Large Numbers Of Single Cells Research Articles

Automated microarray platform for single-cell sorting and collection of lymphocytes following HIV reactivation.

A promising strategy to cure HIV-infected individuals is to use latency reversing agents (LRAs) to reactivate latent viruses, followed by host clearance of infected reservoir cells. However, reactivation of latent proviruses within infected cells is heterogeneous and often incomplete. This fact limits strategies to cure HIV which may require complete elimination of viable virus from all cellular reservoirs. For this reason, understanding the mechanism(s) of reactivation of HIV within cellular reservoirs is critical to achieve therapeutic success. Methodologies enabling temporal tracking of single cells as they reactivate followed by sorting and molecular analysis of those cells are urgently needed. To this end, microraft arrays were adapted to image T-lymphocytes expressing mCherry under the control of the HIV long terminal repeat (LTR) promoter, in response to the application of LRAs (prostratin, iBET151, and SAHA). In response to prostratin, iBET151, and SAHA, 30.5%, 11.2%, and 12.1% percentage of cells, respectively. The arrays enabled large numbers of single cells (>25,000) to be imaged over time. mCherry fluorescence quantification identified cell subpopulations with differing reactivation kinetics. Significant heterogeneity was observed at the single-cell level between different LRAs in terms of time to reactivation, rate of mCherry fluorescence increase upon reactivation, and peak fluorescence attained. In response to prostratin, subpopulations of T lymphocytes with slow and fast reactivation kinetics were identified. Single T-lymphocytes that were either fast or slow reactivators were sorted, and single-cell RNA-sequencing was performed. Different genes associated with inflammation, immune activation, and cellular and viral transcription factors were found.

Single-cell proteomics enabled by next-generation sequencing or mass spectrometry.

In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.

Revealing the spatio-phenotypic patterning of cells in healthy and tumor tissues with mLSR-3D and STAPL-3D.

Despite advances in three-dimensional (3D) imaging, it remains challenging to profile all the cells within a large 3D tissue, including the morphology and organization of the many cell types present. Here, we introduce eight-color, multispectral, large-scale single-cell resolution 3D (mLSR-3D) imaging and image analysis software for the parallelized, deep learning-based segmentation of large numbers of single cells in tissues, called segmentation analysis by parallelization of 3D datasets (STAPL-3D). Applying the method to pediatric Wilms tumor, we extract molecular, spatial and morphological features of millions of cells and reconstruct the tumor’s spatio-phenotypic patterning. In situ population profiling and pseudotime ordering reveals a highly disorganized spatial pattern in Wilms tumor compared to healthy fetal kidney, yet cellular profiles closely resembling human fetal kidney cells could be observed. In addition, we identify previously unreported tumor-specific populations, uniquely characterized by their spatial embedding or morphological attributes. Our results demonstrate the use of combining mLSR-3D and STAPL-3D to generate a comprehensive cellular map of human tumors.

Development and Application of High-Throughput Single Cell Lipid Profiling: A Study of SNCA-A53T Human Dopamine Neurons.

SummaryAdvances in single cell genomics and transcriptomics have shown that at tissue level there is complex cellular heterogeneity. To understand the effect of this inter-cell heterogeneity on metabolism it is essential to develop a single cell lipid profiling approach that allows the measurement of lipids in large numbers of single cells from a population. This will provide a functional readout of cell activity and membrane structure. Using liquid extraction surface analysis coupled with high-resolution mass spectrometry we have developed a high-throughput method for untargeted single cell lipid profiling. This technological advance highlighted the importance of cellular heterogeneity in the functional metabolism of individual human dopamine neurons, suggesting that A53T alpha-synuclein (SNCA) mutant neurons have impaired membrane function. These results demonstrate that this single cell lipid profiling platform can provide robust data that will expand the frontiers in biomedical research.

Development and Application of High-Throughput Single Cell Lipid Profiling: A Study of <i>SNCA-A53T</i> Human Dopamine Neurons

Advances in single cell genomics and transcriptomics have shown that at tissue level there is complex cellular heterogeneity. To understand the effect of this inter-cell heterogeneity on metabolism it is essential to develop a single cell lipid profiling approach that allows the measurement of lipids in large numbers of single cells from a population. This will provide a functional read out of cell activity and membrane structure. Using liquid extraction surface analysis (LESA) coupled with high-resolution mass spectrometry we have developed a new high-throughput method for untargeted single cell lipid profiling. This technological advance revealed new insights into the importance of cellular heterogeneity in the functional metabolism of individual human dopamine neurons, suggesting that A53T alpha-synuclein ( SNCA ) mutant neurons have impaired membrane function. These results demonstrate that this novel single cell lipid profiling platform can provide robust data that will open new frontiers in biomedical research.

Sci-Hi-C: A single-cell Hi-C method for mapping 3D genome organization in large number of single cells.

The highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

Growth, morphology and maturation ability of Pinus pinea embryogenic suspension cultures

Stone pine (Pinus pinea L.) is a Mediterranean forest species used for the commercial harvesting of its edible seeds, known as pine nuts. Plant regeneration by somatic embryogenesis on semi-solid medium has been achieved in stone pine, but suspension culture systems for the large-scale production of somatic embryos need to be developed. Suspension cultures were established from several embryogenic lines. Growth parameters as settled cell volume (SCV) and dry weight (DW) in suspension cultures were influenced by genotype, initial inoculum density, orbiting speed and type of carbohydrate in the culture medium. A higher linear growth (DW) up to week four was obtained using 20 mg mL−1 inoculum. The orbiting speed affected the SCV as well as the morphology of embryogenic cultures. Growth parameters decreased when orbiting speed was increased from 50 rpm to 150 rpm. Cultures at 50 rpm most commonly grew as clusters of embryonal cell masses, whereas at 150 rpm there were large numbers of single cells. Cultures grew slowly in medium with maltose instead of sucrose, but growth was not affected when plant growth regulators (PGRs) concentration was decreased. A sustainable suspension culture could be maintained by transferring 250 mg of filtered cell mass to 25 mL of liquid medium at 3-week intervals. Culture scaling-up in liquid medium resulted in a 23-fold DW increase in 6 weeks, compared with 12 fold increase on semi-solid medium. Cotyledonary embryos were obtained on semi-solid maturation medium from lines proliferated in suspension cultures. Increased somatic embryo production was obtained when proliferation was performed at 100 rpm orbiting speed in liquid medium with low PGRs concentration and sucrose instead of maltose.

Tn5Prime, a Tn5 based 5' capture method for single cell RNA-seq.

RNA-sequencing (RNA-seq) is a powerful technique to investigate and quantify entire transcriptomes. Recent advances in the field have made it possible to explore the transcriptomes of single cells. However, most widely used RNA-seq protocols fail to provide crucial information regarding transcription start sites. Here we present a protocol, Tn5Prime, that takes advantage of the Tn5 transposase-based Smart-seq2 protocol to create RNA-seq libraries that capture the 5′ end of transcripts. The Tn5Prime method dramatically streamlines the 5′ capture process and is both cost effective and reliable. By applying Tn5Prime to bulk RNA and single cell samples, we were able to define transcription start sites as well as quantify transcriptomes at high accuracy and reproducibility. Additionally, similar to 3′ end-based high-throughput methods like Drop-seq and 10× Genomics Chromium, the 5′ capture Tn5Prime method allows the introduction of cellular identifiers during reverse transcription, simplifying the analysis of large numbers of single cells. In contrast to 3′ end-based methods, Tn5Prime also enables the assembly of the variable 5′ ends of the antibody sequences present in single B-cell data. Therefore, Tn5Prime presents a robust tool for both basic and applied research into the adaptive immune system and beyond.

Single-Cell RNA-Seq Reveals the Differentiation Hierarchy of Normal Human Bone Marrow and a Distinct Transcriptome Signature of Monosomy 7 Cells

Monosomy 7 is a frequent cytogenetic abnormality in hematopoietic malignancies and a general indicator of poor prognosis. Due to lack of distinct cell surface markers between monosomy 7 cells and normal cells, it is not feasible to physically separate aneuploid from diploid cells. We performed single-cell RNA-seq (scRNA-seq), which allows the entire transcriptome of large numbers of single cells to be assayed in an unbiased way, to investigate hematopoietic differentiation of normal and aneuploid human hematopoietic cells. Bone marrow samples were collected from four patients (P1-P4) with myelodysplastic syndrome, and four healthy volunteers. Conventional cytogenetics showed -7/7q- in bone marrow cells from P1, P3 and P4, and dup(1)(q21q32) in cells from P2; retrospectively, P2 was found positive for monosomy 7 as well as trisomy 8 by fluorescence in situ hybridization. Fresh CD34+CD38- and CD34+CD38+cells were sorted by flow cytometry and then subjected to Fluidigm C1 Single-Cell Auto Prep System for scRNA-seq. After excluding cells with low transcriptome coverage, 326 cells from P1 and P2 (analysis is in progress for P3 and P4), and 391 cells from healthy subjects were analyzed by comparison of transcriptomes from 17,071 genes. Nonlinear dimension reduction and visualization were achieved using t-distributed Stochastic Neighbor Embedding (tSNE). Cells from healthy controls clustered into seven subgroups based on their gene expression pattern, and each group could be associated with a previously reported hematopoietic cell type by known marker genes (Laurenti E, Nat Immunol, 2013). These cell types included hematopoietic stem cell (HSC), multilymphoid progenitor (MLP), granulocyte-monocyte progenitor (GMP), Pro-B cell (ProB), earliest thymic progenitor (ETP), and megakaryocytic-erythroid progenitor (MEP) (Fig 1a). Individual cells from healthy controls were ordered by Monocle software based on their expression profile similarity to uncover a differentiation hierarchy. A two-branch trajectory of development from HSC was revealed, with one branch progressing towards erythroid cell and the other to lymphoid/myeloid cells (Fig 1b). This pattern differs from the classic hematopoietic model, but is consistent with reports claiming existence of early-lymphoid-biased progenitors that retain myeloid but not erythroid potential (Doulatov S, Nat Immunol, 2010), and of dominance of multipotent and unipotent progenitors over scarce oligopotent progenitors in the adult marrow hematopoietic hierarchy (Notta F, Science, 2016). We compared single cells from patients and healthy controls for regional and chromosomal copy number differences in gene expression. We identified subclonal populations of cells from patients that showed decreased expression of chromosome 7 genes (60% in P1, and 55% in P2; Fig 1c and 1d), and increased expression of chromosome 8 (77% in P2) and chromosome 1 long arm genes (P2), at FDR=0.05 estimated with cells from health donors. Gene Ontology enrichment analysis using topGO indicated that cells with low global expression of chromosome 7 genes had dysregulated expression of immune related genes, including B cell receptor signaling pathway, T cell activation and differentiation, antigen receptor-mediated signaling pathway, as well as signal transduction and Fc-γ Receptor signaling pathway. ScRNA-seq analysis reveals a simple pattern of normal human hematopoietic development and the molecular signature of aneuploid cells from patients with developing "clonal evolution". This powerful method should improve characterization of functional changes in human cells with chromosome abnormalities. [Display omitted] DisclosuresDesierto:GSK/Novartis: Research Funding. Townsley:GSK/Novartis: Research Funding. Young:Novartis: Research Funding.

Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates

The genomes of large numbers of single cells must be sequenced to further understanding of the biological significance of genomic heterogeneity in complex systems. Whole genome amplification (WGA) of single cells is generally the first step in such studies, but is prone to nonuniformity that can compromise genomic measurement accuracy. Despite recent advances, robust performance in high-throughput single-cell WGA remains elusive. Here, we introduce droplet multiple displacement amplification (MDA), a method that uses commercially available liquid dispensing to perform high-throughput single-cell MDA in nanoliter volumes. The performance of droplet MDA is characterized using a large dataset of 129 normal diploid cells, and is shown to exceed previously reported single-cell WGA methods in amplification uniformity, genome coverage, and/or robustness. We achieve up to 80% coverage of a single-cell genome at 5× sequencing depth, and demonstrate excellent single-nucleotide variant (SNV) detection using targeted sequencing of droplet MDA product to achieve a median allelic dropout of 15%, and using whole genome sequencing to achieve false and true positive rates of 9.66 × 10(-6) and 68.8%, respectively, in a G1-phase cell. We further show that droplet MDA allows for the detection of copy number variants (CNVs) as small as 30 kb in single cells of an ovarian cancer cell line and as small as 9 Mb in two high-grade serous ovarian cancer samples using only 0.02× depth. Droplet MDA provides an accessible and scalable method for performing robust and accurate CNV and SNV measurements on large numbers of single cells.

Advancing haematopoietic stem and progenitor cell biology through single‐cell profiling

Haematopoietic stem and progenitor cells (HSPCs) sit at the top of the haematopoietic hierarchy, and their fate choices need to be carefully controlled to ensure balanced production of all mature blood cell types. As cell fate decisions are made at the level of the individual cells, recent technological advances in measuring gene and protein expression in increasingly large numbers of single cells have been rapidly adopted to study both normal and pathological HSPC function. In this review we emphasise the importance of combining the correct computational models with single-cell experimental techniques, and illustrate how such integrated approaches have been used to resolve heterogeneities in populations, reconstruct lineage differentiation, identify regulatory relationships and link molecular profiling to cellular function.

A microfluidic platform for trapping, releasing and super-resolution imaging of single cells

A multi-layer device, combining hydrodynamic trapping with microfluidic valving techniques, has been developed for on-chip manipulation and imaging of single cells and particles. Such a device contains a flow layer with trapping channels to capture single particles or cells and a control layer with valve channels to selectively control the trap and release processes. Particles and cells have been successfully trapped and released using the proposed device. The device enables the trapping of single particles with a trapping efficiency of greater than 95%, and allows for single particles and cells to be trapped, released and manipulated by simply controlling corresponding valves. Moreover, the trap and release processes are found to be compatible with biological samples like cells. Our device allows stable immobilisation of large numbers of single cells in a few minutes, significantly easing the experiment setup for single-cell characterisation and offering a stable platform for both single-molecule and super-resolution imaging. Proof-of-concept super- resolution imaging experiments with mouse embryonic stem cells (mESCs) have been conducted by exploiting super-resolution photoactivated localisation microscopy (PALM). Cells and nuclei were stably trapped and imaged. Centromeres of ∼200nm size could be identified with a localisation precision of <15nm.

Computational assignment of cell-cycle stage from single-cell transcriptome data

The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.

Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA amplification and library preparation for highly multiplexed sequencing, yielding a method that can produce genome-wide CNV profiles of up to a hundred individual cells on a single lane of an Illumina HiSeq instrument. We apply the method to human cancer cell lines and biopsied cancer tissue, thereby illustrating its efficiency, reproducibility, and power to reveal underlying genetic heterogeneity and clonal phylogeny. The capacity of the method to facilitate the rapid profiling of hundreds to thousands of single-cell genomes represents a key step in making single-cell profiling an easily accessible tool for studying cell lineage.

Measurement of intracellular ions by flow cytometry.

Using flow cytometry, single-cell measurements of calcium can be made on isolated populations identified by one or more phenotypic characteristics. Most earlier techniques for measuring cellular activation parameters determined the mean value for a population of cells, which did not permit optimal resolution of the responses. The flow cytometer is particularly useful for this purpose because it can measure ion concentrations in large numbers of single cells and thereby allows ion concentration to be correlated with other parameters such as immunophenotype and cell cycle stage. A limitation of flow cytometry, however, is that it does not permit resolution of certain complex kinetic responses such as cellular oscillatory responses. This unit describes the preparation of cells, including labeling with antibodies and with calcium probes, and discusses the principles of data analysis and interpretation.

Abstract 3491: Rapid and ultra-sensitive single-cell transcript profiling with droplet digital PCR (ddPCR): Application to cell cycle analysis

Abstract The recognition that tumors and other apparently homogeneous cell populations are often heterogeneous and display stochastic expression of their genes, has motivated the development of methods to profile gene expression in single cells. Among these, RNA-Seq is the most powerful for broad-scale profiling and discovery of genes and pathways influencing biological processes and states of interest. However, given the extent of sample manipulation required to prepare each RNA-Seq library – and the trade-off between sensitivity and sample throughput – it is important to validate discovery findings by orthogonal digital methods with comparable or greater sensitivity and accuracy. Droplet Digital PCR is such a method that can rapidly and cost-effectively replicate (or challenge) RNA-Seq results with minimal sample processing when defined targets are to be verified (Chen et al, 2012). Furthermore, ddPCR is performed in 96-well plates and is well-suited to high throughput studies of focused sets of genes in large numbers of single cells (e.g. hundreds in a day). Here we present a simple and robust workflow for profiling multiplexed, transcript targets in flow-sorted, Jurkat single-cells using Taqman 5’ probe hydrolysis assays. We demonstrate that Bio-Rad's QX100 &amp; QX200 Droplet Digital PCR systems provide absolute counts of transcripts from &amp;gt;100,000 copies to &amp;lt;10 copies per cell, using unpurified and unamplified cDNA synthesized in 30’ in the original wells used for flow-sorting. The results are highly reproducible and the method is currently being used to explore the contribution of the cell-cycle to expression differences among single-cell populations. More than 30 cell-cycle genes have been evaluated by ddPCR in exponentially growing Jurkat cells. Several genes, including CCNB1, CDK1 and DNM2, show an unusually broad range of transcript levels (over 2 logs), suggestive of their known phase-specific cell-cycle roles. Jurkat cells have been FACS sorted for either G0/G1 (“G1”) or G2/M (“G2”) single-cells using DyeCycle Orange to stain for DNA content, and are being assessed for phase-specific expression profiles. Preliminary results indicate that G2 single-cells are enriched for higher CCNB1 and CDK1 transcript levels as expected from their role in promoting early events in mitosis; however, even G2 cells still show expression levels spanning ∼2 logs, suggesting cell-cycle phase heterogeneity and/or bursty transcription within the sorted G2 population. While the average mRNA level of even the house-keeping gene, GAPDH, is several-fold higher in G2 cells as compared to G1 cells, transcripts for CCNB1 and CDK1 average ∼6-fold higher in G2 than in G1. In this work, we demonstrate both the analytical performance and the biological utility of droplet digital PCR for single-cell analysis. This same workflow could be used for fingerprinting and study of sub-populations in tumors. Citation Format: George Karlin-Neumann, Shenglong Wang, Camille Troup, Yann Jouvenot, Eli Hefner. Rapid and ultra-sensitive single-cell transcript profiling with droplet digital PCR (ddPCR): Application to cell cycle analysis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3491. doi:10.1158/1538-7445.AM2014-3491

Flow cytometry bioinformatics.

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

Microfluidic chemical cytometry of peptide degradation in single drug-treated acute myeloid leukemia cells.

Microfluidic systems show great promise for single-cell analysis; however, as these technologies mature, their utility must be validated by studies of biologically relevant processes. An important biomedical application of these systems is characterization of tumor cell heterogeneity. In this work, we used a robust microfluidic platform to explore the heterogeneity of enzyme activity in single cells treated with a chemotherapeutic drug. Using chemical cytometry, we measured peptide degradation in the U937 acute myeloid leukemia (AML) cell line in the presence and absence of the aminopeptidase inhibitor Tosedostat (CHR-2797). The analysis of 99 untreated cells revealed rapid and consistent degradation of the peptide reporter within 20 min of loading. Results from drug-treated cells showed inhibited, but ongoing degradation of the reporter. Because the device operates at an average sustained throughput of 37 ± 7 cells/h, we were able to sample cells over the course of this time-dependent degradation. In data from 498 individual drug-treated cells, we found a linear dependence of degradation rate on amount of substrate loaded superimposed upon substantial heterogeneity in peptide processing in response to inhibitor treatment. Importantly, these data demonstrated the potential of microfluidic systems to sample biologically relevant analytes and time-dependent processes in large numbers of single cells.

Composite Single Cell Genetics and Clonal Phylogeny in Acute Lymphoblastic Leukaemia

AbstractAbstract 122Cancer clone development is widely regarded as an evolutionary or Darwinian process of genetic diversification and natural (or therapeutic) selection within tissue ecosystems. Emerging studies are providing strong evidence that dynamic and complex branching sub-clonal genetic architectures are a common feature of cancer (Greaves M and Maley CC Nature 2012). This complexity may underpin the intransigence of advanced cancer to therapeutic control, particularly as the critical ’driver’ cells – cancer or leukaemic stem cells, also appear to be genetically diverse within individual patients (Anderson K et al Nature 2011, Notta F et al Nature 2011). Sub-clonal architecture can only be fully determined through the study of large numbers of single cells uniformly sampled from the individual cancer of interest and assessed for composite genotype. Various technologies and approaches from fluorescent in situ hybridisation (FISH) to whole-genome sequencing of single cells have been applied to cancer and leukaemic cells but each approach has limitations. We have developed a novel multiplex microfluidic Q-PCR approach that allows unbiased single cell sampling, high throughput analysis of hundreds of individual cells and simultaneous detection of multiple genetic alterations in a single cell, including fusion genes, DNA copy number alterations (CNAs) and sequence-based mutations.As a proof of principle study we have applied this technique to REH, an acute lymphoblastic leukaemia (ALL) cell line that harbors the ETV6-RUNX1 fusion and a SNP in the EPO receptor gene, which we used as a surrogate mutation. We further determined a detailed sub-clonal genetic architecture for two ETV6-RUNX1 positive ALL patient samples with multiple point mutations and copy number alterations (determined by whole-genome sequencing) by interrogating approximately 400 flow cytometry sorted single cells with validation by FISH and standard sequencing. Briefly, single cells were lysed prior to multiplex specific (DNA) target amplification (STA) and Q-PCR using the 96.96 dynamic microfluidic array and the BioMarkï HD (Fluidigm, UK). Phylogenetic trees were constructed using maximum parsimony with PAUP analysis software.Interrogation of REH revealed that all single cells registered the ETV6-RUNX1 fusion and EPO receptor SNP, but 42% of cells gained either 1 or 2 additional copies of chromosome 21. Patient sample data revealed branching sub-clonal architectures in Case A in which all leukaemic cells harbored the fusion with additional point mutations but only sub-clones showed CNAs. In contrast, the sub-clonal architecture of Case B showed that whilst the ETV6-RUNX1 fusion was the earliest (or universal) genomic event, CNAs were relatively early events preceding the acquisition of point mutations (Figure 1). In both cases, the numerically predominant sub-clone harbored both point mutations and CNAs in addition to the presumptive initiating lesion, ETV6-RUNX1.These detailed and complex sub-clonal architectures would be masked by other genetic techniques. Single cell genetics coupled with deep genome sequencing is now technically feasible and provides an accurate portrait of the dynamic clonal complexity in leukaemia (and other cancers). Variegated genetics and clonal complexity in individual leukaemias has important implications for our understanding of molecular pathogenesis and for therapeutic targeting. [Display omitted] Disclosures:No relevant conflicts of interest to declare.

Characterization of B cell repertoires in Sjögren’s syndrome by microengraving (44.48)

Abstract Microengraving is a novel technology that uses an array of microfabricated wells to isolate and characterize large numbers of single cells. Using printing technique, secreted antibodies can be captured and characterized on glass slides. Using microengraving, we profiled the antigenic repertoires of B cells reacting against salivary gland (SG) tissues in Sjögren’s syndrome (SjS), an autoimmune disease targeting the exocrine glands. Cells from spleens and cervical lymph nodes (CLN) of normal C57BL/6 (B6) and SjS-susceptible (SjSs) C57BL/6.NOD-AecAec2 mice were isolated and loaded onto microwells. Capture slides preincubated with mouse Igs were used for printing. Detection antibodies included IgG1, IgG3, and SG lysates of B6 and SjSs mice that were labeled with different fluorescent dyes. Data indicated an increase in the frequency of IgG1 antibodies against SG lysates of B6 and SjSs mice when printed using CLN cells of SjSs mice but not B6 mice. In contrast, an increase in the frequency of IgG3 antibodies against SG tissues of B6 and SjSs mice was only observed when printed with SjSs mice splenocytes. These data demonstrate the diversity in the isotypic repertoire of antibodies during autoimmune process, and further revealed a dichotomy in peripheral tolerance. The result supports the generation of self-reactive B cell repertories during the autoimmune process. Furthermore, the use of micrograving of single cells might allow for identification of novel biomarkers.