Name: single-cell gene expression profiling using facs and qpcr with internal standards
Uploaded: 2017-02-25T14:00:00.000+00:00
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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.

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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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The authors have nothing to disclose.

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

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 ...

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16.4K Views.  National Cancer Institute. 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.

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

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 ...

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of taxonomic and functional groups in environmental samples. Used in combination with a reverse transcriptase reaction (RT-q-PCR), it can also be employed to quantify gene transcripts. q-PCR makes use of highly sensitive fluorescent detection chemistries that allow quantification of PCR amplicons during the exponential phase of the reaction. Therefore, the biases associated with &#39;end-point&#39; PCR detected in the plateau phase of the PCR reaction are avoided. A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. First, a method for the co-extraction of DNA and RNA from coastal sediments, including the additional steps required for the preparation of DNA-free RNA, is outlined. Second, a step-by-step guide for the quantification of 16S rRNA genes and transcripts from the extracted nucleic acids via q-PCR and RT-q-PCR is outlined. This includes details for the construction of DNA and RNA standard curves. Key considerations for the use of RT-q-PCR assays in microbial ecology are included.

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of ...

Environment

Isolation and Characterization of Single Cells from Zebrafish Embryos

The zebrafish (Danio rerio) is a powerful model organism to study vertebrate development. Though many aspects of zebrafish embryonic development have been described at the morphological level, little is known about the molecular basis of cellular changes that occur as the organism develops. With recent advancements in microfluidics and multiplexing technologies, it is now possible to characterize gene expression in single cells. This allows for investigation of heterogeneity between individual cells of specific cell populations to identify and classify cell subtypes, characterize intermediate states that occur during cell differentiation, and explore differential cellular responses to stimuli. This study describes a protocol to isolate viable, single cells from zebrafish embryos for high throughput multiplexing assays. This method may be rapidly applied to any zebrafish embryonic cell type with fluorescent markers. An extension of this method may also be used in combination with high throughput sequencing technologies to fully characterize the transcriptome of single cells. As proof of principle, the relative abundance of cardiac differentiation markers was assessed in isolated, single cells derived from nkx2.5 positive cardiac progenitors. By evaluation of gene expression at the single cell level and at a single time point, the data support a model in which cardiac progenitors coexist with differentiating progeny. The method and work flow described here is broadly applicable to the zebrafish research community, requiring only a labeled transgenic fish line and access to microfluidics technologies.

This protocol describes a method for isolating single cells from zebrafish embryos, enriching for cells of interest, capturing zebrafish cells in microfluidic based single cell multiplex systems, and assessing gene expression from single cells.

The zebrafish (Danio rerio) is a powerful model organism to study vertebrate development. Though many aspects of zebrafish embryonic development have been ...

Developmental Biology

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding ...

Single Cell Analysis Of Transcriptionally Active Alleles By Single Molecule FISH

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk population methods (Northern blot, PCR, and RNAseq) that measure mRNA levels lack the ability to provide information on cell-to-cell variation in responses. Precise single cell and allelic visualization and quantification is possible via single molecule RNA fluorescence in situ hybridization (smFISH). RNA-FISH is performed by hybridizing target RNAs with labeled oligonucleotide probes. These can be imaged in medium/high throughput modalities, and, through image analysis pipelines, provide quantitative data on both mature and nascent RNAs, all at the single cell level. The fixation, permeabilization, hybridization and imaging steps have been optimized in the lab over many years using the model system described herein, which results in successful and robust single cell analysis of smFISH labeling. The main goal with sample preparation and processing is to produce high quality images characterized by a high signal-to-noise ratio to reduce false positives and provide data that are more accurate. Here, we present a protocol describing the pipeline from sample preparation to data analysis in conjunction with suggestions and optimization steps to tailor to specific samples.&#160;

Single molecule RNA fluorescence in situ hybridization (smFISH) is a method to accurately quantify levels and localization of specific RNAs at the single cell level. Here, we report our validated lab protocols for wet-bench processing, imaging and image analysis for single cell quantification of specific RNAs.

Gene transcription is an essential process in cell biology, and allows cells to interpret and respond to internal and external cues. Traditional bulk ...

Biochemistry

Cerebrospinal Fluid MicroRNA Profiling Using Quantitative Real Time PCR

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their translational repression. Changes in miRNA expression have been shown to be involved in the development of all major complex diseases. Furthermore, recent findings showed that miRNAs can be secreted to the extracellular environment and enter the bloodstream and other body fluids where they can circulate with high stability. The function of such circulating miRNAs remains largely elusive, but systematic high throughput approaches, such as miRNA profiling arrays, have lead to the identification of miRNA signatures in several pathological conditions, including neurodegenerative disorders and several types of cancers. In this context, the identification of miRNA expression profile in the cerebrospinal fluid, as reported in our recent study, makes miRNAs attractive candidates for biomarker analysis.
There are several tools available for profiling microRNAs, such as microarrays, quantitative real-time PCR (qPCR), and deep sequencing. Here, we describe a sensitive method to profile microRNAs in cerebrospinal fluids by quantitative real-time PCR. We used the Exiqon microRNA ready-to-use PCR human panels I and II V2.R, which allows detection of 742 unique human microRNAs. We performed the arrays in triplicate runs and we processed and analyzed data using the GenEx Professional 5 software.
Using this protocol, we have successfully profiled microRNAs in various types of cell lines and primary cells, CSF, plasma, and formalin-fixed paraffin-embedded tissues.

We describe a protocol of real time PCR to profile microRNAs in the cerebrospinal fluid (CSF). With the exception of RNA extraction protocols, the procedure can be extended to RNA extracted from other body fluids, cultured cells, or tissue specimens.

MicroRNAs (miRNAs) constitute a potent layer of gene regulation by guiding RISC to target sites located on mRNAs and, consequently, by modulating their ...

Medicine

Gene Expression Profiling of Infecting Microbes Using a Digital Bar-coding Platform

For most mammalian pathogens, gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene transcripts from infected tissues. Pathogen RNA constitutes a tiny portion of the total RNA isolated from infected tissue samples. Both microarray and RNAseq technologies have difficulties in generating reliable reads for weakly expressed pathogen genes. Mutant pathogen strains with reduced in vivo proliferation pose an even bigger challenge. Here we describe an in vivo gene expression profiling protocol that is very fast, extremely sensitive and highly reproducible. We developed this protocol during our investigation of the fungal pathogen Candida albicans in a murine model of hematogenously disseminated candidiasis. Using this protocol, we have documented time courses of dynamically regulated C. albicans gene expression during kidney infection, and discovered unexpected features of gene expression responses to antifungal drug treatment in vivo.

We developed a protocol that is fast, sensitive and reproducible for pathogen gene expression profiling during an infection.

For most mammalian pathogens, gene expression profiling studies have been limited by technical difficulties to accurately quantify pathogen gene ...

Immunology and Infection

Single Cell Transcriptional Profiling of Adult Mouse Cardiomyocytes

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation between cells within a tissue. Isolation of pure cardiomyocyte populations through a collagenase perfusion of mouse hearts facilitates the generation of single cell microarrays for whole transcriptome gene expression, or qPCR of specific targets using nanofluidic arrays. We describe here a procedure to examine single cell gene expression profiles of cardiomyocytes isolated from the heart. This paradigm allows for the evaluation of metrics of interest which are not reliant on the mean (for example variance between cells within a tissue) which is not possible when using conventional whole tissue workflows for the evaluation of gene expression (Figure 1). We have achieved robust amplification of the single cell transcriptome yielding micrograms of double stranded cDNA that facilitates the use of microarrays on individual cells. In the procedure we describe the use of NimbleGen arrays which were selected for their ease of use and ability to customize their design. Alternatively, a reverse transcriptase - specific target amplification (RT-STA) reaction, allows for qPCR of hundreds of targets by nanofluidic PCR. Using either of these approaches, it is possible to examine the variability of expression between cells, as well as examining expression profiles of rare cell types from within a tissue. Overall, the single cell gene expression approach allows for the generation of data that can potentially identify idiosyncratic expression profiles that are typically averaged out when examining expression of millions of cells from typical homogenates generated from whole tissues.

Single cell expression profiling allows the detailed gene expression analysis of individual cells. We describe methods for the isolation of cardiomyocytes, and preparing the resulting lysates for either whole transcriptome microarray or qPCR of specific targets.

While numerous studies have examined gene expression changes from homogenates of heart tissue, this prevents studying the inherent stochastic variation ...

Profiling Individual Human Embryonic Stem Cells by Quantitative RT-PCR

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different lineages. A single cell transcriptome assay can be a new approach for dissecting individual variation. We have developed the single cell qRT-PCR method, and confirmed that this method works well in several gene expression profiles. In single cell level, each human embryonic stem cell, sorted by OCT4::EGFP positive cells, has high expression in OCT4, but a different level of NANOG expression. Our single cell gene expression assay should be useful to interrogate population heterogeneities.

Single cell gene expression assay is needed for understanding stem cell heterogeneities.

Heterogeneity of stem cell population hampers detailed understanding of stem cell biology, such as their differentiation propensity toward different ...