system.</li>
	<li>Incubate all the plates at 30 &#176;C for up to 7 days and program the microplate reader system to read the dye color change every 15 min. 
	NOTE: As most microplate readers do not provide a source of continuous light during incubation, the algae should be able to carry out heterotrophic respiration.</li>
</ol>
2. Data Analysis
<ol>
	<li>Export the raw kinetic data from the microplate reader as CSV files, which will subsequently be used as input to the Omnilog Phenotype Microarray (OPM) package in R. Add the biological information as metadata (e.g., strain designation, growth media, temperature, etc.).
	<ol>
		<li>Using the PM Kinetic data converter software; load the D5E data files, and convert them to OKA files using the following command lines in the PM kinetic analysis software: 
		Load | Import (locate the folder of the OKA files) | Populate Filters | Import | Add All Plates | Close. 
		&#8203;Export | choose read data (Kinetic), choose format (CSV) (Tabulate Header), and choose plates (every plate (individual Files)) | Export data | Save.</li>
		<li>To carry out the Phenotype Microarray (PM) data analysis, use the OPM software package21,22&#160;that runs within the R software environment. The package, tutorial, and reference documentation&#160;are available at:&#160;http://www.goeker.org/opm/. In RStudio, a graphical user interface for R, install the opm package and its dependencies using the following commands: 
		source (http://www.goeker.org/opm/install_opm.R) 
		library (opm)</li>
		<li>Navigate to the directory that contains the CSV files of the kinetic data and import the data using the read_opm function: 
		&#8203;x &#60;- read_opm(&#34;.&#34;, convert=&#34;grp&#34;, include=list (&#34;csv:&#34;))</li>
		<li>Aggregate and discretize the kinetic data using curve-parameter estimation. 
		For (i in 1 :length(x)) { 
		x[[i]] &#60;I do _aggre(x[[i]], boot = 0 L, cores = 1 L, method =&#34;splines&#34;, options = set_spline_options(&#34;smooth.spline&#34;)) 
		x[[i]] &#60;- do_disc(x[[i]], cutoff = FALSE) 
		} 
		#Collection of the metadata 
		metadata &#60;- collect_template(&#34;.&#34;) 
		metadata$Strain &#60;- c(&#34;BLANK&#34;,&#34;CC- 503&#34;) 
		for (I in 1 :length(x)) {x[[i]] &#60;- include_metadata(x[[i]], md = metadata, replace = TRUE)}</li>
		<li>Use the function xy_plot to map the respiration (or growth) measurements (y-axis) as a function of time (x-axis) for the assayed 96-well plates. 
		print (xy_plot(x[[ 1 ]], include =&#34;Strain&#34;, theor.max = FALSE))</li>
		<li>Visualize the data as a heat map using the function level_plot to allow for a quick comparative overview of the kinetic data. 
		level_plot(x, main = list(), colors = opm_opt(&#34;color.borders&#34;), panel.headers = metadata$Strain, cex = NULL, strip.fmt = list(), striptext.fmt = list(), legend.sep =&#34; &#34;, space =&#34;Lab&#34;, bias = 0.7 , num.colors = 200 L)</li>
		<li>Extract important biological information, the curve parameters, from the raw kinetic curves and include the lag phase (&#955;), the growth rate (&#956;), the maximum cell respiration (A), and the area under the curve (AUC)21. To identify positive metabolites, use the A values of the negative control, which represents the abiotic reactivity of the dye with the medium, in addition to the blank of each microwell plate as background subtraction values. The extract function is used to obtain the A parameter. 
		opm_opt(&#34;curve.param&#34;) 
		param &#60;- extract (x, as.labels = list(&#34;Strain&#34;)))</li>
	</ol>
	</li>
</ol>
3. Identification of Reactions and Genes Associated with New Metabolites
<ol>
	<li>Search KEGG(Kyoto Encyclopedia of Genes and Genomes) (http://www.genome.jp/kegg/)&#160;and MetaCyc (http://metacyc.org/) to identify Enzyme Commission numbers (ECs) for reactions using metabolites found from chemical compound arrays23,2423,24.</li>
	<li>Use the identified EC numbers as a search basis in multiple available algal annotation resources such as Joint Genome Institute (JGI), Phytozome (http://www.phytozome.net), and peer-reviewed publications23,25,26,27.</li>
	<li>If a query returns no genetic evidence for a given EC number, identify the relevant associated proteins in other organisms, starting with species closest to the C. reinhardtii, then carry out a profile-based search using the NCBI PSI-BLAST server with default settings and use non-redundant proteins (nr) in C. reinhardtii (taxid:3055) to identify candidate genes associated to the reaction12.</li>
	<li>Manually curate PSI-BLAST hits with E values of&#160;&#60; 0.05 for relevance to the searched EC number through querying those BLAST hits through EMBL-EBI Pfam (http://pfam.xfam.org/search), or InterPro (http://www.ebi.ac.uk/interpro/) protein domain prediction servers. Note that the latter two scans are critical steps to ensure the identification of the correct enzymatic activity for the protein.</li>
</ol>
4. Model Refinement and Evaluation
<ol>
	<li>Use the latest COBRA Toolbox v.3.028&#160;in MATLAB29,30&#160;platform to carry out the following steps for model refinement. The COBRA Toolbox can be installed by following the steps in: https://opencobra.github.io/cobratoolbox/stable/installation.html . Alternatively, note that the COBRA Toolbox is also implemented across other open-source programming languages, such as Python (COBRApy31) and is available at: https://opencobra.github.io/cobrapy/ .
	<ol>
		<li>After installing the COBRA Toolbox v.3.0, open MATLAB and execute the following command to initialize the toolbox: 
		&#8203;initCobraToolbox;</li>
		<li>Add the identified reactions with their associated genes to the metabolic model, such as iRC1080, using the COBRA Toolbox functions addReaction and changeGeneAssociation. Navigate to the directory that contains the iRC1080 model, downloaded from http://bigg.ucsd.edu/models/iRC1080 and execute the following commands to load the model, rename it, and add a new reaction and its associated gene. 
		Load(&#39;iRC 1080 .mat&#39;) 
		modelNew = iRC 1080; 
		modelNew = addReaction(modelNew, &#39;D-ALA 2&#39; , &#8230; 
		{&#39;d-ala[c]&#39; , &#39;atp[c]&#39; , &#8230; 
		&#39;D-aladata[c]&#39; , &#39;adp[c]&#39; , &#39;pi[c]&#39; , &#8230; 
		&#39;h[c]&#39; },[- 2 - 1 1 1 1 1 ],false); 
		modelNEW = changeGeneAssociation(modelNew, &#8230; 
		&#39;D-ALA 2&#39;,&#39;au.g 14655 _t 1&#39; );</li>
		<li>In some cases when the metabolite is not produced intracellularly but is taken up from the medium, add transport reactions for the new metabolites to the model. These transport reactions represent passive diffusion of a metabolite from the extracellular medium to the cytosol. In addition, add a corresponding artificial exchange reaction using the addExchangeRxn function to input or output the metabolite into the extracellular medium. 
		modelNew = addReaction(modelNew, &#39;CYCPt&#39; , &#8230; 
		{&#39;cycp[e]&#39;,&#39;cycp[c]&#39; },[- 1 1 ],true)&#39; 
		modelNew = addExchangeRxn(modelNew, &#39;cycp[e]&#39; ,- 1000 ,1000 );</li>
		<li>Test the behavior of the new resultant model, e.g., iBD1106, by carrying out flux balance analysis (FBA) using the function optimizeCbModel under light and dark conditions for the maximization of biomass as the objective function. For light growth, set the lower and upper bounds of the PRISM solar litho&#39; light reactions to 646.07 (maximum rate). For dark growth, set the bounds of all PRISM light reactions to zero. Use the Biomass function defined previously10&#160;for growth under dark and light conditions. The FBA solution will output two vectors corresponding to reaction fluxes (solution.v) and reduced cost (solution.w), as well as one vector corresponding to metabolites&#39; shadow prices (solution.y). 
		%Simulate growth under light condition: 
		modelNew = changeRxnBounds(modelNew,{ &#8230; 
		% &#39;PRISM_solar_litho&#39; , &#8230; 
		&#39;PRISM_solar_exo&#39; , &#8230; 
		&#39;PRISM_incandescent_ 60 W&#39; , &#8230; 
		&#39;PRISM_fluorescent_cool_ 215 W&#39; , &#8230; 
		&#39;PRISM_metal_halide&#39; , &#8230; 
		&#39;PRISM_high_pressure_sodium&#39; , &#8230; 
		&#39;PRISM_growth_room&#39; , &#8230; 
		&#39;PRISM_white_LED&#39; , &#8230; 
		&#39;PRISM_red_LED_array_ 653 nm&#39; , &#8230; 
		&#39;PRISM_red_LED_ 674 nm&#39; , &#8230; 
		&#39;PRISM_fluorescent_warm_ 18 W&#39; , &#8230; 
		&#39;PRISM_design_growth&#39; , &#8230; 
		}, 0, &#39;b&#39; ); 
		modelNew = changeObjective(modelNew, &#39;BIOMASS_Chlamy_mixo&#39;); 
		FBAsolutionNew = optimizeCbModel(modelNew, &#39;max&#39;);</li>
		<li>Repeat step 4.1.4 for iRC1080 to compare FBA solutions obtained for iBD1106 with those obtained for iRC1080.</li>
		<li>There is a range of COBRA methods available that can be used to compare models (e.g., flux variability analysis, gene deletion studies, robustness analyses, flux split predictions, FBA, sampling, etc.). Detailed tutorials can be found at&#160;https://opencobra.github.io/cobratoolbox/stable/tutorials/index.html. Here, an example is provided where the&#160;iRC1080 model is compared with its refined version, iBD1106, by obtaining the shadow prices (sensitivity of the biomass objective function to changes in system variable) of the metabolites accounted for in each model. 
		Obtain the shadow prices for the metabolites: 
		shadowPrices = table(modelNew.mets, &#8230; 
		modelNew.metNames, FBAsolutionNew.y);</li>
	</ol>
	</li>
</ol>

The authors have nothing to disclose.

Metabolic phenotyping of the green microalga, C. reinhardtii, was described here using high throughput PM assay plates and an unmodified PMI. The assays were utilized for a total of 190 carbon sources (PM01 and PM02), 95 nitrogen sources (PM03), 59 phosphorus sources, and 35 sulfur sources (PM04), along with peptide nitrogen sources (PM06-08). Positive respiration was observed for 148 nutrients (one positive assay for C-source utilization, four positive assays for each the S-source and P-source utilizations, and 139 positive assays for N-source utilization). The substrates or nutrients (carbon, nitrogen, phosphorus, or sulfur) component of the media should not be added to the defined medium when applied to the relevant PM microplates that test for each of those sources.
The method shown here is effective for characterizing metabolic microalgae phenotypes that can be used to extend existing metabolic network models or direct the reconstruction of new models. Further, as the&#160;nutritional requirements of most microalgae are not known, this platform can be used to define these rapidly. Nelson et al.43&#160;had successfully applied these methods to identify new compounds that support the growth of the microalgae Chloroidium sp. UTEX 3007 and used the obtained information to define the species entry metabolites, which, unlike Chlamydomonas, include&#160;40 different carbon sources.
One major limitation of the PM for profiling microalgae is that the PMI has no illumination in the incubation chamber, and the microalgae need to be able to carry out heterotrophic metabolism. The absence of light could affect the interpretation of models that incorporate light to calculate metabolic fluxes. Gene pairs with coordinating functions have co-evolved to constitute metabolic network hubs, and the distinction between photosynthetic and non-photosynthetic network hubs can be made44. In general, photosynthetic network hubs (i.e., highly-connected nodes in the model) would be left out of heterotrophic models. For practical purposes, modeling heterotrophism in mixotrophic species should omit reactions known to be driven by light and account for the energy balance differences between conditions. Thus, modeling light-dependent and light-independent metabolism is standard practice in Chlamydomonas metabolic modeling6,45.
Some green microalgae, like Trebouxiophytes, are known to assimilate a variety of carbon molecules for growth, and this is thought to have arisen from their long evolutionary history as members of lichens46. While chlorophytes like Chlamydomonas can use acetate for growth, the brown marine microalga Tisochrysis lutea, known for its potential to commercially produce very-long-chain polyunsaturated fatty acids (VLC-PUFAs), cannot use acetate but can use glycerol for growth47. Biomass concentration of more than 100 g l&#8722;1 dry cell weight has been achieved with Chlorella with optimized addition of organic carbon sources in a fed-batch mode48. Further, the addition of sugar to Chlorellavulgaris can elevate its sequestration of CO2, thus providing an additive benefit during photosynthetic growth49. Most heterotrophic microalgae can also grow mixotrophically, but the Chlorophyte Chromochloris zofingiensis has been shown to shut off photosynthesis upon the addition of sugar50.
Diatoms, belonging to the division Bacillariophyta, are a major group of phytoplankton. Although most of the diatoms can only grow photoautotrophically, some of them can be cultivated mixotrophically or heterotrophically51. For example, glycerol was found to support growth in the light in the absence of CO2&#160;in some diatoms, including the model species Phaeodactylum tricornutum52. Also, some benthic diatoms like Nitzschia linearis can grow on carbohydrates in the dark53. It is likely to extend the PM assays to diatoms and other algal groups by supplementing suitable organic carbon sources to enable the cells to grow heterotrophically, and a mixotrophy strategy can also be potentially used for the obligate autotrophic microalgae providing a minimally required light supply.
To assess the reproducibility of the data, it is highly recommended to carry out duplicate assays for all plates. An assay may be considered positive only if, after subtraction from the negative control and the respective blank wells, the absorbance (PMI value) is positive. This description, in the presence of the tested compound, is a reflection of the dye&#39;s abiotic reaction with the medium.

Optimizing algal metabolism for enhanced and stable production of targeted metabolites requires the development of complex metabolic engineering strategies through systems-level analyses of metabolic networks. Metabolic network models can guide the rational designs for the rapid development of optimization strategies1,2,3,4. Although approximately 160 microalgal species have been sequenced5, there are, to our knowledge, only 44 algal metabolic models available4,6,7. Due to the difficulty in obtaining high-throughput metabolic phenotypic data for experimental validation of genomic information, the reconstruction of high-quality network models lags behind the rapid development of algal genome sequencing.
C. reinhardtii is an attractive model system for algal-based studies. This species can grow photoautotrophically or heterotrophically and has been widely used as a model organism in basic and applied research. Its genome sequence was published in 20078, with genome-scale metabolic models subsequently reconstructed for the species9,10,11. The genome-scale model for C. reinhardtii (iRC1080) was reconstructed by Chang et al.10&#160;based on genomic and literature evidence (entailing ~250 sources). It has 1,706 metabolites with 2,190 reactions10; however, the completeness of the model could not be verified beyond the available published experimental evidence at the time.
The phenotype microarrays (PMs) technology is a high-throughput platform that can provide metabolic profiling information for heterotrophic microorganisms as well as tissue-culture cells. In particular, it can be used to address the phenotype-to-genotype knowledge gap in microalgae, as first reported for Chlamydomonas reinhardtii12&#160;and subsequently for a species of Chloroidium13&#160;and Chlorella14. By studying cell responses to thousands of metabolites, signaling molecules, osmolytes, and effector molecules, the PM assays can provide functional metabolic profiling and offer insights into the function, metabolism, and environmental sensitivity15,16,17. Specifically, PM assays detect cells metabolite utilization in 96-well microplates with different nutrients, metabolites, or osmolytes contained in each well. Moreover, it is also possible to assay bioactive molecules, such as antibiotics and hormones. As determined by the intensity of color production by the NADH reduction of a tetrazolium-based redox dye, the metabolic utilization of substrates is evaluated in terms of cell respiration15,16,17. The experiments in 96-well microplates can be monitored and determined automatically over time with the phenotype microarray instrument (PMI) platform. Twenty 96-well microplates are designed to represent the common set metabolites to study cellular phenotypes to utilize carbon, nitrogen, sulfur, and phosphorus sources, along with different osmotic/ion and pH effects. The PM technology has been successfully used for updating and upgrading a number of existing genome-scale metabolic models for microorganisms15,16,17,18.
The protocol and data shown here are based on previously published work by Chaiboonchoe et al.12&#160;The presented work details the use of the PM assay method to characterize the metabolic phenotypes of microalgae and to expand an existing algal metabolic model of C. reinhardtii as well as to guide the reconstruction of new metabolic models.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ampicillin</td>
			<td>VWR</td>
			<td>97062-796</td>
			<td></td>
		</tr>
		<tr>
			<td>Biolog assay plates [ PM01-08]</td>
			<td>Biolog, Hayward, CA, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Biolog Omnilog Instrument</td>
			<td>Biolog, Hayward, CA, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Chlamydomonas reinhardtii strain CC-503</td>
			<td>Chlamydomonas Resource Center at the University of Minnesota, USA.</td>
			<td></td>
			<td>Regents of the University of Minnesota</td>
		</tr>
		<tr>
			<td>Kanamycin</td>
			<td>VWR</td>
			<td>0408-EU-10G</td>
			<td></td>
		</tr>
		<tr>
			<td>Tetrazolium Violet Dye &#8220;D&#8221;</td>
			<td>Biolog, Hayward, CA, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Timentin</td>
			<td>GlaxoSmithKline Australia Pty Ltd</td>
			<td>42010012-2</td>
			<td></td>
		</tr>
	</tbody>
</table>

high-throughput metabolic profiling for model refinements of microalgae

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a systems-level. Genome-scale metabolic models may include gaps as well as reactions that are unverified experimentally. Reconstructed models of newly isolated microalgal species will result in weaknesses due to these gaps, as there is usually sparse biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology is an effective, high-throughput method that functionally determines cellular metabolic activities&#160;in response to a wide array of entry metabolites. Combining the high throughput phenotypic assays with metabolic modeling can allow existing metabolic network models to be rapidly reconstructed or optimized by providing biochemical evidence to support and expand genomic evidence. This work will show the use of PM assays for the study of microalgae by using the green microalgal model species Chlamydomonas reinhardtii as an example. Experimental evidence for over 254 reactions obtained by PM was used in this study to expand and refine a genome-scale C. reinhardtii metabolic network model, iRC1080, by approximately 25 percent. The protocol created here can be used as a basis for functionally profiling the metabolism of other microalgae, including known microalgae mutants and new isolates.

Watch this Scientific Journal Video about High-Throughput Metabolic Profiling for Model Refinements of Microalgae at JoVE.com

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

This protocol demonstrates the use of a phenotype microarray (PM) technology platform to define metabolic requirements of Chlamydomonas reinhardtii, a green microalga, and refine an existing metabolic network model.

Metabolic models are reconstructed based on an organism&#39;s available genome annotation and provide predictive tools to study metabolic processes at a ...

high-throughput-metabolic-profiling-for-model-refinements

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3.6K Views.  New York University Abu Dhabi Research Institute. This protocol demonstrates the use of a phenotype microarray (PM) technology platform to define metabolic requirements of Chlamydomonas reinhardtii, a green microalga, and refine an existing metabolic network model.

Video: High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-throughput Saccharification Assay for Lignocellulosic Materials

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing a biorefinery. These raw materials would constitute a new industrial platform, which is both sustainable and carbon neutral, to replace 
the current dependency on fossil fuel. The recalcitrance to deconstruction observed in lignocellulosic materials is produced by several intrinsic properties 
of plant cell walls. Crystalline cellulose is embedded in matrix polysaccharides such as xylans and arabinoxylans, and the whole structure is encased by the 
phenolic polymer lignin, that is also difficult to digest 1. In order to improve the digestibility of plant materials we need to discover the main bottlenecks 
for the saccharification of cell walls and also screen mutant and breeding populations to evaluate the variability in saccharification 2. These tasks require 
a high throughput approach and here we present an analytical platform that can perform saccharification analysis in a 96-well plate format. This platform has 
been developed to allow the screening of lignocellulose digestibility of large populations from varied plant species. We have scaled down the reaction volumes 
for gentle pretreatment, partial enzymatic hydrolysis and sugar determination, to allow large numbers to be assessed rapidly in an automated system.
This automated platform works with milligram amounts of biomass, performing ball milling under controlled conditions to reduce the plant 
materials to a standardised particle size in a reproducible manner. Once the samples are ground, the automated formatting robot dispenses specified and recorded 
amounts of material into the corresponding wells of 96 deep well plate (Figure 1). Normally, we dispense the same material into 4 wells to have 4 replicates for 
analysis. Once the plates are filled with the plant material in the desired layout, they are manually moved to a liquid handling station (Figure 2). In this 
station the samples are subjected to a mild pretreatment with either dilute acid or alkaline and incubated at temperatures of up to 90&deg;C. The pretreatment solution 
is subsequently removed and the samples are rinsed with buffer to return them to a suitable pH for hydrolysis. The samples are then incubated with an enzyme
mixture for a variable length of time at 50&deg;C. An aliquot is taken from the hydrolyzate and the reducing sugars are automatically determined by the MBTH 
colorimetric method.

A simple, rapid method for determining the saccharification potential of large numbers of plant biomass samples is described. The automated platform for this analysis involves the preparation of the plant biomass for analysis in 96 well plates and the subsequent performance of pretreatment, hydrolysis and quantification of the sugars released.

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing ...

High-throughput Crystallization of Membrane Proteins Using the Lipidic Bicelle Method

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane bilayer that surrounds all cells and organelles. Alterations in the function of MPs result in many human diseases and disorders; thus, an intricate understanding of their structures remains a critical objective for biological research. However, structure determination of MPs remains a significant challenge often stemming from their hydrophobicity. 
MPs have substantial hydrophobic regions embedded within the bilayer. Detergents are frequently used to solubilize these proteins from the bilayer generating a protein-detergent micelle that can then be manipulated in a similar manner as soluble proteins. Traditionally, crystallization trials proceed using a protein-detergent mixture, but they often resist crystallization or produce crystals of poor quality. These problems arise due to the detergent&prime;s inability to adequately mimic the bilayer resulting in poor stability and heterogeneity. In addition, the detergent shields the hydrophobic surface of the MP reducing the surface area available for crystal contacts. To circumvent these drawbacks MPs can be crystallized in lipidic media, which more closely simulates their endogenous environment, and has recently become a de novo technique for MP crystallization.
Lipidic cubic phase (LCP) is a three-dimensional lipid bilayer penetrated by an interconnected system of aqueous channels1. Although monoolein is the lipid of choice, related lipids such as monopalmitolein and monovaccenin have also been used to make LCP2. MPs are incorporated into the LCP where they diffuse in three dimensions and feed crystal nuclei. A great advantage of the LCP is that the protein remains in a more native environment, but the method has a number of technical disadvantages including high viscosity (requiring specialized apparatuses) and difficulties in crystal visualization and manipulation3,4. Because of these technical difficulties, we utilized another lipidic medium for crystallization-bicelles5,6 (Figure 1). Bicelles are lipid/amphiphile mixtures formed by blending a phosphatidylcholine lipid (DMPC) with an amphiphile (CHAPSO) or a short-chain lipid (DHPC). Within each bicelle disc, the lipid molecules generate a bilayer while the amphiphile molecules line the apolar edges providing beneficial properties of both bilayers and detergents. Importantly, below their transition temperature, protein-bicelle mixtures have a reduced viscosity and are manipulated in a similar manner as detergent-solubilized MPs, making bicelles compatible with crystallization robots. 
Bicelles have been successfully used to crystallize several membrane proteins5,7-11 (Table 1). This growing collection of proteins demonstrates the versatility of bicelles for crystallizing both alpha helical and beta sheet MPs from prokaryotic and eukaryotic sources. Because of these successes and the simplicity of high-throughput implementation, bicelles should be part of every membrane protein crystallographer&prime;s arsenal. In this video, we describe the bicelle methodology and provide a step-by-step protocol for setting up high-throughput crystallization trials of purified MPs using standard robotics.

Bicelles are lipid/amphiphile mixtures that maintain membrane proteins (MPs) within a lipid bilayer but have unique phase behavior that facilitates high-throughput screening by crystallization robots. This technique has successfully produced a number of high-resolution structures from both prokaryotic and eukaryotic sources. This video describes protocols for generating the lipidic bicelle mixture, incorporating MPs into the bicelle mixture, setting up crystallizations trials (manually as well as robotically) and harvesting crystals from the medium.

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane ...

Chemically-blocked Antibody Microarray for Multiplexed High-throughput Profiling of Specific Protein Glycosylation in Complex Samples

In this study, we describe an effective protocol for use in a multiplexed high-throughput antibody microarray with glycan binding protein detection that allows for the glycosylation profiling of specific proteins. Glycosylation of proteins is the most prevalent post-translational modification found on proteins, and leads diversified modifications of the physical, chemical, and biological properties of proteins. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases. However, current methods to study protein glycosylation typically are too complicated or expensive for use in most normal laboratory or clinical settings and a more practical method to study protein glycosylation is needed. The new protocol described in this study makes use of a chemically blocked antibody microarray with glycan-binding protein (GBP) detection and significantly reduces the time, cost, and lab equipment requirements needed to study protein glycosylation. In this method, multiple immobilized glycoprotein-specific antibodies are printed directly onto the microarray slides and the N-glycans on the antibodies are blocked. The blocked, immobilized glycoprotein-specific antibodies are able to capture and isolate glycoproteins from a complex sample that is applied directly onto the microarray slides. Glycan detection then can be performed by the application of biotinylated lectins and other GBPs to the microarray slide, while binding levels can be determined using Dylight 549-Streptavidin. Through the use of an antibody panel and probing with multiple biotinylated lectins, this method allows for an effective glycosylation profile of the different proteins found in a given human or animal sample to be developed.
Introduction
Glycosylation of protein, which is the most ubiquitous post-translational modification on proteins, modifies the physical, chemical, and biological properties of a protein, and plays a fundamental role in various biological processes1-6. Because the glycosylation machinery is particularly susceptible to disease progression and malignant transformation, aberrant glycosylation has been recognized as early detection biomarkers for cancer and other diseases 7-12. In fact, most current cancer biomarkers, such as the L3 fraction of &alpha;-1 fetoprotein (AFP) for hepatocellular carcinoma 13-15, and CA199 for pancreatic cancer 16, 17 are all aberrant glycan moieties on glycoproteins. However, methods to study protein glycosylation have been complicated, and not suitable for routine laboratory and clinical settings. Chen et al. has recently invented a chemically blocked antibody microarray with a glycan-binding protein (GBP) detection method for high-throughput and multiplexed profile glycosylation of native glycoproteins in a complex sample 18. In this affinity based microarray method, multiple immobilized glycoprotein-specific antibodies capture and isolate glycoproteins from the complex mixture directly on the microarray slide, and the glycans on each individual captured protein are measured by GBPs. Because all normal antibodies contain N-glycans which could be recognized by most GBPs, the critical step of this method is to chemically block the glycans on the antibodies from binding to GBP. In the procedure, the cis-diol groups of the glycans on the antibodies were first oxidized to aldehyde groups by using NaIO4 in sodium acetate buffer avoiding light. The aldehyde groups were then conjugated to the hydrazide group of a cross-linker, 4-(4-N-MaleimidoPhenyl)butyric acid Hydrazide HCl (MPBH), followed by the conjugation of a dipeptide, Cys-Gly, to the maleimide group of the MPBH. Thus, the cis-diol groups on glycans of antibodies were converted into bulky none hydroxyl groups, which hindered the lectins and other GBPs bindings to the capture antibodies. This blocking procedure makes the GBPs and lectins bind only to the glycans of captured proteins. After this chemically blocking, serum samples were incubated with the antibody microarray, followed by the glycans detection by using different biotinylated lectins and GBPs, and visualized with Cy3-streptavidin. The parallel use of an antibody panel and multiple lectin probing provides discrete glycosylation profiles of multiple proteins in a given sample 18-20. This method has been used successfully in multiple different labs 1, 7, 13, 19-31. However, stability of MPBH and Cys-Gly, complicated and extended procedure in this method affect the reproducibility, effectiveness and efficiency of the method. In this new protocol, we replaced both MPBH and Cys-Gly with one much more stable reagent glutamic acid hydrazide (Glu-hydrazide), which significantly improved the reproducibility of the method, simplified and shorten the whole procedure so that the it can be completed within one working day. In this new protocol, we describe the detailed procedure of the protocol which can be readily adopted by normal labs for routine protein glycosylation study and techniques which are necessary to obtain reproducible and repeatable results.

In this study, we describe an improved protocol for a multiplexed high-throughput antibody microarray with lectin detection method that can be used in glycosylation profiling of specific proteins. This protocol features new reliable reagents and significantly reduces the time, cost, and lab equipment requirements as compared to the previous procedure.

 In this study, we describe an effective protocol for use in a multiplexed high-throughput antibody microarray with glycan binding protein detection that ...

Metabolic Labeling of Newly Transcribed RNA for High Resolution Gene Expression Profiling of RNA Synthesis, Processing and Decay in Cell Culture

The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene expression. Along with a better understanding of the involved molecular mechanisms, precise measurements of the underlying kinetics have become increasingly important. Here, these powerful methodologies face major limitations due to intrinsic properties of the template samples they study, i.e. total cellular RNA. In many cases changes in total cellular RNA occur either too slowly or too quickly to represent the underlying molecular events and their kinetics with sufficient resolution. In addition, the contribution of alterations in RNA synthesis, processing, and decay are not readily differentiated.
We recently developed high-resolution gene expression profiling to overcome these limitations. Our approach is based on metabolic labeling of newly transcribed RNA with 4-thiouridine (thus also referred to as 4sU-tagging) followed by rigorous purification of newly transcribed RNA using thiol-specific biotinylation and streptavidin-coated magnetic beads. It is applicable to a broad range of organisms including vertebrates, Drosophila, and yeast. We successfully applied 4sU-tagging to study real-time kinetics of transcription factor activities, provide precise measurements of RNA half-lives, and obtain novel insights into the kinetics of RNA processing. Finally, computational modeling can be employed to generate an integrated, comprehensive analysis of the underlying molecular mechanisms.

Total cellular RNA provides a poor template for studying short-term changes in RNA synthesis and decay as well as the kinetics of RNA processing. Here, we describe metabolic labeling of newly transcribed RNA with 4-thiouridine followed by thiol-specific biotinylation and purification of newly transcribed RNA allowing to overcome these limitations.

The development of whole-transcriptome microarrays and next-generation sequencing has revolutionized our understanding of the complexity of cellular gene ...

RNA Secondary Structure Prediction Using High-throughput SHAPE

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.

High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed ...

High-throughput Titration of Luciferase-expressing Recombinant Viruses

Standard plaque assays to determine infectious viral titers can be time consuming, are not amenable to a high volume of samples, and cannot be done with viruses that do not form plaques. As an alternative to plaque assays, we have developed a high-throughput titration method that allows for the simultaneous titration of a high volume of samples in a single day. This approach involves infection of the samples with a Firefly luciferase tagged virus, transfer of the infected samples onto an appropriate permissive cell line, subsequent addition of luciferin, reading of plates in order to obtain luminescence readings, and finally the conversion from luminescence to viral titers. The assessment of cytotoxicity using a metabolic viability dye can be easily incorporated in the workflow in parallel and provide valuable information in the context of a drug screen. This technique provides a reliable, high-throughput method to determine viral titers as an alternative to a standard plaque assay.

This article presents a high-throughput luciferase expression-based method of titrating various RNA and DNA viruses using automated and manual liquid handlers.

Standard plaque assays to determine infectious viral titers can be time consuming, are not amenable to a high volume of samples, and cannot be done with ...

Recovery of Adult Zebrafish Hearts for High-throughput Applications

Use of the zebrafish model system for studying development, regeneration, and disease is expanding toward use of adult hearts for cell dissociation and purification of RNA, DNA, and proteins. All of these applications demand the rapid recovery of significant numbers of zebrafish hearts to avoid gene regulatory, metabolic, and other changes that begin after death. Adult zebrafish hearts are also required for studying heart structure for a variety of mutants and for studying heart regeneration. However, the traditional zebrafish heart dissection is slow and difficult and requires specialized tools, making large-scale dissection of adult zebrafish hearts tedious. Traditional methods also harbor the risk of damaging the heart during the dissection. Here, we describe a method for dissection of adult zebrafish hearts that is fast, reproducible, and preserves heart architecture. Furthermore, this method does not require specialized tools, is painless for the zebrafish, can be performed on fresh or fixed specimens, and can be performed on zebrafish as young as one month old. The approach described expands the use of adult zebrafish for cardiovascular research.

Use of zebrafish for cardiovascular research is expanding towards research on adult hearts. For these applications, quick and simple isolation of cardiac tissues is key to avoid post-mortem changes and to obtain an adequate number of samples. Here, we describe a fast and reproducible method for dissecting adult zebrafish hearts.

Use of the zebrafish model system for studying development, regeneration, and disease is expanding toward use of adult hearts for cell dissociation and ...

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

Isolation of Mitochondria from Minimal Quantities of Mouse Skeletal Muscle for High Throughput Microplate Respiratory Measurements

Dysfunctional skeletal muscle mitochondria play a role in altered metabolism observed with aging, obesity and Type II diabetes. Mitochondrial respirometric assays from isolated mitochondrial preparations allow for the assessment of mitochondrial function, as well as determination of the mechanism(s) of action of drugs and proteins that modulate metabolism. Current isolation procedures often require large quantities of tissue to yield high quality mitochondria necessary for respirometric assays. The methods presented herein describe how high quality purified mitochondria (~ 450 &#181;g) can be isolated from minimal quantities (~75-100 mg) of mouse skeletal muscle for use in high throughput respiratory measurements. We determined that our isolation method yields 92.5&#177; 2.0% intact mitochondria by measuring citrate synthase activity spectrophotometrically. In addition, Western blot analysis in isolated mitochondria resulted in the faint expression of the cytosolic protein, GAPDH, and the robust expression of the mitochondrial protein, COXIV. The absence of a prominent GAPDH band in the isolated mitochondria is indicative of little contamination from non-mitochondrial sources during the isolation procedure. Most importantly, the measurement of O2 consumption rate with micro-plate based technology and determining the respiratory control ratio (RCR) for coupled respirometric assays shows highly coupled (RCR; &#62;6 for all assays) and functional mitochondria. In conclusion, the addition of a separate mincing step and significantly reducing motor driven homogenization speed of a previously reported method has allowed the isolation of high quality and purified mitochondria from smaller quantities of mouse skeletal muscle that results in highly coupled mitochondria that respire with high function during microplate based respirometirc assays.

Here, we present a modification of a previously reported method that allows for the isolation of high quality and purified mitochondria from smaller quantities of mouse skeletal muscle. This procedure results in highly coupled mitochondria that respire with high function during microplate based respirometirc assays.

Dysfunctional skeletal muscle mitochondria play a role in altered metabolism observed with aging, obesity and Type II diabetes. Mitochondrial ...

Mixed Primary Cultures of Murine Small Intestine Intended for the Study of Gut Hormone Secretion and Live Cell Imaging of Enteroendocrine Cells

The gut is the largest endocrine organ of the body, with hormone-secreting enteroendocrine cells located along the length of the gastrointestinal epithelium. Despite their physiological importance, enteroendocrine cells represent only a small fraction of the epithelial cell population and in the past, their characterization has presented a considerable challenge resulting in a reliance on cell line models. Here, we provide a detailed protocol for the isolation and culture of mixed murine small intestinal cells. These primary cultures have been used to identify the signaling pathways underlying the stimulation and inhibition of gut peptide secretion in response to a number of nutrients and neuropeptides as well as pharmacological agents. Furthermore, in combination with the use of transgenic fluorescent reporter mice, we have demonstrated that these primary cultures become a powerful tool for the examination of fluorescently-tagged enteroendocrine cells at the intracellular level, using methods such as patch clamping and single-cell calcium and cAMP-FRET imaging.

This protocol describes the isolation and culture of mixed murine small intestinal cells. These primary intestinal cultures enable the investigation of signaling pathways underlying gut peptide secretion using a number of techniques.