Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation

Abstract

The advent of single-cell sequencing techniques has not only revolutionized the investigation of biological processes but also significantly contributed to unraveling cellular heterogeneity at unprecedented levels. Among the various methods, single-cell transcriptome sequencing stands out as the best established, and has been employed in exploring many physiological and pathological activities. The recently developed single-cell epigenetic sequencing techniques, especially chromatin accessibility sequencing, have further deepened our understanding of gene regulatory networks. In this review, we summarize the recent breakthroughs in single-cell transcriptome and chromatin accessibility sequencing methodologies. Additionally, we describe current bioinformatic strategies to integrate data obtained through these single-cell sequencing methods and highlight the application of this analysis strategy on a deeper understanding of tumorigenesis and tumor progression. Finally, we also discuss the challenges and anticipated developments in this field.

Keywords

single-cell transcriptome sequencing (scRNA-seq) / single-cell chromatin accessibility sequencing / tumor microenvironment / single-cell multi-omics / assay for transposase-accessible chromatin

References

[1] Kim S , Wysocka J . Deciphering the multi-scale, quantitative cis-regulatory code. Mol Cell 2023; 83: 373- 92.

[2] Furey TS . ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 2012; 13: 840- 52.

[3] Boyle AP , Song L , Lee BK , et al. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res 2011; 21: 456- 64.

[4] Buenrostro JD , Giresi PG , Zaba LC , et al. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 2013; 10: 1213- 8.

[5] Giresi PG , Kim J , McDaniell RM , et al. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res 2007; 17: 877- 85.

[6] Aran D . Single-cell RNA sequencing for studying human cancers. Annu Rev Biomed Data Sci 2023; 6: 1- 22.

[7] Tang F , Barbacioru C , Wang Y , et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009; 6: 377- 82.

[8] Baysoy A , Bai Z , Satija R , et al. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24: 695- 713.

[9] Salmen F , De Jonghe J , Kaminski TS , et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat Biotechnol 2022; 40: 1780- 93.

[10] Hagemann-Jensen M , Ziegenhain C , Chen P , et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat Biotechnol 2020; 38: 708- 14.

[11] Zheng GX , Terry JM , Belgrader P , et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun 2017; 8: 14049.

[12] Islam S , Zeisel A , Joost S , et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 2014; 11: 163- 6.

[13] Macosko EZ , Basu A , Satija R , et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015; 161: 1202- 14.

[14] Grun D , Kester L , van Oudenaarden A . Validation of noise models for single-cell transcriptomics. Nat Methods 2014; 11: 637- 40.

[15] Klein AM , Mazutis L , Akartuna I , et al. Droplet barcoding for singlecell transcriptomics applied to embryonic stem cells. Cell 2015; 161: 1187- 201.

[16] Ramskold D , Luo S , Wang YC , et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 2012; 30: 777- 82.

[17] Nakamura T , Yabuta Y , Okamoto I , et al. SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression. Nucleic Acids Res 2015; 43: e60.

[18] Picelli S , Bjorklund AK , Faridani OR , et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 2013; 10: 1096- 8.

[19] Fan HC , Fu GK , Fodor SP . Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 2015; 347: 1258367.

[20] Sasagawa Y , Nikaido I , Hayashi T , et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.Genome Biol 2013; 14: R31.

[21] Jaitin DA , Kenigsberg E , Keren-Shaul H , et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014; 343: 776- 9.

[22] Gierahn TM , Wadsworth MH 2nd , Hughes TK , et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 2017; 14: 395- 8.

[23] Hashimshony T , Senderovich N , Avital G , et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 2016; 17: 77.

[24] Han X , Wang R , Zhou Y , et al. Mapping the mouse cell atlas by microwell-seq. Cell 2018; 172: 1091- 107.e17.

[25] Fan X , Tang D , Liao Y , et al. Single-cell RNA-seq analysis of mouse preimplantation embryos by third-generation sequencing. PLoS Biol 2020; 18: e3001017.

[26] Al’Khafaji AM , Smith JT , Garimella KV , et al. High-throughput RNA isoform sequencing using programmed cDNA concatenation. Nat Biotechnol 2023; 42: 582- 6.

[27] Lawson DA , Bhakta NR , Kessenbrock K , et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature 2015; 526: 131- 5.

[28] Stewart CA , Gay CM , Xi Y , et al. Single-cell analyses reveal increased intratumoral heterogeneity after the onset of therapy resistance in small-cell lung cancer. Nat Cancer 2020; 1: 423- 36.

[29] de Sousa e Melo F , Kurtova AV , Harnoss JM , et al. A distinct role for Lgr5(+) stem cells in primary and metastatic colon cancer. Nature 2017; 543: 676- 80.

[30] Fumagalli A , Oost KC , Kester L , et al. Plasticity of Lgr5-negative cancer cells drives metastasis in colorectal cancer. Cell Stem Cell 2020; 26: 569- 78.e7.

[31] Vasquez EG , Nasreddin N , Valbuena GN , et al. Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia. Cell Stem Cell 2022; 29: 1213- 28.e8.

[32] Min J , Zhang C , Bliton RJ , et al. Dysplastic stem cell plasticity functions as a driving force for neoplastic transformation of precancerous gastric mucosa. Gastroenterology 2022; 163: 875- 90.

[33] Li K , Du Y , Cai Y , et al. Single-cell analysis reveals the chemotherapy-induced cellular reprogramming and novel therapeutic targets in relapsed/refractory acute myeloid leukemia. Leukemia 2023; 37: 308- 25.

[34] Chen G , Gong T , Wang Z , et al. Colorectal cancer organoid models uncover oxaliplatin-resistant mechanisms at single cell resolution. Cell Oncol 2022; 45: 1155- 67.

[35] Sharma A , Seow JJW , Dutertre CA , et al. Onco-fetal reprogramming of endothelial cells drives immunosuppressive macrophages in hepatocellular carcinoma. Cell 2020; 183: 377- 94.e21.

[36] Ren X , Zhang L , Zhang Y , et al. Insights gained from single-cell analysis of immune cells in the tumor microenvironment. Annu Rev Immunol 2021; 39: 583- 609.

[37] Elyada E , Bolisetty M , Laise P , et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov 2019; 9: 1102- 23.

[38] Wang Y , Liang Y , Xu H , et al. Single-cell analysis of pancreatic ductal adenocarcinoma identifies a novel fibroblast subtype associated with poor prognosis but better immunotherapy response. Cell Discov 2021; 7: 36.

[39] Kieffer Y , Hocine HR , Gentric G , et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov 2020; 10: 1330- 51.

[40] Wu SZ , Roden DL , Wang C , et al. Stromal cell diversity associated with immune evasion in human triple-negative breast cancer. EMBO J 2020; 39: e104063.

[41] Bota-Rabassedas N , Banerjee P , Niu Y , et al. Contextual cues from cancer cells govern cancer-associated fibroblast heterogeneity. Cell Rep 2021; 35: 109009.

[42] Affo S , Nair A , Brundu F , et al. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations. Cancer Cell 2021; 39: 866- 82.e11.

[43] Lavie D , Ben-Shmuel A , Erez N , et al. Cancer-associated fibroblasts in the single-cell era. Nat Cancer 2022; 3: 793- 807.

[44] Rohlenova K , Goveia J , Garcia-Caballero M , et al. Single-cell RNA sequencing maps endothelial metabolic plasticity in pathological angiogenesis. Cell Metab 2020; 31: 862- 77.e14.

[45] Zhang J , Lu T , Lu S , et al. Single-cell analysis of multiple cancer types reveals differences in endothelial cells between tumors and normal tissues. Comput Struct Biotechnol J 2023; 21: 665- 76.

[46] Zheng L , Qin S , Si W , et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 2021; 374: abe6474.

[47] Wu K , Lin K , Li X , et al. Redefining tumor-associated macrophage subpopulations and functions in the tumor microenvironment. Front Immunol 2020; 11: 1731.

[48] Gerhard GM , Bill R , Messemaker M , et al. Tumor-infiltrating dendritic cell states are conserved across solid human cancers. J Exp Med 2021; 218: e20200264.

[49] Del Prete A , Salvi V , Soriani A , et al. Dendritic cell subsets in cancer immunity and tumor antigen sensing. Cell Mol Immunol 2023; 20: 432- 47.

[50] Paijens ST , Vledder A , de Bruyn M , et al. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol 2021; 18: 842- 59.

[51] Bod L , Kye YC , Shi J , et al. B-cell-specific checkpoint molecules that regulate anti-tumour immunity. Nature 2023; 619: 348- 56.

[52] Xue R , Zhang Q , Cao Q , et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 2022; 612: 141- 7.

[53] Smallwood SA , Lee HJ , Angermueller C , et al. Single-cell genomewide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 2014; 11: 817- 20.

[54] Ogbeide S , Giannese F , Mincarelli L , et al. Into the multiverse: advances in single-cell multiomic profiling. Trends Genet 2022; 38: 831- 43.

[55] Grosselin K , Durand A , Marsolier J , et al. High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nat Genet 2019; 51: 1060- 6.

[56] Kaya-Okur HS , Wu SJ , Codomo CA , et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun 2019; 10: 1930.

[57] Buenrostro JD , Wu B , Litzenburger UM , et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 2015; 523: 486- 90.

[58] Cusanovich DA , Hill AJ , Aghamirzaie D , et al. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 2018; 174: 1309- 24.e18.

[59] Muto Y , Wilson PC , Ledru N , et al. Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney. Nat Commun 2021; 12: 2190.

[60] Ma S , Zhang B , LaFave LM , et al. Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 2020; 183: 1103- 16.e20.

[61] Morabito S , Miyoshi E , Michael N , et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat Genet 2021; 53: 1143- 55.

[62] Wu X , Lu M , Yun D , et al. Single-cell ATAC-Seq reveals cell typespecific transcriptional regulation and unique chromatin accessibility in human spermatogenesis. Hum Mol Genet 2022; 31: 321- 33.

[63] Zhang TQ , Chen Y , Liu Y , et al. Single-cell transcriptome atlas and chromatin accessibility landscape reveal differentiation trajectories in the rice root. Nat Commun 2021; 12: 2053.

[64] Joung J , Ma S , Tay T , et al. A transcription factor atlas of directed differentiation. Cell 2023; 186: 209- 29.e26.

[65] LaFave LM , Kartha VK , Ma S , et al. Epigenomic state transitions characterize tumor progression in mouse lung adenocarcinoma. Cancer Cell 2020; 38: 212- 28.e13.

[66] Satpathy AT , Granja JM , Yost KE , et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat Biotechnol 2019; 37: 925- 36.

[67] Jiang P , Zhang Z , Hu Y , et al. Single-cell ATAC-seq maps the comprehensive and dynamic chromatin accessibility landscape of CAR-T cell dysfunction. Leukemia 2022; 36: 2656- 68.

[68] O’Connell BL , Nichols RV , Pokholok D , et al. Atlas-scale single-cell chromatin accessibility using nanowell-based combinatorial indexing. Genome Res 2023; 33: 208- 17.

[69] Lareau CA , Liu V , Muus C , et al. Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility. Nat Protoc 2023; 18: 1416- 40.

[70] Cusanovich DA , Daza R , Adey A , et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 2015; 348: 910- 4.

[71] Chen S , Lake BB , Zhang K . High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat Biotechnol 2019; 37: 1452- 7.

[72] Pott S . Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. Elife 2017; 6: e23203.

[73] Zhu C , Yu M , Huang H , et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat Struct Mol Biol 2019; 26: 1063- 70.

[74] Cohen JD , Li L , Wang Y , et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 2018; 359: 926- 30.

[75] Rubin AJ , Parker KR , Satpathy AT , et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 2019; 176: 361- 76.e17.

[76] Cao J , Cusanovich DA , Ramani V , et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 2018; 361: 1380- 5.

[77] Xing QR , Farran CAE , Zeng YY , et al. Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility. Genome Res 2020; 30: 1027- 39.

[78] Clark SJ , Argelaguet R , Kapourani CA , et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun 2018; 9: 781.

[79] Swanson E , Lord C , Reading J , et al. Simultaneous trimodal single-cell measurement of transcripts, epitopes, and chromatin accessibility using TEA-seq. Elife 2021; 10: e63632.

[80] Liu L , Liu C , Quintero A , et al. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat Commun 2019; 10: 470.

[81] Mimitou EP , Lareau CA , Chen KY , et al. Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells. Nat Biotechnol 2021; 39: 1246- 58.

[82] Gu C , Liu S , Wu Q , et al. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res 2019; 29: 110- 23.

[83] Pierce SE , Granja JM , Greenleaf WJ . High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat Commun 2021; 12: 2969.

[84] Plongthongkum N , Diep D , Chen S , et al. Scalable dual-omics profiling with single-nucleus chromatin accessibility and mRNA expression sequencing 2 (SNARE-seq2). Nat Protoc 2021; 16: 4992- 5029.

[85] Fiskin E , Lareau CA , Ludwig LS , et al. Single-cell profiling of proteins and chromatin accessibility using PHAGE-ATAC. Nat Biotechnol 2022; 40: 374- 81.

[86] Yan R , Gu C , You D , et al. Decoding dynamic epigenetic landscapes in human oocytes using single-cell multi-omics sequencing. Cell Stem Cell 2021; 28: 1641- 56.e7.

[87] Chen AF , Parks B , Kathiria AS , et al. NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells. Nat Methods 2022; 19: 547- 53.

[88] Liscovitch-Brauer N , Montalbano A , Deng J , et al. Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens. Nat Biotechnol 2021; 39: 1270- 7.

[89] Tedesco M , Giannese F , Lazarevic D , et al. Chromatin velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nat Biotechnol 2022; 40: 235- 44.

[90] Luo C , Liu H , Xie F , et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom 2022; 2: 100107.

[91] Xu W , Yang W , Zhang Y , et al. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods 2022; 19: 1243- 9.

[92] Wang Y , Yuan P , Yan Z , et al. Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos. Nat Commun 2021; 12: 1247.

[93] Stanojevic S , Li Y , Ristivojevic A , et al. Computational methods for single-cell multi-omics integration and alignment. Genomics Proteomics Bioinformat 2022; 20: 836- 49.

[94] Trevino AE , Muller F , Andersen J , et al. Chromatin and generegulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell 2021; 184: 5053- 69.e23.

[95] Ameen M , Sundaram L , Shen M , et al. Integrative single-cell analysis of cardiogenesis identifies developmental trajectories and non-coding mutations in congenital heart disease. Cell 2022; 185: 4937- 53.e23.

[96] Granja JM , Corces MR , Pierce SE , et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet 2021; 53: 403- 11.

[97] Stuart T , Srivastava A , Madad S , et al. Single-cell chromatin state analysis with Signac. Nat Methods 2021; 18: 1333- 41.

[98] Jafari E , Johnson T , Wang Y , et al. AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency. Bioinformatics 2022; 38: 5236- 44.

[99] Cao ZJ , Gao G . Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat Biotechnol 2022; 40: 1458- 66.

[100] Ashuach T , Gabitto MI , Koodli RV , et al. MultiVI: deep generative model for the integration of multimodal data. Nat Methods 2023; 20: 1222- 31.

[101] Wang L , Trasanidis N , Wu T , et al. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 2023; 20: 1368- 78.

[102] Fleck JS , Jansen SMJ , Wollny D , et al. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 2023; 621: 365- 72.

[103] Jansen C , Ramirez RN , El-Ali NC , et al. Building gene regulatory networks from scATAC-seq and scRNA-seq using linked self organizing maps. PLoS Comput Biol 2019; 15: e1006555.

[104] Kamal A , Arnold C , Claringbould A , et al. GRaNIE and GRaNPA: inference and evaluation of enhancer-mediated gene regulatory networks. Mol Syst Biol 2023; 19: e11627.

[105] Chen X , Wang Y , Cappuccio A , et al. Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data. Nat Comput Sci 2023; 3: 644- 57.

[106] Jin S , Zhang L , Nie Q . scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles. Genome Biol 2020; 21: 25.

[107] Jia Q , Chu H , Jin Z , et al. High-throughput single- cell sequencing in cancer research. Signal Transduct Target Ther 2022; 7: 145.

[108] Song Q , Zhu X , Jin L , et al. SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data. NAR Genom Bioinform 2022; 4: lqac056.

[109] Duren Z , Lu WS , Arthur JG , et al. Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data. Nat Commun 2021; 12: 4763.

[110] Becker WR , Nevins SA , Chen DC , et al. Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer. Nat Genet 2022; 54: 985- 95.

[111] Long Z , Sun C , Tang M , et al. Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma. Cell Discov 2022; 8: 68.

[112] Regner MJ , Wisniewska K , Garcia-Recio S , et al. A multi-omic single-cell landscape of human gynecologic malignancies. Mol Cell 2021; 81: 4924- 41.e10.

[113] Li S , Yang M , Teng S , et al. Chromatin accessibility dynamics in colorectal cancer liver metastasis: uncovering the liver tropism at single cell resolution. Pharmacol Res 2023; 195: 106896.

[114] Babikir H , Wang L , Shamardani K , et al. ATRX regulates glial identity and the tumor microenvironment in IDH-mutant glioma. Genome Biol 2021; 22: 311.

[115] Foster DS , Januszyk M , Delitto D , et al. Multiomic analysis reveals conservation of cancer-associated fibroblast phenotypes across species and tissue of origin. Cancer Cell 2022; 40: 1392- 406.e7.

[116] Terekhanova NV , Karpova A , Liang WW , et al. Epigenetic regulation during cancer transitions across 11 tumour types. Nature 2023; 623: 432- 41.

[117] Rautenstrauch P , Vlot AHC , Saran S , et al. Intricacies of single-cell multi-omics data integration. Trends Genet 2022; 38: 128- 39.

[118] Liu J , Huang Y , Singh R , et al. Jointly embedding multiple single-cell omics measurements. Algorithms Bioinform 2019; 143: 10.

[119] Cao K , Hong Y , Wan L . Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona. Bioinformatics 2021; 38: 211- 9.

[120] Demetci P , Santorella R , Sandstede B , et al. Single-cell multi-omics alignment with optimal transport. J Comput Biol 2022; 29: 3- 18.

[121] Cao K , Bai X , Hong Y , et al. Unsupervised topological alignment for single-cell multi-omics integration. Bioinformatics 2020; 36: i48- 56.

[122] Stuart T , Butler A , Hoffman P , et al. Comprehensive integration of single-cell data. Cell 2019; 177: 1888- 902.e21.

[123] Zhang S , Pyne S , Pietrzak S , et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 2023; 14: 3064.

[124] Skok Gibbs C , Jackson CA , Saldi GA , et al. High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0. Bioinformatics 2022; 38: 2519- 28.

[125] Ma A , Wang X , Li J , et al. Single-cell biological network inference using a heterogeneous graph transformer. Nat Commun 2023; 14: 964.

[126] Zhang L , Zhang J , Nie Q . DIRECT-NET. An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data. Sci Adv 2022; 8: eabl7393.

[127] Kartha VK , Duarte FM , Hu Y , et al. Functional inference of gene regulation using single-cell multi-omics. Cell Genom 2022; 2: eabl7393.

[128] Bravo Gonzalez-Blas C , De Winter S , Hulselmans G , et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat Methods 2023; 20: 1355- 67.

[129] Wang H , Mei Y , Luo C , et al. Single-cell analyses reveal mechanisms of cancer stem cell maintenance and epithelial-mesenchymal transition in recurrent bladder cancer. Clin Cancer Res 2021; 27: 6265- 78.

[130] Xu K , Zhang W , Wang C , et al. Integrative analyses of scRNA-seq and scATAC-seq reveal CXCL14 as a key regulator of lymph node metastasis in breast cancer. Hum Mol Genet 2021; 30: 370- 80.

[131] Yu Z , Lv Y , Su C , et al. Integrative single-cell analysis reveals transcriptional and epigenetic regulatory features of clear cell renal cell carcinoma. Cancer Res 2023; 83: 700- 19.

[132] Sun X , Zhou L , Wang Y , et al. Single-cell analyses reveal cannabidiol rewires tumor microenvironment via inhibiting alternative activation of macrophage and synergizes with anti-PD-1 in colon cancer. J Pharm Anal 2023; 13: 726- 44.

[133] Kim H , Wisniewska K , Regner MJ , et al. Single-cell transcriptional and epigenetic profiles of male breast cancer nominate salient cancer-specific enhancers. Int J Mol Sci 2023; 24: 13053.

[134] Poos AM , Prokoph N , Przybilla MJ , et al. Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis. Blood 2023; 142: 1633- 46.

[135] Leblay N , Ahn S , Tilmont R , et al. Integrated epigenetic and transcriptional single-cell analysis of t(11;14) multiple myeloma and its BCL2 dependency. Blood 2024; 143: 42- 56.

[136] Collin J , Queen R , Zerti D , et al. Dissecting the transcriptional and chromatin accessibility heterogeneity of proliferating cone precursors in human retinoblastoma tumors by single cell sequencing-opening pathways to new therapeutic strategies? Invest Ophthalmol Vis Sci 2021; 62: 18.

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