Cited by 762Single-cell and spatial transcriptomicsRenal cell carcinoma treatmentRenal and related cancers

Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors

Open access
Young, Matthew D., Mitchell, Thomas J., Vieira Braga, Felipe A., Tran, Maxine G. B., Stewart, Benjamin J., Ferdinand, John R., Collord, Grace, Botting, Rachel A. +30 more·2018·Science

Abstract

Pediatric and adult kidney tumors differ: Understanding tumor origins and the similarities and differences between organ-specific cancers is important for determining treatment options. Young et al. generated more than 72,000 single-cell transcriptomes from healthy and cancerous human kidneys. From these data, they determined that Wilms tumor, a pediatric kidney cancer, originates from aberrant fetal cells, whereas adult kidney cancers are likely derived from a specific subtype of proximal convoluted tubular cell. Science , this issue p. 594

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1126/science.aat1699

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Solid-tissue experiments are frequently highly contaminated with red blood cells and red cell lysis is used to prepare the samples.

    “For example, solid-tissue experiments are frequently highly contaminated with red blood cells and red cell lysis is used to prepare the samples [15].”

    From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [15]

    The paper’s tissue-processing description (Abstract; page not provided) says kidney samples were made into viable single-cell solutions, but does not mention red-blood-cell contamination or red-cell lysis. Searches for red blood cells, erythrocyte lysis, and lysis buffer found no passage addressing either part of the claim. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 41

  1. [3]Pritchard-Jones K Omission of doxorubicin from the treatment of stage II-III, intermediate-risk Wilms' tumour (SIOP WT 2001): an open-label, non-inferiority, randomised controlled trial Lancet (London, England) 2015 386 1156 1164 26164096
  2. [4]Perlman EJ MLLT1 YEATS domain mutations in clinically distinctive Favourable Histology Wilms tumours Nature communications 2015 6 10013 PMC4686660 26635203
  3. [5]Gadd S A Children's Oncology Group and TARGET initiative exploring the genetic landscape of Wilms tumor Nature genetics 2017 49 1487 1494 28825729 PMC5712232
  4. [6]Seron D Cameron JS Haskard DO Expression of VCAM-1 in the normal and diseased kidney Nephrology, dialysis, transplantation: official publication of the European Dialysis and Transplant Association - European Renal Association 1991 6 917 922 1724689
  5. [7]Ljungberg B EAU guidelines on renal cell carcinoma: 2014 update European urology 2015 67 913 924 25616710
  6. [8]Fernandez-Pello S A Systematic Review and Meta-analysis Comparing the Effectiveness and Adverse Effects of Different Systemic Treatments for Non-clear Cell Renal Cell Carcinoma European urology 2017 71 426 436 27939075
  7. [9]Thiriot A Differential DARC/ACKR1 expression distinguishes venular from non-venular endothelial cells in murine tissues BMC biology 2017 15 45 28526034 PMC5438556
  8. [10]Gerrelli D Lisgo S Copp AJ Lindsay S Enabling research with human embryonic and fetal tissue resources Development (Cambridge, England) 2015 142 3073 3076 PMC4640175 26395135
  9. [11]Kozarewa I Amplification-free Illumina sequencing-library preparation facilitates improved mapping and assembly of (G+C)-biased genomes Nat Methods 2009 6 291 295 19287394 PMC2664327
  10. [13]Jones D cgpCaVEManWrapper: Simple Execution of CaVEMan in Order to Detect Somatic Single Nucleotide Variants in NGS Data Curr Protoc Bioinformatics 2016 56 15 10 11 15 10 18 PMC6097605 27930805
  11. [14]Menzies A VAGrENT: Variation Annotation Generator Curr Protoc Bioinformatics 2015 52 15 18 11 11 PMC6101192 26678383
  12. [15]Van Loo P Allele-specific copy number analysis of tumors Proceedings of the National Academy of Sciences 2010 107 16910 16915 PMC2947907 20837533
  13. [16]Lun A Riesenfeld S Andrews T Dao TP Gomes T On the correct detection of empty droplets in droplet-based single-cell RNA sequencing protocols 2017 (https://github.com/TimothyTickle/hca-jamboree-cell-identification/blob/master/docs/EmptyDrops_group4_report.pdf) PMC6431044 30902100
  14. [17]Chen YJ Single-cell RNA sequencing identifies distinct mouse medial ganglionic eminence cell types Sci Rep 2017 7 45656 PMC5374502 28361918
  15. [18]Johnson WE Li C Rabinovic A Adjusting batch effects in microarray expression data using empirical Bayes methods Biostatistics 2007 8 118 127 16632515
  16. [19]v d Maaten LJP Hinton GE Visualizing High-Dimensional Data Using t-SNE Journal of Machine Learning Research 2008 9 2579 2605
  17. [20]v d Maaten LJP Accelerating t-SNE using Tree-Based Algorithms Journal of Machine Learning Research 2014 15 3221 3245
  18. [21]Macosko EZ Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets Cell 2015 161 1202 1214 26000488 PMC4481139
  19. [22]Rajaraman A Ullman JD Mining of Massive Datasets Cambridge University Press Cambridge 2011
  20. [23]Qiu X Single-cell mRNA quantification and differential analysis with Census Nat Methods 2017 14 309 315 28114287 PMC5330805
  21. [25]Zhang HM AnimalTFDB: a comprehensive animal transcription factor database Nucleic Acids Res 2012 40 D144 149 22080564 PMC3245155
  22. [26]Zhang HM AnimalTFDB 2.0: a resource for expression, prediction and functional study of animal transcription factors Nucleic Acids Res 2015 43 D76 81 25262351 PMC4384004
  23. [27]Friedman JH Hastie T Tibshirani R Regularization Paths for Generalized Linear Models via Coordinate Descent 2010 2010 33 22 PMC2929880 20808728
  24. [28]N Cancer Genome Atlas Research Comprehensive molecular characterization of clear cell renal cell carcinoma Nature 2013 499 43 49 23792563 PMC3771322
  25. [30]Perlman EJ MLLT1 YEATS domain mutations in clinically distinctive Favourable Histology Wilms tumours Nature communications 2015 6 10013 PMC4686660 26635203
  26. [31]Walz AL Recurrent DGCR8, DROSHA, and SIX homeodomain mutations in favorable histology Wilms tumors Cancer cell 2015 27 286 297 25670082 PMC4800737
  27. [32]Colaprico A TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data Nucleic Acids Res 2016 44 e71 26704973 PMC4856967
  28. [34]Hern WM Correlation of fetal age and measurements between 10 and 26 weeks of gestation Obstetrics & Gynecology 1984 63 26 32 6691014
  29. [35]Brunskill EW Atlas of gene expression in the developing kidney at microanatomic resolution Developmental cell 2008 15 781 791 19000842 PMC2653061
  30. [36]Metsuyanim S Expression of stem cell markers in the human fetal kidney PloS one 2009 4 e6709 19696931 PMC2725321
  31. [37]Lee JW Chou CL Knepper MA Deep Sequencing in Microdissected Renal Tubules Identifies Nephron Segment-Specific Transcriptomes Journal of the American Society of Nephrology: JASN 2015 26 2669 2677 25817355 PMC4625681
  32. [38]Habuka M The kidney transcriptome and proteome defined by transcriptomics and antibody-based profiling PloS one 2014 9 e116125 25551756 PMC4281243
  33. [39]Chabardes-Garonne D A panoramic view of gene expression in the human kidney Proceedings of the National Academy of Sciences of the United States of America 2003 100 13710 13715 14595018 PMC263878
  34. [40]Han X Amar S Secreted Frizzled-related Protein 1 (SFRP1) Protects Fibroblasts from Ceramide-induced Apoptosis Journal of Biological Chemistry 2004 279 2832 2840 14581477
  35. [41]Matsuyama M Nomori A Nakakuni K Shimono A Fukushima M Secreted Frizzled-related Protein 1 (Sfrp1) Regulates the Progression of Renal Fibrosis in a Mouse Model of Obstructive Nephropathy Journal of Biological Chemistry 2014 289 31526 31533 25253698 PMC4223350
  36. [42]Lennon R Global analysis reveals the complexity of the human glomerular extracellular matrix Journal of the American Society of Nephrology: JASN 2014 25 939 951 24436468 PMC4005295
  37. [43]LeBleu VS Origin and Function of Myofibroblasts in Kidney Fibrosis Nature medicine 2013 19 1047 1053 PMC4067127 23817022
  38. [44]Wang L NDUFA4L2 is associated with clear cell renal cell carcinoma malignancy and is regulated by ELK1 PeerJ 2017 5 e4065 29158991 PMC5695248
  39. [45]Habuka M The Urinary Bladder Transcriptome and Proteome Defined by Transcriptomics and Antibody-Based Profiling PloS one 2015 10 e0145301 26694548 PMC4687928
  40. [46]Aird WC Phenotypic Heterogeneity of the Endothelium Circulation Research 2007 100 174 17272819
  41. [47]Nawroth R VE-PTP and VE-cadherin ectodomains interact to facilitate regulation of phosphorylation and cell contacts The EMBO journal 2002 21 4885 4895 12234928 PMC126293

Cited by

Sorted by citations

Authors
Young, M. D., Mitchell, T. J., Vieira Braga, F. A., Tran, M. G. B., Stewart, B. J., Ferdinand, J. R., Collord, G., Botting, R. A., Popescu, D.-M., Loudon, K. W., Vento-Tormo, R., Stephenson, E., Cagan, A., Farndon, S. J., Del Castillo Velasco-Herrera, M., Guzzo, C., Richoz, N., Mamanova, L., Aho, T., Armitage, J. N., Riddick, A. C. P., Mushtaq, I., Farrell, S., Rampling, D., Nicholson, J., Filby, A., Burge, J., Lisgo, S., Maxwell, P. H., Lindsay, S., Warren, A. Y., Stewart, G. D., Sebire, N., Coleman, N., Haniffa, M., Teichmann, S. A., Clatworthy, M., & Behjati, S.
Affiliations
  • Wellcome Sanger Institute (GB)
  • University of Cambridge (GB)
  • Wellcome Sanger Institute, Hinxton CB10 1SA, UK.
  • The Royal Free Hospital (GB)
  • MRC Laboratory of Molecular Biology (GB)
  • Newcastle University (GB)
  • Great Ormond Street Hospital (GB)
  • Cambridge University Hospitals NHS Foundation Trust (GB)
  • Royal Victoria Infirmary (GB)
Corresponding author
  • Young, Matthew D.
  • Mitchell, Thomas J.
  • Haniffa, Muzlifah
  • Teichmann, Sarah A.
  • Clatworthy, Menna
  • Behjati, Sam
Venue
Science
Published
2018-08-10
Volume
361
Issue
6402
Pages
594-599
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2887326710
Cited by
762
References
5
Citations by year
2026: 57 · 2025: 95 · 2024: 107 · 2023: 126 · 2022: 105 · 2021: 110 · 2020: 102 · 2019: 51 · 2018: 6 · 2017: 1
FWCI
26.06
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • Pure Amsterdam UMC (publishedVersion, other-oa)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion)
  • Apollo
Funders
  • Human Frontier Science Program (Long-Term Fellowship)
  • Wellcome Trust (110104/Z/15/Z, WT107931/Z/15/Z, Studentship, 099175/Z/12/Z)
  • Cancer Research UK (Core Infrastructure)
  • Cambridge Biomedical Research Campus (Core Infrastructure, MR/N024907/1)
  • Kidney Cancer UK (Nick Maude Memorial Fund)
  • Facing up 2 Kidney Cancer
  • St Baldrick’s Foundation (Robert J Arceci International Award)
  • Lister Institute of Preventive Medicine
  • NIHR and Newcastle-Biomedical Research Centre
  • Arthritis Research UK (21777)
  • EMBO (Long-Term Fellowship)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Renal cell carcinoma treatment
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Renal and related cancers
Keywords
kidney cancer, Wilms tumor, renal cell carcinoma, single-cell transcriptomics, kidney development, fetal kidney, proximal convoluted tubule, tumor microenvironment, VEGF signaling, tumor cell of origin, childhood cancer
MeSH terms
Adult, Carcinoma, Renal Cell, Child, Humans, Kidney, Kidney Neoplasms, Wilms Tumor, Genetic Variation, Single-Cell Analysis, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review