New avenues for systematically inferring cellcell communication: through single-cell transcriptomics data

Xin Shao, Xiaoyan Lu, Jie Liao, Huajun Chen, Xiaohui Fan

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Protein Cell ›› 2020, Vol. 11 ›› Issue (12) : 866-880. DOI: 10.1007/s13238-020-00727-5
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New avenues for systematically inferring cellcell communication: through single-cell transcriptomics data

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Abstract

For multicellular organisms, cell-cell communication is essential to numerous biological processes. Drawing upon the latest development of single-cell RNA-sequencing (scRNA-seq), high-resolution transcriptomic data have deepened our understanding of cellular phenotype heterogeneity and composition of complex tissues, which enables systematic cell-cell communication studies at a single-cell level. We first summarize a common workflow of cell-cell communication study using scRNA-seq data, which often includes data preparation, construction of communication networks, and result validation. Two common strategies taken to uncover cell-cell communications are reviewed, e.g., physically vicinal structure-based and ligand-receptor interaction-based one. To conclude, challenges and current applications of cell-cell communication studies at a single-cell resolution are discussed in details and future perspectives are proposed.

Keywords

cell-cell communication / single-cell RNA sequencing / physical contact-dependent communication / chemical signal-dependent communication / ligand-receptor interaction / network biology

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Xin Shao, Xiaoyan Lu, Jie Liao, Huajun Chen, Xiaohui Fan. New avenues for systematically inferring cellcell communication: through single-cell transcriptomics data. Protein Cell, 2020, 11(12): 866‒880 https://doi.org/10.1007/s13238-020-00727-5

References

[1]
Albert R, Jeong H, Barabasi AL (2000) Error and attack tolerance of complex networks. Nature 406:378–382
CrossRef Google scholar
[2]
Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, Chak S, Naikawadi RP, Wolters PJ, Abate AR (2019) Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 20:163–172
CrossRef Google scholar
[3]
Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R, Lou X, Pavlov S, Vorobiev S, Dick JE, Tanner SD (2009) Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem 81:6813–6822
CrossRef Google scholar
[4]
Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113
CrossRef Google scholar
[5]
Bessis M(1958) Erythroblastic island, functional unity of bone marrow. Rev Hematol 13:8–11
[6]
Boisset JC, Vivie J, Grun D, Muraro MJ, Lyubimova A, van Oudenaarden A(2018) Mapping the physical network of cellular interactions. Nat Methods 15:547–553
CrossRef Google scholar
[7]
Braga VM (2002) Cell-cell adhesion and signalling. Curr Opin Cell Biol 14:546–556
CrossRef Google scholar
[8]
Budnik B, Levy E, Harmange G, Slavov N (2018) SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol 19:161
CrossRef Google scholar
[9]
Burns JC, Kelly MC, Hoa M, Morell RJ, Kelley MW (2015) Single-cell RNA-Seq resolves cellular complexity in sensory organs from the neonatal inner ear. Nat Commun 6:8557
CrossRef Google scholar
[10]
Camp JG, Sekine K, Gerber T, Loeffler-Wirth H, Binder H, Gac M, Kanton S, Kageyama J, Damm G, Seehofer D (2017) Multilineage communication regulates human liver bud development from pluripotency. Nature 546:533–538
CrossRef Google scholar
[11]
Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C, Daza R, Qiu X, Lee C, Furlan SN, Steemers FJ (2017) Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–667
CrossRef Google scholar
[12]
Cao J, Cusanovich DA, Ramani V, Aghamirzaie D, Pliner HA, Hill AJ, Daza RM, McFaline-Figueroa JL, Packer JS, Christiansen L (2018) Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361:1380–1385
CrossRef Google scholar
[13]
Cheow LF, Courtois ET, Tan Y, Viswanathan R, Xing Q, Tan RZ, Tan DS, Robson P,Loh YH, Quake SR(2016) Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Methods 13:833–836
CrossRef Google scholar
[14]
Cohen M, Giladi A, Gorki AD, Solodkin DG, Zada M, Hladik A, Miklosi A, Salame TM, Halpern KB, David E (2018) Lung single-cell signaling interaction map reveals basophil role in macrophage imprinting. Cell 175:1031–1044 e1018
CrossRef Google scholar
[15]
Collins BC, Aebersold R (2018) Proteomics goes parallel. Nat Biotechnol 36:1051–1053
CrossRef Google scholar
[16]
Duan L, Zhang XD, Miao WY, Sun YJ, Xiong G, Wu Q,Li G, Yang P, Yu H, Li H (2018) PDGFRbeta cells rapidly relay inflammatory signal from the circulatory system to neurons via chemokine CCL2. Neuron 100:183–200 e188
CrossRef Google scholar
[17]
Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R (2020) CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc 15(4):1484–1506
CrossRef Google scholar
[18]
Eng CL, Lawson M, Zhu Q, Dries R, Koulena N, Takei Y, Yun J, Cronin C, Karp C,Yuan GC (2019) Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568:235–239
CrossRef Google scholar
[19]
Evans WH (2015) Cell communication across gap junctions: a historical perspective and current developments. Biochem Soc Trans 43:450–459
CrossRef Google scholar
[20]
Fernandez DM, Rahman AH, Fernandez NF, Chudnovskiy A, Amir ED, Amadori L, Khan NS, Wong CK, Shamailova R, Hill CA (2019) Single-cell immune landscape of human atherosclerotic plaques. Nat Med 25:1576–1588
CrossRef Google scholar
[21]
Gao S,Yan L, Wang R, Li J, Yong J, Zhou X, Wei Y, Wu X, Wang X, Fan X (2018) Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing. Nat Cell Biol 20:721–734
CrossRef Google scholar
[22]
Gartner ZJ, Prescher JA, Lavis LD (2017) Unraveling cell-to-cell signaling networks with chemical biology. Nat Chem Biol 13:564–568
CrossRef Google scholar
[23]
Grun D, Lyubimova A, Kester L,Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251–255
CrossRef Google scholar
[24]
Halpern KB, Shenhav R, Matcovitch-Natan O,Toth B, Lemze D, Golan M, Massasa EE, Baydatch S,Landen S, Moor AE (2017) Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542:352–356
CrossRef Google scholar
[25]
Hashimshony T,Wagner F, Sher N, Yanai I (2012) CEL-Seq: singlecell RNA-Seq by multiplexed linear amplification. Cell Rep 2:666–673
CrossRef Google scholar
[26]
Hu Y,Wang X, Hu B, Mao Y, Chen Y, Yan L,Yong J, Dong J, Wei Y, Wang W (2019) Dissecting the transcriptome landscape of the human fetal neural retina and retinal pigment epithelium by single-cell RNA-seq analysis. PLoS Biol 17:e3000365
CrossRef Google scholar
[27]
Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I,Mildner A, Cohen N,Jung S, Tanay A (2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776–779
CrossRef Google scholar
[28]
Kirouac DC, Madlambayan GJ, Yu M, Sykes EA, Ito C, Zandstra PW (2009) Cell-cell interaction networks regulate blood stem and progenitor cell fate. Mol Syst Biol 5:293
CrossRef Google scholar
[29]
Kiselev VY, Yiu A, Hemberg M (2018) scmap: projection of singlecell RNA-seq data across data sets. Nat Methods 15:359–362
CrossRef Google scholar
[30]
Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V,Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201
CrossRef Google scholar
[31]
Kumar V, Donthireddy L, Marvel D, Condamine T, Wang F, Lavilla-Alonso S, Hashimoto A, Vonteddu P, Behera R, Goins MA (2017) Cancer-associated fibroblasts neutralize the anti-tumor effect of CSF1 receptor blockade by inducing PMN-MDSC infiltration of tumors. Cancer Cell 32:654–668 e655
CrossRef Google scholar
[32]
Kumar MP, Du J, Lagoudas G,Jiao Y, Sawyer A, Drummond DC, Lauffenburger DA, Raue A (2018) Analysis of single-cell RNASeq identifies cell-cell communication associated with tumor characteristics. Cell Rep 25:1458–1468e1454
CrossRef Google scholar
[33]
Li L, Dong J, Yan L, Yong J, Liu X, Hu Y,Fan X, Wu X, Guo H, Wang X (2017) Single-cell RNA-Seq analysis maps development of human germline cells and gonadal niche interactions. Cell Stem Cell 20:858–873 e854
CrossRef Google scholar
[34]
Liao J, Hao C, Huang W,Shao X, Song Y,Liu L, Ai N, Fan X (2018) Network pharmacology study reveals energy metabolism and apoptosis pathways-mediated cardioprotective effects of Shenqi Fuzheng. J Ethnopharmacol 227:155–165
CrossRef Google scholar
[35]
Lin X, Spindler TJ, de Souza Fonseca MA, Corona RI, Seo JH, Dezem FS, Li L, Lee JM,Long HW, Sellers TA (2019) Superenhancer-associated LncRNA UCA1 interacts directly with AMOT to activate YAP target genes in epithelial ovarian cancer. iScience 17:242–255
CrossRef Google scholar
[36]
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214
CrossRef Google scholar
[37]
Manwani D, Bieker JJ (2008) The erythroblastic island. Curr Top Dev Biol 82:23–53
CrossRef Google scholar
[38]
Martin JC, Chang C, Boschetti G,Ungaro R, Giri M, Grout JA, Gettler K, Chuang LS, Nayar S, Greenstein AJ (2019) Single-cell analysis of Crohn’s disease lesions identifies a pathogenic cellular module associated with resistance to anti- TNF therapy. Cell 178:1493–1508e1420
CrossRef Google scholar
[39]
Marx V (2019) A dream of single-cell proteomics. Nat Methods 16:809–812
CrossRef Google scholar
[40]
Mittal K, Eremenko E, Berner O, Elyahu Y, Strominger I, Apelblat D, Nemirovsky A, Spiegel I, Monsonego A (2019) CD4 T cells induce a subset of MHCII-expressing microglia that attenuates Alzheimer pathology. iScience 16:298–311
CrossRef Google scholar
[41]
Nestorowa S, Hamey FK, Pijuan Sala B, Diamanti E, Shepherd M, Laurenti E, Wilson NK, Kent DG, Gottgens B (2016) A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128:e20–31
CrossRef Google scholar
[42]
Nitzan M, Karaiskos N, Friedman N, Rajewsky N (2019) Gene expression cartography. Nature 576:132–137
CrossRef Google scholar
[43]
Pan G, Cavalli M, Carlsson B, Skrtic S, Kumar C, Wadelius C (2020) rs953413 Regulates polyunsaturated fatty acid metabolism by modulating ELOVL2 expression. iScience 23:100808
CrossRef Google scholar
[44]
Petersen F, Bock L, Flad HD, Brandt E (1999) Platelet factor 4-induced neutrophil-endothelial cell interaction: involvement of mechanisms and functional consequences different from those elicited by interleukin-8. Blood 94:4020–4028
CrossRef Google scholar
[45]
Peterson VM, Zhang KX, Kumar N, Wong J, Li L, Wilson DC, Moore R, McClanahan TK,Sadekova S, Klappenbach JA (2017) Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol 35:936–939
CrossRef Google scholar
[46]
Pfaff DW, Baum MJ (2018) Hormone-dependent medial preoptic/ lumbar spinal cord/autonomic coordination supporting male sexual behaviors. Mol Cell Endocrinol 467:21–30
CrossRef Google scholar
[47]
Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10:1096–1098
CrossRef Google scholar
[48]
Rajbhandari P, Arneson D, Hart SK, Ahn IS, Diamante G, Santos LC, Zaghari N, Feng AC, Thomas BJ, Vergnes L (2019) Single cell analysis reveals immune cell-adipocyte crosstalk regulating the transcription of thermogenic adipocytes. Elife 8:e49501
CrossRef Google scholar
[49]
Ramilowski JA, Goldberg T, Harshbarger J, Kloppmann E, Lizio M, Satagopam VP, Itoh M, Kawaji H, Carninci P, Rost B (2015) A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun 6:7866
CrossRef Google scholar
[50]
Ramos P,Casu C, Gardenghi S, Breda L,Crielaard BJ, Guy E,Marongiu MF, Gupta R, Levine RL, Abdel-Wahab O (2013) Macrophages support pathological erythropoiesis in polycythemia vera and beta-thalassemia. Nat Med 19:437–445
CrossRef Google scholar
[51]
Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J,Chen LM, Chen F, Macosko EZ (2019) Slide-seq: a scalable technology for measuring genomewide expression at high spatial resolution. Science 363:1463–1467
CrossRef Google scholar
[52]
Rothbauer M, Zirath H, Ertl P (2018) Recent advances in microfluidic technologies for cell-to-cell interaction studies. Lab Chip 18:249–270
CrossRef Google scholar
[53]
Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33:495–502
CrossRef Google scholar
[54]
Scott CL, Guilliams M (2018) Tissue unit-ed: lung cells team up to drive alveolar macrophage development. Cell 175:898–900
CrossRef Google scholar
[55]
Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, Chen P, Gertner RS, Gaublomme JT, Yosef N (2014) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510:363–369
CrossRef Google scholar
[56]
Shao X, Ai N, Xu D, Fan X (2016) Exploring the interaction between Salvia miltiorrhiza and human serum albumin: Insights from herbdrug interaction reports, computational analysis and experimental studies. Spectrochim Acta A Mol Biomol Spectrosc 161:1–7
CrossRef Google scholar
[57]
Shao X, Lv N, Liao J,Long J,Xue R, Ai N, Xu D, Fan X (2019) Copy number variation is highly correlated with differential gene expression: a pan-cancer study. BMC Med Genet 20:175
CrossRef Google scholar
[58]
Shao X, Liao J, Lu X, Xue R, Ai N, Fan X (2020) scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience 23:100882
CrossRef Google scholar
[59]
Shrestha BR, Chia C, Wu L, Kujawa SG, Liberman MC, Goodrich LV (2018) Sensory neuron diversity in the inner ear is shaped by activity. Cell 174:1229–1246 e1217
CrossRef Google scholar
[60]
Sicard RE (1986) Hormones, neurosecretions, and growth factors as signal molecules for intercellular communication. Dev Comp Immunol 10:269–272
CrossRef Google scholar
[61]
Singer SJ (1992) Intercellular communication and cell-cell adhesion. Science 255:1671–1677
CrossRef Google scholar
[62]
Skelly DA, Squiers GT, McLellan MA, Bolisetty MT, Robson P, Rosenthal NA, Pinto AR (2018) Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep 22:600–610
CrossRef Google scholar
[63]
Song Y,Xu X, Wang W, Tian T, Zhu Z, Yang C (2019) Single cell transcriptomics: moving towards multi-omics. Analyst 144:3172–3189
CrossRef Google scholar
[64]
Stagg RB, Fletcher WH (1990) The hormone-induced regulation of contact-dependent cell-cell communication by phosphorylation. Endocr Rev 11:302–325
CrossRef Google scholar
[65]
Stoeckius M, Hafemeister C, Stephenson W,Houck-Loomis B,Chattopadhyay PK, Swerdlow H, Satija R, Smibert P (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14:865–868
CrossRef Google scholar
[66]
Stuart T, Satija R (2019) Integrative single-cell analysis. Nat Rev Genet 20:257–272
CrossRef Google scholar
[67]
Sugiyama E, Guerrini MM, Honda K, Hattori Y, Abe M, Kallback P, Andren PE, Tanaka KF, Setou M, Fagarasan S (2019) Detection of a high-turnover serotonin circuit in the mouse brain using mass spectrometry imaging. iScience 20:359–372
CrossRef Google scholar
[68]
Swaminathan J, Boulgakov AA, Hernandez ET, Bardo AM, Bachman JL, Marotta J, Johnson AM, Anslyn EV, Marcotte EM (2018) Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures. Nat Biotechnol 36:1076–1082
CrossRef Google scholar
[69]
Szczerba BM, Castro-Giner F, Vetter M, Krol I,Gkountela S, Landin J, Scheidmann MC, Donato C, Scherrer R,Singer J (2019) Neutrophils escort circulating tumour cells to enable cell cycle progression. Nature 566:553–557
CrossRef Google scholar
[70]
Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–196
CrossRef Google scholar
[71]
Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M, Meyer KB, Park JE, Stephenson E, Polanski K, Goncalves A (2018) Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563:347–353
CrossRef Google scholar
[72]
Vitak SA, Torkenczy KA, Rosenkrantz JL, Fields AJ, Christiansen L, Wong MH, Carbone L, Steemers FJ, Adey A (2017) Sequencing thousands of single-cell genomes with combinatorial indexing. Nat Methods 14:302–308
CrossRef Google scholar
[73]
Vogel C, Marcotte EM (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13:227–232
CrossRef Google scholar
[74]
Wang X, Song W, Kawazoe N, Chen G (2013) The osteogenic differentiation of mesenchymal stem cells by controlled cell-cell interaction on micropatterned surfaces. J Biomed Mater Res A 101:3388–3395
CrossRef Google scholar
[75]
Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N, Vesuna S, Evans K, Liu C, Ramakrishnan C, Liu J (2018) Threedimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691
CrossRef Google scholar
[76]
Wang S, Karikomi M, MacLean AL, Nie Q(2019) Cell lineage and communication network inference via optimization for single-cell transcriptomics. Nucleic Acids Res 47(11):e66
CrossRef Google scholar
[77]
Xiong X, Kuang H, Ansari S, Liu T, Gong J, Wang S, Zhao XY, Ji Y, Li C, Guo L(2019) Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis. Mol Cell 75:644–660e645
CrossRef Google scholar
[78]
Xu Y, Ji K, Wu M, Hao B, Yao KT, Xu Y (2019) A miRNA-HERC4 pathway promotes breast tumorigenesis by inactivating tumor suppressor LATS1. Protein Cell 10:595–605
CrossRef Google scholar
[79]
Xue R, Liao J, Shao X, Han K, Long J, Shao L, Ai N, Fan X (2020) Prediction of adverse drug reactions by combining biomedical tripartite network and graph representation model. Chem Res Toxicol 33:202–210
CrossRef Google scholar
[80]
Zepp JA, Zacharias WJ, Frank DB, Cavanaugh CA, Zhou S, Morley MP, Morrisey EE (2017) Distinct mesenchymal lineages and niches promote epithelial self-renewal and myofibrogenesis in the lung. Cell 170:1134–1148 e1110
CrossRef Google scholar
[81]
Zhang L, Vertes A (2018) Single-cell mass spectrometry approaches to explore cellular heterogeneity. Angew Chem Int Ed Engl 57:4466–4477
CrossRef Google scholar
[82]
Zhang Y, Yan Z, Qin Q, Nisenblat V, Chang HM, Yu Y, Wang T, Lu C,Yang M, Yang S(2018) Transcriptome landscape of human folliculogenesis reveals oocyte and granulosa cell interactions. Mol Cell 72:1021–1034 e1024
CrossRef Google scholar
[83]
Zhang X, Lan Y, Xu J, Quan F, Zhao E, Deng C, Luo T, Xu L, Liao G, Yan M (2019) CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res 47:D721–D728
CrossRef Google scholar
[84]
Zhang L, Vertes A (2018) Single-cell mass spectrometry approaches to explore cellular heterogeneity. Angew Chem Int Ed Engl 57:4466–4477
CrossRef Google scholar
[85]
Zheng GX, Terry JM, Belgrader P,Ryvkin P,Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049
CrossRef Google scholar
[86]
Zheng G, Jiang C, Li Y,Yang D, Ma Y,Zhang B, Li X, Zhang P, Hu X, Zhao X(2019) TMEM43-S358L mutation enhances NFkappaB- TGFbeta signal cascade in arrhythmogenic right ventricular dysplasia/cardiomyopathy. Protein Cell 10:104–119
CrossRef Google scholar
[87]
Zhou B, Liu C, Xu L, Yuan Y, Zhao J, Zhao W, Chen Y, Qiu J, Meng M, Zheng Y(2020) N(6)-methyladenosine reader protein Ythdc2 suppresses liver steatosis via regulation of mRNA stability of lipogenic genes. Hepatology. https://doi.org/10.1002/ hep.31220
CrossRef Google scholar
[88]
Zhu C,Preissl S, Ren B (2020) Single-cell multimodal omics: the power of many. Nat Methods 17:11–14
CrossRef Google scholar

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