Microarrays and the gene expression profile of a single cell

A Zwitterionic Detergent and Catalyst-Based Single-Cell Proteomics Using a Loss-Free Microhole-Collection Disc

Recent advances in single-cell proteomics have solved many bottlenecks, such as throughput, sample recovery, and scalability via nanoscale sample handling. In this study, we aimed for a sensitive mass spectrometry (MS) analysis capable of handling single cells with a conventional mass spectrometry workflow without additional equipment. We achieved seamless cell lysis and TMT labeling in a micro-HOLe Disc (microHOLD) by developing a mass-compatible single solution based on a zwitterionic detergent and a catalyst for single-cell lysis and tandem mass tag labeling without a heat incubation step. This method was developed to avoid peptide loss by surface adsorption and buffer or tube changes by collecting tandem mass tag-labeled peptide through microholes placed in the liquid chromatography injection vials in a single solution. We successfully applied the microHOLD single-cell proteomics method for the analysis of proteome reprogramming in hormone-sensitive prostate cells to develop castration-resistant prostate cancer cells. This novel single-cell proteomics method is not limited by cutting-edge nanovolume handling equipment and achieves high throughput and ultrasensitive proteomics analysis of limited samples, such as single cells.

Single-Cell (Multi)omics Technologies

Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

Heterogeneity of Metazoan Cells and Beyond: To Integrative Analysis of Cellular Populations at Single-Cell Level

In this paper, we review some of the recent advances in cellular heterogeneity and single-cell analysis methods. In modern research of cellular heterogeneity, there are four major approaches: analysis of pooled samples, single-cell analysis, high-throughput single-cell analysis, and lately integrated analysis of cellular population at a single-cell level. Recently developed high-throughput single-cell genetic analysis methods such as RNA-Seq require purification step and destruction of an analyzed cell often are providing a snapshot of the investigated cell without spatiotemporal context. Correlative analysis of multiparameter morphological, functional, and molecular information is important for differentiation of more uniform groups in the spectrum of different cell types. Simplified distributions (histograms and 2D plots) can underrepresent biologically significant subpopulations. Future directions may include the development of nondestructive methods for dissecting molecular events in intact cells, simultaneous correlative cellular analysis of phenotypic and molecular features by hybrid technologies such as imaging flow cytometry, and further progress in supervised and non-supervised statistical analysis algorithms.

RNA Sequencing and Analysis

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.

Evaluation and validation of a robust single cell RNA-amplification protocol through transcriptional profiling of enriched lung cancer initiating cells

BACKGROUND

Although profiling of RNA in single cells has broadened our understanding of development, cancer biology and mechanisms of disease dissemination, it requires the development of reliable and flexible methods. Here we demonstrate that the EpiStem RNA-Amp™ methodology reproducibly generates microgram amounts of cDNA suitable for RNA-Seq, RT-qPCR arrays and Microarray analysis.

RESULTS

Initial experiments compared amplified cDNA generated by three commercial RNA-Amplification protocols (Miltenyi μMACS™ SuperAmp™, NuGEN Ovation® One-Direct System and EpiStem RNA-Amp™) applied to single cell equivalent levels of RNA (25-50 pg) using Affymetrix arrays. The EpiStem RNA-Amp™ kit exhibited the highest sensitivity and was therefore chosen for further testing. A comparison of Affymetrix array data from RNA-Amp™ cDNA generated from single MCF7 and MCF10A cells to reference controls of unamplified cDNA revealed a high degree of concordance. To assess the flexibility of the amplification system single cell RNA-Amp™ cDNA was also analysed using RNA-Seq and high-density qPCR, and showed strong cross-platform correlations. To exemplify the approach we used the system to analyse RNA profiles of small populations of rare cancer initiating cells (CICs) derived from a NSCLC patient-derived xenograft. RNA-Seq analysis was able to identify transcriptional differences in distinct subsets of CIC, with one group potentially enriched for metastasis formation. Pathway analysis revealed that the distinct transcriptional signatures demonstrated in the CIC subpopulations were significantly correlated with published stem-cell and epithelial-mesenchymal transition signatures.

CONCLUSIONS

The combined results confirm the sensitivity and flexibility of the RNA-Amp™ method and demonstrate the suitability of the approach for identifying clinically relevant signatures in rare, biologically important cell populations.

Temporally and spatially restricted gene expression profiling

Identifying gene function in specific cells is critical for understanding the processes that make cells unique. Several different methods are available to isolate actively transcribed RNA or actively translated RNA in specific cells at a chosen time point. Cell-specific mRNA isolation can be accomplished by the expression of transgenes in cells of interest, either directly from a specific promoter or using a modular system such as Gal4/UAS or Cre/lox. All of the methods described in this review, namely thiol-labeling of RNA (TU-tagging or RABT), TRAP (translating ribosome affinity purification) and INTACT (isolation of nuclei tagged in specific cell types), allow next generation sequencing, permitting the identification of enriched gene transcripts within the specific cell-type. We describe here the general concept of each method, include examples, evaluate possible problems related to each technique, and suggest the types of questions for which each method is best suited.

Single cell genomics: advances and future perspectives

Advances in whole-genome and whole-transcriptome amplification have permitted the sequencing of the minute amounts of DNA and RNA present in a single cell, offering a window into the extent and nature of genomic and transcriptomic heterogeneity which occurs in both normal development and disease. Single-cell approaches stand poised to revolutionise our capacity to understand the scale of genomic, epigenomic, and transcriptomic diversity that occurs during the lifetime of an individual organism. Here, we review the major technological and biological breakthroughs achieved, describe the remaining challenges to overcome, and provide a glimpse into the promise of recent and future developments.

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.

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.

The challenge of gene expression profiling in heterogeneous clinical samples

Almost all samples used in tumor biology, such as tissues and bodily fluids, are heterogeneous, i.e., consist of different cell types. Evaluating the degree of heterogeneity in samples can increase our knowledge on processes such as clonal selection and metastasis. In addition, generating expression profiles from specific sub populations of cells can reveal their distinct functions. Tissue heterogeneity also poses a challenge, as it can confound the interpretation of gene expression data. This chapter will (1) give a brief overview on how heterogeneity may influence gene expression profiling data and (2) describe the methods that are currently available to assess transcriptional biomarkers in a heterogeneous cell population.

Development and applications of single-cell transcriptome analysis

Dissecting the relationship between genotype and phenotype is one of the central goals in developmental biology and medicine. Transcriptome analysis is a powerful strategy to connect genotype to phenotype of a cell. Here we review the history, progress, potential applications and future developments of single-cell transcriptome analysis. In combination with live cell imaging and lineage tracing, it will be possible to decipher the full gene expression network underlying physiological functions of individual cells in embryos and adults, and to study diseases.

"One cell-one well": a new approach to inkjet printing single cell microarrays

A new approach to prepare arrays of sessile droplets of living single cell cultures using a liquid hydrophobic barrier prevents the samples from dehydrating, and allows for spatially addressable arrays for statistical quantitative single cell studies. By carefully advancing a thin layer of mineral oil on the substrate over the droplets during the printing, dehydration of the droplets can be prevented, and the vitality of the cells can be maintained. The net result of this confluence of submerged cell culturing and inkjet printing is facile access to spatially addressable arrays of isolated single cells on surfaces. Such single cell arrays may be particularly useful as high-throughput tools in the rapidly emerging "omics" fields of cell biology.

RNA-Seq analysis to capture the transcriptome landscape of a single cell

We describe here a protocol for digital transcriptome analysis in a single mouse oocyte and blastomere using a deep-sequencing approach. In this method, individual cells are isolated and transferred into lysate buffer by mouth pipette, followed by reverse transcription carried out directly on the whole cell lysate. Free primers are removed by exonuclease I and a poly(A) tail is added to the 3' end of the first-strand cDNAs by terminal deoxynucleotidyl transferase. Single-cell cDNAs are then amplified by 20 + 9 cycles of PCR. The resulting 100-200 ng of amplified cDNAs are used to construct a sequencing library, which can be used for deep sequencing using the SOLiD system. Compared with cDNA microarray techniques, our assay can capture up to 75% more genes expressed in early embryos. This protocol can generate deep-sequencing libraries for 16 single-cell samples within 6 d.

Imaging approach for monitoring cellular metabolites and ions using genetically encoded biosensors

The spatiotemporal patterns of ion and metabolite levels in living cells are important in understanding signal transduction and metabolite flux. Imaging approaches using genetically encoded sensors are ideal for detecting such molecule dynamics, which are hard to capture otherwise. Recent years have seen iterative improvements and evaluations of sensors, which in turn are starting to make applications in more challenging experimental settings possible. In this review, we will introduce recent progress made in the variety and properties of biosensors, and how biosensors are used for the measurement of metabolite and ion in live cells. The emerging field of applications, such as parallel imaging of two separate molecules, high-resolution transport studies and high-throughput screening using biosensors, will be discussed.

Elevated alpha-synuclein mRNA levels in individual UV-laser-microdissected dopaminergic substantia nigra neurons in idiopathic Parkinson's disease

The presynaptic protein α-synuclein is involved in several neurodegenerative diseases, including Parkinson's disease (PD). In rare familial forms of PD, causal mutations (PARK1) as well as multiplications (PARK4) of the α-synuclein gene have been identified. In sporadic, idiopathic PD, abnormal accumulation and deposition of α-synuclein might also cause degeneration of dopaminergic midbrain neurons, the clinically most relevant neuronal population in PD. Thus, cell-specific quantification of α-synuclein expression-levels in dopaminergic neurons from idiopathic PD patients in comparison to controls would provide essential information about contributions of α-synuclein to the etiology of PD. However, a number of previous studies addressing this question at the tissue-level yielded varying results regarding α-synuclein expression. To increase specificity, we developed a cell-specific approach for mRNA quantification that also took into account the important issue of variable RNA integrities of the individual human postmortem brain samples. We demonstrate that PCR –amplicon size can confound quantitative gene-expression analysis, in particular of partly degraded RNA. By combining optimized UV-laser microdissection- and quantitative RT–PCR-techniques with suitable PCR assays, we detected significantly elevated α-synuclein mRNA levels in individual, surviving neuromelanin- and tyrosine hydroxylase-positive substantia nigra dopaminergic neurons from idiopathic PD brains compared to controls. These results strengthen the pathophysiologic role of transcriptional dysregulation of the α-synuclein gene in sporadic PD.

Integrating whole transcriptome assays on a lab-on-a-chip for single cell gene profiling

To correlate gene expression profiles to fundamental biological processes such as cell growth, differentiation and migration, it is essential to work at the single cell level. Gene expression analysis always starts with the relatively low efficient reverse transcription (RT) of RNA into complementary DNA (cDNA), an essential step as unprocessed RNAs will not be analysed further. In this paper, we present a novel method for RT that uses microfluidics to manipulate nanolitre volumes. We compare our method to conventional protocols performed in microlitre volumes. More specifically, reverse transcription was performed either in a polydimethylsiloxane (PDMS) rotary mixer or in a tube, using a single cell amount of mouse brain RNA (10 pg), and was followed by a template-switching PCR (TS-PCR) amplification step. We demonstrate that, using the microfluidic protocol, 74% of the genes expressed in mouse brain were detected, while only 4% were found with the conventional approach. We next profiled single neuronal progenitors. Using our microfluidic approach, i.e. performing cell capture, lysis and reverse transcription on-chip followed by TS-PCR amplification in tube, a mean of 5000 genes were detected in each neuron, which corresponds to the expected number of genes expressed in a single cell. This demonstrates the outstanding sensitivity of the microfluidic method.

Gene expression analysis at the single cell level using the human bone marrow stromal cell as a model: sample preparation methods

Recent advances in molecular technology, including gene expression microarray analysis, have allowed researchers to examine global patterns of gene expression at high resolution in populations of cultured cells or tissues. Although these techniques can be applied with great sophistication and are useful for address ing many biological questions in cell populations, it is also of great value to assess gene expression at the level of the single cell. This can be achieved by one of two different approaches: (1) specific cell types can be purified from heterogeneous tissues or cultures using immunological methods such as fluorescence-based or magnetic cell sorting or laser capture microdissection, followed by amplification of target cell nucleic acids, and analysis of expressed genes; or (2) immunohisto-chemical studies and in situ expression studies on identical tissue sections can be used to identify genes or sets of genes whose expression correlates with a morpho logically or immunochemically distinct cell-type. Using either approach, the target cell types are identified by their morphological or immunohistochemical properties. This chapter is a primer on using single cell gene expression technology to study human bone marrow stromal cells that express mixed lineage markers. Cytomorphological, cytochemical, and immunocytochemical methods as well as gene expression microarray studies demonstrated that single stromal cells simulta neously express markers associated with osteoblast, fibroblast, muscle, and adi-pocyte differentiation, suggesting that these stromal cells are mesenchymal progenitor cells that have multilineage differentiation capacity. These data charac terize human bone marrow stromal cells as adult stem cells. Because of their pluripotent nature, single cell gene expression technology is particularly critical for characterizing and developing the therapeutic potential of these cells.

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

Background

To discover prostate cancer biomarkers, we profiled gene expression in benign and malignant cells laser capture microdissected (LCM) from prostate tissues and metastatic prostatic adenocarcinomas. Here we present methods developed, optimized, and validated to obtain high quality gene expression data.

Results

RNase inhibitor was included in solutions used to stain frozen tissue sections for LCM, which improved RNA quality significantly. Quantitative PCR assays, requiring minimal amounts of LCM RNA, were developed to determine RNA quality and concentration. SuperScript II™ reverse transcriptase was replaced with SuperScript III™, and SpeedVac concentration was eliminated to optimize linear amplification. The GeneChip^® IVT labeling kit was used rather than the Enzo BioArray™ HighYield™ RNA transcript labeling kit since side-by-side comparisons indicated high-end signal saturation with the latter. We obtained 72 μg of labeled complementary RNA on average after linear amplification of about 2 ng of total RNA.

Conclusion

Unsupervised clustering placed 5/5 normal and 2/2 benign prostatic hyperplasia cases in one group, 5/7 Gleason pattern 3 cases in another group, and the remaining 2/7 pattern 3 cases in a third group with 8/8 Gleason pattern 5 cases and 3/3 metastatic prostatic adenocarcinomas. Differential expression of alpha-methylacyl coenzyme A racemase (AMACR) and hepsin was confirmed using quantitative PCR.

Microarray Technology for Mutation Analysis of Low-Template DNA Samples

Microarrays containing oligonucleotide mutation probes are emerging as useful platforms for the diagnosis of genetic disease. Herein, we describe the development and validation of an in-house microarray suitable for the diagnosis of common cystic fibrosis (CF) mutations in low-template DNA samples such as those taken for preimplantation genetic diagnosis and prenatal diagnosis. The success of the CF microarray was based on the ability to generate sufficient target DNA for hybridization to the array probes using either direct polymerase chain reaction (PCR) amplification or whole-genome amplification followed by PCR. From replicate experiments using target DNA carrying known CF mutations, it was possible to define strict diagnostic parameters for the accurate diagnosis of CF. This protocol serves as a general guide for DNA-testing laboratories to develop other microarray platforms that may eventually replace traditional PCR-based genetic testing in the near future.

The use of needle biopsy for assessment of placental gene expression

OBJECTIVE

The purpose of this study was to test the hypothesis that placental samples that are obtained by needle aspiration ex vivo are useful for the determination of villus gene expression.

STUDY DESIGN

Placental biopsy was performed with a spinal needle after uncomplicated deliveries. Villi were inspected microscopically, and RNA was extracted and analyzed with capillary electrophoresis. Gene expression was determined with quantitative polymerase chain reaction.

RESULTS

We obtained more placental villous fragments per aspiration using a 20-gauge needle (5.2 +/- 1.8 fragments) than with a 22-gauge needle (3.3 +/- 1.6 fragments; P < .01). RNA quality was adequate, based on the 28S and 18S recombinant RNA bands, with a mean 260/280 ratio of 1.88. The amount of extracted RNA correlated with the number of villous fragments per aspirate. Importantly, the expression of NDRG1 and hPL, both markedly altered in hypoxia, was consistent between villi that were obtained by either needle or standard biopsy.

CONCLUSION

Placental samples that are obtained by ex vivo needle aspiration are useful for the extraction of RNA and for the determination of villous gene expression.

Functional genomic methodologies

The ability to form tenable hypotheses regarding the neurobiological basis of normative functions as well as mechanisms underlying neurodegenerative and neuropsychiatric disorders is often limited by the highly complex brain circuitry and the cellular and molecular mosaics therein. The brain is an intricate structure with heterogeneous neuronal and nonneuronal cell populations dispersed throughout the central nervous system. Varied and diverse brain functions are mediated through gene expression, and ultimately protein expression, within these cell types and interconnected circuits. Large-scale high-throughput analysis of gene expression in brain regions and individual cell populations using modern functional genomics technologies has enabled the simultaneous quantitative assessment of dozens to hundreds to thousands of genes. Technical and experimental advances in the accession of tissues, RNA amplification technologies, and the refinement of downstream genetic methodologies including microarray analysis and real-time quantitative PCR have generated a wellspring of informative studies pertinent to understanding brain structure and function. In this review, we outline the advantages as well as some of the potential challenges of applying high throughput functional genomics technologies toward a better understanding of brain tissues and diseases using animal models as well as human postmortem tissues.

The expression profile of purified Drosophila germline stem cells

We developed a method to highly purify germline stem cells (GSCs) from the Drosophila ovary, one of the best understood types of adult stem cell. GSCs express variant isoforms of general transcriptional components, translation initiation factors, and several variant ribosomal proteins, including RpL22, a protein enriched in several mammalian stem cells. These novel isoforms may help regulate stem cell gene expression because a reversion assay indicated that at least four were specific for GSCs. By comparative analysis, we identify additional genes enriched in GSCs, including Psc, the Drosophila homolog of the Bmi-1 Polycomb group gene, as well as genes that may delay cytokinesis in pre-meiotic germ cells. By comparing GSCs arrested by BMP over-expression and bam mutation, we hypothesize that mRNA utilization is modulated in differentiating GSC daughters. Our findings suggest that Drosophila and mammalian stem cells utilize at least two regulatory mechanisms in common.

Embryonic reversions and lineage infidelities in tumour cells: genome-based models and role of genetic instability

Reversions to "embryonic precursor"-type cells and infidelities of tumour cell lineage (including metaplasias) have been recognized as aspects of various tumour types since the 19th century. Since then, evidence of these phenomena has been obtained from numerous clinical, biochemical, immunological and molecular biological studies. In particular, microarray studies have suggested that "aberrant" expressions of relevant genes are common. An unexplained aspect of the results of these studies is that, in many tumour types, the embryonic reversion or lineage infidelity only occurs in a proportion of cases. As a parallel development during the molecular biological investigation of tumours over the last several decades, genetic instability has been found much more marked, at least in some preparations of tumour cells, than that identified by means of previous karyotypic investigations of tumours. This study reviews examples of embryonic reversion and lineage infidelity phenomena, which have derived from the various lines of investigation of cancer over the last 150 or so years. Four categories of circumstances of the occurrence of embryonic reversions or lineage infidelities have been identified - (i) as part of the defining phenotype of the tumour, and hence being presumably integral to the tumour type, (ii) present ab initio in only some cases of the tumour type, and presumably being regularly associated with, but incidental to, the essential features of the tumour type, (iii) occurring later in the course of the disease and thus being possibly a manifestation of in vivo genetic instability and "tumour progression" and (iv) arising probably by genetic instability, during the processes, especially cell culture, associated with ex vivo investigations. Genomic models are described which might account for the origin of these phenomena in each of these circumstances.