REFERENCES
1. Valadi H, Ekström K, Bossios A, Sjöstrand M, Lee JJ, Lötvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9:654-9.
2. National Library of Medicine (US). Available from https://pubmed.ncbi.nlm.nih.gov/. [accessed 23 September 2026].
3. Colombo M, Raposo G, Théry C. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol. 2014;30:255-89.
4. Tóth EÁ, Turiák L, Visnovitz T, et al. Formation of a protein corona on the surface of extracellular vesicles in blood plasma. J Extracell Vesicles. 2021;10:e12140.
5. Wolf M, Poupardin RW, Ebner-Peking P, et al. A functional corona around extracellular vesicles enhances angiogenesis, skin regeneration and immunomodulation. J Extracell Vesicles. 2022;11:e12207.
6. Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013;200:373-83.
7. Weng Q, Wang Y, Xie Y, et al. Extracellular vesicles-associated tRNA-derived fragments (tRFs): biogenesis, biological functions, and their role as potential biomarkers in human diseases. J Mol Med. 2022;100:679-95.
8. Torres AG, Martí E. Toward an understanding of extracellular tRNA biology. Front Mol Biosci. 2021;8:662620.
9. Liu DSK, Yang QZC, Asim M, Krell J, Frampton AE. The clinical significance of transfer RNAs present in extracellular vesicles. Int J Mol Sci. 2022;23:3692.
10. Pan Y, Zhang B, Li Z, Hu K. Extracellular vesicles-associated tRNA-derived fragments: Emerging insights into cancer progression and clinical application potential. Genes Dis. 2026;13:101682.
11. Tan GC, Chan E, Molnar A, et al. 5’ isomiR variation is of functional and evolutionary importance. Nucleic Acids Res. 2014;42:9424-35.
12. Bofill-De Ros X, Yang A, Gu S. IsomiRs: Expanding the miRNA repression toolbox beyond the seed. Biochim Biophys Acta Gene Regul Mech. 2020;1863:194373.
13. Tomasello L, Distefano R, Nigita G, Croce CM. The microRNA family gets wider: the isomirs classification and role. Front Cell Dev Biol. 2021;9:668648.
14. Weston M, Ripan RC, Li X, Hu H. New characteristics of MiRNA and isomiR interactions with mRNA. Sci Rep. 2025;15:37694.
15. Baglio SR, Rooijers K, Koppers-Lalic D, et al. Human bone marrow- and adipose-mesenchymal stem cells secrete exosomes enriched in distinctive miRNA and tRNA species. Stem Cell Res Ther. 2015;6:127.
16. Koppers-Lalic D, Hackenberg M, Bijnsdorp IV, et al. Nontemplated nucleotide additions distinguish the small RNA composition in cells from exosomes. Cell Rep. 2014;8:1649-58.
17. Godoy PM, Barczak AJ, DeHoff P, et al. Comparison of reproducibility, accuracy, sensitivity, and specificity of miRNA quantification platforms. Cell Rep. 2019;29:4212-22.e5.
18. Pérez-Rodríguez D, López-Fernández H, and Agís-Balboa RC. On the reproducibility of miRNA-Seq differential expression analyses in neuropsychiatric diseases. In. Lecture Notes in Networks and Systems: Springer International Publishing; 2022. pp. 41-51.
19. Pfaffl MW, Kubista M, Vandesompele J, Bustin SA. MISEV and MIQE: integrating domain-specific and general standards to strengthen extracellular vesicle biomarker research. Extracell Vesicles Circ Nucl Acids. 2025;6:669-76.
20. Pfaffl MW. How laboratory guidelines promote the validity of circulating extracellular vesicle-associated nucleic acid biomarker signatures in liquid biopsy. Int J Mol Sci. 2025;26:12115.
21. Simoneau J, Dumontier S, Gosselin R, Scott MS. Current RNA-seq methodology reporting limits reproducibility. Brief Bioinform. 2021;22:140-5.
22. Dillies MA, Rau A, Aubert J, et al. ; French StatOmique Consortium. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013;14:671-83.
23. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics. 2010;11:94.
24. Düren Y, Lederer J, Qin LX. Depth normalization of small RNA sequencing: using data and biology to select a suitable method. Nucleic Acids Res. 2022;50:e56.
25. Zhao S, Zhang B. A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics. 2015;16:97.
26. Hansen KD, Wu Z, Irizarry RA, Leek JT. Sequencing technology does not eliminate biological variability. Nat Biotechnol. 2011;29:572-3.
27. Baroin-Tourancheau A, Jaszczyszyn Y, Benigni X, Amar L. Evaluating and correcting inherent bias of microRNA expression in illumina sequencing analysis. Front Mol Biosci. 2019;6:17.
28. Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform. 2020;2:lqaa078.
29. Esteve-Codina A. RNA-Seq data analysis, applications and challenges. In: Jaumot, J, Bedia, C and Tauler, R, editors. Data analysis for omic sciences: methods and applications. Amsterdam: Elsevier; 2018. pp. 71-106.
30. Wang B. Base composition characteristics of mammalian miRNAs. J Nucleic Acids. 2013;2013:951570.
31. Bleazard T, Lamb JA, Griffiths-Jones S. Bias in microRNA functional enrichment analysis. Bioinformatics. 2015;31:1592-8.
33. Fridrich A, Hazan Y, Moran Y. Too many false targets for microRNAs: challenges and pitfalls in prediction of miRNA targets and their gene ontology in model and non-model organisms. Bioessays. 2019;41:e1800169.
34. Lim LP, Lau NC, Garrett-Engele P, et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005;433:769-73.
36. Hildebrandt A, Kirchner B, Nolte-’t Hoen ENM, Pfaffl MW. miREV: an online database and tool to uncover potential reference RNAs and biomarkers in small-RNA sequencing data sets from extracellular vesicles enriched samples. J Mol Biol. 2021;433:167070.
37. Patil AH, Halushka MK. miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline. NAR Genom Bioinform. 2021;3:lqab068.
38. Wu X, Kim TK, Baxter D, et al. sRNAnalyzer-a flexible and customizable small RNA sequencing data analysis pipeline. Nucleic Acids Res. 2017;45:12140-51.
40. Kuksa PP, Amlie-Wolf A, Katanic Ž, Valladares O, Wang LS, Leung YY. SPAR: small RNA-seq portal for analysis of sequencing experiments. Nucleic Acids Res. 2018;46:W36-42.
41. Peltzer A, Trigila A, Pantano L, Ewels P, Wang C, et al. nf-core/smrnaseq (2026). Version 2.4.1. Available from https://nf-co.re/smrnaseq/2.2.4/. [accessed 23 September 2026].
42. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j 2011;17:10.
43. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.
44. Loher P, Telonis AG, Rigoutsos I. MINTmap: fast and exhaustive profiling of nuclear and mitochondrial tRNA fragments from short RNA-seq data. Sci Rep. 2017;7:41184.
45. Zhang X, Ping P, Hutvagner G, Blumenstein M, Li J. Aberration-corrected ultrafine analysis of miRNA reads at single-base resolution: a k-mer lattice approach. Nucleic Acids Res. 2021;49:e106.
46. Schuster M, Braun FK, Chiang DM, et al. Extracellular vesicles secreted by 3D tumor organoids are enriched for immune regulatory signaling biomolecules compared to conventional 2D glioblastoma cell systems. Front Immunol. 2024;15:1388769.
47. Köster J, Rahmann S. Snakemake--a scalable bioinformatics workflow engine. Bioinformatics. 2012;28:2520-2.
48. Mölder F, Jablonski KP, Letcher B, et al. Sustainable data analysis with Snakemake. F1000Res. 2021;10:33.
49. Andrews S. FastQC: A quality control tool for high throughput sequence data (2010). Available from https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. [accessed 23 September 2026].
50. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47:D155-62.
51. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68-73.
52. Goldfarb T, Kodali VK, Pujar S, et al. NCBI RefSeq: reference sequence standards through 25 years of curation and annotation. Nucleic Acids Res. 2025;53:D243-57.
53. Piuco R and Galante PAF. piRNAdb: a piwi-interacting RNA database. bioRxiv 2021; bioRxiv 2021.09.21.461238.
54. R Core Team. R: a language and environment for statistical computing (2024). Version 4.4.1. Available from https://www.r-project.org/. [accessed 23 September 2026].
55. Ushey K, Wickham H. renv: Project Environments (2026). Available from https://cran.r-project.org/web/packages/renv/index.html. [accessed 23 September 2026].
56. Kassambara A. fastqcr: quality control of sequencing data (2023). Available from https://cran.r-project.org/web/packages/fastqcr/index.html. [accessed 23 September 2026].
57. Barrett T, Dowle M, Srinivasan A, et al. data.table: Extension of ‘data.frame’ (2026). Available from https://cran.r-project.org/web/packages/data.table/index.html. [accessed 23 September 2026].
58. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
59. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
60. Blighe K, Rana S, Lewis M. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling (2018). Available from https://github.com/kevinblighe/EnhancedVolcano. [accessed 23 September 2026].
61. Xie Y, Cheng J, Tan X, Aden-Buie G. DT: a wrapper of the JavaScript library ‘DataTables’ (2025). Available from https://cran.r-project.org/web/packages/DT/index.html. [accessed 23 September 2026].
62. Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: visualization of Intersecting Sets. IEEE Trans Vis Comput Graph. 2014;20:1983-92.
63. Krassowski M. ComplexUpset (2020). Version 1.3.3. Available from https://zenodo.org/records/7314197. [accessed 23 September 2026].
64. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: an R package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. 2017;13:e1005752.
65. Kern F, Fehlmann T, Solomon J, et al. miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems. Nucleic Acids Res. 2020;48:W521-8.
66. Rezwani M, Pourfathollah AA, Noorbakhsh F. rbioapi: user-friendly R interface to biologic web services’ API. Bioinformatics. 2022;38:2952-3.
67. Ru Y, Kechris KJ, Tabakoff B, et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res. 2014;42:e133.
68. Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015;4:e05005.
69. Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015;347:1260419.
70. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318-30.
71. Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44:W90-7.
72. Xie Y, Allaire JJ, and Grolemund G. R Markdown: the definitive guide. 1st ed. London: Chapman & Hall/CRC; 2018.
73. Xie Y. knitr: a comprehensive tool for reproducible research in R. 1st ed. London: Chapman & Hall/CRC; 2014.
74. Sievert C. Interactive Web-Based Data visualization with R, plotly, and shiny. 1st ed. London: Chapman & Hall/CRC; 2020.
75. Vaidyanathan R, Xie Y, Allaire J, et al. htmlwidgets: HTML Widgets for R (2023). Available from https://cran.r-project.org/web/packages/htmlwidgets/index.html. [accessed 23 September 2026].
76. Wickham H. ggplot2: elegant graphics for data analysis. 2nd ed. Use R! Cham: Springer; 2016. Available from https://link.springer.com/book/10.1007/978-3-319-24277-4. [accessed 23 September 2026].
77. Buschmann D, Kirchner B, Hermann S, et al. Evaluation of serum extracellular vesicle isolation methods for profiling miRNAs by next-generation sequencing. J Extracell Vesicles. 2018;7:1481321.
78. Céspedes PF, Jainarayanan A, Fernández-Messina L, et al. T-cell trans-synaptic vesicles are distinct and carry greater effector content than constitutive extracellular vesicles. Nat Commun. 2022;13:3460.
79. Love MI, Anders S, and Huber W. Analyzing RNA-seq data with DESeq2 (2025). Available from https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html. [accessed 23 September 2026].
80. Aparicio-Puerta E, Hirsch P, Schmartz GP, Kern F, Fehlmann T, Keller A. miEAA 2023: updates, new functional microRNA sets and improved enrichment visualizations. Nucleic Acids Res. 2023;51:W319-25.
81. Kehl T, Kern F, Backes C, et al. miRPathDB 2.0: a novel release of the miRNA Pathway Dictionary Database. Nucleic Acids Res. 2020;48:D142-7.
82. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27-30.
83. Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 2025;53:D672-7.
84. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353-61.
85. Milacic M, Beavers D, Conley P, et al. The reactome pathway knowledgebase 2024. Nucleic Acids Res. 2024;52:D672-8.
86. Agrawal A, Balcı H, Hanspers K, et al. WikiPathways 2024: next generation pathway database. Nucleic Acids Res. 2024;52:D679-89.
87. Cui S, Yu S, Huang HY, et al. miRTarBase 2025: updates to the collection of experimentally validated microRNA-target interactions. Nucleic Acids Res. 2025;53:D147-56.
88. Hsu SD, Lin FM, Wu WY, et al. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res. 2011;39:D163-9.
89. Huang HY, Lin YC, Li J, et al. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 2020;48:D148-54.
90. Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS One. 2018;13:e0206239.
91. Aleksander SA, Balhoff J, Carbon S, et al. ; Gene Ontology Consortium. The Gene Ontology knowledgebase in 2023. Genetics. 2023;224:iyad031.
92. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25-9.
93. Russo F, Di Bella S, Nigita G, et al. miRandola: extracellular circulating microRNAs database. PLoS One. 2012;7:e47786.
95. Teng X, Chen X, Xue H, et al. NPInter v4.0: an integrated database of ncRNA interactions. Nucleic Acids Res. 2020;48:D160-5.
96. Wu T, Wang J, Liu C, et al. NPInter: the noncoding RNAs and protein related biomacromolecules interaction database. Nucleic Acids Res. 2006;34:D150-2.
97. Wu L, Wang L, Hu S, et al. RNALocate v3.0: advancing the repository of RNA subcellular localization with dynamic analysis and prediction. Nucleic Acids Res. 2025;53:D284-92.
98. Clarke AW, Høye E, Hembrom AA, et al. MirGeneDB 3.0: improved taxonomic sampling, uniform nomenclature of novel conserved microRNA families and updated covariance models. Nucleic Acids Res. 2025;53:D116-28.
99. Fromm B, Billipp T, Peck LE, et al. A uniform system for the annotation of vertebrate microRNA genes and the evolution of the human microRNAome. Annu Rev Genet. 2015;49:213-42.
100. Patil AH, Baran A, Brehm ZP, McCall MN, Halushka MK. A curated human cellular microRNAome based on 196 primary cell types. Gigascience. 2022;11:giac083.
101. Rishik S, Hirsch P, Grandke F, Fehlmann T, Keller A. miRNATissueAtlas 2025: an update to the uniformly processed and annotated human and mouse non-coding RNA tissue atlas. Nucleic Acids Res. 2025;53:D129-37.
102. Aparicio-Puerta E, Hirsch P, Schmartz GP, et al. isomiRdb: microRNA expression at isoform resolution. Nucleic Acids Res. 2023;51:D179-85.
103. Ning L, Cui T, Zheng B, et al. MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation. Nucleic Acids Res. 2021;49:D160-4.
104. Liu X, Wang S, Meng F, et al. SM2miR: a database of the experimentally validated small molecules’ effects on microRNA expression. Bioinformatics. 2013;29:409-11.
105. McGeary SE, Lin KS, Shi CY, et al. The biochemical basis of microRNA targeting efficacy. Science. 2019;366:eaav1741.
106. Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.
107. Xie Z, Bailey A, Kuleshov MV, et al. Gene set knowledge discovery with enrichr. Curr Protoc. 2021;1:e90.
108. Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48:D845-55.
109. McKusick-Nathans Institute of Genetic Medicine - Johns Hopkins University (Baltimore MD). Online Mendelian Inheritance in Man, OMIM. Available from https://omim.org/. [accessed 23 September 2026].
110. Gargano MA, Matentzoglu N, Coleman B, et al. The Human Phenotype Ontology in 2024: phenotypes around the world. Nucleic Acids Res. 2024;52:D1333-46.
111. Conesa A, Madrigal P, Tarazona S, et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
112. Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014;32:896-902.
113. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11:R106.
114. Leek JT, Scharpf RB, Bravo HC, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11:733-9.
115. Wang J, Chen HC, Sheng Q, et al. Systematic assessment of small RNA profiling in human extracellular vesicles. Cancers. 2023;15:3446.
116. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118-27.
117. Miceli RT, Chen TY, Nose Y, et al. Extracellular vesicles, RNA sequencing, and bioinformatic analyses: Challenges, solutions, and recommendations. J Extracell Vesicles. 2024;13:e70005.
118. Chen HC, Wang J, Coffey RJ, et al. EVPsort: an atlas of small ncRNA profiling and sorting in extracellular vesicles and particles. J Mol Biol. 2024;436:168571.
119. Kumar P, Anaya J, Mudunuri SB, Dutta A. Meta-analysis of tRNA derived RNA fragments reveals that they are evolutionarily conserved and associate with AGO proteins to recognize specific RNA targets. BMC Biol. 2014;12:78.
120. Ivanov P, Emara MM, Villen J, Gygi SP, Anderson P. Angiogenin-induced tRNA fragments inhibit translation initiation. Mol Cell. 2011;43:613-23.
121. Goodarzi H, Liu X, Nguyen HC, Zhang S, Fish L, Tavazoie SF. Endogenous tRNA-derived fragments suppress breast cancer progression via YBX1 displacement. Cell. 2015;161:790-802.
122. Makarenkov N, Yoel U, Haim Y, et al. Circulating isomiRs may be superior biomarkers compared to their corresponding miRNAs: a pilot biomarker study of using isomiR-Ome to detect coronary calcium-based cardiovascular risk in patients with NAFLD. Int J Mol Sci. 2024;25:890.
123. Wang J, Ma G, Ge H, et al. Circulating tRNA-derived small RNAs (tsRNAs) signature for the diagnosis and prognosis of breast cancer. NPJ Breast Cancer. 2021;7:4.
124. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005;23:7332-41.
126. Baran AM, Patil AH, Aparicio-Puerta E, Jun SH, Halushka MK, McCall MN. miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs. Genome Biol. 2025;26:102.
127. Grätz C, Schuster M, Brandes F, et al. A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics. Mol Aspects Med. 2024;97:101269.
128. Grätz C, Bui MLU, Thaqi G, Kirchner B, Loewe RP, Pfaffl MW. Obtaining reliable RT-qPCR results in molecular diagnostics-MIQE goals and pitfalls for transcriptional biomarker discovery. Life. 2022;12:386.
129. Mussack V, Hermann S, Buschmann D, Kirchner B, Pfaffl MW. MIQE-compliant validation of microRNA biomarker signatures established by small RNA sequencing. Methods Mol Biol. 2020;2065:23-38.
130. Buschmann D, Haberberger A, Kirchner B, et al. Toward reliable biomarker signatures in the age of liquid biopsies - how to standardize the small RNA-Seq workflow. Nucleic Acids Res. 2016;44:5995-6018.
131. Pinzón N, Li B, Martinez L, et al. microRNA target prediction programs predict many false positives. Genome Res. 2017;27:234-45.
132. Witkos TM, Koscianska E, Krzyzosiak WJ. Practical aspects of microRNA target prediction. Curr Mol Med. 2011;11:93-109.
133. Chi SW, Zang JB, Mele A, Darnell RB. Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature. 2009;460:479-86.
134. Parikh K, Cang S, Sekhri A, Liu D. Selective inhibitors of nuclear export (SINE)--a novel class of anti-cancer agents. J Hematol Oncol. 2014;7:78.
135. Li X, Peng J, Yi C. The epitranscriptome of small non-coding RNAs. Noncoding RNA Res. 2021;6:167-73.
136. Zhang Y, Lu L, Li X. Detection technologies for RNA modifications. Exp Mol Med. 2022;54:1601-16.
137. Elmentaite R, Domínguez Conde C, Yang L, Teichmann SA. Single-cell atlases: shared and tissue-specific cell types across human organs. Nat Rev Genet. 2022;23:395-410.
138. Regev A, Teichmann SA, Lander ES, et al. ; Human Cell Atlas Meeting Participants. The human cell atlas. Elife. 2017;6:e27041.
