Single-cell mRNA Sequencing Research Articles

Abstract LB-078: Improved single-cell mRNA sequencing for transcriptome and paired-chain TCR analysis of primary human CD3+ T cells

Abstract The low RNA content of single human T cells has limited the success of single-cell transcriptome profiling, an approach that can generate data to reconstruct paired α,β T cell receptor (TCR) chains¹. Single-cell TCR sequencing is a powerful molecular tool to analyze tumor-infiltrating lymphocytes and production of cellular therapeutics. We compared the sensitivity and overall performance of the new Clontech® SMART-Seq® v4 Kit to the previously validated SMART-Seq v1 Kit on single primary human CD3+ cells captured and processed on the C1™ system (Fluidigm®). Sequenced libraries were analyzed using the TraCeR bioinformatics pipeline¹ to identify the TCR α and β chains expressed in each T cell. Cryopreserved CD3+ T cells were prepared as a single-cell suspension for capture on C1 using SMART-Seq v1 and v4, according to manufacturer instructions (AllCells®, Fluidigm, Clontech). Single-cell libraries were sequenced at 1.9 million reads per single cell. After data processing, the aligned reads were analyzed using the TraCeR bioinformatics pipeline. Transcriptome-related metrics and paired-chain TCR information were compared to evaluate chemistry performance (Table 1). Results are comparable to single-cell data published by Clontech for cultured Jurkat cells², which do not pose as high a technical challenge to molecular profiling as the primary T cells used in this study. The v4 chemistry produced a higher number of single cells with paired α,β chains detected. These results show that combining the C1 system with SMART-Seq v4 chemistry for processing primary single human T cells provides a powerful tool to investigate and enumerate T cell clones and their phenotypes and functions. 1. Stubbington, M.J.T. et al. Nature Methods, 2016. 2. Bostick, M. et al. Poster, NIH Single Cell Analysis Investigators Meeting, 2016. Table 1.Metrics evaluated in the comparison of SMART-Seq v1 and v4 chemistry performance.MetricSMART-Seq v1 (98 cells)SMART-Seq v4 (122 cells)cDNA yield per single cell (ng)3.4 (±1.4)4.9 (±2.0)Reads aligned to the genome from total (%)82 (±3.2)77 (±6.4)Reads with unique alignment to the genome from total aligned (%)97 (±0.04)97 (±0.05)Reads aligned to RefSeq (%)38 (±0.7)56 (±0.06)Reads aligned to rRNA (%)0.61 (±0.03)1.31 (±0.05)Bases aligned to coding sequences (%)22 (±0.5)40 (±0.5)Bases aligned to intronic sequences (%)40 (±1.0)18 (±0.5)Genes detected per T cell in average (above 1 TPM)2,618 (±431)1,866 (±386)Number of cells for which paired α,β T cell receptor (TCR) chains were identified25 (25%)67 (55%) Citation Format: Camila Egidio, Michael Gonzales, Joel Brockman, Shuwen Chen, Robert Durruthy-Durruthy, Clare Rogers, Manisha Ray. Improved single-cell mRNA sequencing for transcriptome and paired-chain TCR analysis of primary human CD3+ T cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-078. doi:10.1158/1538-7445.AM2017-LB-078

Abstract 2923: Label-free enrichment and integrated full-length mRNA transcriptome analysis of single live circulating tumor cells from breast cancer patients

Abstract Background Label-free methods for isolating circulating tumor cells (CTCs) are attractive because they provide an opportunity to analyze a larger set of CTCs that may otherwise be missed due to variable or no expression of protein (label) markers. Understanding genetic and functional heterogeneity in CTCs allows us to gain insight into the mechanisms underscoring metastasis, drug resistance, and tumor aggressiveness. Currently, a simple workflow for isolation and molecular characterization of single CTCs by mRNA sequencing is lacking. In order to address this challenge, we developed a label-free workflow to isolate CTCs from breast cancer patients for full-length mRNA sequencing analysis by integrating the ClearCell® FX System with the Polaris™ system. The ClearCell FX system processes blood samples from cancer patients and enriches for CTCs in a label-free antibody-independent manner. The low level of nonspecifically isolated white blood cells from ClearCell FX is further depleted on the Polaris system by negative enrichment of viable CTCs. This unique integration of systems will enable researchers to perturb single CTCs in a controlled environment, monitor and measure the response due to perturbation, and link these response measurements to downstream genomic and transcriptomic analysis. Method and Results CTCs from 7.5 mL of peripheral blood sample from breast cancer patients were enriched using ClearCell FX. To differentiate larger blood cells from putative CTCs, we stained the enriched cells with Alexa Fluor® 647-conjugated CD45 and CD31 to identify leukocytes and endothelial cells, respectively. Calcein AM (live cell marker) and CellTracker™ Orange (universal cell marker) were added to identify live cells. Single CTCs were selected on Polaris (Fluidigm) system, lysed and reverse-transcribed, and cDNA were preamplified on the Polaris integrated fluidic circuit (IFC). Sequencing libraries were generated using the Nextera® kit and sequenced on Illumina® MiSeq™ and NextSeq™ systems. We successfully processed blood samples from four patients. Sequenced data showed high-quality metrics, with read depth of up to 2.5 million reads (MiSeq) or 60 million reads (NextSeq), with a low percentage of mapped reads to ribosomal RNA and mitochondrial RNA. Unsupervised hierarchical clustering of gene expression data showed clustering by patient, but considerable heterogeneity was also observed among the CTCs from the same patient. We will provide insights into full-length mRNA transcriptome of single CTCs from triple negative breast cancer patient. Conclusion We present the feasibility of integrating two microfluidics platforms to capture single CTCs for transcriptome and functional study. Our data suggests that the heterogeneity of tumor sample and characterization of metastatic processes can be elucidated from single-cell mRNA sequencing of CTCs. Citation Format: Naveen Ramalingam, Yi Fang Lee, Lukasz Szpankowski, Anne Leyrat, Brian Fowler, Jovina Tan, Chong Tracy Lu, Ninez Delos Angeles, Chad Sanada, Cassandra Greene, Kyle Hukari, Andrew Wu, Yoon-Sim Yap, Jay West, Ali Asgar Bhagat. Label-free enrichment and integrated full-length mRNA transcriptome analysis of single live circulating tumor cells from breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2923. doi:10.1158/1538-7445.AM2017-2923

Reconstructing cell cycle pseudo time-series via single-cell transcriptome data

Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.

Single-Cell mRNA Sequencing in Cancer Research: Integrating the Genomic Fingerprint.

Critical cancer mutations are often regional and mosaic, confounding the efficacy of targeted therapeutics. Single cell mRNA sequencing (scRNA-seq) has enabled unprecedented studies of intra-tumor heterogeneity and its role in cancer progression, metastasis, and treatment resistance. When coupled with DNA sequencing, scRNA-seq allows one to infer the in vivo impact of genomic alterations on gene expression. This combination can be used to reliably distinguish neoplastic from non-neoplastic cells, to correlate paracrine-signaling pathways between neoplastic cells and stroma, and to map expression signatures to inferred clones and phylogenies. Here we review recent advances in scRNA-seq, with a special focus on cancer. We discuss the challenges and prospects of combining scRNA-seq with DNA sequencing to assess intra-tumor heterogeneity.

Vertical flow array chips reliably identify cell types from single-cell mRNA sequencing experiments

Single-cell mRNA sequencing offers an unbiased approach to dissecting cell types as functional units in multicellular tissues. However, highly reliable cell typing based on single-cell gene expression analysis remains challenging because of the lack of methods for efficient sample preparation for high-throughput sequencing and evaluating the statistical reliability of the acquired cell types. Here, we present a highly efficient nucleic reaction chip (a vertical flow array chip (VFAC)) that uses porous materials to reduce measurement noise and improve throughput without a substantial increase in reagent. We also present a probabilistic evaluation method for cell typing depending on the amount of measurement noise. Applying the VFACs to 2580 monocytes provides 1967 single-cell expressions for 47 genes, including low-expression genes such as transcription factors. The statistical method can distinguish two cell types with probabilistic quality values, with the measurement noise level being considered for the first time. This approach enables the identification of various sub-types of cells in tissues and provides a foundation for subsequent analyses.

Abstract 165: Single-cell transcriptome analysis guides tailored combinatorial therapeutics in refractory kidney cancer

Abstract Metastatic renal cell carcinoma (mRCC) evolves from primary RCC (pRCC) and harbors multiple subpopulations with distinct molecular and phenotypic features. Such underlying intratumoral heterogeneity (ITH) imposes difficulties in designing marker-based clinical trials because targeted mono-therapy eliminates a specific subpopulation of tumor cells while leaving others unharmed. Accordingly, a rational combination strategy that minimizes the survival of the drug-resistant subpopulation in a given heterogeneous tumor is essential for long-term therapeutic efficacy. Here, we examined the ITH of a paired mRCC and pRCC using single-cell mRNA sequencing (scRNA-seq) to identify specific tumor cell populations with drug target pathway activation. From the single cell expression profiles, we found the highly activated status of the EGFR and Src signaling pathways in the mRCC compared to the pRCC, with supporting in vitro high-throughput drug screening results. Distinct features of intratumoral expression variability across mRCC single cells that were masked in the bulk measurement prompted us to test the co-targeting strategy for the EGFR and Src pathways with increased likelihood for complete response. This combinatorial strategy showed significantly better treatment effects on mRCC-derived xenograft platforms in vitro and in vivo than monotherapies. Taken together, our findings show clinical implications of scRNA-seq in designing effective treatment regimens for overcoming treatment failure to conventional monotherapies, and also provide novel insights to the unmet clinical needs in effective personalized treatments. Citation Format: Kyu-Tae Kim, Hye Won Lee, Hae-Ock Lee, Hye Jin Song, Da Eun Jeong, Sang Shin, Hyunho Kim, Yoojin Shin, Do-Hyun Nam, Byong Chang Jeong, David G. Kirsch, Kyeung Min Joo, Woong-Yang Park. Single-cell transcriptome analysis guides tailored combinatorial therapeutics in refractory kidney cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 165.

A rapid method to verify single-cell deposition setup for cell sorters.

Studying single cells reveals biology that cannot be explored using bulk techniques. Cell sorters provide the opportunity of separating single cells either for cell culture or for downstream molecular applications such as qPCR to study specific gene expression and single cell mRNA sequencing. Some of these molecular studies can be expensive so the investigator will often want reassurance that the cell sorter can reliably deposit a single droplet into each well of a 96-well or 384-well plate. Such plates may contain very small volumes of fluid as reducing the volume of fluid used can reduce the cost of the assay. To miss some of the wells could leave the data set incomplete requiring costly repetition. To verify this by microscopy is at best very time consuming and at worst impossible. Here, an inexpensive colorimetric method is described for verifying whether a well, in either a 96- or 384-well plate, did receive a single sorted droplet from a cell sorter into the fluid at the bottom of the well. The droplet consists of particles suspended in an enzymatic solution, horseradish peroxidase, which is deposited into microtiter plate wells containing a substrate, 3,3',5,5'-tetramethylbenzidine. This method requires no special equipment or expertise and is rapid enough to be performed directly prior to the single-cell sorting experiment. © 2016 International Society for Advancement of Cytometry.

Thymic expression of tissue-restricted self-antigens is a highly coordinated and evolutionary conserved process

Abstract Promiscuous gene expression (pGE) of tissue-restricted antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential for tolerance imposition in the thymus. PGE is characterized on the one hand by inclusion of a broad range of TRAs and on the other hand by its mosaic patterns, whereby each antigen is only expressed in 1–3% of mTECs at a given point in time. Yet, this mosaic pattern at the single cell level faithfully adds up to the full repertoire of self-antigens at the population level. In order to analyze the regulatory mechanisms underlying this transcriptional heterogeneity among mTECs, we applied two complementing approaches, the isolation of minor mTEC subsets as defined by TRA-selected gene co-expression groups in conjunction with single cell mRNA sequencing. Different TRA-selected mTEC subfractions, each expressing distinct sets of genes in a mutually overlapping fashion, mapped to distinct stages of mTEC development. These co-expression patterns were evolutionary conserved between mouse and human (Rattay et al., J. Autoimmunity 2015). Applying an unbiased single cell mRNA sequencing approach, we extended these findings to the single cell level and showed that the mouse mTEC population essentially represents a composite of multiple co-expression groups (Brennecke et al., Nat. Immunology 2015). These co-expression groups may represent only snapshots of a continuum of changing co-expression groups along the lifetime of an individual mTEC, as captured in the model of “sliding co-expression groups” (Pinto et al., PNAS 2013). Continuous genome scanning would potentially enlarge the overall diversity of self-antigens displayed by a single mTEC.

Single neuron transcriptome analysis can reveal more than cell type classification: Does it matter if every neuron is unique?

A recent single cell mRNA sequencing study by Dueck et al. compares neuronal transcriptomes to the transcriptomes of adipocytes and cardiomyocytes. Single cell ‘omic approaches such as those used by the authors are at the leading edge of molecular and biophysical measurement. Many groups are currently employing single cell sequencing approaches to understand cellular heterogeneity in cancer and during normal development. These single cell approaches also are beginning to address long‐standing questions regarding nervous system diversity. Beyond an innate interest in cataloging cell type diversity in the brain, single cell neuronal diversity has important implications for neurotypic neural circuit function and for neurological disease. Herein, we review the authors’ methods and findings, which most notably include evidence of unique expression profiles in some single neurons.

BCL11B/CTIP2 is highly expressed in GABAergic interneurons of the mouse somatosensory cortex

In the nervous system, BCL11B is crucial for the development of deep layer corticospinal projection neurons and striatal medium spiny neurons and is often used as a marker for the aforementioned cell types. However, the expression of BCL11B in subtypes of non-excitatory neurons in the primary somatosensory cortex (S1) has not been reported in the mouse. In this study we show that BCL11B is extensively expressed in S1 GABAergic interneurons, throughout the three main subgroups (somatostatin-, parvalbumin- and 5HT3a-expresssing). Almost all BCL11B positive cells in the upper S1 layers were GABAergic interneurons and surprisingly, almost 40% of the BCL11B positive neurons in layer V were GABAergic interneurons. Single cell mRNA sequencing data revealed higher Bcl11b expression in S1 interneurons compared to deep layer pyramidal neurons. The highest levels of Bcl11b expression were found within the 5HT3a population, specifically in putative neurogliaform interneuron subclasses (5HT3a-positive but not expressing vasoactive intestinal peptide). In the light of our findings we suggest caution using BCL11B as a single marker to identify neurons.

Computational biology: How to catch rare cell types.

The development of an algorithm called RaceID enables the identification of rare cell types by single-cell RNA sequencing, even when they are part of a complex mixture of similar cells. See Letter p.251 Identifying and tracking rare cell types of physiological importance in a mixed population is a challenge. This study, a collaboration between the labs of Hans Clevers and Alexander van Oudenaarden, tackles the issue by applying single-cell mRNA sequencing to mouse intestinal organoids. The authors develop an algorithm, called RaceID, that allows rare cell type identification in a complex population of single cells. They define a marker for rare enteroendocrine cells of the intestine. Analysis of cells from a primary mouse intestine using this method, reveals that the Lgr5-expressing population consists of a homogenous stem cell population with a few rare secretory cells, including Paneth cells.

BASiCS: Bayesian Analysis of Single-Cell Sequencing Data.

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of unexplained technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model where: (i) cell-specific normalisation constants are estimated as part of the model parameters, (ii) technical variability is quantified based on spike-in genes that are artificially introduced to each analysed cell’s lysate and (iii) the total variability of the expression counts is decomposed into technical and biological components. BASiCS also provides an intuitive detection criterion for highly (or lowly) variable genes within the population of cells under study. This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. We demonstrate our method using gene expression measurements from mouse Embryonic Stem Cells. Cross-validation and meaningful enrichment of gene ontology categories within genes classified as highly (or lowly) variable supports the efficacy of our approach.

Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.

BackgroundIntra-tumoral genetic and functional heterogeneity correlates with cancer clinical prognoses. However, the mechanisms by which intra-tumoral heterogeneity impacts therapeutic outcome remain poorly understood. RNA sequencing (RNA-seq) of single tumor cells can provide comprehensive information about gene expression and single-nucleotide variations in individual tumor cells, which may allow for the translation of heterogeneous tumor cell functional responses into customized anti-cancer treatments.ResultsWe isolated 34 patient-derived xenograft (PDX) tumor cells from a lung adenocarcinoma patient tumor xenograft. Individual tumor cells were subjected to single cell RNA-seq for gene expression profiling and expressed mutation profiling. Fifty tumor-specific single-nucleotide variations, including KRASG12D, were observed to be heterogeneous in individual PDX cells. Semi-supervised clustering, based on KRASG12D mutant expression and a risk score representing expression of 69 lung adenocarcinoma-prognostic genes, classified PDX cells into four groups. PDX cells that survived in vitro anti-cancer drug treatment displayed transcriptome signatures consistent with the group characterized by KRASG12D and low risk score.ConclusionsSingle-cell RNA-seq on viable PDX cells identified a candidate tumor cell subgroup associated with anti-cancer drug resistance. Thus, single-cell RNA-seq is a powerful approach for identifying unique tumor cell-specific gene expression profiles which could facilitate the development of optimized clinical anti-cancer strategies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0692-3) contains supplementary material, which is available to authorized users.

SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression.

Single-cell mRNA sequencing (RNA-seq) methods have undergone rapid development in recent years, and transcriptome analysis of relevant cell populations at single-cell resolution has become a key research area of biomedical sciences. We here present single-cell mRNA 3-prime end sequencing (SC3-seq), a practical methodology based on PCR amplification followed by 3-prime-end enrichment for highly quantitative, parallel and cost-effective measurement of gene expression in single cells. The SC3-seq allows excellent quantitative measurement of mRNAs ranging from the 10,000-cell to 1-cell level, and accordingly, allows an accurate estimate of the transcript levels by a regression of the read counts of spike-in RNAs with defined copy numbers. The SC3-seq has clear advantages over other typical single-cell RNA-seq methodologies for the quantitative measurement of transcript levels and at a sequence depth required for the saturation of transcript detection. The SC3-seq distinguishes four distinct cell types in the peri-implantation mouse blastocysts. Furthermore, the SC3-seq reveals the heterogeneity in human-induced pluripotent stem cells (hiPSCs) cultured under on-feeder as well as feeder-free conditions, demonstrating a more homogeneous property of the feeder-free hiPSCs. We propose that SC3-seq might be used as a powerful strategy for single-cell transcriptome analysis in a broad range of investigations in biomedical sciences.

Abstract 4664: Single-cell transcriptome analysis on lung squamous cell carcinoma derived from a patient

Abstract Distinct genomic signature of a patient's cancer could provide a clue to design and predict accurate therapeutic responses to targeted medicine. Current approaches regarding heterogeneous cancer cells en masse as a pooled population, however, hardly reflect complete genomic landscape of tumor diversity, missing potentially important minor clones' implications regarding metastasis and drug resistance. Here we report a comprehensive analysis to discover different patterns of subclonal behaviors in functional modalities and signaling pathways out of heterogeneous population derived from a patient with lung squamous cell carcinoma, using single-cell mRNA sequencing. As a result of nonnegative matrix factorization as a clustering, we identified discrete 4 subgroups with a major group in accordance with overall expression profile of a pooled population of cells, and the other 3 minor groups that were masked in a pooled sample. Genomic variants were also reliably identified and compared across single cells. We integrated these genomic signatures from single cells to address the different responses in drug screening. Together, our approach provides insights into the more accurate strategy to identify minor but potentially important subclones that are relevant to drug resistance. Citation Format: Kyu-Tae Kim, Hye-Won Lee, Kyeung Min Joo, Sang-Chul Kim, Jin-Ku Lee, Nakho Chang, Jason K. Sa, Yu Jin Cho, Do-Hyun Nam, Hae-Ock Lee, Woong-Yang Park. Single-cell transcriptome analysis on lung squamous cell carcinoma derived from a patient. [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 4664. doi:10.1158/1538-7445.AM2014-4664

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships, but require efficient methods for cell capture and mRNA sequencing1–4. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths5, the limitations of shallow sequencing have not been directly investigated. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In developing cortex we identify diverse cell types including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.

Efficacy, Safety, and Tolerability of Low-Dose Hormone Therapy in Managing Menopausal Symptoms

Use of the lowest clinically effective dose of postmenopausal hormone therapy conforms to current recommendations and good clinical practice. Although accumulating evidence demonstrates the efficacy and tolerability of low hormone therapy doses, data about their use are limited by a lack of long-term, randomized studies. This review evaluates current evidence on the efficacy, safety, and tolerability of these preparations and their role in menopausal management.