During the development of next-generation sequencing, RNA sequencing has become an indispensable deep-sequencing technology for measurement of levels of transcripts and isoforms . But traditional RNA-seq from tissue and/or cells cannot easily resolve specific cell types. Now, exciting new applications are being explored, RNA-seq meet the greatly chance for its new technology that is single-cell RNA sequencing(scRNA-seq). Unlike traditional bulk RNA sequencing analyzing gene expression in bulk level, single-cell RNA sequencing can detect the global transcriptome of thousands of isolated cells on single cell level . The applications of single-cell sequencing are widely, such as cancer research, developmental biology  and neurosciences . In this review, we focus on the application of scRNA-seq on tumor-infiltrating T cells research. scRNA-seq can give us a map of tumor microenvironment by decomposition of complex tumor tissues into functionally distinct cell types and reveal cell types that are unknown in what were considered well-studied tumor diseases. scRNA-seq increase our understanding of the tumor-infiltrating T cells and potentially help us identify new immunotherapy targets.
The steps of scRNA-seq method can borrow from earlier bulk
RNA-seq protocols. Single lymphocytes can isolate from peripheral
blood, tumor, and adjacent normal tissues from patients. Most labs
have access to flow-cytometry instrumentation and use microtiter
plates containing lysis buffer . For higher-throughput experiments
can refer to droplet-microfluidic isolation, such as Drop-Seq
 or InDrop . Each single cell that tagged with Unique Molecular
Identifiers (UMIs) is reverse transcribed in order to produce
cDNA, and the cDNA is used as the input for RNA-seq library preparation.
The transcriptional profiles of these individual cells, coupled
with assembled T cell receptor (TCR), are sequenced . Using unsupervised
algorithms to cluster cell types and then assigned to
cell types according to aggregated cluster-level expression profiles
and delineate their developmental trajectory . Most study may
focus on analyze the cell-type composition and study dynamics of
mixed cell population in various biological contexts. Tumor-infiltrating
lymphocytes are highly heterogeneous, because of a variety
of compositions of cell-type and gene expression profiles on tumor
microenvironment. T cell patterns are distinct in both tumors and
adjacent normal tissue. Yannick Simoni et al. reported that in tumor
microenvironment CD8+ T cells are phenotypically heterogeneous
within a tumor and across patients, and bystander CD8+ T cells are
abundant and distinct in human tumor infiltrates .
The state of tumor-infiltrating T cells can be divided into cytotoxic, bystander cytotoxic, exhausted and dysfunctional state. The functional of different state T cells within tumors remain unknown. Analysis of paired single-cell RNA and T cell receptor sequencing data, Hanjie Li et al. reported a gradient of dysfunctional T cell are associated with tumor reactivity and are the major intratumoral proliferating immune cell compartment on melanoma . Tirosh et al. revealed T cell exhaustion signature may connect to T cell activation and clonal expansion on melanoma turmors . These findings provide evidence that dysfunctional T cells may be a driver of tumor reactive, equally to cytotoxic T cells. Tumor microenvironment have differential impact on T cell dysfunction across tumor types. We need scRNA-seq to describe tumor infiltrates. The transcriptomes of T cell subset help to identify previously unknown marker for prognosis. For example, Xinyi Guo et al. reported a ratio of pre-exhausted to exhausted T cells are relative to better prognosis of lung adenocarcinoma . Peter Savas et al. demonstrated that tumor-infiltrating lymphocytes in breast cancer contains several CD8+ T cells with features of tissue-resident memory expressing high levels of immune checkpoint molecules and effector proteins, which are associated with good prognosis in breast cancer . Chuanhong zheng et al. reported primary CD8+ T cells over-expressing LAYN results in inhibition of interferon-gama production, which suggesting LAYN is linked to the suppressive function of tumor Treg and exhausted CD8 T cells . Overall, these findings provided an exciting vision of how we use scRNA-seq to discover tumor immune markers and understand their roles in regulating immune response and tissue-specific functions.
The development and migration of T cells within tumors remain
unknown. scRNA-seq has also been instrumental in resolving details
of the trajectory and regulation of T cells. T Cell Receptor (TCR)
clonotypes determine the developmental trajectories of T cells and
reveal phenotypic diversity. Tumor antigen specific TCR clusters
also are key components in anti-tumor immune response. scRNAseq
of TCR gene repertoires are useful for to reveal the intrinsic
heterogeneity among antigen-specific T cells and their function in
tumor response . David Redmond et al. reported a method to
identification and assembly of full-length rearranged V(D)J T cell
receptor sequences from scRNA-seq data . Combined with TCR
analysis, EIham Azizi et al. yielded an immune map of breast cancer
that points to continuous T cell activation and differentiation
trajectories . In short, identifying clonal TCRs at single-cell
levels allows us to discover their developmental trajectory in
various T cell clusters, and deduce their activation status in tumor
In conclusion, scRNA-seq measures the expression levels of genes in cells in a comprehensive, sensitive and accurate way. scRNA-seq is aiding in the discovery of the heterogeneity of tumorinfiltrating lymphocytes, unreported subpopulations and states, potential biomarkers, tumor antigen-specific TCR clusters and their relationship to physiology and disease.
The author acknowledges support from the National Key Research and Development Program of China (2017YFA0103501, 2016YFC1305502) and the Chinese Academy of Sciences (XDA12010203, QYZDJ-SSW-SMC017).
- Z Wang, M Gerstein, M Snyder (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1): 57-63.
- AA Kolodziejczyk, J K Kim, V Svensson, J C Marioni, SA Teichmann (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58(4): 610-620.
- SS Potter (2018) Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol 14(8): 479-492.
- D Ofengeim, N Giagtzoglou, D Huh, C Zou, J Yuan (2017) Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time. Trends Mol Med 23(6): 563-576.
- C Ziegenhain, Vieth B, Parekh S, Reinius B, Guillaumet Adkins A, et al. (2017) Comparative Analysis of Single-Cell RNA Sequencing Methods. Mol Cell 65(4): 631-643.
- J Bageritz and G Raddi (2019) Single-Cell RNA Sequencing with Drop-Seq. Methods Mol Biol 1979: 73-85
- A Han, J Glanville, L Hansmann, MM Davis (2014) Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat Biotechnol. 32(7): 684-692.
- AA AlJanahi, M Danielsen, CE Dunbar (2018) An Introduction to the Analysis of Single-Cell RNA-Sequencing Data. Mol Ther Methods Clin Dev 10: 189-196.
- Y Simoni, Becht E, Fehlings M, Loh CY, Koo SL, et al. (2018) Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557(7706): 575-579.
- H Li, van der Leun AM, Yofe I, Lubling Y, Gelbard Solodkin D, et al. (2019) Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma. Cell 176(4): 775-789.
- I Tirosh, Izar B, Prakadan SM, Wadsworth MH, Treacy D, et al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352(6282): 189-196.
- X Guo, Zhang Y, Zheng L, Zheng C, Song J, et al. (2018) Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat Med 24(7): 978-985.
- P Savas, Virassamy B, Ye C, Salim A, Mintoff CP, et al. (2018) Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 24(7): 986-993.
- C Zheng, Zheng L, Yoo JK, Guo H, Zhang Y, et al. (2017) Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing. Cell 169(7): 1342-1356.
- D Redmond, A Poran, and O Elemento (2016) Single-cell TCRseq: paired recovery of entire T-cell alpha and beta chain transcripts in T-cell receptors from single-cell RNAseq. Genome Med 8: 80.
- E Azizi, Carr AJ, Plitas G, Cornish AE, Konopacki C, et al. (2018) Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Cell 174(5): 1293-1308.