New papers with focus on single-cell gene expression noise:

Stochastic gene expression in a single cell
Elowitz MB, Levine AJ, Siggia ED, Swain PS.
Science. 2002 Aug 16;297(5584): 1183-1186.
Laboratory of Cancer Biology, Center for Studies in Physics and Biology, Rockefeller University, New York, NY 10021, USA
Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.

Nature, nurture, or chance: stochastic gene expression and its consequences
Raj A, van Oudenaarden A.
Cell. 2008 Oct 17;135(2):216-26.
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Gene expression is a fundamentally stochastic process, with randomness in transcription and translation leading to cell-to-cell variations in mRNA and protein levels. This variation appears in organisms ranging from microbes to metazoans, and its characteristics depend both on the biophysical parameters governing gene expression and on gene network structure. Stochastic gene expression has important consequences for cellular function, being beneficial in some contexts and harmful in others. These situations include the stress response, metabolism, development, the cell cycle, circadian rhythms, and aging.

Noise in Gene Expression: Origins, Consequences, and Control
Jonathan M. Raser & Erin K. O'Shea
Science 23 September 2005:
Vol. 309. no. 5743, pp. 2010 - 2013

Genetically identical cells and organisms exhibit remarkable diversity even when they have identical histories of environmental exposure. Noise, or variation, in the process of gene expression may contribute to this phenotypic variability. Recent studies suggest that this noise has multiple sources, including the stochastic or inherently random nature of the biochemical reactions of gene expression. In this review, we summarize noise terminology and comment on recent investigations into the sources, consequences, and control of noise in gene expression.

Use of high throughput sequencing to observe genome dynamics at a single cell level
Parkhomchuk D, Amstislavskiy V, Soldatov A, Ogryzko V.
Proc Natl Acad Sci U S A. 2009 Nov 23.
Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, Berlin, Germany.
With the development of high throughput sequencing technology, it becomes possible to directly analyze mutation distribution in a genome-wide fashion, dissociating mutation rate measurements from the traditional underlying assumptions. Here, we sequenced several genomes of Escherichia coli from colonies obtained after chemical mutagenesis and observed a strikingly nonrandom distribution of the induced mutations. These include long stretches of exclusively G to A or C to T transitions along the genome and orders of magnitude intra- and intergenomic differences in mutation density. Whereas most of these observations can be explained by the known features of enzymatic processes, the others could reflect stochasticity in the molecular processes at the single-cell level. Our results demonstrate how analysis of the molecular records left in the genomes of the descendants of an individual mutagenized cell allows for genome-scale observations of fixation and segregation of mutations, as well as recombination events, in the single genome of their progenitor.

Transcriptional noise and cellular heterogeneity in mammalian macrophages
Ramsey S, Ozinsky A, Clark A, Smith KD, de Atauri P, Thorsson V, Orrell D, Bolouri H.
Philos Trans R Soc Lond B Biol Sci. 2006 Mar 29;361(1467):495-506.
Institute for Systems Biology 1441 North 34th Street, Seattle, WA 98103-8904, USA.
Transcriptional noise is known to play a crucial role in heterogeneity in bacteria and yeast. Mammalian macrophages are known to exhibit cell-to-cell variation in their responses to pathogens, but the source of this heterogeneity is not known. We have developed a detailed stochastic model of gene expression that takes into account scaling effects due to cell size and genome complexity. We report the results of applying this model to simulating gene expression variability in mammalian macrophages, demonstrating a possible molecular basis for heterogeneity in macrophage signalling responses. We note that the nature of predicted transcriptional noise in macrophages is different from that in yeast and bacteria. Some molecular interactions in yeast and bacteria are thought to have evolved to minimize the effects of the high-frequency noise observed in these species. Transcriptional noise in macrophages results in slow changes to gene expression levels and would not require the type of spike-filtering circuits observed in yeast and bacteria.

Stochastic gene expression during cell differentiation: order from disorder?
Paldi A.
Cell Mol Life Sci. 2003 Sep;60(9): 1775-1778.
Institut Jacques Monod, INSERM E0367, Ecole Pratique des Hautes Etudes, 2, place Jussieu, 75005 Paris, France.
Understanding cell differentiation in multicellular organ-isms remains one of the central questions of biology. Ac-cording to the prevailing view of contemporary develop-mental biology, differentiation of multicellular organismsis based on precisely regulated communication betweencells via diffusible molecules or direct cell-to-cell con-tacts. These signals are the basis of embryonic induction,where one cell instructs others to adopt a particular de-velopmental fate. A molecular signal is supposed to initi-ate differentiation by specifically activating one or sev-eral regulatory genes, which, in turn, also specifically ac-tivate other downstream regulator and/or effector genes. The activation of such a regulatory cascade leads to theexpression of a new set of genes that progressively guidesthe cell toward commitment into a lineage and ultimatelyto its differentiated state. This process of hierarchicallyordered and sequential expression of genes is usually re-ferred to as a ‘genetic program’. The role of the regula-tory genes is, of course, crucial, since they code for the‘instructions’of the program in the form of transcriptionfactors that are able to bind nucleotide sequence motifs inthe regulatory regions of the target genes and initiate theprocess of transcription by recruiting the other compo-nents of the transcription machinery. ... ... ...

Resolving cell population heterogeneity: real-time PCR for simultaneous multiplexed gene detection in multiple single-cell samples
Diercks A, Kostner H, Ozinsky A.
PLoS One. 2009 Jul 27;4(7):e6326.
Institute for Systems Biology, Seattle, WA, USA.
Decoding the complexity of multicellular organisms requires analytical procedures to overcome the limitations of averaged measurements of cell populations, which obscure inherent cell-cell heterogeneity and restrict the ability to distinguish between the responses of individual cells within a sample. For example, defining the timing, magnitude and the coordination of cytokine responses in single cells is critical for understanding the development of effective immunity. While approaches to measure gene expression from single cells have been reported, the absolute performance of these techniques has been difficult to assess, which likely has limited their wider application. We describe a straightforward method for simultaneously measuring the expression of multiple genes in a multitude of single-cell samples using flow cytometry, parallel cDNA synthesis, and quantification by real-time PCR. We thoroughly assess the performance of the technique using mRNA and DNA standards and cell samples, and demonstrate a detection sensitivity of approximately 30 mRNA molecules per cell, and a fractional error of 15%. Using this method, we expose unexpected heterogeneity in the expression of 5 immune-related genes in sets of single macrophages activated by different microbial stimuli. Further, our analyses reveal that the expression of one 'pro-inflammatory' cytokine is not predictive of the expression of another 'pro-inflammatory' cytokine within the same cell. These findings demonstrate that single-cell approaches are essential for studying coordinated gene expression in cell populations, and this generic and easy-to-use quantitative method is applicable in other areas in biology aimed at understanding the regulation of cellular responses.

Circulating tumour cells in clinical practice: Methods of detection and possible characterization
Alunni-Fabbroni M, Sandri MT.
Methods. 2010 Apr;50(4):289-97. Epub 2010 Jan 29.
Beckman Coulter Biomedical GmbH, Sauerbruchstrasse 50 - 81377 Munich, Germany
Circulating Tumour Cells (CTCs) can be released from the primary tumour into the bloodstream and may colonize distant organs giving rise to metastasis. The presence of CTCs in the blood has been documented more than a century ago, and in the meanwhile various methods have been described for their detection. Most of them require an initial enrichment step, since CTCs are a very rare event. The different technologies and also the differences among the screened populations make the clinical significance of CTCs detection difficult to interprete. Here we will review the different assays up to now available for CTC detection and analysis. Moreover, we will focus on the relevance of the clinical data, generated so far and based on the CTCs analysis. Since the vast majority of data have been produced in breast cancer patients, the review will focus especially on this malignancy.

Quantitative transcription factor analysis of undifferentiated single human embryonic stem cells.
Ståhlberg A, Bengtsson M, Hemberg M, Semb H.
Clin Chem. 2009 Dec;55(12): 2162-2170
Lundberg Laboratory for Cancer, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
BACKGROUND: Human embryonic stem cells (hESCs) require expression of transcription factor genes POU5F1 (POU class 5 homeobox 1), NANOG (Nanog homeobox), and SOX2 [SRY (sex determining region Y)-box 2] to maintain their capacity for self-renewal and pluripotency. Because of the heterogeneous nature of cell populations, it is desirable to study the gene regulation in single cells. Large and potentially important fluctuations in a few cells cannot be detected at the population scale with microarrays or sequencing technologies. We used single-cell gene expression profiling to study cell heterogeneity in hESCs.
METHODS: We collected 47 single hESCs from cell line SA121 manually by glass capillaries and 57 single hESCs from cell line HUES3 by flow cytometry. Single hESCs were lysed and reverse-transcribed. Reverse-transcription quantitative real-time PCR was then used to measure the expression POU5F1, NANOG, SOX2, and the inhibitor of DNA binding genes ID1, ID2, and ID3. A quantitative noise model was used to remove measurement noise when pairwise correlations were estimated.
RESULTS: The numbers of transcripts per cell varied >100-fold between cells and showed lognormal features. POU5F1 expression positively correlated with ID1 and ID3 expression (P < 0.05) but not with NANOG or SOX2 expression. When we accounted for measurement noise, SOX2 expression was also correlated with ID1, ID2, and NANOG expression (P < 0.05). CONCLUSIONS: We demonstrate an accurate method for transcription profiling of individual hESCs. Cell-to-cell variability is large and is at least partly nonrandom because we observed correlations between core transcription factors. High fluctuations in gene expression may explain why individual cells in a seemingly undifferentiated cell population have different susceptibilities for inductive cues.

Single cell analysis of transcriptional activation dynamics
Rafalska-Metcalf IU, Powers SL, Joo LM, LeRoy G, Janicki SM.
PLoS One. 2010 Apr 21;5(4): e10272.
Gene Expression and Regulation Program, The Wistar Institute, Philadelphia, Pennsylvania, United States of America.
BACKGROUND: Gene activation is thought to occur through a series of temporally defined regulatory steps. However, this process has not been completely evaluated in single living mammalian cells.
METHODOLOGY/PRINCIPAL FINDINGS: To investigate the timing and coordination of gene activation events, we tracked the recruitment of GCN5 (histone acetyltransferase), RNA polymerase II, Brd2 and Brd4 (acetyl-lysine binding proteins), in relation to a VP16-transcriptional activator, to a transcription site that can be visualized in single living cells. All accumulated rapidly with the VP16 activator as did the transcribed RNA. RNA was also detected at significantly more transcription sites in cells expressing the VP16-activator compared to a p53-activator. After alpha-amanitin pre-treatment, the VP16-activator, GCN5, and Brd2 are still recruited to the transcription site but the chromatin does not decondense.
CONCLUSIONS/SIGNIFICANCE: This study demonstrates that a strong activator can rapidly overcome the condensed chromatin structure of an inactive transcription site and supercede the expected requirement for regulatory events to proceed in a temporally defined order. Additionally, activator strength determines the number of cells in which transcription is induced as well as the extent of chromatin decondensation. As chromatin decondensation is significantly reduced after alpha-amanitin pre-treatment, despite the recruitment of transcriptional activation factors, this provides further evidence that transcription drives large-scale chromatin decondensation.

Single-cell gene expression profiling using reverse transcription quantitative real-time PCR
Ståhlberg A, Bengtsson M.
Methods. 2010 Apr;50(4): 282-288
Lundberg Laboratory for Cancer, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gula Straket 8, 413 45 Gothenburg, Sweden
Even in an apparently homogeneous population of cells there are considerable differences between individual cells. A response to a stimulus of a cell population or tissue may be consistent and gradual while the single-cell response might be binary and apparently irregular. The origin of this variability may be preprogrammed or stochastic and a study of this phenomenon will require quantitative measurements of individual cells. Here, we describe a method to collect dispersed single cells either by glass capillaries or flow cytometry, followed by quantitative mRNA profiling using reverse transcription and real-time PCR. We present a single cell lysis protocol and optimized priming conditions for reverse transcription. The large cell-to-cell variability in single-cell gene expression measurements excludes it from standard data analysis. Correlation studies can be used to find common regulatory elements that are indistinguishable at the population level. Single-cell gene expression profiling has the potential to become common practice in many laboratories and a powerful research tool for deeper understanding of molecular mechanisms.

Spatial expression profiles in the Xenopus laevis oocytes measured with qPCR tomography
Sindelka R, Sidova M, Svec D, Kubista M.
Methods. 2010 May;51(1): 87-91
Whitehead Institute, Cambridge, USA.
qPCR tomography was developed to study mRNA localization in complex biological samples that are embedded and cryo-sectioned. After total RNA extraction and reverse transcription, the spatial profiles of mRNAs and other functional RNAs were determined by qPCR. The Xenopus laevis oocyte was selected as model, because of its large size (more than 1mm) and large amount of total RNA (approximately 5microg). Fifteen sections along the animal-vegetal axis were cut and prepared for quantification of 31 RNA targets using the high-throughput real-time RT-PCR (qPCR) BioMark platform. mRNAs were found to have two localization patterns, animal/central or vegetal. Because of the high resolution in sectioning, it was possible to distinguish two subgroups of the vegetal gene patterns: germ plasm determinant pattern and profile of other vegetal genes.

Quantitative RT-PCR gene expression analysis of laser microdissected tissue samples
Heidi S Erickson, Paul S Albert, John W Gillespie, Jaime Rodriguez-Canales, W Marston Linehan, Peter A Pinto, Rodrigo F Chuaqui & Michael R Emmert-Buck
Nature Protocols 4, - 902 - 922 (2009)
Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) is a valuable tool for measuring gene expression in biological samples. However, unique challenges are encountered when studies are performed on cells microdissected from tissues derived from animal models or the clinic, including specimen-related issues, variability of RNA template quality and quantity, and normalization. qRT-PCR using small amounts of mRNA derived from dissected cell populations requires adaptation of standard methods to allow meaningful comparisons across sample sets. The protocol described here presents the rationale, technical steps, normalization strategy and data analysis necessary to generate reliable gene expression measurements of transcripts from dissected samples. The entire protocol from tissue microdissection through qRT-PCR analysis requires approx 16 h.

Laser capture microdissection and single-cell RT-PCR without RNA purification
Keays KM, Owens GP, Ritchie AM, Gilden DH, Burgoon MP.
J Immunol Methods. 2005 Jul;302(1-2):90-8.
Department of Neurology, University of Colorado Health Sciences Center, 4200 East 9th Avenue, Mail Stop B182, Denver, CO 80262, United States.
Chronic infectious diseases of the central nervous system (CNS) are characterized by intrathecal synthesis of increased amounts of immunoglobulin G (IgG) directed against the agent that causes disease. In other inflammatory CNS diseases such as multiple sclerosis and CNS sarcoid, the targets of the humoral immune response are uncertain. To identify the IgGs expressed by individual CD38(+) plasma cells seen in human brain sections, we merged the techniques of laser capture microdissection (LCM) and single-cell RT-PCR. Frozen brain sections from a patient who died of subacute sclerosing panencephalitis (SSPE), were rapidly immunostained and examined by LCM to dissect individual CD38(+) cells. After cell lysis, we developed two techniques for reverse-transcription (RT) of unpurified total RNA in the cell lysates. The first method performed repeated and rapid freeze-thawing, followed by centrifugation of the cell lysate into tubes for subsequent RT. The second, more successful method performed RT in situ on detergent-solubilized cells directly on the cap surface; subsequent nested PCR identified heavy and light chain sequences expressed by two-thirds of individually isolated plasma cells. These techniques will streamline the identification of gene expression products in single cells from complex tissues and have the potential to identify IgGs expressed in the CNS of inflammatory diseases of unknown etiology.
Imaging intracellular RNA distribution and dynamics in living cells.
Sanjay Tyagi
Nature Methods vol 6 no 5 2009:  331

Powerful methods now allow the imaging of specific mRNAs in living cells. These methods enlist fluorescent proteins to illuminate mRNAs, use labeled oligonucleotide probes and exploit aptamers that render organic dyes fluorescent. The intracellular dynamics of mRNA synthesis, transport and localization can be analyzed at higher temporal resolution with these methods than has been possible with traditional fixed-cell or biochemical approaches. These methods have also been adopted to visualize and track single mRNA molecules in real time. This review explores the promises and limitations of these methods.

mRNA-Seq whole-transcriptome analysis of a single cell.
Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA.
Wellcome Trust-Cancer Research UK Gurdon Institute of Cancer and Developmental
Biology, University of Cambridge, Cambridge, UK.
Nat Methods. 2009 6(5): 377-82
Next-generation sequencing technology is a powerful tool for transcriptome analysis. However, under certain conditions, only a small amount of material is available, which requires more sensitive techniques that can preferably be used at the single-cell level. Here we describe a single-cell digital gene expression profiling assay. Using our mRNA-Seq assay with only a single mouse blastomere, we detected the expression of 75% (5,270) more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads. Moreover, 8-19% of the genes with multiple known transcript isoforms expressed at least two isoforms in the same blastomere or oocyte, which unambiguously demonstrated the complexity of the transcript variants at whole-genome scale in individual cells. Finally, for Dicer1(-/-) and Ago2(-/-) (Eif2c2(-/-)) oocytes, we found that 1,696 and 1,553 genes, respectively, were abnormally upregulated compared to wild-type controls, with 619 genes in common.

Quantitative analysis of gene expression in a single cell by qPCR.
Taniguchi K, Kajiyama T, Kambara H.
Hitachi Central Research Laboratory, Tokyo, Japan.
Nat Methods. 2009 6(7): 503-506
We developed a quantitative PCR method featuring a reusable single-cell cDNA library immobilized on beads for measuring the expression of multiple genes in a single cell. We used this method to analyze multiple cDNA targets (from several copies to several hundred thousand copies) with an experimental error of 15.9% or less. This method is sufficiently accurate to investigate the heterogeneity of single cells.

Genomic expression analysis by single-cell mRNA differential display of quiescent
CD8 T cells from tumour-infiltrating lymphocytes obtained from in vivo liver tumours.
Zhang W, Ding J, Qu Y, Hu H, Lin M, Datta A, Larson A, Liu GE, Li B.
Department of Biochemistry, Case Western Reserve University School of Medicine,
Cleveland, OH 44106-4935, USA.
Immunology. 2009 May;127(1): 83-90.
We performed a genomic study combining single-cell mRNA differential display and RNA subtractive hybridization to elucidate CD8 T-cell quiescence/ignorance. By comparing actively maintained quiescent CD8 T cells from liver tumour tumour-infiltrating lymphocytes (TILs) with quiescent T cells at the single-cell level, we identified differentially expressed candidate genes by high-throughput screening and comparative analysis of expressed sequence tags (ESTs). While genes for the T-cell receptor, tumour necrosis factor (TNF) receptor, TNF-related apoptosis inducing ligand (TRAIL) and perforin were down-regulated, key genes such as Tob, transforming growth factor (TGF)-beta, lung Krüpple-like factor (LKLF), Sno-A, Ski, Myc, Ets-2 repressor factor (ERF) and RE1-silencing transcription factor (REST/NRSF) complex were highly expressed in the quiescent TIL CD8 cells. Real-time polymerase chain reaction (PCR) further confirmed these results. A regulation model is proposed for actively maintained quiescence in CD8 T cells, including three components: up-regulation of the TGF-beta pathway, a shift in the MYC web and inhibition of the cell cycle.

Rac1 regulates pancreatic islet morphogenesis.
Greiner TU, Kesavan G, Stahlberg A, Semb H.
Stem Cell and Pancreas Developmental Biology, Stem Cell Center, Lund University,
BMC B10, Klinikgatan 26, SE-221 84 Lund, Sweden.
BMC Dev Biol. 2009 Jan 6;9:2.
BACKGROUND: Pancreatic islets of Langerhans originate from endocrine progenitors within the pancreatic ductal epithelium. Concomitant with differentiation of these progenitors into hormone-producing cells such cells delaminate, aggregate and migrate away from the ductal epithelium. The cellular and molecular mechanisms regulating islet cell delamination and cell migration are poorly understood. Extensive biochemical and cell biological studies using cultured cells demonstrated that Rac1, a member of the Rho family of small GTPases, acts as a key regulator of cell migration.
RESULTS: To address the functional role of Rac1 in islet morphogenesis, we generated transgenic mice expressing dominant negative Rac1 under regulation of the Rat Insulin Promoter. Blocking Rac1 function in beta cells inhibited their migration away from the ductal epithelium in vivo. Consistently, transgenic islet cell spreading was compromised in vitro. We also show that the EGF-receptor ligand betacellulin induced actin remodelling and cell spreading in wild-type islets, but not in transgenic islets. Finally, we demonstrate that cell-cell contact E-cadherin increased as a consequence of blocking Rac1 activity.
CONCLUSION: Our data support a model where Rac1 signalling controls islet cell migration by modulating E-cadherin-mediated cell-cell adhesion. Furthermore, in vitro experiments show that betacellulin stimulated islet cell spreading and actin remodelling is compromised in transgenic islets, suggesting that betacellulin may act as a regulator of Rac1 activity and islet migration in vivo. Our results further emphasize Rac1 as a key regulator of cell migration and cell adhesion during tissue and organ morphogenesis.

A novel single-cell quantitative real-time RT-PCR method for quantifying foot-and-mouth disease viral RNA.
Huang X, Li Y, Zheng CY.
State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, China.
J Virol Methods. 2009 155(2): 150-156

Foot-and-mouth disease virus is a positive-sense, single-stranded RNA virus with a negative strand as its replication intermediate, which can cause severe acute infection in sensitive cell lines. To investigate better the actual state of virus infection, there is a need to measure the amount of FMDV RNA in a single acutely infected cell rather than in a large number of cells. Therefore, in the present study, a strand-specific single-cell quantitative real-time RT-PCR was developed to analyze the RNA or FMDV. This new method uses two techniques in concert with each other: a technique for isolating single cells with micromanipulators, which is coupled to an assay for detecting viral RNA by real-time RT-PCR. In the assay of acute infection, 185 of 224 (82.6%) single-cell samples were positive and contained viral genome copies ranging from several to thousands, and up to 1,000,000 copies. However, not all cells were infected and there were differences in the number of viral RNA copies between cells. A single-cell quantitative RT-PCR was validated to be feasible and effective.

Quantification of circulating endothelial and progenitor cells: comparison of quantitative PCR and four-channel flow cytometry.
Steurer M, Kern J, Zitt M, Amberger A, Bauer M, Gastl G, Untergasser G, Gunsilius E.
Tumor Biology and Angiogenesis Laboratory, Division of Hematology and Oncology,
Innrain 66, Innsbruck Medical University, 6020 Innsbruck, Austria.
BMC Res Notes. 2008 28;1: 71
BACKGROUND: Circulating endothelial cells (CEC) and endothelial precursor cells (CEP) have been suggested as markers for angiogenesis in cancer. However, CEC/CEP represent a tiny and heterogeneous cell population, rendering a standardized monitoring in peripheral blood difficult. Thus, we investigated whether a PCR-based detection method of CEC/CEP might overcome the limitations of
rare-event flow cytometry.
FINDINGS: To test the sensitivity of both assays endothelial colony forming cell clones (ECFC) and cord blood derived CD45- CD34+ progenitor cells were spiked into peripheral blood mononuclear cells (PBMNC) of healthy volunteers. Samples were analyzed for the expression of CD45, CD31, CD34, KDR or CD133 by 4-color flow cytometry and for the expression of CD34, CD133, KDR
and CD144 by qPCR. Applying flow cytometry, spiked ECFC and progenitor cells were detectable at frequencies >/= 0.01%, whereas by qPCR a detection limit of 0.001% was achievable. Furthermore, PBMNC from healthy controls (n = 30), patients with locally advanced rectal cancer (n = 20) and metastatic non-small cell lung cancer (NSCLC, n = 25) were analyzed. No increase of CEC/CEP was detectable by flow cytometry. Furthermore, only CD34 and KDR gene expression was significantly elevated in patients with metastatic NSCLC. However, both markers are not specific for endothelial cells.
CONCLUSION: QPCR is more sensitive, but less specific than 4-channel flow cytometry for the detection of CEC/CEP cell types. However, both methods failed to reliably detect an increase of CEC/CEP in tumor patients. Thus, more specific CEC/CEP markers are needed to validate and improve the detection of these rare cell types by PCR-based assays.
Prognosis of non-small cell lung cancer patients by detecting circulating cancer cells
in the peripheral blood with multiple marker genes.

Sher YP, Shih JY, Yang PC, Roffler SR, Chu YW, Wu CW, Yu CL, Peck K.
Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, Republic of China.
Clin Cancer Res. 2005 Jan 1;11(1): 173-179
PURPOSE: Current lung cancer staging and prognosis methods are based on imaging methods, which may not be sensitive enough for early and accurate detection of metastasis. This study aims to validate the use of a panel of markers for circulating cancercell detection to improve the accuracy of cancer staging, prognosis, and as a rapid assessment of therapeutic response.
EXPERIMENTAL DESIGN: We analyzed the National Cancer Institute-Cancer Genome Anatomy Project database to identify potential marker genes for the detection of circulating cancer cells in peripheral blood. Nested real-time quantitative PCR and a scoring method using cancer cell load Lc were employed to correlate the amount of circulating cancer cells with clinical outcomes in 54 non-small cell lung cancer (NSCLC) patients. The Kaplan-Meier method was employed for analysis of prognostic variables.
RESULTS: A panel of four marker genes was identified and experimentally validated. With these marker genes, we achieved an overall positive detection rate of 72% for circulating cancer cells in the peripheral blood of NSCLC patients. Patients who had higher Lc values had worse outcomes and shorter survival times. Patients with poor therapeutic response were revealed by positive detection of circulating cancer cells after therapy. The results correlated well with the patients' survival time.
CONCLUSION: Circulating cancer cell detection by a panel of markers and the Lc scoring method can supplement the current tumor, node, metastasis staging method for improved prognosis and for rapid assessment of therapeutic response. Together, they may facilitate the design of better therapeutic strategies for the treatment of NSCLC patients.

Nanoliter reactors improve multiple displacement amplification of genomes from single cells.
Marcy Y, Ishoey T, Lasken RS, Stockwell TB, Walenz BP, Halpern AL, Beeson KY, Goldberg SM, Quake SR.
Department of Bioengineering, Stanford University, Stanford, California, USA.
PLoS Genet. 2007 Sep;3(9): 1702-1708
Since only a small fraction of environmental bacteria are amenable to laboratory culture, there is great interest in genomic sequencing directly from singlecells. Sufficient DNA for sequencing can be obtained from one cell by theMultiple Displacement Amplification (MDA) method, thereby eliminating the need todevelop culture methods. Here we used a microfluidic device to isolate individual Escherichia coli and amplify genomic DNA by MDA in 60-nl reactions. Our resultsconfirm a report that reduced MDA reaction volume lowers nonspecific synthesis that can result from contaminant DNA templates and unfavourable interaction between primers. The quality of the genome amplification was assessed by qPCR and compared favourably to single-cell amplifications performed in standard 50-microl volumes. Amplification bias was greatly reduced in nanoliter volumes, thereby providing a more even representation of all sequences. Single-cell amplicons from both microliter and nanoliter volumes provided high-quality sequence data by high-throughput pyrosequencing, thereby demonstrating a straightforward route to sequencing genomes from single cells.
Fluidigm Dynamic Arrays provide a platform for single-cell gene expression analysis.
Fluidigm application note 2009 (1)
Historically, single-cell gene expression experiments have been difficult and expensive to perform. Now, however, single-cell gene expression results from single-cell samples can be inexpensive and easily reproducible using Fluidigm’s Dynamic Array™ integrated fluidic circuits and BioMark™ system for genetic analysis. This method is ideally suited for high-throughput cell-line studies to determine individual cell behavior in a homozygous population. To demonstrate this capability, we chose single human cells from eight-cell-stage embryos, collected and analyzed for expression of 46 developmental genes.

Quantification of mRNA in single cells and modelling of RT-qPCR induced noise.
Bengtsson M, Hemberg M, Rorsman P, Stahlberg A.
BMC Mol Biol. 2008 9: 63.
Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford,
The Churchill Hospital, Oxford, OX3 7LJ, UK.
BACKGROUND: Gene expression has a strong stochastic element resulting in highly variable mRNA levels between individual cells, even in a seemingly homogeneous cell population. Access to fundamental information about cellular mechanisms, such as correlated gene expression, motivates measurements of multiple genes in individual cells. Quantitative reverse transcription PCR (RT-qPCR) is the most accessible method which provides sufficiently accurate measurements of mRNA in single cells.
RESULTS: Low concentration of guanidine thiocyanate was used to fully lyse single pancreatic beta-cells followed by RT-qPCR without the need for
purification. The accuracy of the measurements was determined by a quantitative noise-model of the reverse transcription and PCR. The noise is insignificant for initial copy numbers >100 while at lower copy numbers the noise intrinsic of the PCR increases sharply, eventually obscuring quantitative measurements. Importantly, the model allows us to determine the RT efficiency without using artificial RNA as a standard. The experimental setup was applied on single endocrine cells, where the technical and biological noise levels were determined.
CONCLUSION: Noise in single-cell RT-qPCR is insignificant compared to biological cell-to-cell variation in mRNA levels for medium and high abundance transcripts. To minimize the technical noise in single-cell RT-qPCR, the mRNA should be analyzed with a single RT reaction, and a single qPCR reaction per gene.
Intracellular expression profiles measured by real-time PCR tomography in the Xenopus laevis oocyte.
Sindelka R, Jonák J, Hands R, Bustin SA, Kubista M.
Nucleic Acids Res. 2008 36(2):387-92.
Laboratory of Gene Expression, Institute of Molecular Genetics, Academy of
Sciences of the Czech Republic, Videnska 1083, 14220 Prague 4, Czech Republic.
Real-time PCR tomography is a novel, quantitative method for measuring localized RNA expression profiles within single cells. We demonstrate its usefulness by dissecting an oocyte from Xenopus laevis into slices along its animal-vegetal axis, extracting its RNA and measuring the levels of 18 selected mRNAs by real-time RT-PCR. This identified two classes of mRNA, one preferentially located towards the animal, the other towards the vegetal pole. mRNAs within each group show comparable intracellular gradients, suggesting they are produced by similar mechanisms. The polarization is substantial, though not extreme, with around 5% of vegetal gene mRNA molecules detected at the animal pole, and around 50% of the molecules in the far most vegetal section. Most animal pole mRNAs were found in the second section from the animal pole and in the central section, which is where the nucleus is located. mRNA expression profiles did not change following in vitro fertilization and we conclude that the cortical rotation that follows fertilization has no detectable effect on intracellular mRNA gradients.
Intracellular Gene Expression Profi les Revealed with Real-time PCR Tomography
The BioMark system enabled measuring diffenntiation on the single-cell level with high accuracy and throughput.

Fluidigm application note 2009 (2)