Dictionary learning for integrative, multimodal and scalable single-cell analysis

Nature Biotechnology, Год журнала: 2023, Номер 42(2), С. 293 - 304

Опубликована: Май 25, 2023

Язык: Английский

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Single-cell chromatin state analysis with Signac

Nature Methods, Год журнала: 2021, Номер 18(11), С. 1333 - 1341

Опубликована: Ноя. 1, 2021

Язык: Английский

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis

Nature Genetics, Год журнала: 2021, Номер 53(3), С. 403 - 411

Опубликована: Фев. 25, 2021

Abstract The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but outpaced development scalable software rapidly extract biological meaning from these data. Here we present a suite for analysis in R (ArchR; https://www.archrproject.com/ ) that enables fast and comprehensive ArchR provides an intuitive, user-focused interface complex analyses, including doublet removal, clustering cell type identification, unified peak set generation, cellular trajectory DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction multi-omic integration with RNA sequencing (scRNA-seq). Enabling over 1.2 million single cells within 8 h on standard Unix laptop, is end-to-end will accelerate understanding regulation at resolution individual cells.

Язык: Английский

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Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

Cell, Год журнала: 2020, Номер 183(4), С. 1103 - 1116.e20

Опубликована: Окт. 23, 2020

Язык: Английский

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scMC learns biological variation through the alignment of multiple single-cell genomics datasets

Genome biology, Год журнала: 2021, Номер 22(1)

Опубликована: Янв. 4, 2021

Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to removal of both variations. Here, we present an integration method scMC remove while preserving intrinsic variation. learns via variance analysis subtract inferred unsupervised manner. Application simulated real RNA-seq ATAC-seq experiments demonstrates its detecting context-shared context-specific signals accurate alignment.

Язык: Английский

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Lineage tracing meets single-cell omics: opportunities and challenges

Nature Reviews Genetics, Год журнала: 2020, Номер 21(7), С. 410 - 427

Опубликована: Март 31, 2020

Язык: Английский

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Macrophage diversity in cancer revisited in the era of single-cell omics

Trends in Immunology, Год журнала: 2022, Номер 43(7), С. 546 - 563

Опубликована: Июнь 9, 2022

TAMs have diverse functions in cancer, reflecting the heterogenous nature of these immune cells. Here, we propose a new nomenclature to identify TAM subsets.Recent single cell multi-omics technologies, which allow clustering subsets an unbiased manner, significantly advanced our understanding molecular diversity mice and humans.Novel mechanisms potential therapeutic targets been identified that might regulate tumor-promoting function different subsets.TAM opens promising opportunities for envisaging putative cancer treatments. Tumor-associated macrophages (TAMs) multiple potent and, thus, represent important targets. These highlight TAMs. Recent omics technologies However, unifying annotation their signatures is lacking. review recent major studies transcriptome, epigenome, metabolome, spatial with specific focus on We also consensus model present avenues future research. one most abundant types tumors [1.Cassetta L. Pollard J.W. Targeting macrophages: approaches cancer.Nat. Rev. Drug Discov. 2018; 17: 887-904Crossref PubMed Scopus (650) Google Scholar]. Since initial decade ago [2.Qian B.Z. Macrophage enhances tumor progression metastasis.Cell. 2010; 141: 39-51Abstract Full Text PDF (3151) Scholar], functional now widely appreciated, many seminal field [3.Yang M. et al.Diverse microenvironments.Cancer Res. 78: 5492-5503Crossref (202) Scholar, 4.DeNardo D.G. Ruffell B. Macrophages as regulators tumour immunity immunotherapy.Nat. Immunol. 2019; 19: 369-382Crossref (643) 5.Lopez-Yrigoyen al.Macrophage targeting cancer.Ann. N. Y. Acad. Sci. 2021; 1499: 18-41Crossref (25) This array includes promotion growth, lineage plasticity, invasion, remodeling extracellular matrix, crosstalk endothelial, mesenchymal stromal cells, other cells; effects can result progression, metastasis (see Glossary), therapy resistance [6.Mantovani A. al.Tumour-associated treatment oncology.Nat. Clin. Oncol. 2017; 14: 399-416Crossref (1675) Scholar,7.Guc E. Redefining macrophage neutrophil biology metastatic cascade.Immunity. 54: 885-902Abstract (13) With wide application years seen explosion data illustrating cellular heterogeneity resulting unprecedented amount information TAMs, regardless main studies. Links between are emerging. terminology lacking, making direct comparisons full utilization sets difficult. In this review, summarize human data; include traditional nomenclatures, at levels single-cell transcriptomic, epigenomic, metabolic multi-omics, opportunities, directions. subsets. hope will serve starting point help build complete picture dynamic interactions tumor, well microenvironment (TME). A used describe has now-obsolete M1/M2 model, proposed ~20 ago; it separated into two distinct arms: M1 or 'classically' activated; M2 'alternatively' activated, largely based vitro stimulating type 1 2 cytokines [8.Mills C.D. al.M-1/M-2 Th1/Th2 paradigm.J. 2000; 164: 6166-6173Crossref The newer term 'M1-like' phenotype typically described proinflammatory induced by Toll-like receptor (TLR) ligands cytokines, namely IFN-γ TNF-α. Conversely, 'M2-like' having anti-inflammatory characteristics, being activated interleukin (IL)-4 IL-13, producing TGF-β profibrotic factors. nomenclature, albeit used, remains oversimplified [9.Martinez F.O. Gordon S. paradigm activation: time reassessment.F1000Prime Rep. 2014; 6: 13Crossref (2673) Scholar,10.Nahrendorf Swirski F.K. Abandoning network function.Circ. 2016; 119: 414-417Crossref (195) Indeed, significant morphology, function, surface marker expression observed resident-tissue (RTMs) from organs [11.Bleriot C. al.Determinants resident tissue identity function.Immunity. 2020; 52: 957-970Abstract (94) Scholar]; moreover, co-expression both gene almost all [12.Mulder K. al.Cross-tissue landscape monocytes health disease.Immunity. 1883-1900Abstract Therefore, spectrum polarization relates represents more sensible approach describing [10.Nahrendorf Scholar,13.Mosser D.M. Edwards J.P. Exploring activation.Nat. 2008; 8: 958-969Crossref (5864) normal homeostasis, tightly regulated niche-like local environment, recently [14.Guilliams al.Establishment maintenance niche.Immunity. 434-451Abstract (138) Another layer derives origin. Using lineage-tracing mice, illustrated mouse RTMs derived early erythromyeloid progenitors formed either yolk sac fetal liver [15.Geissmann F. al.Blood consist principal migratory properties.Immunity. 2003; 71-82Abstract (2514) Scholar,16.Gomez Perdiguero al.Tissue-resident originate yolk-sac-derived erythro-myeloid progenitors.Nature. 2015; 518: 547-551Crossref (1236) Additionally, adult may derive circulating monocytic precursors (monocytes) bone marrow [17.Cox al.Origins, biology, diseases macrophages.Annu. 39: 313-344Crossref (1) monocyte contribution varies among organs. For example, steady state, microglia central nervous system (CNS) solely [18.Hoeffel G. al.C-Myb(+) progenitor-derived give rise tissue-resident macrophages.Immunity. 42: 665-678Abstract (611) while dermal embryonic origin [19.Kolter J. al.A subset skin contributes surveillance regeneration nerves.Immunity. 50: 1482-1497Abstract (69) appreciated repeatedly reviewed [20.Pathria P. al.Targeting tumor-associated cancer.Trends 40: 310-327Abstract (382) Scholar,21.Guerriero J.L. Macrophages: road less traveled, changing anticancer therapy.Trends Mol. Med. 24: 472-489Abstract (143) Similar counterparts not only its ontogeny, but cues, including type, organ, subanatomic Identifying basis over past [5.Lopez-Yrigoyen advancements unveiling multidimensional complexity manner. research, oncology eventually fully understand cells hopefully use improve precision diagnosis therapy. Single RNA sequencing (scRNA-seq) technology revolutionized providing in-depth transcriptome level [22.Giladi al.Single-cell characterization haematopoietic trajectories homeostasis perturbed haematopoiesis.Nat. Cell Biol. 20: 836-846Crossref (139) substantial advances available experimental techniques bioinformatics pipelines years, scRNA-seq investigate [23.Lawson D.A. al.Tumour resolution.Nat. 1349-1360Crossref (230) Scholar,24.Ren X. al.Insights gained analysis microenvironment.Annu. 583-609Crossref (15) transcriptomic remain Two large-scale pan-cancer provided valuable regarding diversity. One study analyzed myeloid 380 samples across 15 210 patients through combination newly collected eight published [25.Cheng transcriptional atlas infiltrating cells.Cell. 184: 792-809Abstract (111) Comparison consistent presence CD14+ CD16+ tumor-infiltrating (TIMs), LYVE1+ interstitial non-cancer tissues, seven clusters: INHBA+ C1QC+ ISG15+ LNRP3+ SPP1+ compiled mononuclear phagocytes (MNPs) isolated 41 13 types, six common universe, termed MNP-VERSE. Monocyte clusters were then extracted reintegrated generate MoMac-VERSEi. regulatory inference (SCENIC) [26.Aibar al.SCENIC: clustering.Nat. Methods. 1083-1086Crossref (1003) authors classical monocytes, nonclassical five (HES1 TAM, C1Qhi TREM2 IL4I1 proliferating TAMs) Although nomenclatures studies, others, pattern transcriptomics By reviewing journals, found preserved (Table 1). Based signature genes, enriched pathways, predicated naming interferon-primed (IFN-TAMs), (Reg-TAMs), inflammatory cytokine-enriched (Inflam-TAMs), lipid-associated (LA-TAMs), pro-angiogenic (Angio-TAMs), RTM-like (RTM-TAMs), (Prolif-TAMs) Figure 1, Key figure). Furthermore, three TIMs Box 1).Table 1Mouse various TMEsaBlack font: genes clusters; blue protein markers Underline: CITE-seq; Bold: key reported than paper., bAbbreviations: BRCA, breast cancer; CAF, cancer-associated macrophage; CITE-seq, indexing transcriptomes epitopes sequencing; CRC, colorectal CyTOF, Mass cytometry flight; ECM, matrix; ESCA, esophageal carcinoma; GC, gastric HCC, hepatocellular HNC, head neck i.v., intravenous; IF, immunofluorescent staining; INs-seq, intracellular staining LCM, laser capture microdissection; LYM, lymphoma; MEL, melanoma; Mets, metastasis; mIHC, multiplex immunochemistry MMY, myeloma; N/A, available; NPC, nasopharyngeal NSCLC, nonsmall lung OS, osteosarcoma; OVC, ovarian PDAC, pancreatic ductal adenocarcinoma; PRAC, prostate RCC, renal Reg-TAMs, TAMs; SARC, sarcoma; sc-MS, mass spectrometry; SEPN, spinal ependymomas; SKC, ST, transcriptomics; s.c., subcutaneous; macrophages; THCA, thyroid UCEC, uterine corpus endometrial carcinoma.AnnotationSpeciesSignatureTFCancer typeFunction/enriched pathwayAssayRefsIFN-TAMsHumanCASP1, CASP4, CCL2/3/4/7/8, CD274hi, CD40, CXCL2/3/9/10/11, IDO1, IFI6, IFIT1/2/3, IFITM1/3, IRF1, IRF7, ISG15, LAMP3, PDCD1LG2hi, TNFSF10, C1QA/C, CD38, IL4I1, IFI44LSTAT1 IRF1/7BRCACRCCRC metsGBMHCCHNCLYMMELMMYNPCNSCLCOSPDACSEPNTHCAUCECApoptosis regulatorsEnhance proliferationInflammatory responsesPromote Treg entry tumorT exhaustionImmunosuppressionColocalization exhausted T (ST, IF)Decreased antigen presentation (CyTOF)Suppressed activation (in vitro)IFN-α/γ-IFN response signature; IL2/STAT5; IL6/JAK/STAT3scRNA-seqCITE-seqmIHCSTNanoString GeoMx[12.Mulder Scholar,29.Gubin M.M. al.High-dimensional delineates lymphoid compartment during successful immune-checkpoint therapy.Cell. 175: 1014-1030Abstract (165) Scholar,32.Zavidij O. reveals compromised precursor stages myeloma.Nat. Cancer. 1: 493-506Crossref 33.Zhou intratumoral immunosuppressive osteosarcoma.Nat. Commun. 11: 6322Crossref (74) 34.Zhang Q. al.Interrogation microenvironmental ependymomas dual macrophages.Nat. 12: 6867Crossref (0) Scholar,45.Wu al.Spatiotemporal level.Cancer 134-153Crossref (10) Scholar,52.Pombo Antunes A.R. profiling glioblastoma species disease stage competition specialization.Nat. Neurosci. 595-610Crossref (78) Scholar,\81.Wu S.Z. spatially resolved cancers.Nat. Genet. 53: 1334-1347Crossref (47) Scholar,83.Pelka al.Spatially organized multicellular hubs cancer.Cell. 4734-4752Abstract (29) Scholar]CD14+, CD11b+, CD68+, PD-L1hi, PD-L2hi, CD80hi, CD86hi, MHCIIhi, CD86+, MRC1–, SIGLEC1–, HLA-DRlo, CD314+, CD107a+, CD86, TLR4, CD44 (CITE-seq)MouseCcl2/7/8, Cd274, Cxcl9/10/11, Ifit1/2/3, Ifit3, Ifitm1/3, Il7r, Isg15, Nos2, Rsad2, Tnfsf10, Stat1N/ACT26 s.c. CRCCT26 intrasplenic mets modelT3 SARC (s.c.)Orthotopic GL261 GBMIFN signaturescRNA-seqCITE-seqmIHC[29.Gubin Scholar]Inflam-TAMsHumanCCL2/3/4/5/20, CCL3L1, CCL3L3, CCL4L2, CCL4L4, CXCL1/2/3/5/8, G0S2, IL1B, IL1RN, IL6, INHBA, KLF2/6, NEDD9, PMAIP1, S100A8/A9, SPP1EGR3 IKZF1 NFKB1 NFE2L2 RELCRCCRC metsOSSEPNGCRecruiting regulating cellsCNS inflammation-associated chemokinesPromotes inflammationNeutrophil recruitment lumenT interaction (IHC)TNF signaling; WNTImmune check pointsscRNA-seqmIHCNanoString GeoMx[31.Che L.-H. metastases reprogramming preoperative chemotherapy.Cell Discovery. 7: 80Crossref (4) Scholar,33.Zhou Scholar,34.Zhang Scholar,42.Sathe genomic microenvironment.Clin. Cancer 26: 2640-2653Crossref (66) 43.Zhang al.Dissecting underlying premalignant lesions cancer.Cell 27: 1934-1947Abstract (104) 44.Yin H. map development using sequencing.Front. 12728169Crossref 45.Wu Scholar]MouseCxcl1/2/3/5/8, Ccl20, Ccl3l1, Il1rn, Il1b, G0s2, Inhba, Spp1N/ACT26 CRC CT26 modelChemokine productionImmunosuppressionscRNA-seq[45.Wu Scholar]LA-TAMsHumanACP5, AOPE, APOC1, ATF1, C1QA/B/C, CCL18, CD163, CD36, CD63, CHI3L1, CTSB/D/L, F13A1, FABP5, FOLR2, GPNMB, IRF3, LGALS3, LIPA, LPL, MACRO, MerTK, MMP7/9/12, MRC1, NR1H3, NRF1, NUPR1, PLA2G7, RNASE1, SPARC, SPP1, TFDP2, TREM2, ZEB1FOS/JUN HIF1A MAF/MAFB NR1H3 TCF4 TFECBRCACRCCRC metsGBMGCHCCHNCNPCNSCLCOSPDACPhagocytosisPromotion EMTComplement activationECM degradationAntigen processing pathwaysATP biosynthetic processesCanonical M2-like pathwaysFatty acid metabolismImmunosuppressionInflammationIron ion signalingscRNA-seqSMART-seq2CITE-seqmIHCST[12.Mulder Scholar,27.Zilionis R. cancers conserved populations individuals species.Immunity. 1317-1334Abstract (424) Scholar,28.Yang non-small differences sexes.Front. 12756722Google Scholar,30.Zhang analyses inform myeloid-targeted therapies colon 181: 442-459Abstract (246) Scholar,31.Che Scholar,50.Chen Y.P. subtypes associated prognosis carcinoma.Cell 30: 1024-1042Crossref (71) Scholar,81.Wu Scholar]CD9+, CD80+, MAF, CD163lo/-, CD206+/lo, CD71+, CD72+, CD73, ICOSL, CD40LG, Thy-1 (CITE-seq)MouseAcp5, Apoc1, Apoe, C1qa/B/C, Ccl18, Ccl8, Cd163, Cd206, Cd36, Cd63, Ctsb/d/l, Cxcl9, Fabp5, Folr2, Gpnmb, Lgals3, Macro, Mrc1, Trem2MAFCT26 Orthotopic GBM 7940b orthotopic iKras p53 PDAC metsPhagocytosisAntigen presentationFatty metabolismComplement activationscRNA-seqCITE-seqmIHC[45.Wu Scholar,46.Kemp S.B. al.Pancreatic marked complement-high blood tumor–associated macrophages.Life Alliance. 4e202000935Crossref Scholar]Angio-TAMsHumanADAM8, AREG, BNIP3, CCL2/4/20, CD300E, CD44, CD55, CEBPB, CLEC5A, CTSB, EREG, FCN1, FLT1, FN1, HES1, IL8, MIF, OLR1, PPARG, S100A8/9/12, SERPINB2, SLC2A1, SPIC, THBS1, TIMP1, VCAN, VEGFABACH1 CEBPB FOSL2 HIFA KLF5 MAF RUNX1 SPIC TEAD1 ZEB2BRCACRCCRCCRC metsESCAGBMGCHCCMELNPCNPCNSCLCOVCPDACPDAC metsRCCSEPNTHCAUCECAngiogenesisCAF interactionECM proteolysis; ECM interactionPromotion EMTHIF pathway; NF-kB Notch VEGF signalingJuxtaposed PLVAP+/DLL4+ endothelial (IF)scRNA-seqSMART-seq2CITE-seqNanoString GeoMx[25.Cheng Scholar,41.Sharma al.Onco-fetal drives carcinoma.Cell. 183: 377-394Abstract (103) Scholar,49.Raghavan al.Microenvironment drug 6119-6137Abstract Scholar,67.Zhao revealed promoted progression.J. Transl. 454Crossref Scholar]CD52hi, CD163hi, CD206hi, CXCR4+, CD354+, FOSL2, VEGFAMouseArg1, Adam8, Bnip3, Mif, Slc2a1N/AOrthotopic modelHIF signalingAngiogenesisscRNA-seqCITE-seq[52.Pombo Scholar]Reg-TAMsHumanCCL2, CD274, CD80, CHIT1, CX3CR1, HLA-A/C, HLA-DQA1/B1, HLA-DRA/B1/B5, ICOSLG, IL-10, ITGA4, LGALS9, MAC

Язык: Английский

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Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells

Nature Biotechnology, Год журнала: 2021, Номер 39(10), С. 1246 - 1258

Опубликована: Июнь 3, 2021

Язык: Английский

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A human cell atlas of fetal chromatin accessibility

Science, Год журнала: 2020, Номер 370(6518)

Опубликована: Ноя. 13, 2020

The genomics of human development Understanding the trajectory a developing requires an understanding how genes are regulated and expressed. Two papers now present pooled approach using three levels combinatorial indexing to examine single-cell gene expression chromatin landscapes from 15 organs in fetal samples. Cao et al. focus on measurements RNA broadly distributed cell types provide insights into organ specificity. Domcke examined accessibility cells these identify regulatory elements that regulate expression. Together, analyses generate comprehensive atlases early development. Science , this issue p. eaba7721 eaba7612

Язык: Английский

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications, Год журнала: 2021, Номер 12(1)

Опубликована: Фев. 26, 2021

Identification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding origin cellular diversity. Conventional assays to map regulatory via open chromatin analysis primary tissues hindered by sample heterogeneity. Single cell accessible (scATAC-seq) can overcome this limitation. However, high-level noise each single profile and large volume data pose unique computational challenges. Here, we introduce SnapATAC, a software package analyzing scATAC-seq datasets. SnapATAC dissects heterogeneity in an unbiased manner trajectories states. Using Nyström method, process from up million cells. Furthermore, incorporates existing tools into comprehensive ATAC-seq dataset. As demonstration its utility, applied 55,592 single-nucleus profiles mouse secondary motor cortex. The reveals ~370,000 candidate 31 distinct populations brain region inferred transcriptional regulators.

Язык: Английский

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