Full-length RNA-seq from single cells using Smart-seq2. Picelli Simone,Faridani Omid R,Björklund Asa K,Winberg Gösta,Sagasser Sven,Sandberg Rickard Nature protocols Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA. 10.1038/nprot.2014.006
    VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma. Jiang Yanwen,Nie Kui,Redmond David,Melnick Ari M,Tam Wayne,Elemento Olivier Journal of visualized experiments : JoVE Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding the clonal composition changes that occur within a tumor in response to certain micro-environment or treatments may lead to the design of more sophisticated and effective approaches to eradicate tumor cells. However, tracking tumor clonal sub-populations has been challenging due to the lack of distinguishable markers. To address this problem, a VDJ-seq protocol was created to trace the clonal evolution patterns of diffuse large B cell lymphoma (DLBCL) relapse by exploiting VDJ recombination and somatic hypermutation (SHM), two unique features of B cell lymphomas. In this protocol, Next-Generation sequencing (NGS) libraries with indexing potential were constructed from amplified rearranged immunoglobulin heavy chain (IgH) VDJ region from pairs of primary diagnosis and relapse DLBCL samples. On average more than half million VDJ sequences per sample were obtained after sequencing, which contain both VDJ rearrangement and SHM information. In addition, customized bioinformatics pipelines were developed to fully utilize sequence information for the characterization of IgH-VDJ repertoire within these samples. Furthermore, the pipeline allows the reconstruction and comparison of the clonal architecture of individual tumors, which enables the examination of the clonal heterogeneity within the diagnosis tumors and deduction of clonal evolution patterns between diagnosis and relapse tumor pairs. When applying this analysis to several diagnosis-relapse pairs, we uncovered key evidence that multiple distinctive tumor evolutionary patterns could lead to DLBCL relapse. Additionally, this approach can be expanded into other clinical aspects, such as identification of minimal residual disease, monitoring relapse progress and treatment response, and investigation of immune repertoires in non-lymphoma contexts. 10.3791/53215
    T cell fate and clonality inference from single-cell transcriptomes. Stubbington Michael J T,Lönnberg Tapio,Proserpio Valentina,Clare Simon,Speak Anneliese O,Dougan Gordon,Teichmann Sarah A Nature methods We developed TraCeR, a computational method to reconstruct full-length, paired T cell receptor (TCR) sequences from T lymphocyte single-cell RNA sequence data. TraCeR links T cell specificity with functional response by revealing clonal relationships between cells alongside their transcriptional profiles. We found that T cell clonotypes in a mouse Salmonella infection model span early activated CD4(+) T cells as well as mature effector and memory cells. 10.1038/nmeth.3800
    CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Hashimshony Tamar,Senderovich Naftalie,Avital Gal,Klochendler Agnes,de Leeuw Yaron,Anavy Leon,Gennert Dave,Li Shuqiang,Livak Kenneth J,Rozenblatt-Rosen Orit,Dor Yuval,Regev Aviv,Yanai Itai Genome biology Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm's C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2's increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use. 10.1186/s13059-016-0938-8
    Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq. Redmond David,Poran Asaf,Elemento Olivier Genome medicine Accurate characterization of the repertoire of the T-cell receptor (TCR) alpha and beta chains is critical to understanding adaptive immunity. Such characterization has many applications across such fields as vaccine development and response, clone-tracking in cancer, and immunotherapy. Here we present a new methodology called single-cell TCRseq (scTCRseq) for the identification and assembly of full-length rearranged V(D)J T-cell receptor sequences from paired-end single-cell RNA sequencing reads. The method allows accurate identification of the V(D)J rearrangements for each individual T-cell and has the novel ability to recover paired alpha and beta segments. Source code is available at https://github.com/ElementoLab/scTCRseq . 10.1186/s13073-016-0335-7
    A Single-Cell Transcriptome Atlas of the Human Pancreas. Muraro Mauro J,Dharmadhikari Gitanjali,Grün Dominic,Groen Nathalie,Dielen Tim,Jansen Erik,van Gurp Leon,Engelse Marten A,Carlotti Francoise,de Koning Eelco J P,van Oudenaarden Alexander Cell systems To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus. 10.1016/j.cels.2016.09.002
    Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Zheng Chunhong,Zheng Liangtao,Yoo Jae-Kwang,Guo Huahu,Zhang Yuanyuan,Guo Xinyi,Kang Boxi,Hu Ruozhen,Huang Julie Y,Zhang Qiming,Liu Zhouzerui,Dong Minghui,Hu Xueda,Ouyang Wenjun,Peng Jirun,Zhang Zemin Cell Systematic interrogation of tumor-infiltrating lymphocytes is key to the development of immunotherapies and the prediction of their clinical responses in cancers. Here, we perform deep single-cell RNA sequencing on 5,063 single T cells isolated from peripheral blood, tumor, and adjacent normal tissues from six hepatocellular carcinoma patients. The transcriptional profiles of these individual cells, coupled with assembled T cell receptor (TCR) sequences, enable us to identify 11 T cell subsets based on their molecular and functional properties and delineate their developmental trajectory. Specific subsets such as exhausted CD8 T cells and Tregs are preferentially enriched and potentially clonally expanded in hepatocellular carcinoma (HCC), and we identified signature genes for each subset. One of the genes, layilin, is upregulated on activated CD8 T cells and Tregs and represses the CD8 T cell functions in vitro. This compendium of transcriptome data provides valuable insights and a rich resource for understanding the immune landscape in cancers. 10.1016/j.cell.2017.05.035
    Unbiased quantification of immunoglobulin diversity at the DNA level with VDJ-seq. Chovanec Peter,Bolland Daniel J,Matheson Louise S,Wood Andrew L,Krueger Felix,Andrews Simon,Corcoran Anne E Nature protocols For high-throughput sequencing and quantification of immunoglobulin repertoires, most methodologies use RNA. However, output varies enormously between recombined genes due to different promoter strengths and differential activation of lymphocyte subsets, precluding quantitation of recombinants on a per-cell basis. To date, DNA-based approaches have used V gene primer cocktails, with substantial inherent biases. Here, we describe VDJ sequencing (VDJ-seq), which accurately quantitates immunoglobulin diversity at the DNA level in an unbiased manner. This is accomplished with a single primer-extension step using biotinylated J gene primers. By addition of unique molecular identifiers (UMIs) before primer extension, we reliably remove duplicate sequences and correct for sequencing and PCR errors. Furthermore, VDJ-seq captures productive and nonproductive VDJ and DJ recombination events on a per-cell basis. Library preparation takes 3 d, with 2 d of sequencing and 1 d of data processing and analysis. 10.1038/nprot.2018.021
    Single Cell T Cell Receptor Sequencing: Techniques and Future Challenges. De Simone Marco,Rossetti Grazisa,Pagani Massimiliano Frontiers in immunology The peculiarity of T cell is their ability to recognize an infinite range of self and foreign antigens. This ability is achieved during thymic development through a complex molecular mechanism based on somatic recombination that leads to the expression of a very heterogeneous population of surface antigen receptors, the T Cell Receptors (TCRs). TCRs are cell specific and represent a sort of "molecular tag" of T cells and have been widely studied to monitor the dynamics of T cells in terms of clonality and diversity in several contexts including lymphoid malignancies, infectious diseases, autoimmune diseases, and tumor immunology. In this review, we provide an overview of the strategies used to investigate the TCR repertoire from the pioneering techniques based on the V segments identification to the revolution introduced by Next-Generation Sequencing that allows for high-throughput sequencing of alpha and beta chains. Single cell based approaches brought the analysis to a higher level of complexity and now provide the opportunity to sequence paired alpha and beta chains. We also discuss novel approaches that through the integration of TCR tracking and mRNA single cell sequencing offer a valuable tool to associate antigen specificity to transcriptional dynamics and to understand the molecular mechanisms of T cell plasticity. 10.3389/fimmu.2018.01638
    RNase H-dependent PCR-enabled T-cell receptor sequencing for highly specific and efficient targeted sequencing of T-cell receptor mRNA for single-cell and repertoire analysis. Li Shuqiang,Sun Jing,Allesøe Rosa,Datta Krishnalekha,Bao Yun,Oliveira Giacomo,Forman Juliet,Jin Roger,Olsen Lars Rønn,Keskin Derin B,Shukla Sachet A,Wu Catherine J,Livak Kenneth J Nature protocols RNase H-dependent PCR-enabled T-cell receptor sequencing (rhTCRseq) can be used to determine paired alpha/beta T-cell receptor (TCR) clonotypes in single cells or perform alpha and beta TCR repertoire analysis in bulk RNA samples. With the enhanced specificity of RNase H-dependent PCR (rhPCR), it achieves TCR-specific amplification and addition of dual-index barcodes in a single PCR step. For single cells, the protocol includes sorting of single cells into plates, generation of cDNA libraries, a TCR-specific amplification step, a second PCR on pooled sample to generate a sequencing library, and sequencing. In the bulk method, sorting and cDNA library steps are replaced with a reverse-transcriptase (RT) reaction that adds a unique molecular identifier (UMI) to each cDNA molecule to improve the accuracy of repertoire-frequency measurements. Compared to other methods for TCR sequencing, rhTCRseq has a streamlined workflow and the ability to analyze single cells in 384-well plates. Compared to TCR reconstruction from single-cell transcriptome sequencing data, it improves the success rate for obtaining paired alpha/beta information and ensures recovery of complete complementarity-determining region 3 (CDR3) sequences, a prerequisite for cloning/expression of discovered TCRs. Although it has lower throughput than droplet-based methods, rhTCRseq is well-suited to analysis of small sorted populations, especially when analysis of 96 or 384 single cells is sufficient to identify predominant T-cell clones. For single cells, sorting typically requires 2-4 h and can be performed days, or even months, before library construction and data processing, which takes ~4 d; the bulk RNA protocol takes ~3 d. 10.1038/s41596-019-0195-x
    Single-Cell Transcriptome Analysis Reveals Gene Signatures Associated with T-cell Persistence Following Adoptive Cell Therapy. Lu Yong-Chen,Jia Li,Zheng Zhili,Tran Eric,Robbins Paul F,Rosenberg Steven A Cancer immunology research Adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TIL) can mediate responses in some patients with metastatic epithelial cancer. Identifying gene signatures associated with successful ACT might enable the development of improved therapeutic approaches. The persistence of transferred T cells in the peripheral blood is one indication of clinical effectiveness, but many T-cell and host factors may influence T-cell persistence. To limit these variables, we previously studied a patient with metastatic colorectal cancer treated with polyclonal TILs targeting the (G12D) hotspot mutation, who experienced a partial response for 9 months. Three dominant clonotypes specifically recognizing KRAS(G12D) epitopes were identified, but we found that only two clonotypes persisted 40 days after ACT. Because of these findings, in this study, we performed the single-cell transcriptome analysis of the infused TILs. The analysis revealed a total of 472 genes that were differentially expressed between clonotypes 9.1-NP and 9.2-P single cells, and 528 genes between 9.1-NP and 10-P. Following these clonotypes in the peripheral blood after ACT, the gene expression patterns changed, but , and remained expressed in the persistent 9.2-P and 10-P cells, compared with the nonpersistent 9.1-NP cells. In addition, four autologous TILs, which were used for treatment but persisted poorly 1 month after ACT, did not express the gene profiles associated with persistence. These results suggest that certain TIL populations possess a unique gene expression profile that can lead to the persistence of T cells. Thus, this single-patient study provides insight into how to improve ACT for solid cancer. 10.1158/2326-6066.CIR-19-0299
    Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Jaitin Diego Adhemar,Kenigsberg Ephraim,Keren-Shaul Hadas,Elefant Naama,Paul Franziska,Zaretsky Irina,Mildner Alexander,Cohen Nadav,Jung Steffen,Tanay Amos,Amit Ido Science (New York, N.Y.) In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed preprogrammed cell-type hierarchy. These data demonstrate single-cell RNA-seq as an effective tool for comprehensive cellular decomposition of complex tissues. 10.1126/science.1247651
    Integrating single-cell transcriptomic data across different conditions, technologies, and species. Butler Andrew,Hoffman Paul,Smibert Peter,Papalexi Efthymia,Satija Rahul Nature biotechnology Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution. 10.1038/nbt.4096
    Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Aran Dvir,Looney Agnieszka P,Liu Leqian,Wu Esther,Fong Valerie,Hsu Austin,Chak Suzanna,Naikawadi Ram P,Wolters Paul J,Abate Adam R,Butte Atul J,Bhattacharya Mallar Nature immunology Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1SiglecF transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury. 10.1038/s41590-018-0276-y
    Comprehensive Integration of Single-Cell Data. Stuart Tim,Butler Andrew,Hoffman Paul,Hafemeister Christoph,Papalexi Efthymia,Mauck William M,Hao Yuhan,Stoeckius Marlon,Smibert Peter,Satija Rahul Cell Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets. 10.1016/j.cell.2019.05.031
    Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Hafemeister Christoph,Satija Rahul Genome biology Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from "regularized negative binomial regression," where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform, with a direct interface to our single-cell toolkit Seurat. 10.1186/s13059-019-1874-1