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Single-cell sequencing and its applications in bladder cancer

Published online by Cambridge University Press:  28 January 2022

Wang Wei
Affiliation:
Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan 430071, People's Republic of China Center for Single-Cell Omics and Tumor Liquid Biopsy, Zhongnan Hospital of Wuhan University, Wuhan, China Department of Laboratory Medicine, The First Affiliated Hospital of Yangtze University, No.55 North Jianghan Road, Shashi District, Jingzhou 434000, People's Republic of China
Yuan Rong
Affiliation:
Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan 430071, People's Republic of China
Liu Sanhe
Affiliation:
Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China
Yang Chunxiu
Affiliation:
Department of Pathology, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan 430071, People's Republic of China
Erick Thokerunga
Affiliation:
Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan 430071, People's Republic of China
Diansheng Cui*
Affiliation:
Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China Department of Urology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, People's Republic of China
Fubing Wang*
Affiliation:
Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, No.169 Donghu Road, Wuchang District, Wuhan 430071, People's Republic of China Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan, China
*
Author for correspondence: Fubing Wang, E-mail: [email protected] Diansheng Cui, E-mail: [email protected]
Author for correspondence: Fubing Wang, E-mail: [email protected] Diansheng Cui, E-mail: [email protected]

Abstract

Bladder cancer is the most common malignant tumour of the urinary system that is characterised by significant intra-tumoural heterogeneity. While large-scale sequencing projects have provided a preliminary understanding of tumour heterogeneity, these findings are based on the average signals obtained from the pooled populations of diverse cells. Recent advances in single-cell sequencing (SCS) technologies have been critical in this regard, opening up new ways of understanding the nuanced tumour biology by identifying distinct cellular subpopulations, dissecting the tumour microenvironment, and characterizing cellular genomic mutations. By integrating these novel insights, SCS technologies are expected to make powerful and meaningful changes to the current diagnosis and treatment of bladder cancer through the identification and usage of novel biomarkers as well as targeted therapeutics. SCS can discriminate complex heterogeneity in a large population of tumour cells and determine the key molecular properties that influence clinical outcomes. Here, we review the advances in single-cell technologies and discuss their applications in cancer research and clinical practice, with a specific focus on bladder cancer.

Type
Review
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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Footnotes

*

Equal contribution.

References

Sung, H et al. (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer Journal for Clinicians 71, 209249.Google Scholar
McGranahan, N and Swanton, C (2015) Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 1526.CrossRefGoogle ScholarPubMed
Sjödahl, G et al. (2012) A molecular taxonomy for urothelial carcinoma. Clinical Cancer Research 18, 33773386.CrossRefGoogle ScholarPubMed
Choi, W et al. (2014) Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell 25, 152165.CrossRefGoogle ScholarPubMed
Eberwine, J et al. (1992) Analysis of gene expression in single live neurons. Proceedings of the National Academy of Sciences of the USA 89, 30103014.CrossRefGoogle ScholarPubMed
Stuart, T and Satija, R (2019) Integrative single-cell analysis. Nature Reviews Genetics 20, 257272.CrossRefGoogle ScholarPubMed
Gao, Y et al. (2017) Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells. Genome Research 27, 13121322.CrossRefGoogle ScholarPubMed
Wu, H et al. (2017) Evolution and heterogeneity of non-hereditary colorectal cancer revealed by single-cell exome sequencing. Oncogene 36, 28572867.CrossRefGoogle ScholarPubMed
Jahn, K, Kuipers, J and Beerenwinkel, N (2016) Tree inference for single-cell data. Genome Biology 17, 86.CrossRefGoogle ScholarPubMed
Casasent, AK et al. (2018) Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205217.e12.CrossRefGoogle ScholarPubMed
Li, H et al. (2017) Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nature Genetics 49, 708718.CrossRefGoogle ScholarPubMed
Puram, SV et al. (2017) Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 16111624.e24.CrossRefGoogle ScholarPubMed
Lavin, Y et al. (2017) Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750765.e17.CrossRefGoogle ScholarPubMed
Lambrechts, D et al. (2018) Phenotype molding of stromal cells in the lung tumor microenvironment. Nature Medicine 24, 12771289.CrossRefGoogle ScholarPubMed
Carter, L et al. (2017) Molecular analysis of circulating tumor cells identifies distinct copy-number profiles in patients with chemosensitive and chemorefractory small-cell lung cancer. Nature Medicine 23, 114119.CrossRefGoogle ScholarPubMed
Martelotto, LG et al. (2017) Whole-genome single-cell copy number profiling from formalin-fixed paraffin-embedded samples. Nature Medicine 23, 376385.CrossRefGoogle ScholarPubMed
Chen, C et al. (2017) Single-cell whole-genome analyses by linear amplification via transposon insertion (LIANTI). Science 356, 189194.CrossRefGoogle Scholar
Navin, N et al. (2011) Tumour evolution inferred by single-cell sequencing. Nature 472, 9094.CrossRefGoogle ScholarPubMed
Van Loo, P and Voet, T (2014) Single cell analysis of cancer genomes. Current Opinion in Genetics & Development 24, 8291.CrossRefGoogle ScholarPubMed
Zhang, X et al. (2016) Single-cell sequencing for precise cancer research: progress and prospects. Cancer Research 76, 13051312.CrossRefGoogle ScholarPubMed
Blagodatskikh, KA et al. (2017) Improved DOP-PCR (iDOP-PCR): a robust and simple WGA method for efficient amplification of low copy number genomic DNA. PLoS ONE 12, e0184507.CrossRefGoogle ScholarPubMed
Xu, L et al. (2016) Virtual microfluidics for digital quantification and single-cell sequencing. Nature Methods 13, 759762.CrossRefGoogle ScholarPubMed
Zhang, CZ et al. (2015) Calibrating genomic and allelic coverage bias in single-cell sequencing. Nature Communications 6, 6822.CrossRefGoogle ScholarPubMed
Baslan, T et al. (2020) Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing. eLife 9, e51480.CrossRefGoogle ScholarPubMed
Wang, Y et al. (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155160.CrossRefGoogle ScholarPubMed
Tang, F et al. (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377382.CrossRefGoogle ScholarPubMed
Marinov, GK et al. (2014) From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Research 24, 496510.CrossRefGoogle ScholarPubMed
Borel, C et al. (2015) Biased allelic expression in human primary fibroblast single cells. American Journal of Human Genetics 96, 7080.CrossRefGoogle ScholarPubMed
Chen, G, Ning, B and Shi, T (2019) Single-cell RNA-seq technologies and related computational data analysis. Frontiers in Genetics 10, 317.CrossRefGoogle ScholarPubMed
Chung, W et al. (2017) Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nature Communications 8, 15081.CrossRefGoogle ScholarPubMed
Izar, B et al. (2020) A single-cell landscape of high-grade serous ovarian cancer. Nature Medicine 26, 12711279.CrossRefGoogle ScholarPubMed
Zheng, C et al. (2017) Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 13421356.e16.CrossRefGoogle ScholarPubMed
Brouzes, E et al. (2009) Droplet microfluidic technology for single-cell high-throughput screening. Proceedings of the National Academy of Sciences of the USA 106, 1419514200.CrossRefGoogle ScholarPubMed
Gierahn, TM et al. (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nature Methods 14, 395398.CrossRefGoogle ScholarPubMed
Rosenberg, AB et al. (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176182.CrossRefGoogle ScholarPubMed
Baslan, T et al. (2015) Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome Research 25, 714724.CrossRefGoogle ScholarPubMed
Laks, E et al. (2019) Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 12071221.e22.CrossRefGoogle ScholarPubMed
Yin, Y et al. (2019) High-throughput single-cell sequencing with linear amplification. Molecular Cell 76, 676690. e10.CrossRefGoogle ScholarPubMed
Vitak, SA et al. (2017) Sequencing thousands of single-cell genomes with combinatorial indexing. Nature Methods 14, 302308.CrossRefGoogle ScholarPubMed
Nichterwitz, S et al. (2016) Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nature Communications 7, 12139.CrossRefGoogle ScholarPubMed
Shapiro, E, Biezuner, T and Linnarsson, S (2013) Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Reviews Genetics 14, 618630.CrossRefGoogle ScholarPubMed
Xin, Y et al. (2016) Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proceedings of the National Academy of Sciences of the USA 113, 32933298.CrossRefGoogle ScholarPubMed
Macosko, EZ et al. (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 12021214.CrossRefGoogle ScholarPubMed
Fan, HC, Fu, GK and Fodor, SP (2015) Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367.CrossRefGoogle ScholarPubMed
Cao, J et al. (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661667.CrossRefGoogle ScholarPubMed
Jin, W et al. (2015) Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142146.CrossRefGoogle ScholarPubMed
Buenrostro, JD et al. (2015) Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486490.CrossRefGoogle ScholarPubMed
Cusanovich, DA et al. (2015) Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910914.CrossRefGoogle ScholarPubMed
Ramani, V et al. (2017) Massively multiplex single-cell Hi-C. Nature Methods 14, 263266.CrossRefGoogle ScholarPubMed
Rotem, A et al. (2015) Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nature Biotechnology 33, 11651172.CrossRefGoogle ScholarPubMed
Smallwood, SA et al. (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nature Methods 11, 817820.CrossRefGoogle ScholarPubMed
Han, L et al. (2017) Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells. Nucleic Acids Research 45, e77.Google ScholarPubMed
Macaulay, IC et al. (2015) G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nature Methods 12, 519522.CrossRefGoogle ScholarPubMed
Dey, SS et al. (2015) Integrated genome and transcriptome sequencing of the same cell. Nature Biotechnology 33, 285289.CrossRefGoogle ScholarPubMed
Macaulay, IC et al. (2016) Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nature Protocols 11, 20812103.CrossRefGoogle ScholarPubMed
Angermueller, C et al. (2016) Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nature Methods 13, 229232.CrossRefGoogle ScholarPubMed
Clark, SJ et al. (2018) scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nature Communications 9, 781.CrossRefGoogle ScholarPubMed
Hou, Y et al. (2016) Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Research 26, 304319.CrossRefGoogle ScholarPubMed
Gu, C et al. (2019) Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Research 29, 110123.CrossRefGoogle ScholarPubMed
Hu, Y et al. (2016) Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biology 17, 88.CrossRefGoogle ScholarPubMed
Cheow, LF et al. (2016) Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nature Methods 13, 833836.CrossRefGoogle ScholarPubMed
Pott, S (2017) Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, e23203.CrossRefGoogle ScholarPubMed
Guo, F et al. (2017) Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Research 27, 967988.CrossRefGoogle ScholarPubMed
Genshaft, AS et al. (2016) Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biology 17, 188.CrossRefGoogle Scholar
Frei, AP et al. (2016) Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nature Methods 13, 269275.CrossRefGoogle ScholarPubMed
Stoeckius, M et al. (2017) Simultaneous epitope and transcriptome measurement in single cells. Nature Methods 14, 865868.CrossRefGoogle ScholarPubMed
Peterson, VM et al. (2017) Multiplexed quantification of proteins and transcripts in single cells. Nature Biotechnology 35, 936939.CrossRefGoogle ScholarPubMed
Gong, W et al. (2018) DrImpute: imputing dropout events in single cell RNA sequencing data. BMC Bioinformatics 19, 220.CrossRefGoogle ScholarPubMed
Lin, P, Troup, M and Ho, JW (2017) CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biology 18, 59.CrossRefGoogle ScholarPubMed
Haghverdi, L et al. (2018) Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nature Biotechnology 36, 421427.CrossRefGoogle ScholarPubMed
Butler, A et al. (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology 36, 411420.CrossRefGoogle ScholarPubMed
Vallejos, CA et al. (2017) Normalizing single-cell RNA sequencing data: challenges and opportunities. Nature Methods 14, 565571.CrossRefGoogle ScholarPubMed
McCarthy, DJ et al. (2017) Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics (Oxford, England) 33, 11791186.Google ScholarPubMed
Bacher, R et al. (2017) SCnorm: robust normalization of single-cell RNA-seq data. Nature Methods 14, 584586.CrossRefGoogle ScholarPubMed
Kiselev, VY et al. (2017) SC3: consensus clustering of single-cell RNA-seq data. Nature Methods 14, 483486.CrossRefGoogle ScholarPubMed
Ji, Z and Ji, H (2016) TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Research 44, e117.CrossRefGoogle ScholarPubMed
Setty, M et al. (2016) Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature Biotechnology 34, 637645.CrossRefGoogle ScholarPubMed
Ding, J, Condon, A and Shah, SP (2018) Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nature Communications 9, 2002.CrossRefGoogle ScholarPubMed
Wang, B et al. (2017) Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nature Methods 14, 414416.CrossRefGoogle ScholarPubMed
Levine, JH et al. (2015) Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184197.CrossRefGoogle ScholarPubMed
Chen, J et al. (2017) Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nature Protocols 12, 566580.CrossRefGoogle ScholarPubMed
Junker, JP et al. (2014) Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662675.CrossRefGoogle ScholarPubMed
Raj, A et al. (2008) Imaging individual mRNA molecules using multiple singly labeled probes. Nature Methods 5, 877879.CrossRefGoogle ScholarPubMed
Lubeck, E et al. (2014) Single-cell in situ RNA profiling by sequential hybridization. Nature Methods 11, 360361.CrossRefGoogle ScholarPubMed
Shah, S et al. (2016) In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342357.CrossRefGoogle ScholarPubMed
Chen, KH et al. (2015) RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090.CrossRefGoogle ScholarPubMed
Lovatt, D et al. (2014) Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nature Methods 11, 190196.CrossRefGoogle ScholarPubMed
Ståhl, PL et al. (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 7882.CrossRefGoogle ScholarPubMed
Rodriques, SG et al. (2019) Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 14631467.CrossRefGoogle ScholarPubMed
Stickels, RR et al. (2021) Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nature Biotechnology 39, 313319.CrossRefGoogle ScholarPubMed
Kalluri, R (2016) The biology and function of fibroblasts in cancer. Nature Reviews Cancer 16, 582598.CrossRefGoogle ScholarPubMed
Bartoschek, M et al. (2018) Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. Nature Communications 9, 5150.CrossRefGoogle ScholarPubMed
Elyada, E et al. (2019) Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discovery 9, 11021123.CrossRefGoogle ScholarPubMed
Guo, X et al. (2018) Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nature Medicine 24, 978985.CrossRefGoogle ScholarPubMed
Peng, J et al. (2019) Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Research 29, 725738.CrossRefGoogle ScholarPubMed
Savas, P et al. (2018) Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nature Medicine 24, 986993.CrossRefGoogle ScholarPubMed
Zhang, Q et al. (2019) Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179, 829845. e20.CrossRefGoogle ScholarPubMed
Lito, P, Rosen, N and Solit, DB (2013) Tumor adaptation and resistance to RAF inhibitors. Nature Medicine 19, 14011409.CrossRefGoogle ScholarPubMed
Ma, CX et al. (2015) Mechanisms of aromatase inhibitor resistance. Nature Reviews Cancer 15, 261275.CrossRefGoogle ScholarPubMed
Miyamoto, DT et al. (2015) RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349, 13511356.CrossRefGoogle ScholarPubMed
Shaffer, SM et al. (2017) Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431435.CrossRefGoogle ScholarPubMed
Sharma, A et al. (2018) Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy. Nature Communications 9, 4931.CrossRefGoogle ScholarPubMed
Kim, C et al. (2018) Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879893. e13.CrossRefGoogle ScholarPubMed
Park, SR et al. (2020) Single-cell transcriptome analysis of colon cancer cell response to 5-fluorouracil-induced DNA damage. Cell Reports 32, 108077.CrossRefGoogle ScholarPubMed
Nawroth, R, Weckermann, D and Retz, M (2014) [Prostate and bladder cancer: detection of disseminated tumor cells in bone marrow]. Urologe A 53, 514518.CrossRefGoogle Scholar
Yamada, S et al. (2007) Clinical implications of peritoneal cytology in potentially resectable pancreatic cancer: positive peritoneal cytology may not confer an adverse prognosis. Annals of Surgery 246, 254258.CrossRefGoogle Scholar
Naz, S et al. (2015) Role of peritoneal washing cytology in ovarian malignancies: correlation with histopathological parameters. World Journal of Surgical Oncology 13, 315.CrossRefGoogle ScholarPubMed
Ledergor, G et al. (2018) Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nature Medicine 24, 18671876.CrossRefGoogle ScholarPubMed
Pantel, K, Alix-Panabières, C and Riethdorf, S (2009) Cancer micrometastases. Nature Reviews. Clinical Oncology 6, 339351.CrossRefGoogle ScholarPubMed
Sun, YF et al. (2021) Dissecting spatial heterogeneity and the immune-evasion mechanism of CTCs by single-cell RNA-seq in hepatocellular carcinoma. Nature Communications 12, 4091.CrossRefGoogle ScholarPubMed
Barros-Silva, JD et al. (2018) Single-cell analysis identifies LY6D as a marker linking castration-resistant prostate luminal cells to prostate progenitors and cancer. Cell Reports 25, 35043518. e6.CrossRefGoogle ScholarPubMed
Bernard, V et al. (2019) Single-cell transcriptomics of pancreatic cancer precursors demonstrates epithelial and microenvironmental heterogeneity as an early event in neoplastic progression. Clinical Cancer Research 25, 21942205.CrossRefGoogle ScholarPubMed
Alexander, J et al. (2018) Utility of single-cell genomics in diagnostic evaluation of prostate cancer. Cancer Research 78, 348358.CrossRefGoogle ScholarPubMed
Bian, S et al. (2018) Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 10601063.CrossRefGoogle ScholarPubMed
Hong, SP et al. (2019) Single-cell transcriptomics reveals multi-step adaptations to endocrine therapy. Nature Communications 10, 3840.CrossRefGoogle ScholarPubMed
Azizi, E et al. (2018) Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 12931308. e36.CrossRefGoogle ScholarPubMed
Yu, Z et al. (2019) Single-cell transcriptomic map of the human and mouse bladders. Journal of the American Society of Nephrology 30, 21592176.CrossRefGoogle ScholarPubMed
Li, Y et al. (2012) Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer. Gigascience 1, 12.CrossRefGoogle Scholar
Yang, Z et al. (2017) Single-cell sequencing reveals variants in ARID1A, GPRC5A and MLL2 driving self-renewal of human bladder cancer stem cells. European Urology 71, 812.CrossRefGoogle ScholarPubMed
Tanaka, N et al. (2018) Single-cell RNA-seq analysis reveals the platinum resistance gene COX7B and the surrogate marker CD63. Cancer Medicine 7, 61936204.CrossRefGoogle ScholarPubMed
Lee, HW et al. (2020) Single-cell RNA sequencing reveals the tumor microenvironment and facilitates strategic choices to circumvent treatment failure in a chemorefractory bladder cancer patient. Genome Medicine 12, 47.CrossRefGoogle Scholar
Oh, DY et al. (2020) Intratumoral CD4(+) T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell 181, 16121625.e13.CrossRefGoogle ScholarPubMed
Russell, MR et al. (2017) Novel risk models for early detection and screening of ovarian cancer. Oncotarget 8, 785797.CrossRefGoogle ScholarPubMed
Allin, DM et al. (2018) Circulating tumour DNA is a potential biomarker for disease progression and response to targeted therapy in advanced thyroid cancer. European Journal of Cancer 103, 165175.CrossRefGoogle ScholarPubMed
Kim, KT et al. (2015) Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biology 16, 127.CrossRefGoogle ScholarPubMed