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

Trends in Immunology, Journal Year: 2022, Volume and Issue: 43(7), P. 546 - 563

Published: June 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

Language: Английский

---

Applications of single-cell sequencing in cancer research: progress and perspectives

Journal of Hematology & Oncology, Journal Year: 2021, Volume and Issue: 14(1)

Published: June 9, 2021

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics is a powerful tool to decipher the cellular molecular landscape at single-cell resolution, unlike bulk which provides averaged data. The use of sequencing in cancer research has revolutionized our understanding biological characteristics dynamics within lesions. In this review, we summarize emerging technologies recent progress obtained by information related landscapes malignant cells immune cells, tumor heterogeneity, circulating underlying mechanisms behaviors. Overall, prospects facilitating diagnosis, targeted therapy prognostic prediction among spectrum tumors are bright. near future, advances will undoubtedly improve highlight potential precise therapeutic targets for patients.

Language: Английский

---

Cancer-associated fibroblasts in the single-cell era

Nature Cancer, Journal Year: 2022, Volume and Issue: 3(7), P. 793 - 807

Published: July 26, 2022

Language: Английский

---

Single‐Cell, Single‐Nucleus, and Spatial RNA Sequencing of the Human Liver Identifies Cholangiocyte and Mesenchymal Heterogeneity

Hepatology Communications, Journal Year: 2021, Volume and Issue: 6(4), P. 821 - 840

Published: Nov. 18, 2021

The critical functions of the human liver are coordinated through interactions hepatic parenchymal and non-parenchymal cells. Recent advances in single-cell transcriptional approaches have enabled an examination with unprecedented resolution. However, dissociation-related cell perturbation can limit ability to fully capture liver's fraction, which limits comprehensively profile this organ. Here, we report landscape 73,295 cells from using matched RNA sequencing (scRNA-seq) single-nucleus (snRNA-seq). addition snRNA-seq characterization interzonal hepatocytes at a resolution, revealed presence rare subtypes mesenchymal cells, facilitated detection cholangiocyte progenitors that had only been observed during vitro differentiation experiments. T B lymphocytes natural killer were distinguishable scRNA-seq, highlighting importance applying both technologies obtain complete map tissue-resident types. We validated distinct spatial distribution hepatocyte, cholangiocyte, populations by independent transcriptomics data set immunohistochemistry. Conclusion: Our study provides systematic comparison transcriptomes captured scRNA-seq delivers high-resolution healthy liver.

Language: Английский

---

Hepatic stellate cells in physiology and pathology

The Journal of Physiology, Journal Year: 2022, Volume and Issue: 600(8), P. 1825 - 1837

Published: March 21, 2022

Hepatic stellate cells (HSCs) comprise a minor cell population in the liver but serve numerous critical functions normal and response to injury. HSCs are primarily known for their activation upon injury producing collagen-rich extracellular matrix fibrosis. In absence of injury, reside quiescent state, which main function appears be storage retinoids or vitamin A-containing metabolites. Less appreciated include amplifying hepatic inflammatory expressing growth factors that development both initiation termination regeneration. Recent single-cell RNA sequencing studies have corroborated earlier indictaing HSC involves diverse array phenotypic alterations identified unique populations. This review serves highlight these many HSCs, briefly describe recent genetic tools will help thoroughly investigate role physiology pathology.

Language: Английский

---

A single cell atlas of the human liver tumor microenvironment

Molecular Systems Biology, Journal Year: 2020, Volume and Issue: 16(12)

Published: Dec. 1, 2020

Article17 December 2020Open Access Transparent process A single cell atlas of the human liver tumor microenvironment Hassan Massalha orcid.org/0000-0002-9923-6878 Department Molecular Cell Biology, Weizmann Institute Science, Rehovot, Israel Search for more papers by this author Keren Bahar Halpern Samir Abu-Gazala General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Transplant Division, Hospital Pennsylvania, Philadelphia, PA, USA Tamar Jana Efi E Massasa Andreas Moor orcid.org/0000-0001-8715-8449 Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland Lisa Buchauer orcid.org/0000-0002-4722-8390 Milena Rozenberg Eli Pikarsky The Lautenberg Center Immunology, Research Israel-Canada, School, Ido Amit Gideon Zamir Shalev Itzkovitz Corresponding Author [email protected] orcid.org/0000-0003-0685-2522 Information Massalha1, Halpern1, Abu-Gazala2,3, Jana1, Massasa1, Moor4, Buchauer1, Rozenberg1, Pikarsky5, Amit6, Zamir2 *,1 1Department 2Department 3Transplant 4Department 5The 6Department *Corresponding author. Tel: +972 89343104; E-mail: Systems Biology (2020)16:e9682https://doi.org/10.15252/msb.20209682 PDFDownload PDF article text main figures. Peer ReviewDownload a summary editorial decision including letters, reviewer comments responses to feedback. ToolsAdd favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Malignant growth is fueled interactions between cells stromal composing microenvironment. major site tumors metastases, but molecular identities intercellular different types have not been resolved in these pathologies. Here, we apply RNA-sequencing spatial analysis malignant adjacent non-malignant tissues from five patients with cholangiocarcinoma or metastases. We find that exhibit recurring, patient-independent expression programs, reconstruct ligand–receptor map highlights recurring tumor–stroma interactions. By combining transcriptomics laser-capture microdissected regions, zonation hepatocytes sites characterize distribution each type across Our provides resource understanding malignancies may expose potential points interventions. SYNOPSIS Single methods are used generate microenvironment, exposing tumor-stroma patterns healthy tissue. presented. Recurring gene signatures found metastases cholangiocarcinomas. Tumor communicate through conserved ligand-receptor interaction network. Spatial reveal zonated liver. Introduction Cancer heterogeneous disease, exhibiting both interpatient intrapatient variability (Marusyk et al, 2012; Meacham Morrison, 2013; Patel 2014; Alizadeh 2015). do operate isolation, rather closely interact complex milieu supporting form (TME) (Polyak 2009; Hanahan Weinberg, 2011; Lambrechts 2018). These include, among others, range immune cells, cancer-associated fibroblasts (CAFs), endothelial cells. Interactions critical cancer survival (Meacham 2013). Stromal supply factors, facilitate evasion, modulate composition extracellular matrix. Given diversity TME, it essential approaches resolve their (Tirosh 2016; Puram 2017; primary (Llovet 2016). Tumors origin include hepatocellular carcinomas (Guichard 2012), cholangiocarcinomas [tumors originating cholangiocytes (Patel, Sia 2013)], hepatoblastomas. Liver often originate colorectal pancreatic cause mortality (Weinberg, atlases provided important insight into development (Camp Segal 2019; Popescu 2019), physiology (MacParland 2018; Aizarani pathology (Zhang 2019, 2020; Ramachandran Sharma 2020) modalities carcinoma information, tissue identify distinct distributions TME. Results To assemble analyzed six who underwent resection (Fig 1A, Appendix Fig S1). Three Patients hepatic two intrahepatic cholangiocarcinoma, one cyst at benign stage (Dataset EV1). dissociated measured transcriptomes using MARS-seq (Jaitin Materials Methods). In parallel, preserved microdissection (LCM) (Moor 2017, 2018) molecule fluorescence situ hybridization (smFISH) (Bahar Figure 1. Experimental scheme, tumor, non-tumor samples surgeries were scRNA-seq, frozen LCM, fixed smFISH. tSNE plot colored normalized sum pan-carcinoma markers taken al (2017). "n"—indicates number per group. 17 Seurat clusters hepatocytes, (liver sinusoidal cells—LSEC, vascular cells—LVEC, cells—LVECt), mesenchymal (Stellate fibroblasts—CAFs, Pericytes, smooth muscle cells—vSMC), (Kupffer scar-associated macrophages—SAMs, monocytes 1—TM1, cDC1, cDC2, T B cells), proliferating Heatmap showing marker genes (Materials Expression maximal all types. Download figure PowerPoint included 7,947 4,140 3,807 1B). did show histological signs fibrosis, exception patient p2 Methods, Dataset formed clusters, which annotated based on known recent cirrhotic livers (Ramachandran 2019) 1C). Notably, mixture (Appendix S1), demonstrating signatures. Cells several non-parenchymal populations—hepatic stellate (vSMC), Kupffer (LSEC), (LVEC), cholangiocytes, latter clustering marked KRT8, KRT18, EPCAM (Puram 1B) diverse TME populations, fibroblasts, Carcinoma exhibited differences metastatic S1E). Genes elevated higher cholangiocyte Beta-defensin 1 (DEFB1) (Harada 2004) FGFR2. metastasis Cadherin (CDH17) (Panarelli 2012) adhesion molecules CEACAM5 CEACAM6, previously shown correlate colonization (Powell extracted global unique 1D, Datasets EV2 EV3). validated panel 12 smFISH S2). common question whether reconstructed stable regard numbers sample (Mereu 2020). This particularly cancer, due profound levels heterogeneity assessed stability obtained our sampled end, mean subsamples patients, equally sized as controls. compared subsets data those full atlas. gain correlations, when adding new strongly curtailed most beyond three converged correlations subsampling than S3). An was cluster, where changed added thus demonstrates that, while high variability, exhibits uniform patients. Differences matching within same enabled identification populations compose 2). von Willebrand factor VWA1, encoding glycoprotein extravasation (Terraube 2007), well SOX17 (Yang INSR (Nowak-Sliwinska promote angiogenesis 2A). predominantly macrophages (SAMs) 2B). express CD9 TREM2, suppressor (Tang CAPG GPNMB. GSEA Subramanian (2005) SAM resulted significant enrichment apical junction complement system. Their lipid-associated genes, such PLIN2 LPL, overlapping recently identified SPP1+ (LAMs) mouse fatty (Remmerie mononuclear phagocyte composed expressing C1QB, MARCO, CD5L, CD163 S4). Tregs, CTLA4 FOXP3, whereas T-cell cytotoxic CCL5, GZMK, NKG7 2C). divisions suggest recruitment immune-suppressive macrophages, demonstrated other (Lambrechts Binnewies Additional conventional dendritic (cDC1 cDC2), (TM1), FCN1 S100A12 1C, 2. A–D. Top-left—tSNE boxes demarcate clusters. Dashed labels indicate panels A–C. (A) Volcano differential (DGE) samples. (B) DGE phagocytes (C) (D) classified (cholangiocarcinoma dark purple light purple). Wilcoxon rank-sum tests P-values, Benjamini–Hochberg multiple hypotheses correction compute q-values. Labeled dots names selected differentially expressed further phagocytes, 2D). Endothelial etiologies. contrast, up-regulation chemokines CCL4, CCL4L2, CCL3L3 remodeling MMP19, MMP12, HS3ST2 Diversity four 3, S5A). Hepatic retinol binding protein (RBP1), Myosin-11 (MYH11) abundant 3A, S1C). Mesenchymal Cancer-associated matrix (ECM) COL1A1, LUM, BGN, larger cluster. second cluster classic pericytes, periendothelial roles regulating integrity (Armulik 2011). RGS5 CSPG4, neuron-glial antigen 2 (NG2) some suggested DES (Nehls 1992) ANPEP (Kumar 2017), specifically pericytes context Importantly, almost absent demonstrate RGS5+ indeed PDGFB, expected 3B C). CAFs COL1A1 resided farther away D, 3. A. Key (RBP1, RGS5, MYH11). Light gray denote Dark B. Left—Representative image p1 stained localization pericytes. Scale bar 10 µm. lines mark shortest distance (2a) (2b) (1). Middle—zoom-in (1) left panel, blood vessel like structure PDGFB (magenta) wrapped (green). consecutive layers 2.5 Right—zoom-in (2a b) distant signal RGS5. DAPI nuclei staining. C, D. Violin vessels low/high (n = 358 n 360, respectively) 359 359, respectively). "p" P-value determined test. Empty circles medians over repeats. E. Schematic representation top-ranked (bona-fide) detected NicheNet sorted prior LVECt F. Pathway bona-fide EV4) Enrichr tool. Images representative images out eight independent experiments Paracrine juxtacrine attached be proper vascularization (Annika 2005). unbiased signaling pathways could affect physically interacting applied (Browaeys computational method predicts induction downstream target 3E, EV4). via JAG1,2-NOTCH3, PDGFB-PDGFRB S5B), SLIT2-ROBO1,4 S5C) ANGPT2-TEK 3E). ligands receptors mediating cell–pericyte cross-talk enriched pathways, angiogenesis, chemokine cytokine 3F). highly dependent provide factors enhance survival. turn, secrete sensed (Zhou 2017). facilitated an parsed database (Ramilowski 2015) pairs, proteins specific hand SAMs hubs, representing 49.3% carcinoma–TME 4A). focused recurred least 4B, EV5). resulting interactome network highlighted modules, large module, modules centered around ERBB, HGF-MET, TGFbeta, FGF, IGF, VEGFA, lipid trafficking WNT planar polarity module 4B). 4. Human delineates Summary total 20% red box. Network sites. Node colors ligands/receptors enriched. Gray arrows color indicates Zscore shaded. Included significantly appeared Dot-plot highlight shared motifs max (A). For gene, dot size represents fraction positive Top—CAFs comodulate carcinoma–stroma interaction. produce DCN modulates CAF-SAMs-expressed ligand HGF carcinoma-expressed receptor MET. Bottom—CAFs CTHRC1 WNT5A ligand, CAFs, SAMS, TM1 FZD5. Strategy computing scores tumor. score computed products average randomizing real manner preserves outgoing incoming (Scorerand). ratio randomized constitutes score. increases increasing stage. Analysis 383 TCGA (LIHC) (CHOL). median largest consisted proteins. ECM has shaped assembly degrading proteins, collectively matrisome (Naba Varol Sagi, Features stiffness porosity optimal cellular contacts, maximize accessibility control exclusion (Binnewies Within produced collagens laminins, integrin scRNA-seq identifying secreting components S6). Planar (WNT-PCP) pathway invasion (Wang, 2009). PCP activated non-canonical Wnt morphogens, WNT5A, 4B–D). CTHRC1, secreted collagen triple helix filament forms stabilizes its tumor-expressed receptor-FZD (Yamamoto 2008), CAFs. Thus, jointly WNT-PCP signaling. observed similar cooperation MET module. driver (De Silva HGF, activating MET, DCN, decorin protein, turn inhibits HGF-MET (Goldoni revealed additional role interactor carcinoma-specific EGFR summary, details connectivity correlates severity convey selective advantage assess hypothesis, examined cohort bulk-sequenced 4E F). first consists summed degree-preserving random networks 4E). normalization important, since simply reflect receptors, oncogenes, coordinated receptors. increased along stages 4F). severity. identifies solid reside zones oxygen levels, nutrient availability, morphogen concentrations. can Itzkovitz, 2017) result spatially organ, repeating anatomical units termed lobules, polarized centripetal bloo

Language: Английский

---

Identification, discrimination and heterogeneity of fibroblasts

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: June 14, 2022

Abstract Fibroblasts, the principal cell type of connective tissue, secrete extracellular matrix components during tissue development, homeostasis, repair and disease. Despite this crucial role, identification distinction fibroblasts from other types are challenging laden with caveats. Rapid progress in single-cell transcriptomics now yields detailed molecular portraits our bodies, which complement enrich classical histological immunological descriptions, improve class definitions guide further studies on functional heterogeneity subtypes states, origins fates physiological pathological processes. In review, we summarize discuss recent advances understanding fibroblast how they discriminate types.

Language: Английский

---

Liver Zonation – Revisiting Old Questions With New Technologies

Frontiers in Physiology, Journal Year: 2021, Volume and Issue: 12

Published: Sept. 9, 2021

Despite the ever-increasing prevalence of non-alcoholic fatty liver disease (NAFLD), etiology and pathogenesis remain poorly understood. This is due, in part, to liver’s complex physiology architecture. The maintains glucose lipid homeostasis by coordinating numerous metabolic processes with great efficiency. made possible spatial compartmentalization pathways a phenomenon known as zonation. importance zonation normal function, it unresolved if how perturbations can drive hepatic pathophysiology NAFLD development. While hepatocyte heterogeneity has been identified over century ago, its examination had severely hindered due technological limitations. Recent advances single cell analysis imaging technologies now permit further characterization cells across lobule. review summarizes examining elucidating regulatory role pathology. Understanding organization metabolism vital our knowledge provide targeted therapeutic avenues.

Language: Английский

---

Physiological and pathological roles of lipogenesis

Nature Metabolism, Journal Year: 2023, Volume and Issue: 5(5), P. 735 - 759

Published: May 4, 2023

Language: Английский

---

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Nature reviews. Cancer, Journal Year: 2023, Volume and Issue: 23(8), P. 544 - 564

Published: June 22, 2023

Language: Английский

---