Methods and applications for single-cell and spatial multi-omics

Nature Reviews Genetics, Journal Year: 2023, Volume and Issue: 24(8), P. 494 - 515

Published: March 2, 2023

Language: Английский

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The technological landscape and applications of single-cell multi-omics

Nature Reviews Molecular Cell Biology, Journal Year: 2023, Volume and Issue: 24(10), P. 695 - 713

Published: June 6, 2023

Language: Английский

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Multi-omics single-cell data integration and regulatory inference with graph-linked embedding

Nature Biotechnology, Journal Year: 2022, Volume and Issue: 40(10), P. 1458 - 1466

Published: May 2, 2022

Despite the emergence of experimental methods for simultaneous measurement multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle integrating data from is that different layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges gap by modeling regulatory interactions across explicitly. Systematic benchmarking demonstrated more accurate, robust and scalable than state-of-the-art tools heterogeneous multi-omics data. We applied to various challenging tasks, including triple-omics integration, integrative inference human cell atlas construction over millions where was able correct previous annotations. features modular design can be flexibly extended enhanced new analysis tasks. The full package available online at https://github.com/gao-lab/GLUE .

Language: Английский

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Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets

Nature Communications, Journal Year: 2021, Volume and Issue: 12(1)

Published: April 15, 2021

Abstract Determining the epigenetic program that generates unique cell types in kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin gene expression developing adult mouse kidneys at single resolution. We show reliance of on distal regulatory elements (enhancers). reveal key type-specific transcription factors major gene-regulatory circuits cells. Dynamic changes nephron progenitor differentiation demonstrates podocyte commitment occurs early associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a proximal Tfap2b fate. Mapping nucleotide variants human disease implicates types, developmental stages, genes, mechanisms. The multi-omics atlas reveals remodeling events dynamics development.

Language: Английский

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Rolling back human pluripotent stem cells to an eight-cell embryo-like stage

Nature, Journal Year: 2022, Volume and Issue: 605(7909), P. 315 - 324

Published: March 21, 2022

Language: Английский

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Characterizing cis-regulatory elements using single-cell epigenomics

Nature Reviews Genetics, Journal Year: 2022, Volume and Issue: 24(1), P. 21 - 43

Published: July 15, 2022

Language: Английский

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Zygotic genome activation by the totipotency pioneer factor Nr5a2

Science, Journal Year: 2022, Volume and Issue: 378(6626), P. 1305 - 1315

Published: Nov. 24, 2022

Life begins with a switch in genetic control from the maternal to embryonic genome during zygotic activation (ZGA). Despite its importance, essential regulators of ZGA remain largely unknown mammals. On basis de novo motif searches, we identified orphan nuclear receptor Nr5a2 as key activator major mouse two-cell embryos. is required for progression beyond stage. It binds within SINE B1/Alu retrotransposable elements found cis-regulatory regions genes. Chemical inhibition suggests that 72% genes are regulated by and potentially other receptors. promotes chromatin accessibility nucleosomal DNA vitro. We conclude an pioneer factor regulates ZGA.

Language: Английский

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Single-cell technologies: From research to application

The Innovation, Journal Year: 2022, Volume and Issue: 3(6), P. 100342 - 100342

Published: Oct. 18, 2022

Language: Английский

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Single cell cancer epigenetics

Trends in cancer, Journal Year: 2022, Volume and Issue: 8(10), P. 820 - 838

Published: July 9, 2022

The epigenome encompasses several mechanisms controlling gene expression that can be aberrantly regulated during cancer development and progression. Tumors are highly complex heterogeneous biological systems require the study of epigenetic alterations at a single cell resolution.Several technologies developed to different layers epigenome, such as chromatin accessibility or histone modifications, have been applied in research over past few years, improving our understanding driving tumorigenesis.Although these techniques promising, most still nascent present limitations, low throughput limited coverage. In addition, analysis integration various epigenomic data modalities challenges new computational tools. Bulk sequencing methodologies allowed us make great progress research. Unfortunately, lack resolution fully unravel govern tumor heterogeneity. Consequently, many novel cell-sequencing decade, allowing explore components regulate aspects heterogeneity, namely: clonal microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, resistance mechanisms. this review, we enable researchers epigenetics (DNA methylation, accessibility, DNA–protein interactions, 3D architecture) level, their potential applications cancer, current technical limitations. importance both basic clinical is indisputable. field important implications for disease. Indeed, non-mutational reprogramming was recently designated mechanistic determinant enables acquisition hallmark capabilities [1.Hanahan D. Hallmarks cancer: dimensions.Cancer Discov. 2022; 12: 31-46Crossref PubMed Scopus (326) Google Scholar]. Although it well established cells may arise from genetic mutations drive carcinogenesis, types strong drivers could explain malignant processes, progression [2.Turajilic S. et al.Resolving heterogeneity cancer.Nat. Rev. Genet. 2019; 20: 404-416Crossref (228) Scholar], therapy [3.Shaffer S.M. al.Rare variability drug-induced mode drug resistance.Nature. 2017; 546: 431-435Crossref (508) metastasis [4.Chen J.F. Yan Q. roles metastasis.Biochem. J. 2021; 478: 3373-3393Crossref (2) suggesting non-genetic determinants crucial role [5.Nam A.S. al.Integrating evolution by single-cell multi-omics.Nat. 22: 3-18Crossref (83) Thus, affecting non-malignant act critical evolution. These mechanisms, which genes without altering DNA sequence, fall into five main categories: (i) methylation; (ii) accessibility; (iii) modifications; (iv) interactions; (v) tridimensional architecture [6.Allis C.D. Jenuwein T. molecular hallmarks control.Nat. 2016; 17: 487-500Crossref Scholar,7.Darwiche N. Epigenetic an intimate affair.Am. Cancer Res. 2020; 10: 1954-1978PubMed Each type mechanism experimentally studied using bulk (Box 1). due cellular tumor, valuable information lost when techniques, since all possible retrieved point view masked averaging. Nonetheless, with emergence Scholar,8.Yalan L. al.Applications research: perspectives.J. Hematol. Oncol. 14: 91Crossref (15) tumoral were otherwise impossible assess now open exploration.Box 1Bulk analyze mechanismsVarious used understand mechanisms: taking advantage bisulfite chemistry, analyzed whole-genome (WGBS), reduced representation (RRBS), 450k/850k Illumina methylation arrays [128.Ortiz-Barahona V. al.Use profiling translational oncology.Semin. Biol. 83: 523-535Crossref (7) Scholar]; mainly assay transposase-accessible (ATAC-seq) [129.Marinov G.K. Shipony Z. Interrogating accessible landscape eukaryote genomes ATAC-seq.Methods Mol. 2243: 183-226Crossref (0) modifications interactions immunoprecipitation (ChIP-seq) [130.Nakato R. Sakata Methods ChIP-seq analysis: practical workflow advanced applications.Methods. 187: 44-53Crossref (6) explored multiple chromosome conformation capture technology, 3C, 4C, 5C, Hi-C, promoter-capture ChIA-PET [131.Sati Cavalli G. Chromosome impact genome function.Chromosoma. 126: 33-44Crossref (97) Scholar,132.Javierre B.M. al.Lineage-specific links enhancers non-coding disease variants target promoters.Cell. 167: 1369-1384Abstract Full Text PDF (486) One common drawback among need considerable sample size, demanding thousands millions minimal input. considered 'bulk methodologies', obtain average value whole-cell [133.Carter B. Zhao K. basis heterogeneity.Nat. 235-250Crossref (3) Various deconvolution strategies data, but substantial risk retrieving artifacts losing difficult-to-detect minor subclones [134.Chakravarthy A. al.Pan-cancer tumour composition methylation.Nat. Commun. 2018; 9: 3220Crossref (114) Nevertheless, indispensable tools its relationship cancer. For example, they methylation-based classification diffuse gliomas (LGm1-LGm6) [135.Ceccarelli M. al.Molecular reveals biologically discrete subsets pathways glioma.Cell. 164: 550-563Abstract cancers unknown primary [123.Moran al.Epigenetic classify primary: multicentre, retrospective analysis.Lancet 1386-1395Abstract (251) modification-based tracking states [136.Völker-Albert al.Histone stem implications.Stem Cell Rep. 15: 1196-1205Abstract A entity comprising cells, each has [9.Dagogo-Jack I. Shaw A.T. Tumour therapies.Nat. Clin. 81-94Crossref (1187) help properly dissect dependencies complexity. There six biology related key (Figure 1): heterogeneity; TME; organization intercellular crosstalk; developmental programs (phenotypic plasticity); metastasis; (vi) appearance therapy. necessary develop allow cues undetectable methodologies. catalog facilitate characteristics level. We technology based on under architecture), focusing first mono-omic (techniques only one cell) then multi-omic (which simultaneously cell). summarize currently available also highlight recent discoveries insights gained technologies, how contribute solve (mostly derived heterogeneity), translational/clinical scenarios. comprise subclones, unique properties [10.McGranahan Swanton C. Clonal evolution: past, present, future.Cell. 168: 613-628Abstract (1220) Scholar,11.Oakes C.C. al.Evolution linked aberrations chronic lymphocytic leukemia.Cancer 2014; 4: 348-361Crossref (113) As population evolves, accumulate clones harbor novel, selective advantages (e.g., enhanced proliferation, therapy, invasiveness, etc.) detection outcome. Single able detect (especially minor, difficult-to-detect, subclones), thus revealing prognostic information. does not solely harbors myriad distinct T content directly associated types, cytotoxic (Tc) helper (Th1, Th2, Th17) correlating good prognosis [12.Tay R.E. al.Revisiting CD4 + immunotherapy-new old paradigms.Cancer Gene Ther. 28: 5-17Crossref Tumor-associated macrophages progression, depending M1/M2 state [13.Baghban al.Tumor complexity therapeutic glance.Cell Signal. 18: 59Crossref (321) Additionally, natural killer (NK) B endothelial fibroblasts, other participate interactome [14.Anderson N.M. Simon M.C. microenvironment.Curr. 30: R921-R925Abstract (186) microenvironmental profoundly modulate nontumoral generating crosstalk determines [15.Marks D.L. control microenvironment.Epigenomics. 8: 1671-1687Crossref (32) studying signals level mandatory decipher interactome. Malignant randomly distributed inside instead occupy specific positions space, cell–cell [16.Noble al.Spatial structure governs evolution.Nat. Ecol. Evol. 6: 207-217Crossref Knowing distribution assessing 'heat' certain melanoma), relative quantity position [17.Trujillo J.A. al.T cell-inflamed versus non-T tumors: conceptual framework immunotherapy combination selection.Cancer Immunol. 990-1000Crossref (202) dependent colorectal (CRC) patients locoregional relapse-free overall survival [18.Martínez-Cardús homogeneity within tumors predicts shorter times cancer.Gastroenterology. 151: 961-972Abstract Microscopy immunohistochemistry. enabled advances aspect. specificity unveil cell. (including cutting-edge epigenomics) will infer correlate changes value. hypothesis growth depends, least part, asymmetrical divisions differentiate committed [19.Lim J.R. al.Cancer targets.Med. 38: 76Crossref background, follow program glioblastoma, there four program, some higher stemness (neural progenitor-like oligodendrocyte cells), others more differentiated (astrocyte-like mesenchymal-like cells) [20.Neftel al.An integrative model states, plasticity, genetics glioblastoma.Cell. 178: 835-849Abstract (598) identity maintained memory methylation) ensure full commitment transcriptional [21.Lee H.J. Reprogramming methylome: erasing creating diversity.Cell Stem Cell. 710-719Abstract (223) detecting machinery provide trajectories, predicting deciding treatment should applied. Some acquire ability leave site colonize distant tissues, cause cancer-related deaths [22.Fares principles metastasis: revisited.Signal Transduct. Target. 5: 28Crossref (390) From transformation until settlement tissue, metastatic experiences drastic changes, acquiring motility (epithelial–to-mesenchymal transition), avoiding immune surveillance, adapting secondary No driver yet identified, dynamic involved steps Scholar,23.Patel S.A. Vanharanta metastasis.Mol. 11: 79-96Crossref (37) useful confidently sites those metastatic-prone background. Certain abundance epimutations render them resistant treatments affect abundant [24.Wang X. al.Drug combating cancer.Cancer Drug Resist. 2: 141-160PubMed likely become predominant ones after line treatment, representing relapse. Alterations strongly antitumoral [25.Hayashi Konishi Correlation anti-tumour regulation.Br. Cancer. 124: 681-682Crossref (4) bortezomib myeloma, enter slow-cycling, drug-tolerant reversible state, consequence plasticity rather than determinants. Another case non-genetically determined H3 lysine 4 demethylases, KDM5, transcriptomic breast leading decreased sensitivity antiestrogens [26.Hinohara al.KDM5 demethylase activity resistance.Cancer 34: 939-953Abstract (96) taxane-resistant triple-negative (TNBC), global hypomethylation relocation H3K27 trimethylation paclitaxel, vulnerability inhibitors [27.Deblois switch-induced viral mimicry evasion chemotherapy-resistant 1312-1329Crossref well-documented cases [28.Marine J.-C. al.Non-genetic 743-756Crossref early diagnosis, would significantly clinicians select best combinations. residual fundamental, because constitutes biomarker predict relapse [29.Schuurhuis G.J. al.Minimal/measurable AML: consensus document European Leukemia Net MRD Working Party.Blood. 131: 1275-1291Crossref (537) encompass breakthrough methodology revolutionized way characterized looking time. approaches essential examine underlying levels. With advent RNA-sequencing (scRNA-seq), transcriptome exploited technologies. It accelerated biology, enabling characterization unprecedented Scholar,30.van Galen P. al.Single-cell RNA-seq AML hierarchies relevant immunity.Cell. 176: 1265-1281Abstract (288) Scholar, 31.Costa al.Fibroblast immunosuppressive environment human 33: 463-479Abstract (602) 32.Aoki disease-defining t-cell classic hodgkin lymphoma.Cancer 406-421Crossref (82) 33.Campillo-Marcos analyses hematopoiesis hematological malignancies.Exp. 98: 1-13Abstract emerging DNA-sequencing profile, amplicon-based targeted manner, recurrently mutated genes, providing genotype every nucleotide (SNPs) copy number (CNVs) [34.Miles L.A. mutation myeloid malignancies.Nature. 587: 477-482Crossref (117) diversity often independent highlighting developing [35.Chaligne encoding, heritability glioma states.Nat. 53: 1469-1479Crossref (20) aimed evolved rapidly compared transcriptome, being regulation. 5-Methylcytosine (5mC) well-known modification. mammals, mostly occurs cytosines followed guanine, forming 5′-to-3′ CpG pair. Approximately 70% promoters enriched clustered pairs, 'CpG islands' prone 5mC [36.Deaton A.M. Bird islands regulation transcription.Genes Dev. 2011; 25: 1010-1022Crossref (1966) regions, acts repressive switch, restricting expression. found genomic bodies regulatory regions (enhancers CTCF sites), regulating function cis. deregulated. Promoter hyper/hypomethylation suppressors/oncogenes well-established [37.Berdasco Esteller Clinical epigenetics: seizing opportunities translation.Nat. 109-127Crossref (227) deregulation enhancer fostering [38.Bell al.Enhancer dynamics patient mortality.Genome 26: 601-611Crossref (73) helped revolutionize area. Most conversion unmethylated uracil DNA. This allows methylated array-based methods al.Epi

Language: Английский

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Advances in single-cell omics and multiomics for high-resolution molecular profiling

Experimental & Molecular Medicine, Journal Year: 2024, Volume and Issue: 56(3), P. 515 - 526

Published: March 5, 2024

Abstract Single-cell omics technologies have revolutionized molecular profiling by providing high-resolution insights into cellular heterogeneity and complexity. Traditional bulk approaches average signals from heterogeneous cell populations, thereby obscuring important nuances. studies enable the analysis of individual cells reveal diverse types, dynamic states, rare populations. These techniques offer unprecedented resolution sensitivity, enabling researchers to unravel landscape cells. Furthermore, integration multimodal data within a single provides comprehensive holistic view processes. By combining multiple dimensions, can facilitate elucidation complex interactions, regulatory networks, mechanisms. This integrative approach enhances our understanding systems, development disease. review an overview recent advances in single-cell for profiling. We discuss principles methodologies representatives each method, highlighting strengths limitations different techniques. In addition, we present case demonstrating applications various fields, including developmental biology, neurobiology, cancer research, immunology, precision medicine.

Language: Английский

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