Single-cell Map of Diverse Immune Phenotypes Driven by the Tumor Microenvironment
Elham AziziAmbrose CarrGeorge PlitasAndrew CornishCatherine KonopackiSandhya PrabhakaranJuozas NainysKenmin WuVaidotas KiseliovasManu SettyKristy ChoiRachel M. FrommePhuong DaoPeter T. McKenneyRuby WastiKrishna KadaveruLinas MažutisAlexander Y. RudenskyDana Pe’er
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SUMMARY Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We created an immune map of breast cancer using single-cell RNA-seq data from 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph node. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous tumor-specific phenotypic expansions driven by environmental cues. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer, with important implications for characterizing tumor-infiltrating immune cells.Keywords:
Single cell sequencing
Single-Cell Analysis
Cancer immunology
Recent years have seen significant efforts in understanding and modulating the immune response in cancer. In this context, immunosuppressive cells, including regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs), have come under intense investigation for their proposed roles in suppressing tumor-specific immune responses and establishing an immunosuppressive tumor microenvironment, thus enabling tumor immune evasion. Additionally, recent evidence indicates that Tregs comprise diverse and heterogeneous subsets; phenotypically and functionally distinct subsets of tumor-infiltrating Tregs could contribute differently to cancer prognosis and clinical outcomes. Understanding Treg biology in the setting of cancer, and specifically the tumor microenvironment, is important for designing effective cancer therapies. In this review, we critically examine the role of Tregs in the tumor microenvironment and in cancer progression focusing on human studies. We also discuss the impact of current therapeutic modalities on Treg biology and the therapeutic opportunities for targeting Tregs to enhance anti-tumor immune responses and clinical benefits.
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Recently, single cell sequencing has become the cutting-edge technology to reveal the complexity of biological processes at the finest scale. However, it is not easy to handle single cells precisely, nor to perform the small scale reactions accurately. Microfluidics offers a ready solution to control the liquid delivery at the picoliter scale, matching the accuracy requirements for single cell analyses. We review the recently reported applications that using integrated microfluidic devices to facilitate the sample preparation for single cell sequencing. Various sequencing approaches with very limited starting materials, such as single cell whole-genome sequencing, single cell whole-transcriptome sequencing, and 1,000-cell chromatin immunoprecipitation sequencing, have been successfully performed with microfluidic assistance. Compared to conventional methods, integrated microfluidic devices exhibit unique advantages in manipulation and capturing single cells, and provide flexible tools for performing high throughput experiments in parallel. The results are highly repeatable and reproducible, showing great potential in sample preparation for single cell sequencing.
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Microbiologists traditionally study population rather than individual cells, as it is generally assumed that the status of individual cells will be similar to that observed in the population. However, recent studies have shown that the individual behavior of each single cell could be quite different from that of the whole population, suggesting the importance of extending traditional microbiology studies to single-cell level. With recent technological advances, such as flow cytometry, next-generation sequencing (NGS) and microspectroscopy, single-cell microbiology has greatly enhanced the understanding of individuality and heterogeneity of microbes in many biological systems. Notably, the application of multiple 'omics' in single-cell analysis has shed lights on how individual cell perceives, responds and adapts to environments, how heterogeneity arises under external stress and finally determines the fate of the whole population, and how microbes survive under natural conditions. As single-cell analysis involves no axenic cultivation of target microorganism, it has also been demonstrated as a valuable tool for dissecting the microbial 'dark matter'. In this review, current state-of-the-art tools and methods for genomic and transcriptomic analysis of microbes at single-cell level were critically summarized, including single-cell isolation methods and experimental strategies of single-cell analysis with NGS. In addition, perspectives on the future trends of technology development in the field of single-cell analysis was also presented.
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Abstract Molecular analysis of circulating tumor cells (CTCs) at single-cell resolution offers great promise for cancer diagnostics and therapeutics from simple liquid biopsy. Recent development of massively parallel single-cell RNA-sequencing (scRNA-seq) provides a powerful method to resolve the cellular heterogeneity from gene expression and pathway regulation analysis. However, the scarcity of CTCs and the massive contamination of blood cells limit the utility of currently available technologies. Here, we present Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding technique, for high-throughput CTC analysis. High cell-capture efficiency and contamination removal capability of Hydro-Seq enables successful scRNA-seq of 666 CTCs from 21 breast cancer patient samples at high throughput. We identify breast cancer drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells (CSCs) as well as of epithelial/mesenchymal cell state transitions. Transcriptome analysis of these cells provides insights into monitoring target therapeutics and processes underlying tumor metastasis.
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The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.
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Detection of genomic changes at single cell resolution is important for characterizing genetic heterogeneity and evolution in normal tissues, cancers, and microbial populations. Traditional methods for assessing genetic heterogeneity have been limited by low resolution, low sensitivity, and/or low specificity. Single cell sequencing has emerged as a powerful tool for detecting genetic heterogeneity with high resolution, high sensitivity and, when appropriately analyzed, high specificity. Here we provide a protocol for the isolation, whole genome amplification, sequencing, and analysis of single cells. Our approach allows for the reliable identification of megabase-scale copy number variants in single cells. However, aspects of this protocol can also be applied to investigate other types of genetic alterations in single cells.
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We congratulate Wu et al.1 on their successful application of single-nucleus RNA sequencing to characterize renal cell types underrepresented by single-cell RNA sequencing. A particular strength of their workflow is the detection of cells such as podocytes, mesangial cells, and glomerular endothelial cells, which can be underrepresented by conventional single-cell sequencing. These cell types have previously been described as distinct clusters using single-cell sequencing at markedly higher total cell numbers, such as the recent landmark renal single-cell sequencing paper by Park et al.2 As a group characterizing renal injuries using a similar single-cell, high-throughput, droplet-based platform, we are struck by the differences in cell classification between those described by Wu et al. using single-nucleus sequencing and those seen in our own single-cell sequencing of similar kidneys after unilateral ureteric obstruction (UUO). In contrast to the authors, we detect multiple leukocytes in our samples and identify clusters of distinct subsets of mononuclear phagocytes (35%), neutrophils (1.8%), B lymphocytes (3.3%), and T lymphocytes (10%) from a library of 6213 transcriptomes (Figure 1). This correlates well with existing histologic studies and FACS analysis of digested UUO kidneys showing nearly 40% of renal cells are CD45-positive leukocytes3–5 and is in contrast to a single, small macrophage cluster found on the single-nucleus analysis. Several groups have reported the importance of both T and B lymphocytes in the pathogenesis of UUO, yet single-nuclear RNA sequencing seems not to identify such cells, with representative lymphocyte marker genes suggested by Park et al. instead expressed in sparse cells scattered across multiple clusters in the single-nucleus dataset.Figure 1.: Single cell sequencing identifies multiple distinct leukocyte populations in the obstructed murine kidney. (A) t-SNE plot demonstrating distinct cluster formation, annotated by cell type. (B) Leukocyte gene expression projected across these t-SNE clusters. (C) Leukocyte gene expression plotted across clusters, natural logarithmic scale. (D) Cell frequency as percentage of total library size. LH/DCT, loop of Henle/distal convoluted tubule; PTEC, proximal tubular epithelial cell; t-SNE, t-Distributed Stochastic Neighbour Embedding.It is probable that differing technologies may introduce a cell-specific bias into the library preparation. Single-nucleus sequencing appears to bring improved identification of glomerular cell types. Single-cell sequencing may be more biased toward leukocytes, to the detriment of those glomerular cells. The advantages of single-nucleus sequencing are important and include the reduction of stress-response gene signatures and the ability to sequence historical frozen samples. However, we wonder if current protocols lack the ability to characterize the full spectrum of leukocytes present within the kidney. We propose that complimentary roles exist for both single-cell and single-nucleus sequencing, and researchers may wish to consider the cell-type bias in any workflow, and their own cell of interest, when designing sequencing experiments. Disclosures None.
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Significance T cell receptors (TCRs) surveil cellular environment by recognizing peptides presented by the major histocompatibility complex. TCR sequencing allows for understanding the scope of T cell reactivity in health and disease. Specific TCR clones can be used as therapeutics in cancer and autoimmune disease. We present a technique that allows for TCR sequencing based on intracellular signaling molecules, such as cytokines and transcription factors. The core concept is highly generalizable and should be applicable to global gene expression analysis where intracellular marker-based cell isolation is required.
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Cells are the basic unit of life structure and life activities. Because of the complex micro-environment of cells, the content of components that play a key role is relatively small, so single-cell analysis is extremely challenging. In recent years, single-cell sequencing technology has been developed and matured. Single-cell sequencing can reveal the composition and physiological diversity of cells, and the existing single-cell separation technology, single-cell whole genome amplification technology, single The principles and applications of cell whole transcriptome amplification technology and single cell transcriptome sequencing are summarized and summarized.
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Abstract Theoretically, mice can produce 1015 possible αβ T cell receptors (TCRs), with over 106 TCR clonotypes present at a given time. Data driven estimates based on a rigorous analysis of this massive complex repertoire have not been done due to the cost of obtaining a large set of TCR sequences. The advent of next generation DNA sequencing platforms (Illumina, SOLiD, 454) has brought TCR sequence analysis into the realm of possibility. Matching the platform with the TCR template’s unique characteristics is essential. Each T cell rearranges its genome to generate a unique TCR (VDJ recombination), with nongermline bases enzymatically produced at the junctions. Attempts to “assemble” TCR sequences from short reads (35bp, Illumina) that do not span the recombination junctions have introduced an assembly conundrum. However, the 454 pyrosequencing platform’s 250bp sequence reads generate full TCR clonotypes with no need for overlap assembly. Herein, we provide the first large scale C57Bl/6 mouse TCR β repertoire on a long read platform (Roche 454). To illustrate our methods strength, we have generated hundreds of thousands of TCRs from multiple B6 mice. To estimate the procedures error rates we have sequenced TCRs from a recombinase deficient TCR transgenic mouse (P14). This technique enables organism level analysis of T cell lineage specification, selection, and regulation during development and in disease states (infectious, autoimmune, graft and vaccine responses).
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