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Cell types of origin of the cell-free transcriptome

Vorperian, Sevahn K., Moufarrej, Mira N., Tabula Sapiens Consortium, Overall Project Direction and Coordination, Jones, Robert C., Karkanias, Jim, Krasnow, Mark, Pisco, Angela Oliveira +173 more·2022·Nature Biotechnology

Abstract

Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.

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  • Most plasma miRNAs were RBC derived, followed by platelets, mesenchymal cells, and immune cells.

    “Similar to a recent manuscript on cell-free RNA in plasma [40] and consistent with other miRNA-based studies [41–43], we observed that most plasma miRNAs were RBC derived, followed by platelets, mesenchymal cells, and immune cells.”

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  1. [1]Koh W Noninvasive in vivo monitoring of tissue-specific global gene expression in humans Proc. Natl Acad. Sci. USA 2014 111 7361 7366 10.1073/pnas.1405528111 24799715 PMC4034220 · DOI
  2. [2]Ibarra A Non-invasive characterization of human bone marrow stimulation and reconstitution by cell-free messenger RNA sequencing Nat. Commun. 2020 11 400 10.1038/s41467-019-14253-4 31964864 PMC6972916 · DOI
  3. [3]Larson MH A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection Nat. Commun. 2021 12 2357 10.1038/s41467-021-22444-1 33883548 PMC8060291 · DOI
  4. [4]Ngo, T. T. M., Moufarrej, M. N. & Rasmussen, M. L. H. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science360, 1133–1136 (2018). PMC7734383 29880692
  5. [5]Munchel, S. et al. Circulating transcripts in maternal blood reflect a molecular signature of early-onset preeclampsia. Sci. Transl. Med. 12, eaaz0131 (2020). 32611681
  6. [6]Toden, S. et al. Noninvasive characterization of Alzheimer’s disease by circulating, cell-free messenger RNA next-generation sequencing. Sci. Adv. 6, eabb1654 (2020). PMC7821903 33298436
  7. [7]Chalasani N Noninvasive stratification of nonalcoholic fatty liver disease by whole transcriptome cell-free mRNA characterization Am. J. Physiol. Gastrointest. Liver Physiol. 2021 320 G439 G449 10.1152/ajpgi.00397.2020 33501884 PMC8238173 · DOI
  8. [8]Klatt, E. C. Robbins & Cotran Atlas of Pathology (Elsevier, 2021).
  9. [9]Franzén O Gan L-M Björkegren JLM PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data Database (Oxford) 2019 2019 baz046 10.1093/database/baz046 30951143 PMC6450036 · DOI · Paper page
  10. [10]Newman AM Robust enumeration of cell subsets from tissue expression profiles Nat. Methods 2015 12 453 457 10.1038/nmeth.3337 25822800 PMC4739640 · DOI · Paper page
  11. [11]Newman AM Determining cell type abundance and expression from bulk tissues with digital cytometry Nat. Biotechnol. 2019 37 773 782 10.1038/s41587-019-0114-2 31061481 PMC6610714 · DOI
  12. [12]The Tabula Sapiens Consortium & Quake, S. R. The Tabula Sapiens: a single cell transcriptomic atlas of multiple organs from individual human donors. Preprint at https://www.biorxiv.org/content/10.1101/2021.07.19.452956v1 (2021). · DOI
  13. [13]GTEx Consortium Genetic effects on gene expression across human tissues Nature 2017 550 204 213 10.1038/nature24277 29022597 PMC5776756 · DOI · Paper page
  14. [14]Sadeh R ChIP-seq of plasma cell-free nucleosomes identifies gene expression programs of the cells of origin Nat. Biotechnol. 2021 39 586 598 10.1038/s41587-020-00775-6 33432199 PMC7610786 · DOI
  15. [15]Uhlen M A genome-wide transcriptomic analysis of protein-coding genes in human blood cells Science 2019 366 eaax9198 10.1126/science.aax9198 31857451 · DOI · Paper page
  16. [16]András IE Toborek M Extracellular vesicles of the blood–brain barrier Tissue Barriers 2016 4 e1131804 10.1080/21688370.2015.1131804 27141419 PMC4836554 · DOI
  17. [17]Abbott NJ Inflammatory mediators and modulation of blood–brain barrier permeability Cell. Mol. Neurobiol. 2000 20 131 147 10.1023/A:1007074420772 10696506 PMC11537513 · DOI
  18. [18]Ganong WF Circumventricular organs: definition and role in the regulation of endocrine and autonomic function Clin. Exp. Pharmacol. Physiol. 2000 27 422 427 10.1046/j.1440-1681.2000.03259.x 10831247 · DOI · Paper page
  19. [19]Suryawanshi H A single-cell survey of the human first-trimester placenta and decidua Sci. Adv. 2018 4 eaau4788 10.1126/sciadv.aau4788 30402542 PMC6209386 · DOI
  20. [20]Vento-Tormo R Single-cell reconstruction of the early maternal–fetal interface in humans Nature 2018 563 347 353 10.1038/s41586-018-0698-6 30429548 PMC7612850 · DOI · Paper page
  21. [21]Stewart BJ Spatiotemporal immune zonation of the human kidney Science 2019 365 1461 1466 10.1126/science.aat5031 31604275 PMC7343525 · DOI
  22. [22]Aizarani N A human liver cell atlas reveals heterogeneity and epithelial progenitors Nature 2019 572 199 204 10.1038/s41586-019-1373-2 31292543 PMC6687507 · DOI · Paper page
  23. [23]Kaufmann P Black S Huppertz B Endovascular trophoblast invasion: implications for the pathogenesis of intrauterine growth retardation and preeclampsia Biol. Reprod. 2003 69 1 7 10.1095/biolreprod.102.014977 12620937 · DOI
  24. [24]Tsang JCH Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics Proc. Natl Acad. Sci. USA 2017 114 E7786 E7795 28830992 PMC5604038
  25. [25]Nakhoul N Batuman V Role of proximal tubules in the pathogenesis of kidney disease Contrib. Nephrol. 2011 169 37 50 10.1159/000313944 21252510 · DOI
  26. [26]Chevalier RL The proximal tubule is the primary target of injury and progression of kidney disease: role of the glomerulotubular junction Am. J. Physiol. Renal Physiol. 2016 311 F145 F161 10.1152/ajprenal.00164.2016 27194714 PMC4967168 · DOI
  27. [27]Feldstein AE Gores GJ Apoptosis in alcoholic and nonalcoholic steatohepatitis Front. Biosci. 2005 10 3093 3099 10.2741/1765 15970563 · DOI
  28. [28]Mathys H Single-cell transcriptomic analysis of Alzheimer’s disease Nature 2019 570 332 337 10.1038/s41586-019-1195-2 31042697 PMC6865822 · DOI · Paper page
  29. [30]Dhillon P The nuclear receptor ESRRA protects from kidney disease by coupling metabolism and differentiation Cell Metab. 2021 33 379 394 10.1016/j.cmet.2020.11.011 33301705 PMC9259369 · DOI
  30. [31]Schelling JR Tubular atrophy in the pathogenesis of chronic kidney disease progression Pediatr. Nephrol. 2016 31 693 706 10.1007/s00467-015-3169-4 26208584 PMC4726480 · DOI
  31. [32]Meex RCR Watt MJ Hepatokines: linking nonalcoholic fatty liver disease and insulin resistance Nat. Rev. Endocrinol. 2017 13 509 520 10.1038/nrendo.2017.56 28621339 · DOI · Paper page
  32. [33]McCall MA Targeted deletion in astrocyte intermediate filament (Gfap) alters neuronal physiology Proc. Natl Acad. Sci. USA 1996 93 6361 6366 10.1073/pnas.93.13.6361 8692820 PMC39027 · DOI
  33. [34]Lytton J Na+/Ca2+ exchangers: three mammalian gene families control Ca2+ transport Biochem. J. 2007 406 365 382 10.1042/BJ20070619 17716241 · DOI
  34. [35]Friedman LG Cadherin-8 expression, synaptic localization, and molecular control of neuronal form in prefrontal corticostriatal circuits J. Comp. Neurol. 2015 523 75 92 10.1002/cne.23666 25158904 PMC4232472 · DOI
  35. [36]Arlotta P Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo Neuron 2005 45 207 221 10.1016/j.neuron.2004.12.036 15664173 · DOI
  36. [37]Shigemoto R Nakanishi S Mizuno N Distribution of the mRNA for a metabotropic glutamate receptor (mGluR1) in the central nervous system: an in situ hybridization study in adult and developing rat J. Comp. Neurol. 1992 322 121 135 10.1002/cne.903220110 1430307 · DOI
  37. [38]Zhou Q Choi G Anderson DJ The bHLH transcription factor Olig2 promotes oligodendrocyte differentiation in collaboration with Nkx2.2 Neuron 2001 31 791 807 10.1016/S0896-6273(01)00414-7 11567617 · DOI
  38. [39]Nielsen JA Berndt JA Hudson LD Armstrong RC Myelin transcription factor 1 (Myt1) modulates the proliferation and differentiation of oligodendrocyte lineage cells Mol. Cell. Neurosci. 2004 25 111 123 10.1016/j.mcn.2003.10.001 14962745 · DOI
  39. [40]Ichihara-Tanaka K Oohira A Rumsby M Muramatsu T Neuroglycan C is a novel midkine receptor involved in process elongation of oligodendroglial precursor-like cells J. Biol. Chem. 2006 281 30857 30864 10.1074/jbc.M602228200 16901907 · DOI
  40. [41]Levine JM Reynolds R Fawcett JW The oligodendrocyte precursor cell in health and disease Trends Neurosci. 2001 24 39 47 10.1016/S0166-2236(00)01691-X 11163886 · DOI
  41. [44]Moufarrej MN Wong RJ Shaw GM Stevenson DK Quake SR Investigating pregnancy and its complications using circulating cell-free RNA in women’s blood during gestation Front. Pediatr. 2020 8 605219 10.3389/fped.2020.605219 33381480 PMC7767905 · DOI
  42. [46]Robinson MD Oshlack A A scaling normalization method for differential expression analysis of RNA-seq data Genome Biol. 2010 11 R25 10.1186/gb-2010-11-3-r25 20196867 PMC2864565 · DOI · Paper page
  43. [48]Shen-Orr, S. S., Tibshirani, R. & Butte, A. J. Gene expression deconvolution in linear space. Nat. Methods9, 9 (2012). 22205510
  44. [49]Chang C-C Lin C-J Training ν-support vector regression: theory and algorithms Neural Comput. 2002 14 1959 1977 10.1162/089976602760128081 12180409 · DOI
  45. [50]Zhong Y Liu Z Gene expression deconvolution in linear space Nat. Methods 2012 9 8 9 10.1038/nmeth.1830 22205510 · DOI
  46. [52]Qiao W PERT: a method for expression deconvolution of human blood samples from varied microenvironmental and developmental conditions PLoS Comput. Biol. 2012 8 e1002838 10.1371/journal.pcbi.1002838 23284283 PMC3527275 · DOI
  47. [53]Kryuchkova-Mostacci N Robinson-Rechavi M A benchmark of gene expression tissue-specificity metrics Brief. Bioinform. 2017 18 205 214 26891983 PMC5444245
  48. [54]van Rossum D Hanisch U-K Microglia Metab. Brain Dis. 2004 19 393 411 10.1023/B:MEBR.0000043984.73063.d8 15554430 · DOI

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Authors
Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Overall Project Direction and Coordination, Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Recruitment, D., Bulthaup, B., Brown, P., Harper, W., Hemenez, M., Ponnusamy, R., Salehi, A., Sanagavarapu, B. A., Spallino, E., Surgeons, Aaron, K. A., Concepcion, W., Gardner, J. M., Kelly, B., Neidlinger, N., Wang, Z., coordination, L., Crasta, S., Kolluru, S., Morri, M., Tan, S. Y., Travaglini, K. J., Xu, C., Processing, O., Alcántara-Hernández, M., Almanzar, N., Antony, J., Beyersdorf, B., Burhan, D., Calcuttawala, K., Carter, M. M., Chan, C. K. F., Chang, C. A., Chang, S., Colville, A., Culver, R. N., Cvijović, I., D’Amato, G., Ezran, C., Galdos, F. X., Gillich, A., Goodyer, W. R., Hang, Y., Hayashi, A., Houshdaran, S., Huang, X., Irwin, J. C., Jang, S., Juanico, J. V., Kershner, A. M., Kim, S., Kiss, B., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Li, B., Loeb, G. B., Lu, W.-J., Mantri, S., Markovic, M., McAlpine, P. L., de Morree, A., Mrouj, K., Mukherjee, S., Muser, T., Neuhöfer, P., Nguyen, T. D., Perez, K., Phansalkar, R., Puluca, N., Qi, Z., Rao, P., Raquer-McKay, H., Schaum, N., Scott, B., Seddighzadeh, B., Segal, J., Sen, S., Sikandar, S., Spencer, S. P., Steffes, L., Subramaniam, V. R., Swarup, A., Swift, M., Van Treuren, W., Trimm, E., Veizades, S., Vijayakumar, S., Vo, K. C., Vorperian, S. K., Wang, W., Weinstein, H. N. W., Winkler, J., Wu, T. T. H., Xie, J., Yung, A. R., Zhang, Y., Sequencing, Detweiler, A. M., Mekonen, H., Neff, N. F., Sit, R. V., Tan, M., Yan, J., Histology, Bean, G. R., Charu, V., Forgó, E., Martin, B. A., Ozawa, M. G., Silva, O., Toland, A., Vemuri, V. N. P., Analysis, D., Afik, S., Awayan, K., Bierman, R., Botvinnik, O. B., Byrne, A., Chen, M., Dehghannasiri, R., Gayoso, A., Granados, A. A., Li, Q., Mahmoudabadi, G., McGeever, A., Olivieri, J. E., Park, M., Ravikumar, N., Stanley, G., Tan, W., Tarashansky, A. J., Vanheusden, R., Wang, P., Wang, S., Xing, G., Xu, C., Yosef, N., Annotation, E. C. T., Culver, R., Dethlefsen, L., Ho, P.-Y., Liu, S., Maltzman, J. S., Metzger, R. J., Sasagawa, K., Sinha, R., Song, H., Wang, B., Tissue Expert Principal Investigators, Artandi, S. E., Beachy, P. A., Clarke, M. F., Giudice, L. C., Huang, F. W., Huang, K. C., Idoyaga, J., Kim, S. K., Kuo, C. S., Nguyen, P., Rando, T. A., Red-Horse, K., Reiter, J., Relman, D. A., Sonnenburg, J. L., Wu, A., Wu, S. M., Wyss-Coray, T., & Quake, S. R.
Venue
Nature Biotechnology
Published
2022-02-07
Volume
40
Issue
6
Pages
855-861
ISSN
  • 1087-0156
  • 1546-1696
Cited by
115
References
5
Language
en

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