scholarly journals Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Woosung Chung ◽  
Hye Hyeon Eum ◽  
Hae-Ock Lee ◽  
Kyung-Min Lee ◽  
Han-Byoel Lee ◽  
...  
2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A799-A799
Author(s):  
Dhiraj Kumar ◽  
Sreeharsha Gurrapu ◽  
Hyunho Han ◽  
Yan Wang ◽  
Seongyeon Bae ◽  
...  

BackgroundLong non-coding RNAs (lncRNAs) are involved in various biological processes and diseases. Malat1 (metastasis-associated lung adenocarcinoma transcript 1), also known as Neat2, is one of the most abundant and highly conserved nuclear lncRNAs. Several studies have shown that the expression of lncRNA Malat1 is associated with metastasis and serving as a predictive marker for various tumor progression. Metastatic relapse often develops years after primary tumor removal as a result of disseminated tumor cells undergoing a period of latency in the target organ.1–4 However, the correlation of tumor intrinsic lncRNA in regulation of tumor dormancy and immune evasion is largely unknown.MethodsUsing an in vivo screening platform for the isolation of genetic entities involved in either dormancy or reactivation of breast cancer tumor cells, we have identified Malat1 as a positive mediator of metastatic reactivation. To functionally uncover the role of Malat1 in metastatic reactivation, we have developed a knock out (KO) model by using paired gRNA CRISPR-Cas9 deletion approach in metastatic breast and other cancer types, including lung, colon and melanoma. As proof of concept we also used inducible knockdown system under in vivo models. To delineate the immune micro-environment, we have used 10X genomics single cell RNA-seq, ChIRP-seq, multi-color flowcytometry, RNA-FISH and immunofluorescence.ResultsOur results reveal that the deletion of Malat1 abrogates the tumorigenic and metastatic potential of these tumors and supports long-term survival without affecting their ploidy, proliferation, and nuclear speckles formation. In contrast, overexpression of Malat1 leads to metastatic reactivation of dormant breast cancer cells. Moreover, the loss of Malat1 in metastatic cells induces dormancy features and inhibits cancer stemness. Our RNA-seq and ChIRP-seq data indicate that Malat1 KO downregulates several immune evasion and stemness associated genes. Strikingly, Malat1 KO cells exhibit metastatic outgrowth when injected in T cells defective mice. Our single-cell RNA-seq cluster analysis and multi-color flow cytometry data show a greater proportion of T cells and reduce Neutrophils infiltration in KO mice which indicate that the immune microenvironment playing an important role in Malat1-dependent immune evasion. Mechanistically, loss of Malat1 is associated with reduced expression of Serpinb6b, which protects the tumor cells from cytotoxic killing by the T cells. Indeed, overexpression of Serpinb6b rescued the metastatic potential of Malat1 KO cells by protecting against cytotoxic T cells.ConclusionsCollectively, our data indicate that targeting this novel cancer-cell-initiated domino effect within the immune system represents a new strategy to inhibit tumor metastatic reactivation.Trial RegistrationN/AEthics ApprovalFor all the animal studies in the present study, the study protocols were approved by the Institutional Animal Care and Use Committee(IACUC) of UT MD Anderson Cancer Center.ConsentN/AReferencesArun G, Diermeier S, Akerman M, et al., Differentiation of mammary tumors and reduction in metastasis upon Malat1 lncRNA loss. Genes Dev 2016 Jan 1;30(1):34–51.Filippo G. Giancotti, mechanisms governing metastatic dormancy and reactivation. Cell 2013 Nov 7;155(4):750–764.Gao H, Chakraborty G, Lee-Lim AP, et al., The BMP inhibitor Coco reactivates breast cancer cells at lung metastatic sites. Cell 2012b;150:764–779.Gao H, Chakraborty G, Lee-Lim AP, et al., Forward genetic screens in mice uncover mediators and suppressors of metastatic reactivation. Proc Natl Acad Sci U S A 2014 Nov 18; 111(46): 16532–16537.


BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.


2020 ◽  
Author(s):  
Juliane Winkler ◽  
Weilun Tan ◽  
Christopher McGinnis ◽  
Zev Gartner ◽  
Spyros Darmanis ◽  
...  

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e12070-e12070
Author(s):  
Katherine Sanchez ◽  
Maritza Martel ◽  
Shu-Ching Chang ◽  
Yaping Wu ◽  
Zhaoyu Sun ◽  
...  

e12070 Background: In triple negative breast cancer (TNBC), anti-PD-L1 is associated with gains in overall survival, however only in tumors with > 1% PD-L1+ ICs. Locoregional cytokine therapy is being evaluated as a method of priming and redirecting ICs to tumors, which may increase I-O benefit particularly in PD-L1 negative tumors. We report a sensitive method of quantifying I-O-related changes in PD-L1 and ICs using mIHC and single-cell hierarchical regression, which controls for within-tumor and across-patient PD-L1/IC heterogeneity. Methods: Pre-treatment and resection tissues from a phase Ib trial of locoregional cytokines (IRX-2, n = 16 subjects) in ESBC were analyzed. IRX-2 contains immunostimulatory cytokines (GM-CSF, IL-2, IFN-α, INF-γ, and IL-12) and was injected in peri-areolar tissue, which communicates directly with tumor-draining lymphatics. Specimens were analyzed for 1) H&E stromal TILs score; 2) clinical PD-L1 IC expression (Ventana SP142, 2 blinded pathologists); and 3) mIHC (PerkinElmer Vectra). InForm software was used to obtain geospatial outputs of tumor cells (CK+) and ICs (CD3, CD8, CD163) across multiple regions of interest (mean:18; range: 9-32). Mixed-effects modeling was used to evaluate for changes in PD-L1, ICs, and IC/tumor distance metrics. Results: PD-L1 and IC quantity by mIHC was highly concordant with clinical PD-L1 IC classification (JT test = 211, p < .001) and TIL score (r = 0.77). Cytokine therapy was associated with higher PD-L1 IC classification in 9/13 tumors, and increased PD-L1 ICs by mIHC in 14/15 (+337%, 95% CI: 120,769%). After adjusting for heterogeneity, cytokine therapy increased CD3+CD8+ ICs (+173%, CI: 93,287%) and CD3+CD8- ICs (+120%, CI: 21,301%). Therapy was also associated with increased effector-helper T-cell clustering (nearest-neighbor distance, -40%, CI: -29,-50%) and effector cell tumor localization (stromal-tumor interface CD8+ density +90%, Cl: 74, 305%). Conclusions: Our method is concordant with clinical PD-L1 and sTILs scores, and can additionally quantify IC and IC distances. This assay may allow for comparative I-O assessments with increased resolution, and will be used in a randomized phase II trial of pembrolizumab, chemo +/- IRX-2 in stage II/III TNBC.


2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.


Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4885
Author(s):  
Christine M. Pauken ◽  
Shelby Ray Kenney ◽  
Kathryn J. Brayer ◽  
Yan Guo ◽  
Ursa A. Brown-Glaberman ◽  
...  

Fatal metastasis occurs when circulating tumor cells (CTCs) disperse through the blood to initiate a new tumor at specific sites distant from the primary tumor. CTCs have been classically defined as nucleated cells positive for epithelial cell adhesion molecule and select cytokeratins (EpCAM/CK/DAPI), while negative for the common lymphocyte marker CD45. The enumeration of CTCs allows an estimation of the overall metastatic burden in breast cancer patients, but challenges regarding CTC heterogeneity and metastatic propensities persist, and their decryption could improve therapies. CTCs from metastatic breast cancer (mBC) patients were captured using the RareCyteTM Cytefinder II platform. The Lin− and Lin+ (CD45+) cell populations isolated from the blood of three of these mBC patients were analyzed by single-cell transcriptomic methods, which identified a variety of immune cell populations and a cluster of cells with a distinct gene expression signature, which includes both cells expressing EpCAM/CK (“classic” CTCs) and cells possessing an array of genes not previously associated with CTCs. This study put forward notions that the identification of these genes and their interactions will promote novel areas of analysis by dissecting properties underlying CTC survival, proliferation, and interaction with circulatory immune cells. It improves upon capabilities to measure and interfere with CTCs for impactful therapeutic interventions.


2020 ◽  
Author(s):  
Ruoyu Zhang ◽  
Gurinder S. Atwal ◽  
Wei Keat Lim

AbstractWith the rapid advancement of single-cell RNA-seq (scRNA-seq) technology, many data preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene networks reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on human cell atlas bone marrow data with respect to their impact on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data preprocessing steps due to over-smoothing of the raw data. We proposed a model-agnostic noise regularization method that can effectively eliminate the correlation artifacts. The noise regularized gene-gene correlations were further used to reconstruct gene co-expression network and successfully revealed several known immune cell modules.


2020 ◽  
Author(s):  
Xiaomei Li ◽  
Lin Liu ◽  
Greg Goodall ◽  
Andreas Schreiber ◽  
Taosheng Xu ◽  
...  

AbstractBreast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Author summaryVarious computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.


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