Name: single-cell gene expression profiling using facs and qpcr with internal standards
Uploaded: 2017-02-25T14:00:00
Description: Watch this Scientific Journal Video about Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards at JoVE.com

We would like to thank V. Kapoor in the CCR ETIB Flow Cytometry Core for her aid in performing the cell sorting during the development of this protocol. We also thank M. Raffeld and the CCR LP Molecular Diagnostics Unit and J. Zhu and the NHLBI DNA Sequencing and Genomics Core for their aid in performing the qPCR during the development of this protocol. This research was supported by the Intramural Program of the NIH.

1. Advance Preparation
<ol>
	<li>Select up to 96 genes of interest whose expression will be measured. 
	NOTE: At least one of these genes should be a &#34;housekeeping gene,&#34; such as ACTB or GAPDH, that is known to be expressed at a relatively high and constant level under the conditions used in the experiment. This gene will be used to identify positively sorted wells (step 8.1) and amplified samples (step 10.1). 
	NOTE: For the example experiment, well-characterized, direct targets of p53 with a variety...

<ol>
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J., Oren, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+first+30+years+of+p53:+growing+ever+more+complex.">The first 30 years of p53: growing ever more complex.</a> Nat. Rev. Cancer. 9 (10), 749-758 (2009).</li><li>Riley, T., Sontag, E., Chen, P., Levine, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptional+control+of+human+p53-regulated+genes.">Transcriptional control of human p53-regulated genes.</a> Nat. Rev. Mol. Cell Biol. 9 (5), 402-412 (2008).</li><li>Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S., Madden, T. 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We have presented a method for isolating individual mammalian cells from a population of adherent cells grown in culture and for assaying the expression of approximately 96 genes in each cell. Good advance preparation is critical for this method to work well. In particular, designing and testing primer pairs specific to the transcripts of interest (steps 1.2-1.3) are time-consuming but important steps, as the primers determine the quality of the single-cell measurements. Once reliable primer pairs have been obtained, the...

Individual cells in a population can show widely differing responses to a uniform physiological stimulus1,2,3,4. The genetic variation of cells in a population is one mechanism for this variety of responses, but there are also several non-genetic factors that can increase the variability of responses, even in a clonal population of cells. For example, the levels of individual proteins and other important signaling molecules can vary on a cell-by-cell basis, giving rise to variation in downstream gene expression profiles. Additionally, gene activation can occur in short-duration bursts of transcripts5,6 that may be limited to a relatively small number of transcripts per burst7,8,9. Such stochasticity in gene activation can greatly contribute to variability in biological responses and can provide a selective advantage in microorganisms10 and in mammalian cells1,2 responding to a physiological stimulus. Due to both genetic and non-genetic sources of variation, the gene expression profile of any given cell in response to a stimulus may differ greatly from the average gene expression profile obtained from the measurement of the bulk response. Determining the extent to which individual cells show variability in response to a stimulus requires techniques for the isolation of individual cells, the measurement of the expression levels for transcripts of interest, and the computational analysis of the resulting expression data.
There are several approaches for assaying gene expression in single cells, covering a wide range of costs, number of transcripts probed, and accuracy of quantification. For example, single-cell RNA-Seq offers a wide depth of transcript coverage and the ability to quantify thousands of distinct transcripts for the most highly expressed genes in individual cells; however, the cost associated with such sequencing depth can be prohibitive, although costs continue to decrease. Conversely, single-molecule RNA fluorescence in situ hybridization (smRNA FISH) offers precise quantification of transcripts for even low-expressing genes at a reasonable cost per gene of interest; however, only a small number of target genes can be assayed in a given cell by this approach. Quantitative PCR-based assays, described in this protocol, provide a middle ground between these techniques. These assays employ a microfluidic real-time PCR machine to quantify up to 96 transcripts of interest at a time in up to 96 cells. While each of the aforementioned methods has requisite hardware costs, the cost of any individual qPCR assay is relatively low. This protocol is adapted from one suggested by a manufacturer of a microfluidic real-time PCR machine (Protocol ADP 41, Fluidigm). To enable the estimation of the absolute number of each transcript in a PCR-based approach, we have expanded the protocol to make use of internal controls of prepared target gene amplicons that can be used across multiple experiments.
As an example of this technique, the quantification of the expression of genes regulated by the tumor suppressor p53 in MCF-7 human breast carcinoma cells is described11. The cells are challenged with a chemical agent that induces DNA double-strand breaks. Previous studies have shown that the p53 response to DNA double-strand breaks exhibits a great deal of heterogeneity in individual cells, both in terms of p53 levels12 and in the activation of distinct target genes11. Furthermore, p53 regulates the expression of over 100 well-characterized target genes involved in numerous downstream pathways, including cell cycle arrest, apoptosis, and senescence13,14. Since the p53-mediated response in each cell is both complex and variable, the analysis of the system benefits from an approach in which nearly 100 target genes can be probed simultaneously in individual cells, such as that described below. With slight modifications (such as alternative methods for single-cell isolation and lysis), the protocol can be readily adapted to study a wide range of mammalian cell types, transcripts, and cellular responses.
With proper advance preparation, a round of cell sorting and gene expression measurement can be conducted according to this protocol over a period of three days. The following timing is suggested: in advance, select the transcripts of interest, identify and validate the primer pairs that amplify the cDNA from those transcripts, and prepare the standards and primer mixes using those primers. On Day 1, following cell treatment, harvest and sort the cells, perform reverse transcription and specific target amplification, and treat the samples with an exonuclease to remove unincorporated primers. On Day 2, perform quality control on sorted cells using qPCR. Finally, on Day 3, measure the gene expression in the sorted cells using microfluidic qPCR. Figure 1 summarizes the steps involved.

<table border="0" cellpadding="0" cellspacing="0" width="988">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:488px;">RNeasy Plus Mini Kit</td>
			<td style="width:187px;">Qiagen</td>
			<td style="width:147px;">74134</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">High Capacity cDNA Reverse Transcription Kit with RNase Inhibitor</td>
			<td>ThermoFisher</td>
			<td>4374966</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phusion High-Fidelity DNA Polymerase</td>
			<td>New England BioLabs</td>
			<td>M0530S</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">QIAquick Gel Extraction Kit</td>
			<td>Qiagen</td>
			<td>28704</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Quant-iT High-Sensitivity dsDNA Assay Kit</td>
			<td>ThermoFisher</td>
			<td>Q33120&#160;</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2.0-mL low adhesion microcentrifuge tubes</td>
			<td>USA Scientific</td>
			<td>1420-2600</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">DNA Suspension Buffer</td>
			<td>Teknova</td>
			<td>T0221</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Axygen 0.2-mL Maxymum Recovery Thin Wall PCR Tubes</td>
			<td>Corning</td>
			<td>PCR-02-L-C</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">GE 96.96 Dynamic Array DNA Binding Dye Sample &#38; Assay Loading Reagent Kit</td>
			<td>Fluidigm</td>
			<td>100-3415</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HyClone RPMI 1640 media</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30027.01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fetal Bovine Serum, Certified (US)</td>
			<td>ThermoFisher</td>
			<td>16000-044</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Antibiotic-Antimycotic Solution</td>
			<td>Corning</td>
			<td>30-004-CI</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Neocarzinostatin</td>
			<td>Sigma</td>
			<td>N9162</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ELIMINase</td>
			<td>Decon Labs</td>
			<td>1101</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SUPERase-In</td>
			<td>ThermoFisher</td>
			<td>AM2696</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">CellsDirect One-Step qRT-PCR Kit</td>
			<td>ThermoFisher</td>
			<td>11753500</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">E. coli DNA</td>
			<td>Affymetrix</td>
			<td>14380 10 MG</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">ThermalSeal Sealing Film, Sterile</td>
			<td>Excel Scientific</td>
			<td>STR-THER-PLT</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BD FACSAria IIu</td>
			<td>BD Biosciences</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">HyClone Trypsin 0.05%</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30236.01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">PBS, 1x</td>
			<td>Corning</td>
			<td>21-040-CV</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Falcon 40&#181;m Cell Strainer</td>
			<td>Corning</td>
			<td>352340</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Exonuclease I</td>
			<td>New England BioLabs</td>
			<td>M0293S</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">SsoFast EvaGreen Supermix with Low ROX</td>
			<td>Bio-Rad</td>
			<td>172-5210</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">96.96 Dynamic Array IFC for Gene Expression (microfluidic qPCR chip)</td>
			<td>Fluidigm</td>
			<td>BMK-M-96.96</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">IFC Controller HX (loading machine)</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BioMark or BioMark HD (microfluidic qPCR machine)</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Real-Time PCR Analysis software&#160;</td>
			<td>Fluidigm</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">MATLAB software</td>
			<td>MathWorks</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

single-cell gene expression profiling using facs and qpcr with internal standards

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those populations. Single-cell gene expression measurements can help assess the role of noise in gene expression, identify correlations in the expression of pairs of genes, and reveal subpopulations of cells that respond differently to a stimulus. Here, we describe a procedure to measure the expression of up to 96 genes in single mammalian cells isolated from a population growing in tissue culture. Cells are sorted into lysis buffer by fluorescence-activated cell sorting (FACS), and the mRNA species of interest are reverse-transcribed and amplified. Gene expression is then measured using a microfluidic real-time PCR machine, which performs up to 96 qPCR assays on up to 96 samples at a time. We also describe the generation and use of PCR amplicon standards to enable the estimation of the absolute number of each transcript. Compared with other methods of measuring gene expression in single cells, this approach allows for the quantification of more distinct transcripts than RNA FISH at a lower cost than RNA-Seq.

A general overview of the protocol is shown in Figure 1, including steps for cell treatment, the isolation of single cells by FACS, the generation and pre-amplification of cDNA libraries from single-cell lysates, the confirmation of single-cell cDNA libraries in sorted wells, and the measurement of gene expression by qPCR.
In preparation for single-cell isolation and gene expression analysis, it is necessary to ...

Watch this Scientific Journal Video about Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards at JoVE.com

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

We describe a method to sort single mammalian cells and to quantify the expression of up to 96 target genes of interest in each cell. This method includes the use of internal qPCR standards to enable the estimation of absolute transcript counts.

Gene expression measurements from bulk populations of cells can obscure the considerable transcriptomic variation of individual cells within those ...

The overall goal of this procedure is to quantify the levels of mRNA in individual cells using microfluidic qPCR with internal standards. This method can help answer key questions in the field of single cell biology, such as whether cell populations of cells respond to a stimulus in characteristically different ways. The main advantage of this technique is that it can measure expression of more genes than RNA FISH at a lower cost than RNA-Seq in single cells.We first had the idea for this method when we were exploring protocols to measure gene expression in single cells by microfluidic qPCR. We wanted to modify existing methods to more accurately estimate the absolute number of different transcripts in single cells. Demonstrating the cell sorting procedure will be William Telford, the manager of the flow cytometry facility used by my lab.After generating and purifying amplicons from genes of interest according to the text protocol, combine 18 microliters of each purified amplicon in a 2.0 milliliter low binding microcentrifuge tube, then add DNA suspension buffer up to 1, 800 microliters so that the concentration of each amplicon is five times 10 to the sixth molecules per microliter. Thoroughly vortex the 5e6 standard and divide it into 20 microliter aliquots in low binding PCR tubes. After plating MCF7 p53-Venus cells according to the text protocol, add 400 nanograms per milliliter of neocarzinostatin to the cells and treat them for three, 8.5, 14 or 24 hours.To make lysis buffer, combine the reagents listed in this table. Transfer 92.4 microliters of the buffer into a separate tube to serve as the lysis buffer for the amplicon standards. The remaining lysis buffer will be for the cells.Prepare and add E.coli DNA to the lysis buffer for the cells, as per the text protocol. To prepare standards, label six low binding centrifuge tubes. Add 90 microliters of DNA suspension buffer to the 5e5 tube.Add 90 microliters of buffer containing 6.2 picograms per microliter of E.coli DNA to each of the other five tubes and keep all the tubes on ice. After removing an aliquot of 5e6 standard from storage, briefly vortex the tube, then spin it to ensure that the liquid is at the bottom of the tube, then using a low binding pipette tip, add 10 microliters of 5e6 standard to the 5e5 tube. Vortex, spin and put the tube back on ice.Next, pipette 10 microliters from the 5e5 tube into the 5e4 tube, then briefly vortex the tube, spin it, and put the tube back on ice. Repeat the serial dilutions with each tube, receiving 10 microliters from the previous tube until all tubes have a dilution of standard. After preparing PCR tubes with lysis buffer, using a 12 channel pipette, distribute nine microliters of cell lysis buffer from the row of PCR tubes into each well of the first seven rows of a PCR plate.Distribute seven microliters of standard lysis buffer to each well of the last row of the plate, then using low binding pipette tips, add two microliters of standard to each well of the bottom row according to the plate map. Add two microliters of 3.1 picograms per microliter of E.coli DNA into the 10 cell and 100 cell wells, then add two microliters of 6.2 picograms per microliter of E.coli DNA into the no cell wells. After programming the fluorescence activated cell sorter to sort into a 96 well plate according to the plate map, to aim the machine, seal an empty PCR plate and use the test stream to sort droplets and PBS onto the seal of the empty plate.If the droplets do not land in the correct position, wipe them off the surface of the seal, recalibrate the cell sorter according to the machine manufacturer's instructions, and repeat until the droplets in the sort stream are deposited correctly. Harvest cells by trypsinization and re-suspend them in 0.5 milliliters of PBS with 2%FBS, then transfer to a flow cytometry tube. To sort cells, insert the tube with the cell suspension into the cell sorter, open the seal on the plate of lysis buffer, and carefully sort the cells of interest into the plate according to the plate map.To ensure that single cells are sorted at maximum purity, run the machine in single cell mode and use standard flow cytometry gating based on forward and side scatter. Following the run, use a new sterile thermal seal to seal the plate and centrifuge it at 400 times g for four degrees Celsius for one minute, then place the plate in the thermal cycler and run the previously prepared RT-STA program. After spinning the completed RT-STA plate, use a 12 channel pipette to distribute 3.6 microliters of dilute exonuclease I into each well of the plate.Seal and briefly spin the plate, then place the plate in the thermal cycler and run the exo I program. To measure the housekeeping gene expression in each sample, prepare 900 microliters of DNA binding dye qPCR master mix for 96 reactions using primers from one of the selected housekeeping genes. Distribute nine microliters of master mix to every well of a 96 well PCR plate, then add one microliter of sample from the sample plate to the corresponding well of the PCR plate.Run the plate on a real time PCR machine using a thermal cycling protocol suggested by the master mix manufacturer or following a standard protocol, then analyze the samples and make a sample mixes plate map according to the text protocol. Label a plate sample mixes and then, using an eight channel pipette, distribute 3.3 microliters of master mix sample loading reagent mixture into each well of a 96 well PCR plate. For each PCR plate of samples, vortex the plate for 10 seconds and spin it at 500 times g for 10 seconds.Remove the cover and transfer 2.7 microliters of each positive one cell sample to its corresponding well on the sample mixes plate for the one cell wells indicated on the plate map. Peel the sticker off the bottom of a new microfluidic qPCR chip, then using a syringe of control line fluid with the cap still on, push down each accumulator spring to loosen it and inject each accumulator with 100 to 200 microliters of control line fluid. Insert the chip in the loading machine and run the prime script, then using an eight channel pipette, transfer 4.5 microliters of each assay mix into the left side of the microfluidic qPCR chip.It's critical to avoid creating air bubbles at the bottom of the wells. This will create loading problems. To prevent this, touch the pipette tips to the side of the well, then pipette only to the first stop.Transfer 4.5 microliters of each sample mix to the right side of the microfluidic qPCR chip, then insert the chip into the loading machine and run the load mix script. When the load mix script is done, verify that there are no lines across the chip, which would indicate a loading problem, then use tape to carefully remove any dust particles from the chip, then insert the chip into the microfluidic real time PCR machine and run the data collection script. Finally, analyze the data according to the text protocol.This figure shows the expression levels of a housekeeping gene measured by qPCR to determine which wells received a single cell. Samples that received a cell have low CT values. Samples without a cell have much higher CT values.The wells for the amplicon standard should show evenly spaced amplification curves. There should also be clear separation in the curves for the no cell, 10 cell and 100 cell wells. Seed DNA samples representing actual cells are cherry picked from multiple sorted 96 well plates and combined in a single plate to prepare for microfluidic qPCR.This heat map summarizes the microfluidic qPCR results. The color variation in each column shows the variability in expression of each gene between different cells. The two rows that are much darker than the rest most likely represent bad samples.While attempting this procedure, it's important to keep your work area RNase free and DNA free, especially when making the standards and lysis buffer. The pre-PCR parts of this protocol are sensitive to contamination. After watching this video, you should have a good understanding of how to quantify expression of multiple genes of interest in individual cells.Following this procedure, other methods, like single molecule RNA FISH can be performed to validate measurements of mRNA abundance in single cells.

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Parallel Measurement of Circadian Clock Gene Expression and Hormone Secretion in Human Primary Cell Cultures

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental changes. Considerable progress in our understanding of the tight connection between the circadian clock and most aspects of physiology has been made in the field over the last decade. However, unraveling the molecular basis that underlies the function of the circadian oscillator in humans stays of highest technical challenge. Here, we provide a detailed description of an experimental approach for long-term (2-5 days) bioluminescence recording and outflow medium collection in cultured human primary cells. For this purpose, we have transduced primary cells with a lentiviral luciferase reporter that is under control of a core clock gene promoter, which allows for the parallel assessment of hormone secretion and circadian bioluminescence. Furthermore, we describe the conditions for disrupting the circadian clock in primary human cells by transfecting siRNA targeting CLOCK. Our results on the circadian regulation of insulin secretion by human pancreatic islets, and myokine secretion by human skeletal muscle cells, are presented here to illustrate the application of this methodology. These settings can be used to study the molecular makeup of human peripheral clocks and to analyze their functional impact on primary cells under physiological or pathophysiological conditions.

Here, we describe settings to monitor in parallel circadian bioluminescence and the secretory activity of human islet cells and primary myotubes. For this, we employed lentiviral gene delivery of a luciferase core clock reporter, followed by in vitro synchronization and collection of outflow medium by continuous cell perifusion.

Circadian clocks are functional in all light-sensitive organisms, allowing for an adaptation to the external world by anticipating daily environmental ...

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in a single reaction. The miRNA assay counts transcripts by directly imaging and digitally counting miRNA molecules that are labeled with color-coded fluorescent barcoded probe sets (a reporter probe and a capture probe). Barcodes are hybridized directly to mature miRNAs that have been elongated by ligating a unique oligonucleotide tag (miRtag) to the 3&#39; end. Reverse transcription and amplification of the transcripts are not required. Reporter probes contain a sequence of six color positions populated using a combination of four fluorescent colors. The four colors over six positions are used to construct a gene-specific color barcode sequence. Post-hybridization processing is automated on a robotic prep station. After hybridization, the excess probes are washed away, and the tripartite structures (capture probe-miRNA-reporter probe) are fixed to a streptavidin-coated slide via the biotin-labeled capture probe. Imaging and barcode counting is done using a digital analyzer. The immobilized barcoded miRNAs are visualized and imaged using a microscope and camera, and the unique barcodes are decoded and counted. Data quality control (QC), normalization, and analysis are facilitated by a custom-designed data processing and analysis software that accompanies the assay software. The assay demonstrates high linearity over a broad range of expression, as well as high sensitivity. Sample and assay preparation does not involve enzymatic reactions, reverse transcription, or amplification; has few steps; and is largely automated, reducing investigator effects and resulting in high consistency and technical reproducibility. Here, we describe the application of this technology to identifying circulating miRNA perturbations in irritable bowel syndrome.

We describe the use of a multiplexed high-throughput gene expression platform that quantitates gene expression by barcoding and counting molecules in biological substrates without the need for amplification. We used the platform to quantitate microRNA (miRNA) expression in whole blood in subjects with and without irritable bowel syndrome.

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in ...

Single-cell Gene Expression Using Multiplex RT-qPCR to Characterize Heterogeneity of Rare Lymphoid Populations

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell activation, stress, or stimulation. Single-cell multiplex gene expression enables the simultaneous assessment of tens of genes1,2,3. At the single-cell level, multiplex gene expression determines population heterogeneity4,5. It allows for the distinction of population heterogeneity by determining both the probable mix of diverse precursor stages among mature cells and also the diversity of cell responses to stimuli.
Innate lymphoid cells (ILC) have been recently described as a population of innate effectors of the immune response6,7. In this protocol, cell heterogeneity of the ILC hepatic compartment is investigated during homeostasis.
Currently, the most widely used technique to assess gene expression is RT-qPCR. This method measures gene expression only one gene at a time. Additionally, this method cannot estimate heterogeneity of gene expression, since multiple cells are needed for one test. This leads to the measurement of the average gene expression of the population. When assessing large numbers of genes, RT-qPCR becomes a time-, reagent-, and sample-consuming method. Hence, the trade-offs limit the number of genes or cell populations that can be evaluated, increasing the risk of missing the global picture.
This manuscript describes how single-cell multiplex RT-qPCR can be used to overcome these limitations. This technique has benefited from recent microfluidics technological advances1,2. Reactions occurring in multiplex RT-qPCR chips do not exceed the nanoliter-level. Hence, single-cell gene expression, as well as simultaneous multiple gene expression, can be performed in a reagent-, sample-, and cost-effective manner. It is possible to test cell gene signature heterogeneity at the clonal level between cell subsets within a population at different developmental stages or under different conditions4,5. Working on rare populations with large numbers of conditions at the single-cell level is no longer a restriction.

This protocol describes how to assess the expression of a large array of genes at the clonal level. Single-cell RT-qPCR produces highly reliable results with a strong sensitivity for hundreds of samples and genes.

Gene expression heterogeneity is an interesting feature to investigate in lymphoid populations. Gene expression in these cells varies during cell ...

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.

Single cell sequencing is an increasingly popular and accessible tool for addressing genomic changes at high resolution. We provide a protocol that uses single cell sequencing to identify copy number alterations in single cells.

Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and ...

Detection of microRNA Expression in Peritoneal Membrane of Rats Using Quantitative Real-time PCR

MicroRNAs (miRNAs) are small noncoding RNAs that regulate messenger RNA expression post-transcriptionally. The miRNA expression profile has been investigated in various organs and tissues in rat. However, standard methods for the purification of miRNAs and detection of their expression in rat peritoneal membrane have not been well established. We have developed an effective and reliable method to purify and quantify miRNAs using quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) in rat peritoneal membrane. This protocol consists of four steps: 1) purification of peritoneal membrane sample; 2) purification of total RNA including miRNA from peritoneal membrane sample; 3) reverse transcription of miRNA to produce cDNA; and 4) qRT-PCR to detect miRNA expression. Using this protocol, we successfully determined that the expression of six miRNAs (miRNA-142-3p, miRNA-21-5p, miRNA-221-3p, miRNA-223-3p, miRNA-327, and miRNA-34a-5p) increased significantly in the peritoneal membrane of a rat peritoneal fibrosis model compared with those in control groups. This protocol can be used to study the profile of miRNA expression in the peritoneal membrane of rats in many pathological conditions.

Here we present a protocol for the detection of microRNA expression in rat peritoneal membrane using quantitative real-time reverse-transcription polymerase chain reaction. This method is suitable for studying the microRNA expression profile in rat peritoneal membrane in several pathological conditions.

MicroRNAs (miRNAs) are small noncoding RNAs that regulate messenger RNA expression post-transcriptionally. The miRNA expression profile has been ...

Studying Cell Cycle-regulated Gene Expression by Two Complementary Cell Synchronization Protocols

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such as cancer. Cell cycle-regulated gene expression analysis depends on cell synchronization into specific phases. Here we describe a method utilizing two complementary synchronization protocols that is commonly used for studying periodic variation of gene expression during the cell cycle. Both procedures are based on transiently blocking the cell cycle in one defined point. The synchronization protocol by hydroxyurea (HU) treatment leads to cellular arrest in late G1/early S phase, and release from HU-mediated arrest provides a cellular population uniformly progressing through S and G2/M. The synchronization protocol by thymidine and nocodazole (Thy-Noc) treatment blocks cells in early mitosis, and release from Thy-Noc mediated arrest provides a synchronized cellular population suitable for G1 phase and S phase-entry studies. Application of both procedures requires monitoring of the cell cycle distribution profiles, which is typically performed after propidium iodide (PI) staining of the cells and flow cytometry-mediated analysis of DNA content. We show that the combined use of two synchronization protocols is a robust approach to clearly determine the transcriptional profiles of genes that are differentially regulated in the cell cycle (i.e. E2F1 and E2F7), and consequently to have a better understanding of their role in cell cycle processes. Furthermore, we show that this approach is useful for the study of mechanisms underlying drug-based therapies (i.e. mitomycin C, an anticancer agent), because it allows to discriminate genes that are responsive to the genotoxic agent from those solely affected by cell cycle perturbations imposed by the agent.

We report two cell synchronization protocols that provide a context for studying events related to specific phases of the cell cycle. We show that this approach is useful for analyzing the regulation of specific genes in an unperturbed cell cycle or upon exposure to agents affecting the cell cycle.

The gene expression program of the cell cycle represents a critical step for understanding cell cycle-dependent processes and their role in diseases such ...

Sample Preparation and Analysis of RNASeq-based Gene Expression Data from Zebrafish

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an excellent model for rapid assessment of whole transcriptome from whole animal or individual cell populations due to the ease of isolation of RNA from large numbers of animals. Here a protocol for global gene expression analysis in zebrafish embryos using RNA sequencing (RNASeq) is presented. We describe preparation of RNA from whole embryos or from cell populations obtained using cell sorting in transgenic animals. We also describe an approach for analysis of RNASeq data to identify enriched pathways and Gene Ontology (GO) terms in global gene expression data sets. Finally, we provide a protocol for validation of gene expression changes using quantitative reverse transcriptase PCR (qRT-PCR). These protocols can be used for comparative analysis of control and experimental sets of zebrafish to identify novel gene expression changes, and provide molecular insight into phenotypes of interest.

This protocol presents an approach for whole transcriptome analysis from zebrafish embryos, larvae, or sorted cells. We include isolation of RNA, pathway analysis of RNASeq data, and qRT-PCR-based validation of gene expression changes.

The analysis of global gene expression changes is a valuable tool for identifying novel pathways underlying observed phenotypes. The zebrafish is an ...

Generation of a Gene-disrupted Streptococcus mutans Strain Without Gene Cloning

Typical methods for the elucidation of the function of a particular gene involve comparative phenotypic analyses of the wild-type strain and a strain in which the gene of interest has been disrupted. A gene-disruption DNA construct containing a suitable antibiotic resistance marker gene is useful for the generation of gene-disrupted strains in bacteria. However, conventional construction methods, which require gene cloning steps, involve complex and time-consuming protocols. Here, a relatively facile, rapid, and cost-effective method for targeted gene disruption in Streptococcus mutans is described. The method utilizes a 2-step fusion polymerase chain reaction (PCR) to generate the disruption construct and electroporation for genetic transformation. This method does not require an enzymatic reaction, other than PCR, and additionally offers greater flexibility in terms of the design of the disruption construct. Employment of electroporation facilitates the preparation of competent cells and improves the transformation efficiency. The present method may be adapted for the generation of gene-disrupted strains of various species.

Generation of a Gene-disrupted Streptococcus mutans Strain Without Gene Cloning

We describe a facile, rapid, and relatively inexpensive method for the generation of gene-disrupted Streptococcus mutans strains; this technique may be adapted for the generation of gene-disrupted strains of various species.

Typical methods for the elucidation of the function of a particular gene involve comparative phenotypic analyses of the wild-type strain and a strain in ...

Measuring Gene Expression in Bombarded Barley Aleurone Layers with Increased Throughput

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for germination or early seedling development are activated by gibberellin (GA), while genes associated with stress responses are activated by abscisic acid (ABA). The mechanisms of GA and ABA signaling can be interrogated by introducing reporter gene constructs into aleurone cells via particle bombardment, with the resulting transient expression measured using enzyme assays. An improved protocol is reported that partially automates and streamlines the grain homogenization step and the enzyme assays, allowing significantly more throughput than existing methods. Homogenization of the grain samples is carried out using an automated tissue homogenizer, and GUS (&#946;-glucuronidase) assays are carried out using a 96-well plate system. Representative results using the protocol suggest that phospholipase D activity may play an important role in the activation of HVA1 gene expression by ABA, through the transcription factor TaABF1.

An improved protocol is presented for the measurement of transient gene expression from reporter constructs in barley aleurone cells after particle bombardment. The combination of automated grain grinding with 96-well plate enzyme assays provides high throughput for the procedure.

The aleurone layer of barley grains is an important model system for hormone-regulated gene expression in plants. In aleurone cells, genes required for ...

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for protein synthesis by translating codons in mRNA into amino acid sequences. tRNAs were long considered as house-keeping molecules that lacked regulatory functions. However, a growing body of evidence indicates that cellular tRNA levels fluctuate in correspondence to varying conditions such as cell type, environment, and stress. The fluctuation of tRNA expression directly influences gene translation, favoring or repressing the expression of particular proteins. Ultimately comprehending the dynamic of protein synthesis requires the development of methods able to deliver high-quality tRNA profiles. The method that we present here is named SPOt, which stands for Streamlined Platform for Observing tRNA. SPOt consists of three steps starting with metabolic labeling of cell cultures with radioactive orthophosphate, followed by guanidinium thiocyanate-phenol-chloroform extraction of radioactive total RNAs and finally hybridization on in-house printed macroarrays. tRNA levels are estimated by quantifying the radioactivity intensities at each probe spot. In the protocol presented here we profile tRNAs in Mycobacterium smegmatis mc2155, a nonpathogenic bacterium often used as a model organism to study tuberculosis.

We describe an original transfer RNA analysis platform named SPOt (Streamlined Platform for Observing tRNA). SPOt simultaneously measures cellular levels of all tRNAs in biological samples, in only three steps, and in less than 24 hours.

Transfer RNAs (tRNA) are abundant short non-coding RNA species that are typically 76 to 90 nucleotides in length. tRNAs are directly responsible for ...