Quantification of mRNA in single cells and modelling of RT-qPCR induced noise

Muscle-specific PGC-1α modulates mitochondrial oxidative stress in aged sarcopenia through regulating Nrf2

BACKGROUND

Aged sarcopenia is characterized by loss of skeletal muscle mass and strength, and mitochondrial dysregulation in skeletal myocyte is considered as a major factor. Here, we aimed to analyze the effects of peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) on mitochondrial reactive oxygen species (ROS) and nuclear factor erythroid 2-related factor 2 (Nrf2) in aged skeletal muscles.

METHODS

C2C12 cells were stimulated by 50 μM 7β-hydroxycholesterol (7β-OHC) to observe the changes of cellular ROS, mitochondrial ROS, and expression of PGC-1α and Nrf2. Different PGC-1α expression in cells was established by transfection with small interfering RNA (siRNA) or plasmids overexpressing PGC-1α (pEX-3-PGC-1α). The effects of different PGC-1α expression on cellular ROS, mitochondrial ROS and Nrf2 expression were measured in cells. Wild type (WT) mice and PGC-1α conditional knockout (CKO) mice were used to analyze the effects of PGC-1α on aged sarcopenia and expression of Nrf2 and CD38 in gastrocnemius muscles. Diethylmaleate, a Nrf2 activator, was used to analyze the connection between PGC-1α and Nrf2 in cells and in mice.

RESULTS

In C2C12 cells, the expressions of PGC-1α and Nrf2 were declined by the 7β-OHC treatment or PGC-1α silence. Moreover, PGC-1α silence increased the harmful ROS and decreased the Nrf2 protein expression in the 7β-OHC-treated cells. PGC-1α overexpression decreased the harmful ROS and increased the Nrf2 protein expression in the 7β-OHC-treated cells. Diethylmaleate treatment decreased the harmful ROS in the 7β-OHC-treated or PGC-1α siRNA-transfected cells. At the same age, muscle-specific PGC-1α deficiency aggravated aged sarcopenia, decreased Nrf2 expression and increased CD38 expression in gastrocnemius muscles compared with the WT mice. Diethylmaleate treatment improved the muscle function and decreased the CD38 expression in the old two genotypes.

CONCLUSIONS

Our study demonstrated that PGC-1α modulated mitochondrial oxidative stress in aged sarcopenia through regulating Nrf2.

A simple joint detection platform for high-throughput single-cell heterogeneity screening

Single cell heterogeneity plays an important role in many biological phenomena and distinguishing cells that exhibit certain mutation in sample could benefit clinical diagnose and drug screening. Typical single cell detection methods such as flow cytometry, in-situ hybridization, real-time amplification or sequencing test either protein or nucleic acid as target and usually require specialized instruments. Joint measurement of the both types of targets could be done by combining the above strategies precisely but also unwieldly. Methods for rapidly and parallelly screening single cells with target genotype and antigen is needed. In this study, we describe a gel plate platform to distinguish cell types based on their phenotypes on target gene and antigen with low equipment requirement. Integrated cell lysis and immobilization were done in the gel solidification step, after which antibody hybridization and real-time amplification were sequentially carried out without losing the original loci information of individual single cells so the three types of information of individual single cells could be combined to distinguished cells with expected genotype and phenotype. The easy-to-use gel platform has potential in point-of-care circumstances and single-cell stimulation response that have high requirements on efficiency and simplicity.

Evaluating Novel Quantification Methods for Infectious Baculoviruses

Accurate and rapid quantification of (infectious) virus titers is of paramount importance in the manufacture of viral vectors and vaccines. Reliable quantification data allow efficient process development at a laboratory scale and thorough process monitoring in later production. However, current gold standard applications, such as endpoint dilution assays, are cumbersome and do not provide true process analytical monitoring. Accordingly, flow cytometry and quantitative polymerase chain reaction have attracted increasing interest in recent years, offering various advantages for rapid quantification. Here, we compared different approaches for the assessment of infectious viruses, using a model baculovirus. Firstly, infectivity was estimated by the quantification of viral nucleic acids in infected cells, and secondly, different flow cytometric approaches were investigated regarding analysis times and calibration ranges. The flow cytometry technique included a quantification based on post-infection fluorophore expression and labeling of a viral surface protein using fluorescent antibodies. Additionally, the possibility of viral (m)RNA labeling in infected cells was investigated as a proof of concept. The results confirmed that infectivity assessment based on qPCR is not trivial and requires sophisticated method optimization, whereas staining of viral surface proteins is a fast and feasible approach for enveloped viruses. Finally, labeling of viral (m)RNA in infected cells appears to be a promising opportunity but will require further research.

RNA cytometry of single-cells using semi-permeable microcapsules

Analytical tools for gene expression profiling of individual cells are critical for studying complex biological systems. However, the techniques enabling rapid measurements of gene expression on thousands of single-cells are lacking. Here, we report a high-throughput RNA cytometry for digital profiling of single-cells isolated in liquid droplets enveloped by a thin semi-permeable membrane (microcapsules). Due to the selective permeability of the membrane, the desirable enzymes and reagents can be loaded, or replaced, in the microcapsule at any given step by simply changing the reaction buffer in which the microcapsules are dispersed. Therefore, complex molecular biology workflows can be readily adapted to conduct nucleic acid analysis on encapsulated mammalian cells, or other biological species. The microcapsules support sequential multi-step enzymatic reactions and remain intact under different biochemical conditions, freezing, thawing, and thermocycling. Combining microcapsules with conventional FACS provides a high-throughput approach for conducting RNA cytometry of individual cells based on their digital gene expression signature.

Single-Cell Mass Spectrometry Enables Insight into Heterogeneity in Infectious Disease

Cellular heterogeneity is generally overlooked in infectious diseases. In this study, we investigated host cell heterogeneity during infection with Trypanosoma cruzi (T. cruzi) parasites, causative agents of Chagas disease (CD). In chronic-stage CD, only a few host cells are infected with a large load of parasites and symptoms may appear at sites distal to parasite colonization. Furthermore, recent work has revealed T. cruzi heterogeneity with regard to replication rates and drug susceptibility. However, the role of cellular-level metabolic heterogeneity in these processes has yet to be assessed. To fill this knowledge gap, we developed a Single-probe SCMS (single-cell mass spectrometry) method compatible with biosafety protocols, to acquire metabolomics data from individual cells during T. cruzi infection. This study revealed heterogeneity in the metabolic response of the host cells to T. cruzi infection in vitro. Our results showed that parasite-infected cells possessed divergent metabolism compared to control cells. Strikingly, some uninfected cells adjacent to infected cells showed metabolic impacts as well. Specific metabolic changes include increases in glycerophospholipids with infection. These results provide novel insight into the pathogenesis of CD. Furthermore, they represent the first application of bioanalytical SCMS to the study of mammalian-infectious agents, with the potential for broad applications to study infectious diseases.

A protocol for extracting immunolabeled murine cardiomyocytes of high-quality RNA by laser capture microdissection

We developed a highly efficient, ultrashort immunohistochemistry-laser capture microdissection (IHC-LMD) protocol, which allows microdissection of up to 250 single cardiomyocytes. Before LMD, murine hearts are excised, snap-frozen, and cryosectioned. RNA isolated from LMD material is of high RNA quality, making it usable for gene expression analysis and RNA sequencing. Challenges and limitations of this protocol include visualization of the immunostaining and nuclei DAPI dye on the PEN slides, and timing and speed to limit RNA degradation as much as possible.

OUP accepted manuscript

Motivation

Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can eliminate the need for reference profiles but show unstable performance when few bulk samples are available.

Results

In this work, we develop a new method using reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g. clonally resolving archived primary tissues using scRNA-seq from metastases. By integrating such information in a Quadratic Programming framework, our method can recover more accurate cell types and corresponding cell type abundances in bulk samples. Application to a breast tumor bone metastases dataset confirms the power of scRNA-seq data to improve cell type inference and quantification in same-patient bulk samples.

Availability and implementation

Source code is available on Github at https://github.com/CMUSchwartzLab/RADs.

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.

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

Shedding light: The importance of reverse transcription efficiency standards in data interpretation

The RNA-to-cDNA conversion step in transcriptomics experiments is widely recognised as inefficient and variable, casting doubt on the ability to do quantitative transcriptomics analyses. Multiple studies have focused on ways to optimise this process, resulting in contradictory recommendations. Here we explore the problem of reverse transcription efficiency using digital PCR and the RT method’s impact on subsequent data analysis. Using synthetic RNA standards, an example experiment is presented, outlining a method to (1) determine relevant efficiency and variability values and then to (2) incorporate this information into downstream analyses as a way to improve the accuracy of quantitative transcriptomics experiments.

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations. In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types. In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

The DNA Methyltransferase 1 (DNMT1) Controls the Shape and Dynamics of Migrating POA-Derived Interneurons Fated for the Murine Cerebral Cortex

The proliferative niches in the subpallium generate a rich cellular variety fated for diverse telencephalic regions. The embryonic preoptic area (POA) represents one of these domains giving rise to the pool of cortical GABAergic interneurons and glial cells, in addition to striatal and residual POA cells. The migration from sites of origin within the subpallium to the distant targets like the cerebral cortex, accomplished by the adoption and maintenance of a particular migratory morphology, is a critical step during interneuron development. To identify factors orchestrating this process, we performed single-cell transcriptome analysis and detected Dnmt1 expression in murine migratory GABAergic POA-derived cells. Deletion of Dnmt1 in postmitotic immature cells of the POA caused defective migration and severely diminished adult cortical interneuron numbers. We found that DNA methyltransferase 1 (DNMT1) preserves the migratory shape in part through negative regulation of Pak6, which stimulates neuritogenesis at postmigratory stages. Our data underline the importance of DNMT1 for the migration of POA-derived cells including cortical interneurons.

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.

Triage of high-risk HPV-positive women in population-based screening by miRNA expression analysis in cervical scrapes; a feasibility study

Background

Primary testing for high-risk HPV (hrHPV) is increasingly implemented in cervical cancer screening programs. Many hrHPV-positive women, however, harbor clinically irrelevant infections, demanding additional disease markers to prevent over-referral and over-treatment. Most promising biomarkers reflect molecular events relevant to the disease process that can be measured objectively in small amounts of clinical material, such as miRNAs. We previously identified eight miRNAs with altered expression in cervical precancer and cancer due to either methylation-mediated silencing or chromosomal alterations. In this study, we evaluated the clinical value of these eight miRNAs on cervical scrapes to triage hrHPV-positive women in cervical screening.

Results

Expression levels of the eight candidate miRNAs in cervical tissue samples (n = 58) and hrHPV-positive cervical scrapes from a screening population (n = 187) and cancer patients (n = 38) were verified by quantitative RT-PCR. In tissue samples, all miRNAs were significantly differentially expressed (p < 0.05) between normal, high-grade precancerous lesions (CIN3), and/or cancer. Expression patterns detected in cervical tissue samples were reflected in cervical scrapes, with five miRNAs showing significantly differential expression between controls and women with CIN3 and cancer. Using logistic regression analysis, a miRNA classifier was built for optimal detection of CIN3 in hrHPV-positive cervical scrapes from the screening population and its performance was evaluated using leave-one-out cross-validation. This miRNA classifier consisted of miR-15b-5p and miR-375 and detected a major subset of CIN3 as well as all carcinomas at a specificity of 70%. The CIN3 detection rate was further improved by combining the two miRNAs with HPV16/18 genotyping. Interestingly, both miRNAs affected the viability of cervical cancer cells in vitro.

Conclusions

This study shows that miRNA expression analysis in cervical scrapes is feasible and enables the early detection of cervical cancer, thus underlining the potential of miRNA expression analysis for triage of hrHPV-positive women in cervical cancer screening.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0509-9) contains supplementary material, which is available to authorized users.

A Strategy to Find Suitable Reference Genes for miRNA Quantitative PCR Analysis and Its Application to Cervical Specimens

miRNAs represent an emerging class of promising biomarkers for cancer diagnostics. To perform reliable miRNA expression analysis using quantitative PCR, adequate data normalization is essential to remove nonbiological, technical variations. Ideal reference genes should be biologically stable and reduce technical variability of miRNA expression analysis. Herein is a new strategy for the identification and evaluation of reference genes that can be applied for miRNA-based diagnostic tests without entailing excessive additional experiments. We analyzed the expression of 11 carefully selected candidate reference genes in different types of cervical specimens [ie, tissues, scrapes, and self-collected cervicovaginal specimens (self-samples)]. To identify the biologically most stable reference genes, three commonly used algorithms (GeNorm, NormFinder, and BestKeeper) were combined. Signal-to-noise ratios and P values between control and disease groups were calculated to validate the reduction in technical variability on expression analysis of two marker miRNAs. miR-423 was identified as a suitable reference gene for all sample types, to be used in combination with RNU24 in cervical tissues, RNU43 in scrapes, and miR-30b in self-samples. These findings demonstrate that the choice of reference genes may differ between different types of specimens, even when originating from the same anatomical source. More important, it is shown that adequate normalization increases the signal-to-noise ratio, which is not observed when normalizing to commonly used reference genes.

Expanded Genomic Profiling of Circulating Tumor Cells in Metastatic Breast Cancer Patients to Assess Biomarker Status and Biology Over Time (CALGB 40502 and CALGB 40503, Alliance)

Purpose: We profiled circulating tumor cells (CTCs) to study the biology of blood-borne metastasis and to monitor biomarker status in metastatic breast cancer (MBC).Methods: CTCs were isolated from 105 patients with MBC using EPCAM-based immunomagnetic enrichment and fluorescence-activated cells sorting (IE/FACS), 28 of whom had serial CTC analysis (74 samples, 2-5 time points). CTCs were subjected to microfluidic-based multiplex QPCR array of 64 cancer-related genes (n = 151) and genome-wide copy-number analysis by array comparative genomic hybridization (aCGH; n = 49).Results: Combined transcriptional and genomic profiling showed that CTCs were 26% ESR1-ERBB2-, 48% ESR1+ERBB2-, and 27% ERBB2+ Serial testing showed that ERBB2 status was more stable over time compared with ESR1 and proliferation (MKI67) status. While cell-to-cell heterogeneity was observed at the single-cell level, with increasingly stable expression in larger pools, patient-specific CTC expression "fingerprints" were also observed. CTC copy-number profiles clustered into three groups based on the extent of genomic aberrations and the presence of large chromosomal imbalances. Comparative analysis showed discordance in ESR1/ER (27%) and ERBB2/HER2 (23%) status between CTCs and matched primary tumors. CTCs in 65% of the patients were considered to have low proliferation potential. Patients who harbored CTCs with high proliferation (MKI67) status had significantly reduced progression-free survival (P = 0.0011) and overall survival (P = 0.0095) compared with patients with low proliferative CTCs.Conclusions: We demonstrate an approach for complete isolation of EPCAM-positive CTCs and downstream comprehensive transcriptional/genomic characterization to examine the biology and assess breast cancer biomarkers in these cells over time. Clin Cancer Res; 24(6); 1486-99. ©2018 AACR.

Highly multiplexed single-cell quantitative PCR

We present a microfluidic device for rapid gene expression profiling in single cells using multiplexed quantitative polymerase chain reaction (qPCR). This device integrates all processing steps, including cell isolation and lysis, complementary DNA synthesis, pre-amplification, sample splitting, and measurement in twenty separate qPCR reactions. Each of these steps is performed in parallel on up to 200 single cells per run. Experiments performed on dilutions of purified RNA establish assay linearity over a dynamic range of at least 10⁴, a qPCR precision of 15%, and detection sensitivity down to a single cDNA molecule. We demonstrate the application of our device for rapid profiling of microRNA expression in single cells. Measurements performed on a panel of twenty miRNAs in two types of cells revealed clear cell-to-cell heterogeneity, with evidence of spontaneous differentiation manifested as distinct expression signatures. Highly multiplexed microfluidic RT-qPCR fills a gap in current capabilities for single-cell analysis, providing a rapid and cost-effective approach for profiling panels of marker genes, thereby complementing single-cell genomics methods that are best suited for global analysis and discovery. We expect this approach to enable new studies requiring fast, cost-effective, and precise measurements across hundreds of single cells.

Identification of inhibitors regulating cell proliferation and FUS-DDIT3 expression in myxoid liposarcoma using combined DNA, mRNA, and protein analyses

FUS-DDIT3 belongs to the FET (FUS, EWSR1, and TAF15) family of fusion oncogenes, which collectively are considered to be key players in tumor development. Even though over 90% of all myxoid liposarcomas (MLS) have a FUS-DDIT3 gene fusion, there is limited understanding of the signaling pathways that regulate its expression. In order to study cell proliferation and FUS-DDIT3 regulation at mRNA and protein levels, we first developed a direct cell lysis approach that allows DNA, mRNA, and protein to be analyzed in the same sample using quantitative PCR, reverse transcription quantitative qPCR and proximity ligation assay, respectively. We screened 70 well-characterized kinase inhibitors and determined their effects on cell proliferation and expression of FUS-DDIT3 and FUS at both mRNA and protein levels in the MLS 402-91 cell line, where twelve selected inhibitors were evaluated further in two additional MLS cell lines. Both FUS-DDIT3 and FUS mRNA expression correlated with cell proliferation and both transcripts were co-regulated in most conditions, indicating that the common 5' FUS promotor is important in transcriptional regulation. In contrast, FUS-DDIT3 and FUS protein levels displayed more cell line dependent expression. Furthermore, most JAK inhibitors caused FUS-DDIT3 downregulation at both mRNA and protein levels. In conclusion, defining factors that regulate FUS-DDIT3 expression opens new means to understand MLS development at the molecular level.

Upregulation of ZBTB7A exhibits a tumor suppressive role in gastric cancer cells

Gastric cancer presents as a complex solid tumor and is the third leading cause of global cancer‑associated mortality. The genetic alterations in gastric cancer remain unclear and deserve further investigation. Mining The Cancer Genome Atlas gastric adenocarcinoma dataset identified a frequent loss of the zinc finger and BTB domain containing 7A (ZBTB7A) gene locus and a significant correlation between low ZBTB7A expression and poor patient survival. ZBTB7A belongs to the POZ/BTB and Kruppel transcription factor family. In the present study, overexpression of ZBTB7A in a gastric cancer cell line induced cell cycle arrest at the S phase. Upregulation of ZBTB7A also promoted apoptosis and repressed cell migration. The results of the present study indicated that ZBTB7A functions as a tumor suppressor in gastric cancer cells. Understanding the role of ZBTB7A in gastric cancer may provide important clinical insight for treatment.

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.

Digital Assays Part II: Digital Protein and Cell Assays

A digital assay is one in which the sample is partitioned into many containers such that each partition contains a discrete number of biological entities (0, 1, 2, 3, . . .). A powerful technique in the biologist’s toolkit, digital assays bring a new level of precision in quantifying nucleic acids, measuring proteins and their enzymatic activity, and probing single-cell genotype and phenotype. Where part I of this review focused on the fundamentals of partitioning and digital PCR, part II turns its attention to digital protein and cell assays. Digital enzyme assays measure the kinetics of single proteins with enzymatic activity. Digital enzyme-linked immunoassays (ELISAs) quantify antigenic proteins with 2 to 3 log lower detection limit than conventional ELISA, making them well suited for low-abundance biomarkers. Digital cell assays probe single-cell genotype and phenotype, including gene expression, intracellular and surface proteins, metabolic activity, cytotoxicity, and transcriptomes (scRNA-seq). These methods exploit partitioning to 1) isolate single cells or proteins, 2) detect their activity via enzymatic amplification, and 3) tag them individually by coencapsulating them with molecular barcodes. When scaled, digital assays reveal stochastic differences between proteins or cells within a population, a key to understanding biological heterogeneity. This review is intended to give a broad perspective to scientists interested in adopting digital assays into their workflows.

Analytical validation of a reverse transcriptase droplet digital PCR (RT-ddPCR) for quantitative detection of infectious hematopoietic necrosis virus

Infectious hematopoietic necrosis virus (IHNV) is an important pathogen of salmonid fishes. A validated universal reverse transcriptase quantitative PCR (RT-qPCR) assay that can quantify levels of IHNV in fish tissues has been previously reported. In the present study, we adapted the published set of IHNV primers and probe for use in a reverse-transcriptase droplet digital PCR (RT-ddPCR) assay for quantification of the virus in fish tissue samples. The RT-ddPCR and RT-qPCR assays detected 13 phylogenetically diverse IHNV strains, but neither assay produced detectable amplification when RNA from other fish viruses was used. The RT-ddPCR assay had a limit of detection (LOD) equating to 2.2 plaque forming units (PFU)/μl while the LOD for the RT-qPCR was 0.2 PFU/μl. Good agreement (69.4-100%) between assays was observed when used to detect IHNV RNA in cell culture supernatant and tissues from IHNV infected rainbow trout (Oncorhynchus mykiss) and arctic char (Salvelinus alpinus). Estimates of RNA copy number produced by the two assays were significantly correlated but the RT-qPCR consistently produced higher estimates than the RT-ddPCR. The analytical properties of the N gene RT-ddPCR test indicated that this method may be useful to assess IHNV RNA copy number for research and diagnostic purposes. Future work is needed to establish the within and between laboratory diagnostic performance of the RT-ddPCR assay.

Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq

Conventional gene expression studies analyze multiple cells simultaneously or single cells, for which the exact in vivo or in situ position is unknown. Although cellular heterogeneity can be discerned when analyzing single cells, any spatially defined attributes that underpin the heterogeneous nature of the cells cannot be identified. Here, we describe how to use Geo-seq, a method that combines laser capture microdissection (LCM) and single-cell RNA-seq technology. The combination of these two methods enables the elucidation of cellular heterogeneity and spatial variance simultaneously. The Geo-seq protocol allows the profiling of transcriptome information from only a small number cells and retains their native spatial information. This protocol has wide potential applications to address biological and pathological questions of cellular properties such as prospective cell fates, biological function and the gene regulatory network. Geo-seq has been applied to investigate the spatial transcriptome of mouse early embryo, mouse brain, and pathological liver and sperm tissues. The entire protocol from tissue collection and microdissection to sequencing requires ∼5 d, Data analysis takes another 1 or 2 weeks, depending on the amount of data and the speed of the processor.

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.

The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms

In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.

Mutation analysis with random DNA identifiers (MARDI) catalogs Pig-a mutations in heterogeneous pools of CD48-deficient T cells derived from DMBA-treated rats

Identification of mutations induced by xenotoxins is a common task in the field of genetic toxicology. Mutations are often detected by clonally expanding potential mutant cells and genotyping each viable clone by Sanger sequencing. Such a "clone-by-clone" approach requires significant time and effort, and sometimes is even impossible to implement. Alternative techniques for efficient mutation identification would greatly benefit both basic and regulatory genetic toxicology research. Here, we report the development of Mutation Analysis with Random DNA Identifiers (MARDI), a novel high-fidelity Next Generation Sequencing (NGS) approach that circumvents clonal expansion and directly catalogs mutations in pools of mutant cells. MARDI uses oligonucleotides carrying Random DNA Identifiers (RDIs) to tag progenitor DNA molecules before PCR amplification, enabling clustering of descendant DNA molecules and eliminating NGS- and PCR-induced sequencing artifacts. When applied to the Pig-a cDNA analysis of heterogeneous pools of CD48-deficient T cells derived from DMBA-treated rats, MARDI detected nearly all Pig-a mutations that were previously identified by conventional clone-by-clone analysis and discovered many additional ones consistent with DMBA exposure: mostly A to T transversions, with the mutated A located on the non-transcribed DNA strand.

Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data

Background

The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions.

Results

We present a discrete, distributional method for differential gene expression (D³E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D³E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D³E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D³E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression.

Conclusion

Evaluation using synthetic data shows that D³E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D³E makes it possible to gain additional biological insights.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-0944-6) contains supplementary material, which is available to authorized users.

A Double-Hybridization Approach for the Transcription- and Amplification-Free Detection of Specific mRNA on a Microarray

A double-hybridization approach was developed for the enzyme-free detection of specific mRNA of a housekeeping gene. Targeted mRNA was immobilized by hybridization to complementary DNA capture probes spotted onto a microarray. A second hybridization step of Cy5-conjugated label DNA to another section of the mRNA enabled specific labeling of the target. Thus, enzymatic artifacts could be avoided by omitting transcription and amplification steps. This manuscript describes the development of capture probe molecules used in the transcription- and amplification-free analysis of RPLP0 mRNA in isolated total RNA. An increase in specific signal was found with increasing length of the target-specific section of capture probes. Unspecific signal comprising spot autofluorescence and unspecific label binding did not correlate with the capture length. An additional spacer between the specific part of the capture probe and the substrate attachment site increased the signal significantly only on a short capture probe of approximately 30 nt length.

Single-Cell Isolation and Gene Analysis: Pitfalls and Possibilities

During the last two decades single-cell analysis (SCA) has revealed extensive phenotypic differences within homogenous cell populations. These phenotypic differences are reflected in the stochastic nature of gene regulation, which is often masked by qualitatively and quantitatively averaging in whole tissue analyses. The ability to isolate transcripts and investigate how genes are regulated at the single cell level requires highly sensitive and refined methods. This paper reviews different strategies currently used for SCA, including harvesting, reverse transcription, and amplification of the RNA, followed by methods for transcript quantification. The review provides the historical background to SCA, discusses limitations, and current and future possibilities in this exciting field of research.

Targeting allergenic fungi in agricultural environments aids the identification of major sources and potential risks for human health

Fungi are, after pollen, the second most important producers of outdoor airborne allergens. To identify sources of airborne fungal allergens, a workflow for qPCR quantification from environmental samples was developed, thoroughly tested, and finally applied. We concentrated on determining the levels of allergenic fungi belonging to Alternaria, Cladosporium, Fusarium, and Trichoderma in plant and soil samples from agricultural fields in which cereals were grown. Our aims were to identify the major sources of allergenic fungi and factors potentially influencing their occurrence. Plant materials were the main source of the tested fungi at and after harvest. Amounts of A. alternata and C. cladosporioides varied significantly in fields under different management conditions, but absolute levels were very high in all cases. This finding suggests that high numbers of allergenic fungi may be an inevitable side effect of farming in several crops. Applied in large-scale studies, the concept described here may help to explain the high number of sensitization to airborne fungal allergens.

The technology and biology of single-cell RNA sequencing

The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.

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.

How good is a PCR efficiency estimate: Recommendations for precise and robust qPCR efficiency assessments

We have examined the imprecision in the estimation of PCR efficiency by means of standard curves based on strategic experimental design with large number of technical replicates. In particular, how robust this estimation is in terms of a commonly varying factors: the instrument used, the number of technical replicates performed and the effect of the volume transferred throughout the dilution series. We used six different qPCR instruments, we performed 1–16 qPCR replicates per concentration and we tested 2–10 μl volume of analyte transferred, respectively. We find that the estimated PCR efficiency varies significantly across different instruments. Using a Monte Carlo approach, we find the uncertainty in the PCR efficiency estimation may be as large as 42.5% (95% CI) if standard curve with only one qPCR replicate is used in 16 different plates. Based on our investigation we propose recommendations for the precise estimation of PCR efficiency: (1) one robust standard curve with at least 3–4 qPCR replicates at each concentration shall be generated, (2) the efficiency is instrument dependent, but reproducibly stable on one platform, and (3) using a larger volume when constructing serial dilution series reduces sampling error and enables calibration across a wider dynamic range.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanism behind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally.

Self-digitization microfluidic chip for absolute quantification of mRNA in single cells

Quantification of mRNA in single cells provides direct insight into how intercellular heterogeneity plays a role in disease progression and outcomes. Quantitative polymerase chain reaction (qPCR), the current gold standard for evaluating gene expression, is insufficient for providing absolute measurement of single-cell mRNA transcript abundance. Challenges include difficulties in handling small sample volumes and the high variability in measurements. Microfluidic digital PCR provides far better sensitivity for minute quantities of genetic material, but the typical format of this assay does not allow for counting of the absolute number of mRNA transcripts samples taken from single cells. Furthermore, a large fraction of the sample is often lost during sample handling in microfluidic digital PCR. Here, we report the absolute quantification of single-cell mRNA transcripts by digital, one-step reverse transcription PCR in a simple microfluidic array device called the self-digitization (SD) chip. By performing the reverse transcription step in digitized volumes, we find that the assay exhibits a linear signal across a wide range of total RNA concentrations and agrees well with standard curve qPCR. The SD chip is found to digitize a high percentage (86.7%) of the sample for single-cell experiments. Moreover, quantification of transferrin receptor mRNA in single cells agrees well with single-molecule fluorescence in situ hybridization experiments. The SD platform for absolute quantification of single-cell mRNA can be optimized for other genes and may be useful as an independent control method for the validation of mRNA quantification techniques.

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.

Analysis of biological and technical variability in gene expression assays from formalin-fixed paraffin-embedded classical Hodgkin lymphomas

Formalin-fixed paraffin-embedded (FFPE) tissues are invaluable sources of biological material for research and diagnostic purposes. In this study, we aimed to identify biological and technical variability in RT-qPCR TaqMan® assays performed with FFPE-RNA from lymph nodes of classical Hodgkin lymphoma samples. An ANOVA-nested 6-level design was employed to evaluate BCL2, CASP3, IRF4, LYZ and STAT1 gene expression. The most variable genes were CASP3 (low expression) and LYZ (high expression). Total variability decreased after normalization for all genes, except by LYZ. Genes with moderate and low expression were identified and suffered more the effects of the technical manipulation than high-expression genes. Pre-amplification was shown to introduce significant technical variability, which was partially alleviated by lowering to a half the amount of input RNA. Ct and Cy0 quantification methods, based on cycle-threshold and the kinetic of amplification curves, respectively, were compared. Cy0 method resulted in higher quantification values, leading to the decrease of total variability in CASP3 and LYZ genes. The mean individual noise was 0.45 (0.31 to 0.61 SD), indicating a variation of gene expression over ~1.5 folds from one case to another. We showed that total variability in RT-qPCR from FFPE-RNA is not higher than that reported for fresh complex tissues, and identified gene-, and expression level-sources of biological and technical variability, which can allow better strategies for designing RT-qPCR assays from highly degraded and inhibited samples.

Validation of high-throughput single cell analysis methodology

High-throughput quantitative polymerase chain reaction (qPCR) approaches enable profiling of multiple genes in single cells, bringing new insights to complex biological processes and offering opportunities for single cell-based monitoring of cancer cells and stem cell-based therapies. However, workflows with well-defined sources of variation are required for clinical diagnostics and testing of tissue-engineered products. In a study of neural stem cell lines, we investigated the performance of lysis, reverse transcription (RT), preamplification (PA), and nanofluidic qPCR steps at the single cell level in terms of efficiency, precision, and limit of detection. We compared protocols using a separate lysis buffer with cell capture directly in RT-PA reagent. The two methods were found to have similar lysis efficiencies, whereas the direct RT-PA approach showed improved precision. Digital PCR was used to relate preamplified template copy numbers to Cq values and reveal where low-quality signals may affect the analysis. We investigated the impact of calibration and data normalization strategies as a means of minimizing the impact of inter-experimental variation on gene expression values and found that both approaches can improve data comparability. This study provides validation and guidance for the application of high-throughput qPCR workflows for gene expression profiling of single cells.

Molecular profiling of single Sca-1+/CD34+,- cells--the putative murine lung stem cells

Murine bronchioalveolar stem cells play a key role in pulmonary epithelial maintenance and repair but their molecular profile is poorly described so far. In this study, we used antibodies directed against Sca-1 and CD34, two markers originally ascribed to pulmonary cells harboring regenerative potential, to isolate single putative stem cells from murine lung tissue. The mean detection rate of positive cells was 8 per 10⁶ lung cells. We then isolated and globally amplified the mRNA of positive cells to analyze gene expression in single cells. The resulting amplicons were then used for molecular profiling by transcript specific polymerase chain reaction (PCR) and global gene expression analysis using microarrays. Single marker-positive cells displayed a striking heterogeneity for the expression of epithelial and mesenchymal transcripts on the single cell level. Nevertheless, they could be subdivided into two cell populations: Sca-1⁺/CD34⁻ and Sca-1⁺/CD34⁺ cells. In these subpopulations, transcripts of the epithelial marker Epcam (CD326) were exclusively detected in Sca-1⁺/CD34⁻ cells (p = 0.03), whereas mRNA of the mesenchymal marker Pdgfrα (CD140a) was detected in both subpopulations and more frequently in Sca-1⁺/CD34⁺ cells (p = 0.04). FACS analysis confirmed the existence of a Pdgfrα positive subpopulation within Epcam⁺/Sca-1⁺/CD34⁻ epithelial cells. Gene expression analysis by microarray hybridization identified transcripts differentially expressed between the two cell types as well as between epithelial reference cells and Sca-1⁺/CD34⁺ single cells, and selected transcripts were validated by quantitative PCR. Our results suggest a more mesenchymal commitment of Sca-1⁺/CD34⁺ cells and a more epithelial commitment of Sca-1⁺/CD34⁻ cells. In summary, the study shows that single cell analysis enables the identification of novel molecular markers in yet poorly characterized populations of rare cells. Our results could further improve our understanding of Sca-1⁺/CD34^+,− cells in the biology of the murine lung.

Monitoring gene expression in a single Xenopus oocyte using multiple cytoplasmic collections and quantitative RT-PCR

Oocytes and eggs of the African clawed frog, Xenopus laevis, are commonly used in gene expression studies. However, monitoring transcript levels in the individual living oocytes remains challenging. To address this challenge, we used a technique based on multiple repeated collections of nanoliter volumes of cytoplasmic material from a single oocyte. Transcript quantification was performed by quantitative RT-PCR. The technique allowed monitoring of heterologous gene expression in a single oocyte without affecting its viability. We also used this approach to profile the expression of endogenous genes in living Xenopus oocytes. Although frog oocytes are traditionally viewed as a homogenous cell population, a significant degree of gene expression variation was observed among the individual oocytes. A lognormal distribution of transcript levels was revealed in the oocyte population. Finally, using this technique, we observed a dramatic decrease in the content of various cytoplasmic mRNAs in aging unfertilized eggs but not in oocytes, suggesting a link between mRNA degradation and egg apoptosis.

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 responses to ionizing radiation

While gene expression studies have proved extremely important in understanding cellular processes, it is becoming more apparent that there may be differences in individual cells that are missed by studying the population as a whole. We have developed a qRT-PCR protocol that allows us to assay multiple gene products in small samples, starting at 100 cells and going down to a single cell, and have used it to study radiation responses at the single-cell level. Since the accuracy of qRT-PCR depends greatly on the choice of "housekeeping" genes used for normalization, initial studies concentrated on determining the optimal panel of such genes. Using an endogenous control array, it was found that for IMR90 cells, common housekeeping genes tend to fall into one of two categories-those that are relatively stably expressed regardless of the number of cells in the sample, e.g., B2M, PPIA, and GAPDH, and those that are more variable (again regardless of the size of the population), e.g., YWHAZ, 18S, TBP, and HPRT1. Further, expression levels in commonly studied radiation-response genes, such as ATF3, CDKN1A, GADD45A, and MDM2, were assayed in 100, 10, and single-cell samples. It is here that the value of single-cell analyses becomes apparent. It was observed that the expression of some genes such as FGF2 and MDM2 was relatively constant over all irradiated cells, while that of others such as FAS was considerably more variable. It was clear that almost all cells respond to ionizing radiation but the individual responses were considerably varied. The analyses of single cells indicate that responses in individual cells are not uniform and suggest that responses observed in populations are not indicative of identical patterns in all cells. This in turn points to the value of single-cell analyses.

Single-cell analysis reveals early manifestation of cancerous phenotype in pre-malignant esophageal cells

Cellular heterogeneity plays a pivotal role in a variety of functional processes in vivo including carcinogenesis. However, our knowledge about cell-to-cell diversity and how differences in individual cells manifest in alterations at the population level remains very limited mainly due to the lack of appropriate tools enabling studies at the single-cell level. We present a study on changes in cellular heterogeneity in the context of pre-malignant progression in response to hypoxic stress. Utilizing pre-malignant progression of Barrett's esophagus (BE) as a disease model system we studied molecular mechanisms underlying the progression from metaplastic to dysplastic (pre-cancerous) stage. We used newly developed methods enabling measurements of cell-to-cell differences in copy numbers of mitochondrial DNA, expression levels of a set of mitochondrial and nuclear genes involved in hypoxia response pathways, and mitochondrial membrane potential. In contrast to bulk cell studies reported earlier, our study shows significant differences between metaplastic and dysplastic BE cells in both average values and single-cell parameter distributions of mtDNA copy numbers, mitochondrial function, and mRNA expression levels of studied genes. Based on single-cell data analysis, we propose that mitochondria may be one of the key factors in pre-malignant progression in BE.