{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T09:44:52Z","timestamp":1792143892276,"version":"4.4.0"},"publisher-location":"New York, NY","reference-count":47,"publisher":"Springer US","isbn-type":[{"value":"9781071655863","type":"print"},{"value":"9781071655870","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-1-0716-5587-0_29","type":"book-chapter","created":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T08:54:00Z","timestamp":1792140840000},"page":"497-536","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Single-Cell RNA Sequencing to Characterize Murine Ewing Sarcoma Xenografts"],"prefix":"10.1007","author":[{"given":"Tobias","family":"Faehling","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelina","family":"Yershova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1016\/j.celrep.2020.01.049","volume":"30","author":"M-M Aynaud","year":"2020","unstructured":"Aynaud M-M et al. (2020) Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single-cell resolution. Cell Rep 30:1767\u20131779.e6","journal-title":"Cell Rep"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1038\/s41587-020-0465-8","volume":"38","author":"J Ding","year":"2020","unstructured":"Ding J et al. (2020) Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38:737\u2013746","journal-title":"Nat Biotechnol"},{"key":"29_CR3","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1186\/s13059-020-02048-6","volume":"21","author":"E Denisenko","year":"2020","unstructured":"Denisenko E et al. (2020) Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol 21:130","journal-title":"Genome Biol"},{"key":"29_CR4","first-page":"3625","volume":"144","author":"M Adam","year":"2017","unstructured":"Adam M, Potter AS, Potter SS (2017) Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development. Development 144:3625\u20133632","journal-title":"Development"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Potter AS, Potter SS (2019) Dissociation of tissues for single-cell analysis. In: (ed Vainio, S) Kidney organogenesis: methods and protocols. Springer, New York, NY, pp 55\u201362. https:\/\/doi.org\/10.1007\/978-1-4939-9021-4_5","DOI":"10.1007\/978-1-4939-9021-4_5"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Truong DD et al (2022) Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing. bioRxiv https:\/\/doi.org\/10.1101\/2022.01.21.476982","DOI":"10.1101\/2022.01.21.476982"},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1038\/s41593-022-01022-8","volume":"25","author":"SE Marsh","year":"2022","unstructured":"Marsh SE et al. (2022) Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain. Nat Neurosci 25:306\u2013316","journal-title":"Nat Neurosci"},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1016\/j.cell.2018.05.060","volume":"174","author":"E Azizi","year":"2018","unstructured":"Azizi E et al. (2018) Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174:1293\u20131308.e36","journal-title":"Cell"},{"key":"29_CR9","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1126\/science.aad0501","volume":"352","author":"I Tirosh","year":"2016","unstructured":"Tirosh I et al. (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189\u2013196","journal-title":"Science"},{"key":"29_CR10","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1038\/s41576-023-00586-w","volume":"24","author":"L Heumos","year":"2023","unstructured":"Heumos L et al. (2023) Best practices for single-cell analysis across modalities. Nat Rev Genet 24:550\u2013572","journal-title":"Nat Rev Genet"},{"key":"29_CR11","doi-asserted-by":"publisher","first-page":"107242","DOI":"10.1016\/j.isci.2023.107242","volume":"26","author":"D Arceneaux","year":"2023","unstructured":"Arceneaux D et al. (2023) A contamination focused approach for optimizing the single-cell RNA-seq experiment. iScience 26:107242","journal-title":"iScience"},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/s13059-019-1830-0","volume":"20","author":"CH O\u2019Flanagan","year":"2019","unstructured":"O\u2019Flanagan CH et al. (2019) Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol 20:210","journal-title":"Genome Biol"},{"key":"29_CR13","unstructured":"Cell Preparation for Single Cell Protocols | Official 10x Genomics Support. 10x Genomics https:\/\/www.10xgenomics.com\/support\/universal-three-prime-gene-expression\/documentation\/steps\/sample-prep\/single-cell-protocols-cell-preparation-guide"},{"key":"29_CR14","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1038\/s41592-023-01943-7","volume":"20","author":"SJ Fleming","year":"2023","unstructured":"Fleming SJ et al. (2023) Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat Methods 20:1323\u20131335","journal-title":"Nat Methods"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Young MD, Behjati S (2020) SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience 9:giaa151","DOI":"10.1093\/gigascience\/giaa151"},{"key":"29_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13059-020-1950-6","volume":"21","author":"S Yang","year":"2020","unstructured":"Yang S et al. (2020) Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol 21:57","journal-title":"Genome Biol"},{"key":"29_CR17","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.cels.2020.05.010","volume":"11","author":"NJ Bernstein","year":"2020","unstructured":"Bernstein NJ et al. (2020) Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning. Cell Syst 11:95\u2013101.e5","journal-title":"Cell Syst"},{"key":"29_CR18","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.cels.2019.03.003","volume":"8","author":"CS McGinnis","year":"2019","unstructured":"McGinnis CS, Murrow LM, Gartner ZJ (2019) DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst 8:329\u2013337.e4","journal-title":"Cell Syst"},{"key":"29_CR19","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1186\/s13059-021-02547-0","volume":"22","author":"W Muskovic","year":"2021","unstructured":"Muskovic W, Powell JE (2021) DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data. Genome Biol 22:329","journal-title":"Genome Biol"},{"key":"29_CR20","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.cels.2020.11.008","volume":"12","author":"NM Xi","year":"2021","unstructured":"Xi NM, Li JJ (2021) Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst 12:176\u2013194.e6","journal-title":"Cell Syst"},{"key":"29_CR21","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1093\/bioinformatics\/btz698","volume":"36","author":"AS Bais","year":"2020","unstructured":"Bais AS, Kostka D (2020) scds: Computational annotation of doublets in single-cell RNA sequencing data. Bioinformatics 36:1150\u20131158","journal-title":"Bioinformatics"},{"key":"29_CR22","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1186\/s13059-019-1662-y","volume":"20","author":"ATL Lun","year":"2019","unstructured":"Lun ATL et al. (2019) EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol 20:63","journal-title":"Genome Biol"},{"key":"29_CR23","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-019-1795-z","volume":"20","author":"T Abdelaal","year":"2019","unstructured":"Abdelaal T et al. (2019) A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol 20: 194","journal-title":"Genome Biol"},{"key":"29_CR24","unstructured":"Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830"},{"key":"29_CR25","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.cels.2019.06.004","volume":"9","author":"Y Tan","year":"2019","unstructured":"Tan Y, Cahan P (2019) SingleCellNet: a computational tool to classify single cell RNA-seq data across platforms and across species. Cell Syst 9:207\u2013213.e2","journal-title":"Cell Syst"},{"key":"29_CR26","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1038\/nmeth.4644","volume":"15","author":"VY Kiselev","year":"2018","unstructured":"Kiselev VY, Yiu A, Hemberg M (2018) scmap: projection of single-cell RNA-seq data across data sets. Nat Methods 15:359\u2013362","journal-title":"Nat Methods"},{"key":"29_CR27","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1186\/s13059-019-1862-5","volume":"20","author":"J Alquicira-Hernandez","year":"2019","unstructured":"Alquicira-Hernandez J, Sathe A, Ji HP, Nguyen Q, Powell JE (2019) scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol 20:264","journal-title":"Genome Biol"},{"key":"29_CR28","doi-asserted-by":"publisher","first-page":"D870","DOI":"10.1093\/nar\/gkac947","volume":"51","author":"C Hu","year":"2023","unstructured":"Hu C et al. (2023) CellMarker 2.0: an updated database of manually curated cell markers in human\/mouse and web tools based on scRNA-seq data. Nucleic Acids Res 51:D870\u2013D876","journal-title":"Nucleic Acids Res"},{"key":"29_CR29","doi-asserted-by":"crossref","unstructured":"Franz\u00e9n O, Gan L-M, Bj\u00f6rkegren JLM (2019) PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019:baz046","DOI":"10.1093\/database\/baz046"},{"key":"29_CR30","doi-asserted-by":"publisher","first-page":"100882","DOI":"10.1016\/j.isci.2020.100882","volume":"23","author":"X Shao","year":"2020","unstructured":"Shao X et al. (2020) scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience 23:100882","journal-title":"iScience"},{"key":"29_CR31","unstructured":"The Human Protein Atlas. https:\/\/www.proteinatlas.org\/ (2026)"},{"key":"29_CR32","unstructured":"GSEA | MSigDB | Human MSigDB Collections. https:\/\/www.gsea-msigdb.org\/gsea\/msigdb\/collections.jsp (2026)"},{"key":"29_CR33","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","volume":"184","author":"Y Hao","year":"2021","unstructured":"Hao Y et al. (2021) Integrated analysis of multimodal single-cell data. Cell 184:3573\u20133587.e29","journal-title":"Cell"},{"key":"29_CR34","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","volume":"36","author":"A Butler","year":"2018","unstructured":"Butler A, Hoffman P, Smibert P, Papalexi E, Satija R (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36:411\u2013420","journal-title":"Nat Biotechnol"},{"key":"29_CR35","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/nbt.3192","volume":"33","author":"R Satija","year":"2015","unstructured":"Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33:495\u2013502","journal-title":"Nat Biotechnol"},{"key":"29_CR36","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","volume":"177","author":"T Stuart","year":"2019","unstructured":"Stuart T et al. (2019) Comprehensive integration of single-cell data. Cell 177:1888\u20131902.e21","journal-title":"Cell"},{"key":"29_CR37","doi-asserted-by":"publisher","first-page":"5890","DOI":"10.1038\/s41467-021-25957-x","volume":"12","author":"JB Kang","year":"2021","unstructured":"Kang JB et al. (2021) Efficient and precise single-cell reference atlas mapping with Symphony. Nat Commun 12:5890","journal-title":"Nat Commun"},{"key":"29_CR38","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1038\/s41587-021-01001-7","volume":"40","author":"M Lotfollahi","year":"2022","unstructured":"Lotfollahi M et al. (2022) Mapping single-cell data to reference atlases by transfer learning. Nat Biotechnol 40:121\u2013130","journal-title":"Nat Biotechnol"},{"key":"29_CR39","doi-asserted-by":"publisher","first-page":"111761","DOI":"10.1016\/j.celrep.2022.111761","volume":"41","author":"MF Orth","year":"2022","unstructured":"Orth MF et al. (2022) Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation. Cell Rep 41:111761","journal-title":"Cell Rep"},{"key":"29_CR40","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1158\/1541-7786.MCR-06-0090","volume":"4","author":"M Kinsey","year":"2006","unstructured":"Kinsey M, Smith R, Lessnick SL (2006) NR0B1 is required for the oncogenic phenotype mediated by EWS\/FLI in Ewing\u2019s sarcoma. Mol Cancer Res MCR 4:851\u2013859","journal-title":"Mol Cancer Res MCR"},{"key":"29_CR41","doi-asserted-by":"publisher","first-page":"8213","DOI":"10.1158\/0008-5472.CAN-03-4059","volume":"64","author":"MS Staege","year":"2004","unstructured":"Staege MS et al. (2004) DNA microarrays reveal relationship of Ewing family tumors to both endothelial and fetal neural crest-derived cells and define novel targets. Cancer Res 64:8213\u20138221","journal-title":"Cancer Res"},{"key":"29_CR42","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1038\/sj.onc.1208455","volume":"24","author":"C Siligan","year":"2005","unstructured":"Siligan C et al. (2005) EWS-FLI1 target genes recovered from Ewing\u2019s sarcoma chromatin. Oncogene 24:2512\u20132524","journal-title":"Oncogene"},{"key":"29_CR43","doi-asserted-by":"publisher","first-page":"e122","DOI":"10.1371\/journal.pmed.0040122","volume":"4","author":"K Stegmaier","year":"2007","unstructured":"Stegmaier K et al. (2007) Signature-based small molecule screening identifies cytosine arabinoside as an EWS\/FLI modulator in Ewing sarcoma. PLOS Med 4:e122","journal-title":"PLOS Med"},{"key":"29_CR44","doi-asserted-by":"publisher","first-page":"e5415","DOI":"10.1371\/journal.pone.0005415","volume":"4","author":"M Kauer","year":"2009","unstructured":"Kauer M et al. (2009) A molecular function map of Ewing\u2019s sarcoma. PLoS ONE 4:e5415","journal-title":"PLoS ONE"},{"key":"29_CR45","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1038\/s41467-024-55340-5","volume":"16","author":"M Kasan","year":"2025","unstructured":"Kasan M et al. (2025) Genomic and phenotypic stability of fusion-driven pediatric sarcoma cell lines. Nat Commun 16:380","journal-title":"Nat Commun"},{"key":"29_CR46","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41572-018-0003-x","volume":"4","author":"TGP Gr\u00fcnewald","year":"2018","unstructured":"Gr\u00fcnewald TGP et al. (2018) Ewing sarcoma. Nat Rev Dis Primer 4:5","journal-title":"Nat Rev Dis Primer"},{"key":"29_CR47","doi-asserted-by":"publisher","first-page":"2010","DOI":"10.1158\/1078-0432.CCR-24-2040","volume":"31","author":"A Goodspeed","year":"2025","unstructured":"Goodspeed A et al. (2025) Single cell RNA-sequencing of Ewing sarcoma tumors demonstrates transcriptional heterogeneity and clonal evolution. Clin. Cancer Res 31:2010\u20132023","journal-title":"Clin. Cancer Res"}],"container-title":["Methods in Molecular Biology","Ewing Sarcoma"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-5587-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T08:54:02Z","timestamp":1792140842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/chapter\/10.1007\/978-1-0716-5587-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,16]]},"ISBN":["9781071655863","9781071655870"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-5587-0_29","relation":{},"ISSN":["1064-3745","1940-6029"],"issn-type":[{"value":"1064-3745","type":"print"},{"value":"1940-6029","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,16]]},"assertion":[{"value":"16 October 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}