Single-cell RNA-seq Platform Research Articles

Performance comparison of high throughput single-cell RNA-Seq platforms in complex tissues

Single-cell transcriptomics has emerged as the preferred tool to define cell identity through the analysis of gene expression signatures. However, there are limited studies that have comprehensively compared the performance of different scRNAseq systems in complex tissues. Here, we present a systematic comparison of two well-established high throughput 3′-scRNAseq platforms: 10× Chromium and BD Rhapsody, using tumours that present high cell diversity. Our experimental design includes both fresh and artificially damaged samples from the same tumours, which also provides a comparable dataset to examine their performance under challenging conditions. The performance metrics used in this study consist of gene sensitivity, mitochondrial content, reproducibility, clustering capabilities, cell type representation and ambient RNA contamination. These analyses showed that BD Rhapsody and 10× Chromium have similar gene sensitivity, while BD Rhapsody has the highest mitochondrial content. Interestingly, we found cell type detection biases between platforms, including a lower proportion of endothelial and myofibroblast cells in BD Rhapsody and lower gene sensitivity in granulocytes for 10× Chromium. Moreover, the source of the ambient noise was different between plate-based and droplet-based platforms. In conclusion, our reported platform differential performance should be considered for the selection of the scRNAseq method during the study experimental designs.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach. We analyzed patient esophageal and colorectal carcinomas and found that reads from distinct genera tend to co-occur in the same host cells, testifying to possible intracellular polymicrobial interactions. Microbial reads are disproportionately abundant within myeloid cells that up-regulate proinflammatory cytokines like IL1Β and CXCL8, while infected tumor cells up-regulate antigen processing and presentation pathways. These results show that myeloid cells with bacteria engulfed are a major source of bacterial RNA within the tumor microenvironment (TME) and may inflame the TME and influence immunotherapy response.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms

Recent advances in sample preparation and sequencing technology have made it possible to profile the transcriptomes of individual cells using single-cell RNA sequencing (scRNA-Seq). Compared to bulk RNA-Seq data, single-cell data often contain a higher percentage of zero reads, mainly due to lower sequencing depth per cell, which affects mostly measurements of low-expression genes. However, discrepancies between platforms are observed regardless of expression level. Using four paired datasets with multiple samples each, we investigated technical and biological factors that can contribute to this expression shift. Using two separate machine learning models we found that, in addition to expression level, RNA integrity, gene or UTR3 length, and the number of transcripts potentially also influence the occurrence of zeros. These findings could enable the development of novel analytical methods for cross-platform expression shift correction. We also identified genes and biological pathways in our diverse datasets that consistently showed differences when assessed at the single cell versus bulk level to assist in interpreting analysis across transcriptomic platforms. At the gene level, 25 genes (0.12%) were found in all datasets as discordant, but at the pathway level, 7 pathways (2.02%) showed shared enrichment in discordant genes.

CeTF: an R/Bioconductor package for transcription factor co-expression networks using regulatory impact factors (RIF) and partial correlation and information (PCIT) analysis

BackgroundFinding meaningful gene-gene interaction and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining.ResultsHere, we developed the R package “CeTF” that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems — for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. To demonstrate the CeTF package application, we analyzed gastric cancer RNA-seq data obtained from TCGA (The Cancer Genome Atlas) and found the HOXB3 gene as the second most relevant TFs with a high regulatory impact (TFs-HRi) regulating gene pathways in the cell cycle.ConclusionThis preliminary finding shows the potential of CeTF to list master regulators of gene networks. CeTF was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on Bioconductor (http://bioconductor.org/packages/CeTF) and GitHub (http://github.com/cbiagii/CeTF).

Single cell transcriptome sequencing on the Nanopore platform with ScNapBar.

The current ecosystem of single-cell RNA-seq platforms is rapidly expanding, but robust solutions for single-cell and single-molecule full-length RNA sequencing are virtually absent. A high-throughput solution that covers all aspects is necessary to study the complex life of mRNA on the single-cell level. The Nanopore platform offers long read sequencing and can be integrated with the popular single-cell sequencing method on the 10× Chromium platform. However, the high error-rate of Nanopore reads poses a challenge in downstream processing (e.g., for cell barcode assignment). We propose a solution to this particular problem by using a hybrid sequencing approach on Nanopore and Illumina platforms. Our software ScNapBar enables cell barcode assignment with high accuracy, especially if sequencing saturation is low. ScNapBar uses unique molecular identifier (UMI) or Naïve Bayes probabilistic approaches in the barcode assignment, depending on the available Illumina sequencing depth. We have benchmarked the two approaches on simulated and real Nanopore data sets. We further applied ScNapBar to pools of cells with an active or a silenced nonsense-mediated RNA decay pathway. Our Nanopore read assignment distinguishes the respective cell populations and reveals characteristic nonsense-mediated mRNA decay events depending on cell status.

Autophagy Regulatory Genes MET and RIPK2 Play a Prognostic Role in Pancreatic Ductal Adenocarcinoma: A Bioinformatic Analysis Based on GEO and TCGA.

Pancreatic ductal adenocarcinoma is a common malignant tumor with a poor prognosis. Autophagy activity changes in both cancer cells and microenvironment and affects the progression of pancreatic ductal adenocarcinoma. The purpose of this study was to predict the prognostic autophagy regulatory genes and their role in the regulation of autophagy in pancreatic ductal adenocarcinoma. We draw conclusions based on gene expression data from different platforms: GSE62165 and GSE85916 from the array platform, TCGA from the bulk RNA-seq platform, and GSE111672 from the single-cell RNA-seq platform. At first, we detected differentially expressed genes in pancreatic ductal adenocarcinoma compared with normal pancreatic tissue based on GSE62165. Then, we screened prognostic genes based on GSE85916 and TCGA. Furthermore, we constructed a risk signature composed of the prognostic differentially expressed genes. Finally, we predicted the probable role of these genes in regulating autophagy and the types of cell expressing these genes. According to our screening criteria, there were only two genes: MET and RIPK2, selected into the development of the risk signature. However, evaluated by log-rank tests, receiver operating characteristic curves, and calibration curves, the risk signature was worth considering its clinical application because of good sensitivity, specificity, and stability. Besides, we predicted that both MET and RIPK2 promote autophagy in pancreatic ductal adenocarcinoma by gene set enrichment analysis. Analysis of single-cell RNA-seq data from GSE111672 revealed that both MET and RIPK2 were expressed in cancer cells while RIPK2 was also expressed in monocytes and neutrophils. After comprehensive analysis, we found that both MET and RIPK2 are related to the prognosis of pancreatic ductal adenocarcinoma and provided some associated clues for clinical application and basic experiment research.

Abstract 5511: Frontline therapy with anti-PD1 enhances the durability of combination targeted therapy in NRAS-mutant melanoma

Abstract Melanoma is one of the deadliest form of skin cancer. Although the activating NRAS mutations are present in 20% of melanoma patients, currently, there are no FDA-approved therapies for treating NRAS-mutant melanoma. In this project, our goal is to prime the immune system by immunotherapy and then improve the targeted therapy responses by suppressing recruitment of inhibitory immune cell populations and by increasing T cell infiltration, which would lead to enhanced responses in NRAS-mutant melanoma. We used a syngeneic C3H/HeNCrl mouse model of NRAS-mutant melanoma. The mice were treated with two doses of either anti-PD1 or IgG every 5 days followed by everyday treatment with either single agent or combination of ceritinib and trametinib for a total period of 40 days. Single cell RNA-seq platform was used to determine the impact of immunotherapy and targeted therapy upon each individual cell type. Flow cytometry experiments were carried out to assess the percentage of T cells and MDSCs recruitment. Altered signaling responses and chemokines/ cytokines responsible for MDSCs recruitment were determined from RPPA and RNA-seq data respectively. The in vivo studies demonstrated that the tumors in the mice treated with anti-PD1 followed by combination shrunk significantly over a period of 40 days without any relapse. Single cell RNA seq analysis and flow cytometry experiments revealed a 15- and 20-fold increase in T cell infiltration in these tumors. Immune cells depletion experiments showed that the cytotoxic effects of combination therapy was dependent upon CD8+ T cells only. Single cell RNA seq analysis also demonstrated that priming with immunotherapy increased IFN γ expression on T cells and subsequent treatment with ceritinib+trametinib increased melanoma antigen presentation by increased MHC class I expression. The RPPA data from tumors treated with anti-PD-1 followed by combination demonstrated inhibition of RTKs implicated in therapy escape such as c-MET, EGFR, IGF1R, HER2/3. Flow cytometric analysis also revealed around 60% decrease in Tregs and 80% decrease in MDSCs recruitment in the tumors treated with anti-PD1 followed by combination. RNA seq analysis revealed that ceritinib decreased mRNA expression of cytokines/ chemokines such as Cxcl5, Cxcl3, Cxcl2, Csf1, Cx3cl1, Cklf, that have been implicated in the recruitment and differentiation of MDSCs. Hence, we demonstrate that frontline immunotherapy primes the immune system, making the tumors more immunogenic with an increased accumulation of activated CD8+ T cells. Successive treatment with combination targeted therapy not only increases antigen presentation to T cells but also decreases recruitment of immunosuppressive MDSCs thus preventing immune escape and tumor progression. In summary, we have developed novel targeted therapy combinations that can be sequentially used with immunotherapy to maximize the durations of response in treatment of NRAS-mutant melanoma. Citation Format: Manali S. Phadke, Zhihua Chen, Eslam Mohamed, Michael Davies, Ann Chen, Uwe Rix, Paulo Rodriguez, Keiran Smalley. Frontline therapy with anti-PD1 enhances the durability of combination targeted therapy in NRAS-mutant melanoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5511.

2027 - SIMULTANEOUS LINEAGE AND TRANSCRIPTIONAL ANALYSIS OF SINGLE HSCS IDENTIFIES MECHANISMS OF FUNCTIONAL HETEROGENEITY

The hematopoietic system follows a hierarchical organization, with long-term repopulating hematopoietic stem cells (LT-HSCs) occupying the top tier. Application of systems-level single cell methods has revealed a remarkable functional and transcriptional heterogeneity within stem cells. To elucidate the molecular underpinnings of this functional heterogeneity, we developed a technique to barcode hematopoietic cells at the RNA level in order to simultaneously capture the lineage relationships and transcriptional states of HSCs with unprecedented robustness. Using a droplet-based single cell RNAseq platform, we analyzed the total fraction of engrafted hematopoietic stem cells together with a sufficiently significant representation of downstream progenitor cells to estimate HSC output. Inspection of the resulting stem cell state maps revealed a variety of clonal behaviors, including cell quiescence, asymmetric and symmetric divisions, and clonal competition. We also correlated these behaviors with some of the previously observed heterogeneity during transplantation, including lineage bias and clonal dominance. Importantly, comparison of single HSC expression profiles revealed significant differences in the transcriptional programs related with some of these behaviors, which illuminated the molecular machineries that operate at the stem cell level to define this heterogeneity. Finally, a loss-of-function in vivo screening for clonal quiescence/expansion regulators identified several novel candidates of this process. Thus, our work has harnessed the power of combined functional/transcriptional single cell readouts to discover novel mechanisms that orchestrate the in vivo complexity of stem cell activities.

An Informative Approach to Single-Cell Sequencing Analysis.

Recent advances in sequencing technologies enable us to obtain genome, epigenome and transcriptome data in individual cells. In this review, we describe various platforms for single-cell sequencing analysis across multiple layers. We mainly introduce an automated single-cell RNA-seq platform, the Chromium Single Cell 3' RNA-seq system, and its technical features and compare it with other single-cell RNA-seq systems. We also describe computational methods for analyzing large, complex single-cell datasets. Due to the insufficient depth of single-cell RNA-seq data, resulting in a critical lack of transcriptome information for low-expressed genes, it is occasionally difficult to interpret the data as is. To overcome the analytical problems for such sparse datasets, there are many bioinformatics reports that provide informative approaches, including imputation, correction of batch effects, dimensional reduction and clustering.

Abstract 5350: Single cell RNA-seq reveals transcriptionally distinct subpopulations within patient-derived pancreatic cancer organoids

Abstract Advances in 3D cell culture over the last decade have enabled the establishment of organoid models from a multitude of organs in varying stages of development and disease. Organoids recapitulate the tissue-specific cell types and exhibit similar morphology and spatial organization as the organ it is derived from. However, to the best of our knowledge, tumor organoids have never been profiled at a single cell level. In this study, we applied Drop-seq, a high throughput single cell RNA-seq platform, to characterize the different cell types present in pancreatic cancer organoids. We sequenced the transcriptomes of 5494 single cells from organoids derived from a patient undergoing a Whipple procedure for pancreatic ductal adenocarcinoma (PDAC). We then developed a novel clustering approach based on a class of probabilistic generative models called topic models, leading to the identification of two subpopulations of cells. One subgroup expressed marker genes known to play important roles in PDAC biology, such as MUC1, CLDN4 and GAS5. Interestingly, this subgroup also bore markers for cancer stem cells (HES1 and SQSTM1) that have been found by multiple studies to mediate chemoresistance in PDAC. The other subpopulation differentially expressed AGR2 and TMBIM6, which are associated with endoplasmic reticulum (ER) stress and correlates with invasiveness of PDAC cell lines. Functional studies are underway to determine the exact role of these subgroups, but our preliminary findings are suggestive of a subpopulation of cells that maybe involved in resistance to chemotherapy. This is consistent with the clinical progression of this patient's cancer, suggesting that rapid, inexpensive single cell transcriptomics might provide more precise molecular information to personalized medicine programs than traditional RNA-seq alone. Citation Format: Mohana Ray, Rajib K. Nandi, Isabel Romero Calvo, Kori Kirby, Christopher Weber, Hedy L. Kindler, Mary Buschmann, Kevin Roggin, Kevin P. White. Single cell RNA-seq reveals transcriptionally distinct subpopulations within patient-derived pancreatic cancer organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5350.

Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response

Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population.

Abstract 4339: Single cell RNA sequencing dissects cellular growth factor dependencies and oncogenic driver effects in an organoid model of gastric cancer

Abstract Gastric cancer is a lethal malignancy with few therapeutic options. Gastric tumors rely on complex intercellular signaling “crosstalk” that enables tumor development, metastasis, and therapeutic resistance. To recapitulate the intercellular communications among various cell populations that exist in vivo, we are using a three-dimensional culture system to grow and manipulate mouse gastric tissue in vitro, otherwise referred to as organoids. Importantly, this organoid model contains epithelium with its endogenous mesenchymal niche and does not require exogenous Wnt stimulation. To systematically analyze the distinct cellular lineages and their interactions, we applied a massively-scaled single cell RNA-Seq platform to sequence thousands of individual cells from organoid cultures. With PCA and t-SNE analysis of the high-dimensional data generated from single cell RNA-seq, we characterized two major cell types, i.e. epithelial and mesenchymal cells. Leverage the information from single cell transcriptome profiles, we identified specific niche factors of the Wnt signaling pathways that are activated in different stomach cell lineages. These results suggest that the mesenchymal cell populations provide a potential source of the R-spondin, a Wnt agonist, that sustains the growth of epithelium. Furthermore, we compared cell populations from Cdh1-/-/Trp53-/- and Trp53-/-organoids, and characterized changes on the transcriptome profiles due to the loss of Cdh1, an early oncogenic event in diffuse gastric cancer development. Overall, using organoid model and high-throughput single cell RNA-Seq provides a novel approach to study early tumor transformation and critical cancer-stroma interactions. Citation Format: Jiamin Chen, Noemi Andor, Susan M. Grimes, Billy Lau, Hanlee P. Ji. Single cell RNA sequencing dissects cellular growth factor dependencies and oncogenic driver effects in an organoid model of gastric cancer [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 4339. doi:10.1158/1538-7445.AM2017-4339