Single-cell gene expression profiling using reverse transcription quantitative real-time PCR

Evaluation of molecular effects associated with apoptosis, tumour progression, angiogenesis and metastasis by a novel combination of drugs with ormeloxifene in triple negative breast cancer cells

Aim

To investigate the molecular effects of a novel combination [sertraline and plumbagin (comb) with ormeloxifene (Orm)] for anticancer activity in triple negative breast cancer cell line "MDA-MB-231".

Methods

The cytotoxic effect of the drugs was analyzed by the MTT assay and nuclear morphological changes by acridine orange/ethidium bromide (AO/EB) staining. Induction of apoptosis by annexin V-FITC staining, active caspase-3 detection and cell cycle analysis were studied in vitro on "MDA-MB-231" cells. The qRT-PCR was done to explore the upregulation and down regulation of targeted genes for angiogenesis, metastasis, tumor suppression and protein folding on the triple negative breast cancer cells. The preliminary anti-angiogenic effect of the drugs was assessed by chorioallantoic membrane (CAM) assay.

Results

Orm showed inhibitory effects in "MDA-MB-231" cells in a dose and time dependent manner whereas; the drugs in combination gave better cytotoxic effects in the screening MTT assay. Orm + comb was more effective than Orm alone in eliciting apoptosis as well as inhibited the single cell to grow into a colony. CAM assay using Orm and Orm + comb suggested the anti-angiogenic potential which was further confirmed by the downregulation of VEGF in "MDA-MB-231" cells by qRT-PCR studies. The combination was found to effectively upregulate the expression of P53 and P21 and downregulate the gene expression of zinc finger E-box binding homeobox 1 (ZEB1) and heat shock protein 70 (HSP70) in "MDA-MB-231" cancer cells.

Conclusions

Collectively this study reveals the efficacy of Orm + comb as more significant than the clinically used tamoxifen (Tam). The study elucidates the promising novelty of the combination as a potential chemotherapeutic intervention for mitigating the aggressiveness of triple negative breast cancer and it addresses the intrinsic resistance caused by single drug treatments.

Mesenchymal stem cells for cartilage regeneration: Insights into molecular mechanism and therapeutic strategies

The use of mesenchymal stem cells (MSCs) in cartilage regeneration has gained significant attention in regenerative medicine. This paper reviews the molecular mechanisms underlying MSC-based cartilage regeneration and explores various therapeutic strategies to enhance the efficacy of MSCs in this context. MSCs exhibit multipotent capabilities and can differentiate into various cell lineages under specific microenvironmental cues. Chondrogenic differentiation, a complex process involving signaling pathways, transcription factors, and growth factors, plays a pivotal role in the successful regeneration of cartilage tissue. The chondrogenic differentiation of MSCs is tightly regulated by growth factors and signaling pathways such as TGF-β, BMP, Wnt/β-catenin, RhoA/ROCK, NOTCH, and IHH (Indian hedgehog). Understanding the intricate balance between these pathways is crucial for directing lineage-specific differentiation and preventing undesirable chondrocyte hypertrophy. Additionally, paracrine effects of MSCs, mediated by the secretion of bioactive factors, contribute significantly to immunomodulation, recruitment of endogenous stem cells, and maintenance of chondrocyte phenotype. Pre-treatment strategies utilized to potentiate MSCs, such as hypoxic conditions, low-intensity ultrasound, kartogenin treatment, and gene editing, are also discussed for their potential to enhance MSC survival, differentiation, and paracrine effects. In conclusion, this paper provides a comprehensive overview of the molecular mechanisms involved in MSC-based cartilage regeneration and outlines promising therapeutic strategies. The insights presented contribute to the ongoing efforts in optimizing MSC-based therapies for effective cartilage repair.

Digital RNA sequencing using unique molecular identifiers enables ultrasensitive RNA mutation analysis

Mutation analysis is typically performed at the DNA level since most technical approaches are developed for DNA analysis. However, some applications, like transcriptional mutagenesis, RNA editing and gene expression analysis, require RNA analysis. Here, we combine reverse transcription and digital DNA sequencing to enable low error digital RNA sequencing. We evaluate yield, reproducibility, dynamic range and error correction rate for seven different reverse transcription conditions using multiplexed assays. The yield, reproducibility and error rate vary substantially between the specific conditions, where the yield differs 9.9-fold between the best and worst performing condition. Next, we show that error rates similar to DNA sequencing can be achieved for RNA using appropriate reverse transcription conditions, enabling detection of mutant allele frequencies <0.1% at RNA level. We also detect mutations at both DNA and RNA levels in tumor tissue using a breast cancer panel. Finally, we demonstrate that digital RNA sequencing can be applied to liquid biopsies, analyzing cell-free gene transcripts. In conclusion, we demonstrate that digital RNA sequencing is suitable for ultrasensitive RNA mutation analysis, enabling several basic research and clinical applications.

Detection of MicroRNA Expression Dynamics Using LNA/DNA Nanobiosensor

The investigation of complex biological processes requires effective tools for probing the spatiotemporal dynamics of individual cells. Single-cell gene expression analysis, such as RNA in situ hybridization and single-cell PCR, has been demonstrated in various biological applications (Tautz and Pfeifle, Chromosoma 98(2):81-5, 1989; Stahlberg and Bengtsson, Methods 50(4):282-288, 2010; Sanchez-Freire et al., Nat Protoc 7(5):829-838, 2012). However, existing techniques require cell lysis or fixation. The dynamic information and spatiotemporal regulation of the biological process cannot be obtained with these methods. Real-time gene expression analysis in living cells remains an outstanding challenge in the field. Here, we described a single-cell gene expression analysis method in living mammalian cells using a locked nucleic acid/DNA (LNA/DNA) nanobiosensor. This LNA/DNA nanobiosensor consists of a fluorophore-labeled detecting strand and a quenching strand. The fluorophore-labeled LNA probe is designed to hybridize with the target microRNA (miRNA) specifically and displace from the quenching strand, allowing the fluorophore to fluorescence. Large-scale single-cell dynamic gene expression monitoring can be performed using time-lapse microscopy to study spatiotemporal distribution and heterogeneity in gene expression. Multiplex detection of miRNAs can be achieved using different fluorophore-labeled LNA/DNA nanobiosensors. This LNA/DNA protocol is fast, generally applicable, and easily accessible.

Promotion of tumor progression by exosome transmission of circular RNA circSKA3

We performed in vitro and in vivo experiments to investigate the role of the circular RNA circSKA3 in tumor development. We examined the effects of circSKA3 on mediating breast cancer metastasis. In vitro, we found that the circular RNA circSKA3 was transferred between breast cancer cells, which were decreased by inhibiting exosome secretion. In vivo, circSKA3-containing exosomes potentiated tumor development and invasion that were inhibited by blocking exosome transmission. The ascites isolated from tumor-bearing mice or breast cancer patients showed high levels of circSKA3 and integrin β1. Single-cell culture and single-cell PCR showed that circSKA3 was heterogeneously expressed, the cells expressing higher levels of circSKA3 had a higher potential to form large colonies. This property was similar to c-myc, but circSKA3 expression had no correlation with c-myc levels. The effects of circSKA3 on cell migration and invasion appeared to predominate c-myc functions. By releasing circSKA3-containing exosomes to cancer cells expressing lower levels of circSKA3, the large colonies could regulate the activities of small colonies, enhancing the tumor-forming capacity of the entire population. Thus, we provide evidence that the transmission of circular RNAs in tumor-derived exosomes may allow for the maintenance of advantageous invasive sub-clones in breast cancer.

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.

Performance Comparison of Reverse Transcriptases for Single-Cell Studies

BACKGROUND

Recent advances allowing quantification of RNA from single cells are revolutionizing biology and medicine. Currently, almost all single-cell transcriptomic protocols rely on reverse transcription (RT). However, RT is recognized as a known source of variability, particularly with low amounts of RNA. Recently, several new reverse transcriptases (RTases) with the potential to decrease the loss of information have been developed, but knowledge of their performance is limited.

METHODS

We compared the performance of 11 RTases in quantitative reverse transcription PCR (RT-qPCR) on single-cell and 100-cell bulk templates, using 2 priming strategies: a conventional mixture of random hexamers with oligo(dT)s and a reduced concentration of oligo(dT)s mimicking common single-cell RNA-sequencing protocols. Depending on their performance, 2 RTases were further tested in a high-throughput single-cell experiment.

RESULTS

All tested RTases demonstrated high precision (R2 > 0.9445). The most pronounced differences were found in their ability to capture rare transcripts (0%-90% reaction positivity rate) and in their absolute reaction yield (7.3%-137.9%). RTase performance and reproducibility were compared with Z scores. The 2 best-performing enzymes were Maxima H- and SuperScript IV. The validity of the obtained results was confirmed in a follow-up single-cell model experiment. The better-performing enzyme (Maxima H-) increased the sensitivity of the single-cell experiment and improved resolution in the clustering analysis over the commonly used RTase (SuperScript II).

CONCLUSIONS

Our comprehensive comparison of 11 RTases in low RNA input conditions identified 2 best-performing enzymes. Our results provide a point of reference for the improvement of current single-cell quantification protocols.

The mevalonate precursor enzyme HMGCS1 is a novel marker and key mediator of cancer stem cell enrichment in luminal and basal models of breast cancer

The definitive characterization of common cancer stem cell (CSCs) subpopulations in breast cancer subtypes with distinct genotypic and phenotypic features remains an ongoing challenge. In this study, we have used a non-biased genome wide screening approach to identify transcriptional networks that may be specific to the CSC subpopulations in both luminal and basal breast cancer subtypes. In depth studies of three CSC-enriched breast cancer cell lines representing various subtypes of breast cancer revealed a striking hyperactivation of the mevalonate metabolic pathway in comparison to control cells. The upregulation of metabolic networks is a key feature of tumour cells securing growth and proliferative capabilities and dysregulated mevalonate metabolism has been associated with tumour malignancy and cellular transformation in breast cancer. Furthermore, accumulating evidence suggests that Simvastatin therapy, a mevalonate pathway inhibitor, could affect breast cancer progression and reduce breast cancer recurrence. When detailing the mevalonate pathway in breast cancer using a single-cell qPCR, we identified the mevalonate precursor enzyme, HMGCS1, as a specific marker of CSC-enriched subpopulations within both luminal and basal tumour subtypes. Down-regulation of HMGCS1 also decreased the CSC fraction and function in various model systems, suggesting that HMGCS1 is essential for CSC-activities in breast cancer in general. These data was supported by strong associations between HMGCS1 expression and aggressive features, such as high tumour grade, p53 mutations as well as ER-negativity in lymph node positive breast cancer. Importantly, loss of HMGCS1 also had a much more pronounced effect on CSC-activities compared to treatment with standard doses of Simvastatin. Taken together, this study highlights HMGCS1 as a potential gatekeeper for dysregulated mevalonate metabolism important for CSC-features in both luminal and basal breast cancer subtypes. Pharmacological inhibition of HMGCS1 could therefore be a superior novel treatment approach for breast cancer patients via additional CSC blocking functions.

Honey Bee Queens and Virus Infections

The honey bee queen is the central hub of a colony to produce eggs and release pheromones to maintain social cohesion. Among many environmental stresses, viruses are a major concern to compromise the queen's health and reproductive vigor. Viruses have evolved numerous strategies to infect queens either via vertical transmission from the queens' parents or horizontally through the worker and drones with which she is in contact during development, while mating, and in the reproductive period in the colony. Over 30 viruses have been discovered from honey bees but only few studies exist on the pathogenicity and direct impact of viruses on the queen's phenotype. An apparent lack of virus symptoms and practical problems are partly to blame for the lack of studies, and we hope to stimulate new research and methodological approaches. To illustrate the problems, we describe a study on sublethal effects of Israeli Acute Paralysis Virus (IAPV) that led to inconclusive results. We conclude by discussing the most crucial methodological considerations and novel approaches for studying the interactions between honey bee viruses and their interactions with queen health.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Embracing Systems Toxicology at Single-Cell Resolution

As systems biology expands its multi-omic spectrum to increasing resolutions, distinguishing cells based on single-cell profiles becomes feasible. Unlike traditional bulk assays that average cellular responses and blur the distinct identities of responsive cells, single-cell technologies enable sensitive detection of small cellular changes and precise identification of those cells perturbed by toxicants. Among the suite of omic technologies that continue to expand and become affordable, single-cell RNA sequencing (scRNA-seq) is at the cutting edge and leading the way to transform systems toxicology. Single-cell systems toxicology can provide a wealth of information to elucidate cell-specific alterations and response trajectories, detect points-of-departure, map and develop dynamical models of toxicity pathways.

Quantification of RNA by Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR)

This protocol describes a real-time reverse transcription-polymerase chain reaction (RT-PCR) assay using a two-enzyme, two-tube approach, carried out using either SYBR Green I or TaqMan chemistries. The protocol uses a PCR volume of 20 µL (although most manufacturers recommend 50-µL reactions). However, if the PCR target is not very abundant (i.e., present at one to 10 copies per sample), a larger volume may yield better reproducibility between samples. Discussion on preparing high-quality RNA, choosing a priming method, selecting an enzyme, and selecting an endogenous reference gene is also included.

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.

A novel ex vivo Huntington's disease model for studying GABAergic neurons and cell grafts by laser microdissection

Organotypic brain slice cultures have been recently used to study neurodegenerative disorders such as Parkinson’s disease and Huntington’s disease (HD). They preserve brain three-dimensional architecture, synaptic connectivity and brain cells microenvironment. Here, we developed an innovative model of Huntington’s disease from coronal rat brain slices, that include all the areas involved in the pathology. HD-like neurodegeneration was obtained in only one week, in a single step, during organotypic slice preparation, without the use of neurotoxins. HD-like histopathology was analysed and after one week, a reduction of 40% of medium spiny neurons was observed. To analyse new therapeutic approaches in this innovative HD model, we developed a novel protocol of laser microdissection to isolate and analyse by RT-qPCR, grafted cells as well as surrounding tissue of fresh organotypic slices. We determined that laser microdissection could be performed on a 400μm organotypic slice after alcohol dehydration protocol, allowing the analysis of mRNA expression in the rat tissue as well as in grafted cells. In conclusion, we developed a new approach for modeling Huntington's disease ex vivo, and provided a useful innovative method for screening new potential therapies for neurodegenerative diseases especially when associated with laser microdissection.

Evaluation of digital real-time PCR assay as a molecular diagnostic tool for single-cell analysis

In a single-cell study, isolating and identifying single cells are essential, but these processes often require a large investment of time or money. The aim of this study was to isolate and analyse single cells using a novel platform, the PanelChip™ Analysis System, which includes 2500 microwells chip and a digital real-time polymerase chain reaction (dqPCR) assay, in comparison with a standard PCR (qPCR) assay. Through the serial dilution of a known concentration standard, namely pUC19, the accuracy and sensitivity levels of two methodologies were compared. The two systems were tested on the basis of expression levels of the genetic markers vimentin, E-cadherin, N-cadherin and GAPDH in A549 lung carcinoma cells at two known concentrations. Furthermore, the influence of a known PCR inhibitor commonly found in blood samples, heparin, was evaluated in both methodologies. Finally, mathematical models were proposed and separation method of single cells was verified; moreover, gene expression levels during epithelial-mesenchymal transition in single cells under TGFβ1 treatment were measured. The drawn conclusion is that dqPCR performed using PanelChip™ is superior to the standard qPCR in terms of sensitivity, precision, and heparin tolerance. The dqPCR assay is a potential tool for clinical diagnosis and single-cell applications.

Quantitative assessment of the CD26+ leukemic stem cell compartment in chronic myeloid leukemia: patient-subgroups, prognostic impact, and technical aspects

Little is known about the function and phenotype of leukemic stem cells (LSCs) in chronic myeloid leukemia (CML) or about specific markers that discriminate LSCs from normal hematopoietic stem cells (HSCs). CD26 has recently been described as a specific marker of CML LSCs. In the current study, we investigated this marker in a cohort of 31 unselected CML patients. BCR/ABL1 positivity was analyzed in highly enriched stem cell fractions using fluorescence in situ hybridization (FISH) and reverse transcription PCR (RT-PCR). The proportion of CD26⁺ LSCs and CD26⁻ HSCs varied considerably among the patients analyzed, and the percentage of CD26⁺ cells correlated with leukocyte count. The CD26 expression robustly discriminated LSCs from HSCs. This required a strict gating of the stem cell compartment. Thus, in patients with very low LSC or HSC numbers, only the highly sensitive RT-PCR method discriminated between clonal and non-clonal cells, while a robust FISH analysis required larger numbers of cells in both compartments. Finally, our data show that the numbers of CD26⁺ CML LSCs correlate with responses to treatment with BCR-ABL1 inhibitors.

On the origin and impact of mesenchymal stem cell heterogeneity: new insights and emerging tools for single cell analysis

Mesenchymal stem cells (MSCs) display substantial cell-to-cell variation. This heterogeneity manifests among donors, among tissue sources, and within cell populations. Such pervasive variability complicates the use of MSCs in regenerative applications and may limit their therapeutic efficacy. Most conventional assays measure MSC properties in bulk and, as a consequence, mask this cell-to-cell variation. Recent studies have identified extensive variability amongst and within clonal MSC populations, in dimensions including functional differentiation capacity, molecular state (e.g. epigenetic, transcriptomic, and proteomic status), and biophysical properties. While the origins of these variations remain to be elucidated, potential mechanisms include in vivo micro-anatomical heterogeneity, epigenetic bistability, and transcriptional fluctuations. Emerging tools for single cell analysis of MSC gene and protein expression may yield further insight into the mechanisms and implications of single cell variation amongst these cells, and ultimately improve the clinical utility of MSCs in tissue engineering and regenerative medicine applications. This review outlines the dimensions across which MSC heterogeneity is present, defines some of the known mechanisms that govern this heterogeneity, and highlights emerging technologies that may further refine our understanding and improve our clinical application of this unique cell type.

Technical aspects and recommendations for single-cell qPCR

Single cells are basic physiological and biological units that can function individually as well as in groups in tissues and organs. It is central to identify, characterize and profile single cells at molecular level to be able to distinguish different kinds, to understand their functions and determine how they interact with each other. During the last decade several technologies for single-cell profiling have been developed and used in various applications, revealing many novel findings. Quantitative PCR (qPCR) is one of the most developed methods for single-cell profiling that can be used to interrogate several analytes, including DNA, RNA and protein. Single-cell qPCR has the potential to become routine methodology but the technique is still challenging, as it involves several experimental steps and few molecules are handled. Here, we discuss technical aspects and provide recommendation for single-cell qPCR analysis. The workflow includes experimental design, sample preparation, single-cell collection, direct lysis, reverse transcription, preamplification, qPCR and data analysis. Detailed reporting and sharing of experimental details and data will promote further development and make validation studies possible. Efforts aiming to standardize single-cell qPCR open up means to move single-cell analysis from specialized research settings to standard research laboratories.

Liquid Biopsies in Oncology and the Current Regulatory Landscape

There is a profound need in oncology to detect cancer earlier, guide individualized therapies, and better monitor progress during treatment. Currently, some of this information can be achieved through solid tissue biopsy and imaging. However, these techniques are limited because of the invasiveness of the procedure and the size of the tumor. A liquid biopsy can overcome these barriers as its non-invasive nature allows samples to be collected over time. Liquid biopsies may also allow earlier detection than traditional imaging. Liquid biopsies include the analysis of circulating tumor cells (CTCs), cell-free nucleic acid (cfNA), or extracellular vesicles obtained from a variety of biofluids, such as peripheral blood. In this review, we discuss different liquid biopsy types and how they fit into the current regulatory landscape.

Single Cell Analysis Linking Ribosomal (r)DNA and rRNA Copy Numbers to Cell Size and Growth Rate Provides Insights into Molecular Protistan Ecology

Ribosomal (r)RNA and rDNA have been golden molecular markers in microbial ecology. However, it remains poorly understood how ribotype copy number (CN)‐based characteristics are linked with diversity, abundance, and activity of protist populations and communities observed at organismal levels. Here, we applied a single‐cell approach to quantify ribotype CNs in two ciliate species reared at different temperatures. We found that in actively growing cells, the per‐cell rDNA and rRNA CNs scaled with cell volume (CV) to 0.44 and 0.58 powers, respectively. The modeled rDNA and rRNA concentrations thus appear to be much higher in smaller than in larger cells. The observed rRNA:rDNA ratio scaled with CV^0.14. The maximum growth rate could be well predicted by a combination of per‐cell ribotype CN and temperature. Our empirical data and modeling on single‐cell ribotype scaling are in agreement with both the metabolic theory of ecology and the growth rate hypothesis, providing a quantitative framework for linking cellular rDNA and rRNA CNs with body size, growth (activity), and biomass stoichiometry. This study also demonstrates that the expression rate of rRNA genes is constrained by cell size, and favors biomass rather than abundance‐based interpretation of quantitative ribotype data in population and community ecology of protists.

cgCorrect: a method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics

Accessing gene expression at a single-cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remains obscured when using traditional population-based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue is differences in cell size, which introduce additional variability into the data and for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers. We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell-growth-corrected mRNA transcript number given the measured, cell size-dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportionally to the cell's volume during the cell cycle. cgCorrect can be used for both data normalization and to analyze the steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time polymerase chain reaction (PCR) data from mouse blood stem and progenitor cells (and to quantitative single-cell RNA-sequencing data obtained from mouse embryonic stem cells). We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.

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.

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.

The correlation between expression profiles measured in single cells and in traditional bulk samples

Reverse transcription quantitative PCR (RT-qPCR) is already an established tool for mRNA detection and quantification. Since recently, this technique has been successfully employed for gene expression analyses, and also in individual cells (single cell RT-qPCR). Although the advantages of single cell measurements have been proven several times, a study correlating the expression measured on single cells, and in bulk samples consisting of a large number of cells, has been missing. Here, we collected a large data set to explore the relation between gene expression measured in single cells and in bulk samples, reflected by qPCR Cq values. We measured the expression of 95 genes in 12 bulk samples, each containing thousands of astrocytes, and also in 693 individual astrocytes. Combining the data, we described the relation between Cq values measured in bulk samples with either the percentage of the single cells that express the given genes, or the average expression of the genes across the single cells. We show that data obtained with single cell RT-qPCR are fully consistent with measurements in bulk samples. Our results further provide a base for quality control in single cell expression profiling, and bring new insights into the biological process of cellular expression.

Identification of Distinct Breast Cancer Stem Cell Populations Based on Single-Cell Analyses of Functionally Enriched Stem and Progenitor Pools

The identification of breast cancer cell subpopulations featuring truly malignant stem cell qualities is a challenge due to the complexity of the disease and lack of general markers. By combining extensive single-cell gene expression profiling with three functional strategies for cancer stem cell enrichment including anchorage-independent culture, hypoxia, and analyses of low-proliferative, label-retaining cells derived from mammospheres, we identified distinct stem cell clusters in breast cancer. Estrogen receptor (ER)α+ tumors featured a clear hierarchical organization with switch-like and gradual transitions between different clusters, illustrating how breast cancer cells transfer between discrete differentiation states in a sequential manner. ERα− breast cancer showed less prominent clustering but shared a quiescent cancer stem cell pool with ERα+ cancer. The cellular organization model was supported by single-cell data from primary tumors. The findings allow us to understand the organization of breast cancers at the single-cell level, thereby permitting better identification and targeting of cancer stem cells.

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ERα+ and ERα− breast cancer stem cells share a quiescent cancer stem cell pool

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Single-cell analysis identified distinct cancer stem cell populations in breast cancer

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Identified ERα+ breast cancer cell populations were hierarchically organized

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Switch-like and gradual transitions exist between ERα+ stem and progenitor pools

BTR: training asynchronous Boolean models using single-cell expression data

Background

Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs. While many algorithms exist to infer a gene regulatory network, very few of them are able to harness the extra expression states present in single-cell expression data without getting adversely affected by the substantial technical noise present.

Results

Here we introduce BTR, an algorithm for training asynchronous Boolean models with single-cell expression data using a novel Boolean state space scoring function. BTR is capable of refining existing Boolean models and reconstructing new Boolean models by improving the match between model prediction and expression data. We demonstrate that the Boolean scoring function performed favourably against the BIC scoring function for Bayesian networks. In addition, we show that BTR outperforms many other network inference algorithms in both bulk and single-cell synthetic expression data. Lastly, we introduce two case studies, in which we use BTR to improve published Boolean models in order to generate potentially new biological insights.

Conclusions

BTR provides a novel way to refine or reconstruct Boolean models using single-cell expression data. Boolean model is particularly useful for network reconstruction using single-cell data because it is more robust to the effect of drop-outs. In addition, BTR does not assume any relationship in the expression states among cells, it is useful for reconstructing a gene regulatory network with as few assumptions as possible. Given the simplicity of Boolean models and the rapid adoption of single-cell genomics by biologists, BTR has the potential to make an impact across many fields of biomedical research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1235-y) contains supplementary material, which is available to authorized users.

Single-Cell Quantitative PCR: Advances and Potential in Cancer Diagnostics

Tissues are heterogeneous in their components. If cells of interest are a minor population of collected tissue, it would be difficult to obtain genetic or genomic information of the interested cell population with conventional genomic DNA extraction from the collected tissue. Single-cell DNA analysis is important in the analysis of genetics of cell clonality, genetic anticipation, and single-cell DNA polymorphisms. Single-cell PCR using Single Cell Ampligrid/GeXP platform is described in this chapter.

Isolation and gene expression analysis of single potential human spermatogonial stem cells

STUDY HYPOTHESIS

It is possible to isolate pure populations of single potential human spermatogonial stem cells without somatic contamination for down-stream applications, for example cell culture and gene expression analysis.

STUDY FINDING

We isolated pure populations of single potential human spermatogonial stem cells (hSSC) without contaminating somatic cells and analyzed gene expression of these cells via single-cell real-time RT-PCR.

WHAT IS KNOWN ALREADY

The isolation of a pure hSSC fraction could enable clinical applications such as fertility preservation for prepubertal boys and in vitro-spermatogenesis. By utilizing largely nonspecific markers for the isolation of spermatogonia (SPG) and hSSC, previously published cell selection methods are not able to deliver pure target cell populations without contamination by testicular somatic cells. However, uniform cell populations free of somatic cells are necessary to guarantee defined growth conditions in cell culture experiments and to prevent unintended stem cell differentiation. Fibroblast growth factor receptor 3 (FGFR3) is a cell surface protein of human undifferentiated A-type SPG and a promising candidate marker for hSSC. It is exclusively expressed in small, non-proliferating subgroups of this spermatogonial cell type together with the pluripotency-associated protein and spermatogonial nuclear marker undifferentiated embryonic cell transcription factor 1 (UTF1).

STUDY DESIGN, SAMPLES/MATERIALS, METHODS

We specifically selected the FGFR3-positive spermatogonial subpopulation from two 30 mg biopsies per patient from a total of 37 patients with full spermatogenesis and three patients with meiotic arrest. We then employed cell selection with magnetic beads in combination with a fluorescence-activated cell sorter antibody directed against human FGFR3 to tag and visually identify human FGFR3-positive spermatogonia. Positively selected and bead-labeled cells were subsequently picked with a micromanipulator. Analysis of the isolated cells was carried out by single-cell real-time RT-PCR, real-time RT-PCR, immunocytochemistry and live/dead staining.

MAIN RESULTS AND THE ROLE OF CHANCE

Single-cell real-time RT-PCR and real-time RT-PCR of pooled cells indicate that bead-labeled single cells express FGFR3 with high heterogeneity at the mRNA level, while bead-unlabeled cells lack FGFR3 mRNA. Furthermore, isolated cells exhibit strong immunocytochemical staining for the stem cell factor UTF1 and are viable.

LIMITATIONS, REASONS FOR CAUTION

The cell population isolated in this study has to be tested for their potential stem cell characteristics via xenotransplantation. Due to the small amount of the isolated cells, propagation by cell culture will be essential. Other potential hSSC without FGFR3 surface expression will not be captured with the provided experimental design.

WIDER IMPLICATIONS OF THE FINDINGS

The technical approach as developed in this work could encourage the scientific community to test other established or novel hSSC markers on single SPG that present with potential stem cell-like features.

STUDY FUNDING AND COMPETING INTERESTS

The project was funded by the DFG Research Unit FOR1041 Germ cell potential (SCH 587/3-2) and DFG grants to K.v.K. (KO 4769/2-1) and A.-N.S. (SP 721/4-1). The authors declare no competing interests.

Piezoelectric inkjet assisted rapid electrospray ionization mass spectrometric analysis of metabolites in plant single cells via a direct sampling probe

Direct sampling probe mass spectrometry (DSP-MS) enables fast and direct profiling of metabolites in biological samples. However, because the solvent amount for the online dissolution of acquired analytes is difficult to control, the detection sensitivity is not satisfactory. In this study, we present a modified version of the DSP-MS system for direct mass spectrometric profiling of metabolites in plant single cells. Two major improvements are introduced in this work, including a pointed-tip probe with high surface wetting properties, which is ten times finer than the previous version, and a piezoelectric inkjet system working as the auxiliary solvent delivery means. The probe tip can be controlled to insert into a cell through the cell wall. Metabolites loaded on the tip surface can be extracted by the auxiliary solvent and electrosprayed after applying a high direct current voltage. The unique features such as low cost, disposability and versatility make this technique a competitive tool for single cell analysis.

Two distinct effector memory cell populations of WT1 (Wilms' tumor gene 1)-specific cytotoxic T lymphocytes in acute myeloid leukemia patients

Wilms' tumor gene 1 (WT1) protein is a promising tumor-associated antigen for cancer immunotherapy. We have been performing WT1 peptide vaccination with good clinical responses in over 750 patients with leukemia or solid cancers. In this study, we generated single-cell gene-expression profiles of the effector memory (EM) subset of WT1-specific cytotoxic T lymphocytes (CTLs) in peripheral blood of nine acute myeloid leukemia patients treated with WT1 peptide vaccine, in order to discriminate responders (WT1 mRNA levels in peripheral blood decreased to undetectable levels, decreased but stayed at abnormal levels, were stable at undetectable levels, or remained unchanged from the initial abnormal levels more than 6 months after WT1 vaccination) from non-responders (leukemic blast cells and/or WT1 mRNA levels increased relative to the initial state within 6 months of WT1 vaccination) prior to WT1 vaccination. Cluster and principal component analyses performed using 83 genes did not discriminate between responders and non-responders prior to WT1 vaccination. However, these analyses revealed that EM subset of WT1-specific CTLs could be divided into two groups: the "activated" and "quiescent" states; in responders, EM subset of the CTLs shifted to the "quiescent" state, whereas in non-responders, those shifted to the "activated" state following WT1 vaccination. These results demonstrate for the first time the existence of two distinct EM states, each of which was characteristic of responders or non-responders, of WT1-specific CTLs in AML patients, and raises the possibility of using advanced gene-expression profile analysis to clearly discriminate between responders and non-responders prior to WT1 vaccination.

Quantitative Analysis of Glutamate Receptors in Glial Cells from the Cortex of GFAP/EGFP Mice Following Ischemic Injury: Focus on NMDA Receptors

Cortical glial cells contain both ionotropic and metabotropic glutamate receptors. Despite several efforts, a comprehensive analysis of the entire family of glutamate receptors and their subunits present in glial cells is still missing. Here, we provide an overall picture of the gene expression of ionotropic (AMPA, kainate, NMDA) and the main metabotropic glutamate receptors in cortical glial cells isolated from GFAP/EGFP mice before and after focal cerebral ischemia. Employing single-cell RT-qPCR, we detected the expression of genes encoding subunits of glutamate receptors in GFAP/EGFP-positive (GFAP/EGFP(+)) glial cells in the cortex of young adult mice. Most of the analyzed cells expressed mRNA for glutamate receptor subunits, the expression of which, in most cases, even increased after ischemic injury. Data analyses disclosed several classes of GFAP/EGFP(+) glial cells with respect to glutamate receptors and revealed in what manner their expression correlates with the expression of glial markers prior to and after ischemia. Furthermore, we also examined the protein expression and functional significance of NMDA receptors in glial cells. Immunohistochemical analyses of all seven NMDA receptor subunits provided direct evidence that the GluN3A subunit is present in GFAP/EGFP(+) glial cells and that its expression is increased after ischemia. In situ and in vitro Ca(2+) imaging revealed that Ca(2+) elevations evoked by the application of NMDA were diminished in GFAP/EGFP(+) glial cells following ischemia. Our results provide a comprehensive description of glutamate receptors in cortical GFAP/EGFP(+) glial cells and may serve as a basis for further research on glial cell physiology and pathophysiology.

Recent advances and current issues in single-cell sequencing of tumors

Intratumoral heterogeneity is a recently recognized but important feature of cancer that underlies the various biocharacteristics of cancer tissues. The advent of next-generation sequencing technologies has facilitated large scale capture of genomic data, while the recent development of single-cell sequencing has allowed for more in-depth studies into the complex molecular mechanisms of intratumoral heterogeneity. In this review, the recent advances and current challenges in single-cell sequencing methodologies are discussed, highlighting the potential power of these data to provide insights into oncological processes, from tumorigenesis through progression to metastasis and therapy resistance.

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.

Pre-amplification in the context of high-throughput qPCR gene expression experiment

BACKGROUND

With the introduction of the first high-throughput qPCR instrument on the market it became possible to perform thousands of reactions in a single run compared to the previous hundreds. In the high-throughput reaction, only limited volumes of highly concentrated cDNA or DNA samples can be added. This necessity can be solved by pre-amplification, which became a part of the high-throughput experimental workflow. Here, we focused our attention on the limits of the specific target pre-amplification reaction and propose the optimal, general setup for gene expression experiment using BioMark instrument (Fluidigm).

RESULTS

For evaluating different pre-amplification factors following conditions were combined: four human blood samples from healthy donors and five transcripts having high to low expression levels; each cDNA sample was pre-amplified at four cycles (15, 18, 21, and 24) and five concentrations (equivalent to 0.078 ng, 0.32 ng, 1.25 ng, 5 ng, and 20 ng of total RNA). Factors identified as critical for a success of cDNA pre-amplification were cycle of pre-amplification, total RNA concentration, and type of gene. The selected pre-amplification reactions were further tested for optimal Cq distribution in a BioMark Array. The following concentrations combined with pre-amplification cycles were optimal for good quality samples: 20 ng of total RNA with 15 cycles of pre-amplification, 20x and 40x diluted; and 5 ng and 20 ng of total RNA with 18 cycles of pre-amplification, both 20x and 40x diluted.

CONCLUSIONS

We set up upper limits for the bulk gene expression experiment using gene expression Dynamic Array and provided an easy-to-obtain tool for measuring of pre-amplification success. We also showed that variability of the pre-amplification, introduced into the experimental workflow of reverse transcription-qPCR, is lower than variability caused by the reverse transcription step.

Analysis of Gene and Protein Expression in Atherosclerotic Mouse Aorta by Western Blot and Quantitative Real-Time PCR

Atherosclerosis involves changes in gene and protein expression patterns in affected arteries. Quantification of these alterations is essential for understanding the molecular mechanisms underlying this pathology. Western blot and real-time PCR-used to quantify protein and messenger RNA levels, respectively-are invaluable molecular biology tools, particularly when material is limited. The availability of many genetically modified mouse models of atherosclerosis makes the mouse aorta an ideal tissue in which to carry out these expression pattern analyses. In this chapter, protocols are presented for mRNA and protein extraction from mouse aorta and for the accurate quantification of mRNA expression by RT-PCR and of proteins by western blot.

Single-cell technologies sharpen up mammalian stem cell research

Analysis of the mechanisms underlying cell fates requires the molecular quantification of cellular features. Classical techniques use population average readouts at single time points. However, these approaches mask cellular heterogeneity and dynamics and are limited for studying rare and heterogeneous cell populations like stem cells. Techniques for single-cell analyses, ideally allowing non-invasive quantification of molecular dynamics and cellular behaviour over time, are required for studying stem cells. Here, we review the development and application of these techniques to stem cell research.

The workflow of single-cell expression profiling using quantitative real-time PCR

Biological material is heterogeneous and when exposed to stimuli the various cells present respond differently. Much of the complexity can be eliminated by disintegrating the sample, studying the cells one by one. Single-cell profiling reveals responses that go unnoticed when classical samples are studied. New cell types and cell subtypes may be found and relevant pathways and expression networks can be identified. The most powerful technique for single-cell expression profiling is currently quantitative reverse transcription real-time PCR (RT-qPCR). A robust RT-qPCR workflow for highly sensitive and specific measurements in high-throughput and a reasonable degree of multiplexing has been developed for targeting mRNAs, but also microRNAs, non-coding RNAs and most recently also proteins. We review the current state of the art of single-cell expression profiling and present also the improvements and developments expected in the next 5 years.

Na+ current properties in islet α- and β-cells reflect cell-specific Scn3a and Scn9a expression

Mouse pancreatic β- and α-cells are equipped with voltage-gated Na⁺ currents that inactivate over widely different membrane potentials (half-maximal inactivation (V_0.5) at −100 mV and −50 mV in β- and α-cells, respectively). Single-cell PCR analyses show that both α- and β-cells have Na_v1.3 (Scn3) and Na_v1.7 (Scn9a) α subunits, but their relative proportions differ: β-cells principally express Na_v1.7 and α-cells Na_v1.3. In α-cells, genetically ablating Scn3a reduces the Na⁺ current by 80%. In β-cells, knockout of Scn9a lowers the Na⁺ current by >85%, unveiling a small Scn3a-dependent component. Glucagon and insulin secretion are inhibited in Scn3a^−/− islets but unaffected in Scn9a-deficient islets. Thus, Na_v1.3 is the functionally important Na⁺ channel α subunit in both α- and β-cells because Na_v1.7 is largely inactive at physiological membrane potentials due to its unusually negative voltage dependence of inactivation. Interestingly, the Na_v1.7 sequence in brain and islets is identical and yet the V_0.5 for inactivation is >30 mV more negative in β-cells. This may indicate the presence of an intracellular factor that modulates the voltage dependence of inactivation.

In vitro clinical trials: the future of cell-based profiling

The drug discovery process classically revolves around a set of biochemical and cellular assays to drive potency optimization and structural-activity relationship models. Layered on top of these concepts is the inclusion of molecular features that affect final drug use, things like: bioavailability, toxicity, stability, solubility, formulation, route of administration, etc. Paradoxically, most drugs entering clinical trials are only tested in a handful of human genetic backgrounds before they are given to people. Here we review efforts and opine on the use of large scale in vitro cellular and in vivo models that attempt to model human disease and include diversity found in the human genetic population. Because hundreds to thousands of individual assays are needed to scratch the surface of human genetic diversity, sophisticated high throughput automation technologies or pooling and deconvolution strategies are required. Characterization of each model needs to be extensive to enable non-biased informatics based modeling. Such approaches will enable deep understanding of genetic to pharmacological response and result in new methods for patient stratification in the clinic. Oncology medicines and cancer genetics have been paving the way for these approaches and we expect to see continued expansion to other fields such as immunology and neuroscience.

A convenient, optimized pipeline for isolation, fluorescence microscopy and molecular analysis of live single cells

Background

Heterogeneity within cell populations is relevant to the onset and progression of disease, as well as development and maintenance of homeostasis. Analysis and understanding of the roles of heterogeneity in biological systems require methods and technologies that are capable of single cell resolution. Single cell gene expression analysis by RT-qPCR is an established technique for identifying transcriptomic heterogeneity in cellular populations, but it generally requires specialized equipment or tedious manipulations for cell isolation.

Results

We describe the optimization of a simple, inexpensive and rapid pipeline which includes isolation and culture of live single cells as well as fluorescence microscopy and gene expression analysis of the same single cells by RT-qPCR. We characterize the efficiency of single cell isolation and demonstrate our method by identifying single GFP-expressing cells from a mixed population of GFP-positive and negative cells by correlating fluorescence microscopy and RT-qPCR.

Conclusions

Single cell gene expression analysis by RT-qPCR is a convenient means for investigating cellular heterogeneity, but is most useful when correlating observations with additional measurements. We demonstrate a convenient and simple pipeline for multiplexing single cell RT-qPCR with fluorescence microscopy which is adaptable to other molecular analyses.

Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development.

Regulation of action potential delays via voltage-gated potassium Kv1.1 channels in dentate granule cells during hippocampal epilepsy

Action potential (AP) responses of dentate gyrus granule (DG) cells have to be tightly regulated to maintain hippocampal function. However, which ion channels control the response delay of DG cells is not known. In some neuron types, spike latency is influenced by a dendrotoxin (DTX)-sensitive delay current (ID) mediated by unidentified combinations of voltage-gated K(+) (Kv) channels of the Kv1 family Kv1.1-6. In DG cells, the ID has not been characterized and its molecular basis is unknown. The response phenotype of mature DG cells is usually considered homogenous but intrinsic plasticity likely occurs in particular in conditions of hyperexcitability, for example during temporal lobe epilepsy (TLE). In this study, we examined response delays of DG cells and underlying ion channel molecules by employing a combination of gramicidin-perforated patch-clamp recordings in acute brain slices and single-cell reverse transcriptase quantitative polymerase chain reaction (SC RT-qPCR) experiments. An in vivo mouse model of TLE consisting of intrahippocampal kainate (KA) injection was used to examine epilepsy-related plasticity. Response delays of DG cells were DTX-sensitive and strongly increased in KA-injected hippocampi; Kv1.1 mRNA was elevated 10-fold, and the response delays correlated with Kv1.1 mRNA abundance on the single cell level. Other Kv1 subunits did not show overt changes in mRNA levels. Kv1.1 immunolabeling was enhanced in KA DG cells. The biophysical properties of ID and a delay heterogeneity within the DG cell population was characterized. Using organotypic hippocampal slice cultures (OHCs), where KA incubation also induced ID upregulation, the homeostatic reversibility and neuroprotective potential for DG cells were tested. In summary, the AP timing of DG cells is effectively controlled via scaling of Kv1.1 subunit transcription. With this antiepileptic mechanism, DG cells delay their responses during hyperexcitation.

RNA-sequencing from single nuclei

It has recently been established that synthesis of double-stranded cDNA can be done from a single cell for use in DNA sequencing. Global gene expression can be quantified from the number of reads mapping to each gene, and mutations and mRNA splicing variants determined from the sequence reads. Here we demonstrate that this method of transcriptomic analysis can be done using the extremely low levels of mRNA in a single nucleus, isolated from a mouse neural progenitor cell line and from dissected hippocampal tissue. This method is characterized by excellent coverage and technical reproducibility. On average, more than 16,000 of the 24,057 mouse protein-coding genes were detected from single nuclei, and the amount of gene-expression variation was similar when measured between single nuclei and single cells. Several major advantages of the method exist: first, nuclei, compared with whole cells, have the advantage of being easily isolated from complex tissues and organs, such as those in the CNS. Second, the method can be widely applied to eukaryotic species, including those of different kingdoms. The method also provides insight into regulatory mechanisms specific to the nucleus. Finally, the method enables dissection of regulatory events at the single-cell level; pooling of 10 nuclei or 10 cells obscures some of the variability measured in transcript levels, implying that single nuclei and cells will be extremely useful in revealing the physiological state and interconnectedness of gene regulation in a manner that avoids the masking inherent to conventional transcriptomics using bulk cells or tissues.

Direct cell lysis for single-cell gene expression profiling

The interest to analyze single and few cell samples is rapidly increasing. Numerous extraction protocols to purify nucleic acids are available, but most of them compromise severely on yield to remove contaminants and are therefore not suitable for the analysis of samples containing small numbers of transcripts only. Here, we evaluate 17 direct cell lysis protocols for transcript yield and compatibility with downstream reverse transcription quantitative real-time PCR. Four endogenously expressed genes are assayed together with RNA and DNA spikes in the samples. We found bovine serum albumin (BSA) to be the best lysis agent, resulting in efficient cell lysis, high RNA stability, and enhanced reverse transcription efficiency. Furthermore, we found direct cell lysis with BSA superior to standard column based extraction methods, when analyzing from 1 up to 512 mammalian cells. In conclusion, direct cell lysis protocols based on BSA can be applied with most cell collection methods and are compatible with most analytical workflows to analyze single-cells as well as samples composed of small numbers of cells.

Single-cell PCR analysis of murine embryonic stem cells cultured on different substrates highlights heterogeneous expression of stem cell markers

BACKGROUND INFORMATION

In the last few years, recent evidence has revealed that inside an apparently homogeneous cell population there indeed appears to be heterogeneity. This is particularly true for embryonic stem (ES) cells where markers of pluripotency are dynamically expressed within the single cells. In this work, we have designed and tested a new set of primers for multiplex PCR detection of pluripotency markers expression, and have applied it to perform a single-cell analysis in murine ES cells cultured on three different substrates that could play an important role in controlling cell behaviour and fate: (i) mouse embryonic fibroblast (MEF) feeder layer, as the standard method for ES cells culture; (ii) Matrigel coating; (iii) micropatterned hydrogel.

RESULTS

Compared with population analysis, using a single-cell approach, we were able to evaluate not only the number of cells that maintained the expression of a specific gene but, most importantly, how many cells co-expressed different markers. We found that micropatterned hydrogel seems to represent a good alternative to MEF, as the expression of stemness markers is better preserved than in Matrigel culture.

CONCLUSIONS

This single-cell assay allows for the assessment of the stemness maintenance at a single-cell level in terms of gene expression profile, and can be applied in stem cell research to characterise freshly isolated and cultured cells, or to standardise, for instance, the method of culture closely linked to the transcriptional activity and the differentiation potential.