Single-cell gene-expression profiling and its potential diagnostic applications

Topological biclustering ARTMAP for identifying within bicluster relationships

Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation. Therefore, to improve the quality of biclustering and module extraction, this work combines the adaptive resonance theory (ART)-based methods of biclustering ARTMAP (BARTMAP) and topological ART (TopoART), to produce TopoBARTMAP. The latter inherits the ability to detect topological associations while performing data reduction. The capabilities of TopoBARTMAP were benchmarked using 35 real world cancer datasets and contrasted with other (bi)clustering methods, where it showed a statistically significant improvement over the other assessed methods on ordered and shuffled data experiments. In experiments with 12 synthetic datasets, the method was observed to perform better at identifying constant, scale, shift, and shift scale type biclusters. The produced graphical representation was refined to represent gene bicluster associations and was assessed on the NCBI GSE89116 dataset containing expression levels of 39,326 probes sampled over 38 observations.

Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.

Tutorial: Guidelines for Single-Cell RT-qPCR

Reverse transcription quantitative PCR (RT-qPCR) has delivered significant insights in understanding the gene expression landscape. Thanks to its precision, sensitivity, flexibility, and cost effectiveness, RT-qPCR has also found utility in advanced single-cell analysis. Single-cell RT-qPCR now represents a well-established method, suitable for an efficient screening prior to single-cell RNA sequencing (scRNA-Seq) experiments, or, oppositely, for validation of hypotheses formulated from high-throughput approaches. Here, we aim to provide a comprehensive summary of the scRT-qPCR method by discussing the limitations of single-cell collection methods, describing the importance of reverse transcription, providing recommendations for the preamplification and primer design, and summarizing essential data processing steps. With the detailed protocol attached in the appendix, this tutorial provides a set of guidelines that allow any researcher to perform scRT-qPCR measurements of the highest standard.

The Trends of Single-Cell Analysis: A Global Study

Objective

The field of single-cell analysis has rapidly grown worldwide, and a bibliometric analysis and visualization of data and publications pertaining to such single-cell research has the potential to offer insights into the development of this field over the past two decades while also highlighting future avenues of research.

Methods

Single-cell analysis-related studies published from 2000-2019 were identified through searches of the Web of Science, Scopus, and PubMed databases, and corresponding bibliometric data were systematically compiled. Extracted data from each study included author names, country of origin, and affiliations. GraphPad Prism was used to analyze these data, while VOSviewer was used to perform global analyses of bibliographic coupling, coauthorship, cocitation, and co-occurrence.

Results

In total, 4,071 relevant studies were included in this analysis. The number of publications increased substantially with time, suggesting that single-cell analyses are becoming increasingly more prevalent in recent years. Studies from the USA had the greatest impact in this field, with higher H-index values and numbers of citations relative to other countries, whereas Israel exhibited the highest average number of citations per publication. Bibliographic coupling, coauthorship, cocitation, and co-occurrence analyses revealed that Analytical Chemistry was associated with the highest number of publications in this field, and the University of Stanford contributed the most to this field. The most cited study included in this analysis was published by Macosko et al. in 2015 in Cell. Co-occurrence analyses revealed that the most common single-cell research topics included “mechanistic studies,” “in vitro studies,” “in vivo studies,” and “fabrication studies.”

Conclusions

Single-cell analyses are a rapidly growing area of scientific interest, and higher volumes of publications in this field are expected in the coming years, particularly for studies conducting fabrication and in vivo single-cell analyses.

Heterogeneous response of endothelial cells to insulin-like growth factor 1 treatment is explained by spatially clustered sub-populations

A common strategy to measure the efficacy of drug treatment is the in vitro comparison of ensemble readouts with and without treatment, such as proliferation and cell death. A fundamental assumption underlying this approach is that there exists minimal cell-to-cell variability in the response to a drug. Here, we demonstrate that ensemble and non-spatial single-cell readouts applied to primary cells may lead to incomplete conclusions due to cell-to-cell variability. We exposed primary fetal pulmonary artery endothelial cells (PAEC) isolated from healthy newborn sheep and persistent pulmonary hypertension of the newborn (PPHN) sheep to the growth hormone, insulin-like growth factor 1 (IGF-1). We found that IGF-1 increased proliferation and branch points in tube formation assays but not angiogenic signaling proteins at the population level for both cell types. We hypothesized that this molecular ambiguity was due to the presence of cellular sub-populations with variable responses to IGF-1. Using high throughput single-cell imaging, we discovered a spatially localized response to IGF-1. This suggests localized signaling or heritable cell response to external stimuli may ultimately be responsible for our observations. Discovering and further exploring these rare cells is critical to finding new molecular targets to restore cellular function.

Patch-Seq Protocol to Analyze the Electrophysiology, Morphology and Transcriptome of Whole Single Neurons Derived From Human Pluripotent Stem Cells

The human brain is composed of a complex assembly of about 171 billion heterogeneous cellular units (86 billion neurons and 85 billion non-neuronal glia cells). A comprehensive description of brain cells is necessary to understand the nervous system in health and disease. Recently, advances in genomics have permitted the accurate analysis of the full transcriptome of single cells (scRNA-seq). We have built upon such technical progress to combine scRNA-seq with patch-clamping electrophysiological recording and morphological analysis of single human neurons in vitro. This new powerful method, referred to as Patch-seq, enables a thorough, multimodal profiling of neurons and permits us to expose the links between functional properties, morphology, and gene expression. Here, we present a detailed Patch-seq protocol for isolating single neurons from in vitro neuronal cultures. We have validated the Patch-seq whole-transcriptome profiling method with human neurons generated from embryonic and induced pluripotent stem cells (ESCs/iPSCs) derived from healthy subjects, but the procedure may be applied to any kind of cell type in vitro. Patch-seq may be used on neurons in vitro to profile cell types and states in depth to unravel the human molecular basis of neuronal diversity and investigate the cellular mechanisms underlying brain disorders.

Global and targeted approaches to single-cell transcriptome characterization

Analysing transcriptomes of cell populations is a standard molecular biology approach to understand how cells function. Recent methodological development has allowed performing similar experiments on single cells. This has opened up the possibility to examine samples with limited cell number, such as cells of the early embryo, and to obtain an understanding of heterogeneity within populations such as blood cell types or neurons. There are two major approaches for single-cell transcriptome analysis: quantitative reverse transcription PCR (RT-qPCR) on a limited number of genes of interest, or more global approaches targeting entire transcriptomes using RNA sequencing. RT-qPCR is sensitive, fast and arguably more straightforward, while whole-transcriptome approaches offer an unbiased perspective on a cell's expression status.

Platforms for Single-Cell Collection and Analysis

Single-cell analysis has become an established method to study cell heterogeneity and for rare cell characterization. Despite the high cost and technical constraints, applications are increasing every year in all fields of biology. Following the trend, there is a tremendous development of tools for single-cell analysis, especially in the RNA sequencing field. Every improvement increases sensitivity and throughput. Collecting a large amount of data also stimulates the development of new approaches for bioinformatic analysis and interpretation. However, the essential requirement for any analysis is the collection of single cells of high quality. The single-cell isolation must be fast, effective, and gentle to maintain the native expression profiles. Classical methods for single-cell isolation are micromanipulation, microdissection, and fluorescence-activated cell sorting (FACS). In the last decade several new and highly efficient approaches have been developed, which not just supplement but may fully replace the traditional ones. These new techniques are based on microfluidic chips, droplets, micro-well plates, and automatic collection of cells using capillaries, magnets, an electric field, or a punching probe. In this review we summarize the current methods and developments in this field. We discuss the advantages of the different commercially available platforms and their applicability, and also provide remarks on future developments.

Laser microdissection: A powerful tool for genomics at cell level

Laser microdissection (LM) has become widely democratized over the last fifteen years. Instruments have evolved to offer more powerful and efficient lasers as well as new options for sample collection and preparation. Technological evolutions have also focused on the post-microdissection analysis capabilities, opening up investigations in all disciplines of experimental and clinical biology, thanks to the advent of new high-throughput methods of genome analysis, including RNAseq and proteomics, now globally known as microgenomics, i.e. analysis of biomolecules at the cell level. In spite of the advances these rapidly developing methods have allowed, the workflow for sampling and collection by LM remains a critical step in insuring sample integrity in terms of histology (accurate cell identification) and biochemistry (reliable analyzes of biomolecules). In this review, we describe the sample processing as well as the strengths and limiting factors of LM applied to the specific selection of one or more cells of interest from a heterogeneous tissue. We will see how the latest developments in protocols and methods have made LM a powerful and sometimes essential tool for genomic and proteomic analyzes of tiny amounts of biomolecules extracted from few cells isolated from a complex tissue, in their physiological context, thus offering new opportunities for understanding fundamental physiological and/or patho-physiological processes.

NFκB pathway analysis: An approach to analyze gene co-expression networks employing feedback cycles

The genes of the NFκB pathway are involved in the control of a plethora of biological processes ranking from inhibition of apoptosis to metastasis in cancer. It has been described that Gliobastoma multiforme (GBM) patients carry aberrant NFκB activation, but the molecular mechanisms are not completely understood. Here, we present a NFκB pathway analysis in tumor specimens of GBM compared to non-neoplasic brain tissues, based on the different kind of cycles found among genes of a gene co-expression network constructed using quantized data obtained from the microarrays. A cycle is a closed walk with all vertices distinct (except the first and last). Thanks to this way of finding relations among genes, a more robust interpretation of gene correlations is possible, because the cycles are associated with feedback mechanisms that are very common in biological networks. In GBM samples, we could conclude that the stoichiometric relationship between genes involved in NFκB pathway regulation is unbalanced. This can be measured and explained by the identification of a cycle. This conclusion helps to understand more about the biology of this type of tumor.

Global preamplification simplifies targeted mRNA quantification

The need to perform gene expression profiling using next generation sequencing and quantitative real-time PCR (qPCR) on small sample sizes and single cells is rapidly expanding. However, to analyse few molecules, preamplification is required. Here, we studied global and target-specific preamplification using 96 optimised qPCR assays. To evaluate the preamplification strategies, we monitored the reactions in real-time using SYBR Green I detection chemistry followed by melting curve analysis. Next, we compared yield and reproducibility of global preamplification to that of target-specific preamplification by qPCR using the same amount of total RNA. Global preamplification generated 9.3-fold lower yield and 1.6-fold lower reproducibility than target-specific preamplification. However, the performance of global preamplification is sufficient for most downstream applications and offers several advantages over target-specific preamplification. To demonstrate the potential of global preamplification we analysed the expression of 15 genes in 60 single cells. In conclusion, we show that global preamplification simplifies targeted gene expression profiling of small sample sizes by a flexible workflow. We outline the pros and cons for global preamplification compared to target-specific preamplification.

Application of unique sequence index (USI) barcode to gene expression profiling in gastric adenocarcinoma

Accurate expression profiling is imperative for understanding the biological roles of mRNAs. Real-time PCR have been at the forefront of biological innovation in detection and monitoring of gene expression, however, fluorophore-labeled oligonucleotides and double-stranded DNA binding dyes, the two most frequently used dyes in RNA detection, are not very cost effective and have poor specificity, respectively. We have developed a cost effective and specific approach for mRNA expression profiling via added unique sequence index (USI) to cDNAs before amplification. USI is a barcode which enable the detection of each target RNA. Using this method, caudal type homeobox 1 (CDX1) and FAT atypical cadherin 4 (FAT4) expressions were investigated in tumoral and non-tumoral tissues of gastric cancer patients and compared with commercial ABI kit. Both methods indicated that FAT4 and CDX1 expression were significantly reduced in gastric cancer tissues compared with adjacent noncancerous tissues. Moreover, we have shown that this assay is highly sensitive, linear and reproducible. USI barcode not only provides a powerful tool for mRNA detection due to its sensitivity, specificity and cost-effectiveness, but also allows comfortable design for real-time qPCR assays within the least time and empowers the analysis of many transcripts of virtually any organism. Furthermore, USI barcode is highly affordable for large numbers of different samples or small sample sizes without microarray and expensive commercial platforms.

Cell Cycle and Cell Size Dependent Gene Expression Reveals Distinct Subpopulations at Single-Cell Level

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 - S - G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

Single-cell technologies are revolutionizing the approach to rare cells

In the last lustrum single-cell techniques such as single-cell quantitative PCR, RNA and DNA sequencing, and the state-of-the-art cytometry by time of flight (CyTOF) mass cytometer have allowed a detailed analysis of the sub-composition of different organs from the bone marrow hematopoietic compartment to the brain. These fine-grained analyses have highlighted the great heterogeneity within each cell compartment revealing previously unknown subpopulations of cells. In this review, we analyze how this fast technological evolution has improved our understanding of the biological processes with a particular focus on rare cells of the immune system.

Properties of targeted preamplification in DNA and cDNA quantification

Objective: Quantification of small molecule numbers often requires preamplification to generate enough copies for accurate downstream enumerations. Here, we studied experimental parameters in targeted preamplification and their effects on downstream quantitative real-time PCR (qPCR). Methods: To evaluate different strategies, we monitored the preamplification reaction in real-time using SYBR Green detection chemistry followed by melting curve analysis. Furthermore, individual targets were evaluated by qPCR. Result: The preamplification reaction performed best when a large number of primer pairs was included in the primer pool. In addition, preamplification efficiency, reproducibility and specificity were found to depend on the number of template molecules present, primer concentration, annealing time and annealing temperature. The amount of nonspecific PCR products could also be reduced about 1000-fold using bovine serum albumin, glycerol and formamide in the preamplification. Conclusion: On the basis of our findings, we provide recommendations how to perform robust and highly accurate targeted preamplification in combination with qPCR or next-generation sequencing.

Making a big thing of a small cell--recent advances in single cell analysis

Single cell analysis is an emerging field requiring a high level interdisciplinary collaboration to provide detailed insights into the complex organisation, function and heterogeneity of life. This review is addressed to life science researchers as well as researchers developing novel technologies. It covers all aspects of the characterisation of single cells (with a special focus on mammalian cells) from morphology to genetics and different omics-techniques to physiological, mechanical and electrical methods. In recent years, tremendous advances have been achieved in all fields of single cell analysis: (1) improved spatial and temporal resolution of imaging techniques to enable the tracking of single molecule dynamics within single cells; (2) increased throughput to reveal unexpected heterogeneity between different individual cells raising the question what characterizes a cell type and what is just natural biological variation; and (3) emerging multimodal approaches trying to bring together information from complementary techniques paving the way for a deeper understanding of the complexity of biological processes. This review also covers the first successful translations of single cell analysis methods to diagnostic applications in the field of tumour research (especially circulating tumour cells), regenerative medicine, drug discovery and immunology.

Single cell transcriptomics: methods and applications

Traditionally, gene expression measurements were performed on “bulk” samples containing populations of thousands of cells. Recent advances in genomic technology have made it possible to measure gene expression in hundreds of individual cells at a time. As a result, cellular properties that were previously masked in “bulk” measurements can now be observed directly. In this review, we will survey emerging technologies for single cell transcriptomics, and describe how they are used to study complex disease such as cancer, as well as other biological phenomena such as tissue regeneration, embryonic development, and immune response.

Atomic force microscopic detection enabling multiplexed low-cycle-number quantitative polymerase chain reaction for biomarker assays

Quantitative polymerase chain reaction is the current "golden standard" for quantification of nucleic acids; however, its utility is constrained by an inability to easily and reliably detect multiple targets in a single reaction. We have successfully overcome this problem with a novel combination of two widely used approaches: target-specific multiplex amplification with 15 cycles of polymerase chain reaction (PCR), followed by single-molecule detection of amplicons with atomic force microscopy (AFM). In test experiments comparing the relative expression of ten transcripts in two different human total RNA samples, we find good agreement between our single reaction, multiplexed PCR/AFM data, and data from 20 individual singleplex quantitative PCR reactions. This technique can be applied to virtually any analytical problem requiring sensitive measurement concentrations of multiple nucleic acid targets.

Single-copy quantification of HIV-1 in clinical samples

HIV replication in humans proceeds with substantial viral RNA levels in plasma. Antiretroviral therapy results in suppression but not eradication of HIV infection. Continuous therapy is essential for durable clinical responses. Discontinuing antiretroviral therapy results in prompt rebound in viremia. The source of HIV during suppressive therapy and mechanisms of persistence remain uncertain. Sensitive assays for HIV have been useful in quantifying viremia in response to antiretroviral therapy and in experimental studies of drug intensification, drug simplification, and potential anatomic sanctuary site investigations. As clinical eradication strategies move forward, robust, sensitive quantitative assays for HIV at low levels represent essential laboratory support modalities. Here we describe in detail an assay for HIV-1 RNA with single-copy sensitivity.

Human C-kit+CD45- cardiac stem cells are heterogeneous and display both cardiac and endothelial commitment by single-cell qPCR analysis

C-kit expressing cardiac stem cells have been described as multipotent. We have previously identified human cardiac C-kit+CD45- cells, but only found evidence of endothelial commitment. A small cardiac committed subpopulation within the C-kit+CD45- population might however be present. To investigate this at single-cell level, right and left atrial biopsies were dissociated and analyzed by FACS. Only right atrial biopsies contained a clearly distinguishable C-kit+CD45- population, which was single-cell sorted for qPCR. A minor portion of the sorted cells (1.1%) expressed early cardiac gene NKX2.5 while most of the cells (81%) expressed late endothelial gene VWF. VWF- cells were analyzed for a wider panel of genes. One group of these cells expressed endothelial genes (FLK-1, CD31) while another group expressed late cardiac genes (TNNT2, ACTC1). In conclusion, human C-kit+CD45- cells were predominantly localized to the right atrium. While most of these cells expressed endothelial genes, a minor portion expressed cardiac genes.

Circulating brain microvascular endothelial cells (cBMECs) as potential biomarkers of the blood-brain barrier disorders caused by microbial and non-microbial factors

Despite aggressive research, central nervous system (CNS) disorders, including blood-brain barrier (BBB) injury caused by microbial infection, stroke, abused drugs [e.g., methamphetamine (METH) and nicotine], and other pathogenic insults, remain the world's leading cause of disabilities. In our previous work, we found that dysfunction of brain microvascular endothelial cells (BMECs), which are a major component of the BBB, could be caused by nicotine, meningitic pathogens and microbial factors, including HIV-1 virulence factors gp41 and gp120. One of the most challenging issues in this area is that there are no available cell-based biomarkers in peripheral blood for BBB disorders caused by microbial and non-microbial insults. To identify such cellular biomarkers for BBB injuries, our studies have shown that mice treated with nicotine, METH and gp120 resulted in increased blood levels of CD146+(endothelial marker)/S100B+ (brain marker) circulating BMECs (cBMECs) and CD133+[progenitor cell (PC) marker]/CD146+ endothelial PCs (EPCs), along with enhanced Evans blue and albumin extravasation into the brain. Nicotine and gp120 were able to significantly increase the serum levels of ubiquitin C-terminal hydrolase 1 (UCHL1) (a new BBB marker) as well as S100B in mice, which are correlated with the changes in cBMECs and EPCs. Nicotine- and meningitic E. coli K1-induced enhancement of cBMEC levels, leukocyte migration across the BBB and albumin extravasation into the brain were significantly reduced in alpha7 nAChR knockout mice, suggesting that this inflammatory regulator plays an important role in CNS inflammation and BBB disorders caused by microbial and non-microbial factors. These results demonstrated that cBMECs as well as EPCs may be used as potential cell-based biomarkers for indexing of BBB injury.

Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of Alzheimer's disease

Alterations in gene expression resulting from Alzheimer’s disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer’s disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer’s disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer’s disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically ‘proximal’) single cell types.

RT-qPCR work-flow for single-cell data analysis

Individual cells represent the basic unit in tissues and organisms and are in many aspects unique in their properties. The introduction of new and sensitive techniques to study single-cells opens up new avenues to understand fundamental biological processes. Well established statistical tools and recommendations exist for gene expression data based on traditional cell population measurements. However, these workflows are not suitable, and some steps are even inappropriate, to apply on single-cell data. Here, we present a simple and practical workflow for preprocessing of single-cell data generated by reverse transcription quantitative real-time PCR. The approach is demonstrated on a data set based on profiling of 41 genes in 303 single-cells. For some pre-processing steps we present options and also recommendations. In particular, we demonstrate and discuss different strategies for handling missing data and scaling data for downstream multivariate analysis. The aim of this workflow is provide guide to the rapidly growing community studying single-cells by means of reverse transcription quantitative real-time PCR profiling.

Single-neuron RNA-Seq: technical feasibility and reproducibility

Understanding brain function involves improved knowledge about how the genome specifies such a large diversity of neuronal types. Transcriptome analysis of single neurons has been previously described using gene expression microarrays. Using high-throughput transcriptome sequencing (RNA-Seq), we have developed a method to perform single-neuron RNA-Seq. Following electrophysiology recording from an individual neuron, total RNA was extracted by aspirating the cellular contents into a fine glass electrode tip. The mRNAs were reverse transcribed and amplified to construct a single-neuron cDNA library, and subsequently subjected to high-throughput sequencing. This approach was applied to both individual neurons cultured from embryonic mouse hippocampus, as well as neocortical neurons from live brain slices. We found that the average pairwise Spearman’s rank correlation coefficient of gene expression level expressed as RPKM (reads per kilobase of transcript per million mapped reads) was 0.51 between five cultured neuronal cells, whereas the same measure between three cortical layer 5 neurons in situ was 0.25. The data suggest that there may be greater heterogeneity of the cortical neurons, as compared to neurons in vitro. The results demonstrate the technical feasibility and reproducibility of RNA-Seq in capturing a part of the transcriptome landscape of single neurons, and confirmed that morphologically identical neurons, even from the same region, have distinct gene expression patterns.

Epithelial mesenchymal transition: a new insight into the detection of circulating tumor cells

Many research groups reported on the relation between circulating tumor cells (CTCs) in peripheral blood and worse prognosis for metastatic cancer patients. These results are based on CTCs counting and did not take into account molecular characteristics of cells. To establish CTCs as a reliable and accurate biological marker, new technologies must be focused on CTC subpopulations: dedifferentiated circulating tumor cells (ddCTCs) arising from epithelial mesenchymal transition (EMT). To select and detect them, different methods have been proposed but none has still reached the goal. Technical progress and translational research are expected to establish CTCs as a real marker. Thus CTC evaluation profiling for each patient will lead to personalize followup and therapy.

Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns

Single-cell quantitative real-time PCR (qRT-PCR) combined with high-throughput arrays allows the analysis of gene expression profiles at a molecular level in approximately 11 h after cell sample collection. We present here a high-content microfluidic real-time platform as a powerful tool for comparatively investigating the regulation of developmental processes in single cells. This approach overcomes the limitations involving heterogeneous cell populations and sample amounts, and may shed light on differential regulation of gene expression in normal versus disease-related contexts. Furthermore, high-throughput single-cell qRT-PCR provides a standardized, comparative assay for in-depth analysis of the mechanisms underlying human pluripotent stem cell self-renewal and differentiation.