Molecular heterogeneity at the single-cell level is now a well-established concept. Recent advances in molecular tools including RT-PCR, microarrays and next-generation sequencing have enabled a more refined analysis of diversity among broad cellular populations. Numerous examples of cellular heterogeneity have been reported, including stem cell variation and intratumor heterogeneity, immune cell regulatory states and genetic consequences of infectious disease Citation[1].
It is becoming apparent therefore that the ability to accurately determine the specific molecular states of individual cells is critical to the understanding of biological phenomena and disease pathogenesis.
Single-cell gene expression analysis
Microarrays and quantitative RT-PCR are powerful tools for gene expression analysis that have facilitated our understanding of the intricate biology of normal and disease-state cells and tissues Citation[2,3]. Recent development of digital PCR technology further enabled very sensitive detection of gene transcript at the single-molecule detection level Citation[4]. Moreover, the next-generation sequencing technologies, in particular transcriptome profiling by RNA-Seq, deliver comprehensive gene expression analysis with a large dynamic range Citation[5]. However, most gene expression studies have used bulk measurements from heterogeneous mixtures of cells and tissues, in which information from rare or specific cell types can be obscured. By analyzing gene expression in individual cells, a more complete picture of the gene expression dynamics within heterogeneous samples can be captured.
A growing number of reports have recently demonstrated single-cell gene expression profiles Citation[5,6] using advanced molecular technologies, including RNA-seq for comprehensive transcriptome information or highly parallel targeted gene expression analysis from a selected limited number of individual cells. However, the outcome of expression profiles can vary depending on the methodology applied to measure the mRNA in individual cells, where the majority of mRNAs are present at low copy numbers. To understand the cellular heterogeneity of mRNA expression in single cells, it will be critical to measure accurately and quantitatively the content of specific mRNAs, without measurement artifacts.
One important technology that has been applied to quantitate RNA transcripts in single cells is FISH. This technique has been extensively applied as a gene-mapping tool for identification and validation of cytogenetic aberrations in single cells in normal and diseased states. FISH probes for the detection of RNA were first developed in the late 1970s. Although numerous improvements have been made to this method, cellular analysis has largely been restricted to the examination of cells or tissues fixed on slides, with limited ability to identify multiple RNA transcripts due to limitation by the number of parameters measured by imaging cytometry platforms.
Flow cytometry as a tool for specific & sensitive gene expression analysis in individual cells
Flow cytometry is a platform technology for measuring multiparametric events simultaneously in each individual cell. This technology has contributed significantly to our knowledge in understanding roles of multiple cell types, including immune cells in cell suspensions. Among single-cell isolation technologies, flow cytometry has been applied in numerous studies for comprehensive molecular profiling of single cells sorted based on specific phenotypes. Comprehensive genome or transcriptome analysis of each single cell or nucleus or specific subsets of cells sorted via FACS has been demonstrated Citation[7]. While this technology is a powerful tool for analyzing complex biological events quantitatively and systematically in any cell suspension, its application has been limited primarily to the identification of cell types and functions based on protein expression using specific antibodies, or to cell cycle or ploidy analysis by total DNA staining. Over the last few decades, flow cytometric detection of intracellular RNAs has been attempted by applying various molecular technologies Citation[8,9]. However, the specificity and sensitivity of all previous attempts have not been suitable for the wide range of intracellular RNA analyses, particularly in the case of low-abundance target RNA sequences. The major limitation of RNA transcript detection by flow cytometry has been its poor sensitivity. Because the flow cytometer interrogates cellular fluorescence as an integrated event, even low levels of background fluorescence significantly compromise the ability to resolve localized fluorescent targets such as RNA transcripts. Consequently, most examples of RNA flow cytometry are applications involving high copy numbers of RNA such as some examples of viral expression, including Sindbis virus and Epstein–Barr virus. However, many RNA transcripts are present in low frequencies (fewer than 50 copies per cell) Citation[10] and until now have not been detectable by flow cytometry. The opportunity to detect multiple species of low-abundance RNA transcripts at very high sensitivity as described by Hanley et al. using a modified form of branched DNA technology now paves the way to explore a number of new applications by flow cytometry, which until now has not been possible Citation[11]. Using quantitative and high-throughput single-cell analysis capability by flow cytometry, the demonstrated technology presents a powerful tool for specific and sensitive single-cell detection of multiple RNA transcripts, even when present at a few copies per cell, from a heterogeneous cell population.
RNA flow cytometry: potential applications & clinical significance
Molecular biomarkers and gene expression signatures have been increasingly utilized for research and clinical applications. With the ability to analyze specific RNA biomarkers in individual cells, RNA flow cytometry provides a unique tool to expand our understanding of the individuality of single-cell molecular phenotypes. It maybe a particularly valuable tool to understand the complex network of pathways in single cells among very heterogeneous cell populations, including the areas of stem-cell biology, developmental biology, immunology and immune cell-related diseases and oncology for various types of cancers. Furthermore, the ability to detect low copy number RNAs by flow cytometry will be extremely valuable for rare cell analysis such as identifying minor subclones associated with resistance to therapy, or detecting rare stem cell populations, or identifying and characterizing rare circulating tumor cells (CTCs) in blood or bone marrow. One interesting technical advantage in targeting RNA biomarkers for clinical applications is its ability to combine multiple biomarkers representing similar phenotypes into the same channel, as recently reported by Yu et al. for the role of epithelial to mesenchymal transition phenotype in CTCs in disease progression Citation[12]. The authors applied assays with combined multiple RNA markers representing either epithelial or mesenchymal phenotypes and demonstrated the association of mesenchymal CTCs to disease progression. While the assay was conducted on cells fixed on slides after pre-enrichment of CTCs, it is also likely that these rare cell phenotypes can be measured by flow cytometry as one streamlined assay. Beyond cancer, RNA analysis may be applicable for characterizing a variety of diseases at the single-cell level where the function of specific genes is known to contribute to altered biological pathways. For example, circulating HIV-infected cells may be characterized to understand the biology of HIV latency and to measure the treatment efficacy as future HIV therapy attempts to control the reservoir of HIV. RNA flow cytometry can serve as a complementary tool for high-throughput single-cell gene expression analysis for a more comprehensive understanding of the implications of cellular heterogeneity in biology and disease. Furthermore, this analysis platform may serve to validate gene expression signatures derived from the high-throughput transcriptome data in individual cells for specific molecular heterogeneity. RNA flow cytometry offers a potentially inexpensive and streamlined assay that can be applicable for many routine clinical samples.
Moving forward: RNA & multiomics flow cytometry
The intracellular RNA analysis capability presented by RNA flow cytometry is sufficiently sensitive to detect specific mRNA biomarkers in cells. However, the technical capability demonstrated to date is still in its infancy in addressing complex genomic networks in single cells. Moving forward, a number of further technical capabilities can be developed for single-cell multiomics flow cytometry, which presents a powerful next-generation tool for comprehensive molecular profiling of disease in individual patients. These include the development of the capability to characterize single cells based on simultaneous protein and RNA expression detection, various types of mutation analysis, detection and quantitation of additional RNA species including small noncoding RNAs.
While flow cytometry can serve as a comprehensive tool for multiparametric cell analysis, at this time, the number of parameters still is limited to fewer than 15 biomarkers. A newly developed mass cytometer may expand the number of parameters to more than 50, but the applicability of this technology should be further validated Citation[13]. Further technical improvement to expand the number of analytical parameters with current fluorescence-based flow cytometry, with unique nonoverlapping fluorescence signal acquisition capability, will enhance flow cytometry as a platform for systematic analysis of molecular biomarkers in single cell.
In conclusion, the authors believe RNA flow cytometry has great potential to be a significant tool to study the molecular heterogeneity of cells and tissues. While specific applications for clinical utility need to be further developed, the ability to detect low-abundance RNA in single cell offers a valuable potential to bring the concept of molecular heterogeneity to clinical utility, ultimately managing patients based on specific molecular phenotypes.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
References
- Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12(5), 323–334 (2012).
- VanGuilder HD, Vrana KE, Freeman WM. Twenty-five years of quantitative PCR for gene expression analysis. BioTechniques 44(5), 619–626 (2008).
- Freeman WM, Robertson DJ, Vrana KE. Fundamentals of DNA hybridization arrays for gene expression analysis. BioTechniques 29(5), 1042–1046, 1048 (2000).
- Chiu RW, Cantor CR, Lo YM. Non-invasive prenatal diagnosis by single molecule counting technologies. Trends Genet. 25(7), 324–331 (2009).
- Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10(1), 57–63 (2009).
- Livak KJ, Wills QF, Tipping AJ et al. Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells. Methods 59(1), 71–79 (2013).
- Dalerba P, Kalisky T, Sahoo D et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29(12), 1120–1127 (2011).
- Crouch J, Leitenberg D, Smith BR, Howe JG. Epstein-Barr virus suspension cell assay using in situ hybridization and flow cytometry. Cytometry 29(1), 50–57 (1997).
- Robertson KL, Verhoeven AB, Thach DC, Chang EL. Monitoring viral RNA in infected cells with LNA flow-FISH. RNA 16(8), 1679–1685 (2010).
- Zhang L, Zhou W, Velculescu VE et al. Gene expression profiles in normal and cancer cells. Science 276(5316), 1268–1272 (1997).
- Hanley MB, Lomas W, Mittar D, Maino V, Park E. Detection of low abundance RNA molecules in individual cells by flow cytometry. PLoS ONE 8(2), e57002 (2013).
- Yu M, Bardia A, Wittner BS et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339(6119), 580–584 (2013).
- Bendall SC, Nolan GP. From single cells to deep phenotypes in cancer. Nat. Biotechnol. 30(7), 639–647 (2012).