CoChat
Sign Up
PreprintCited by 15Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsMetabolomics and Mass Spectrometry Studies

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Spatial top-down proteomics for the functional characterization of human kidney

Open access
Zemaitis, Kevin J., Fulcher, James M., Kumar, Rashmi, Degnan, David J., Lewis, Logan A., Liao, Yen-Chen, Veličković, Marija, Williams, Sarah M., Moore, Ronald J., Bramer, Lisa M., Veličković, Dušan, Zhu, Ying, Zhou, Mowei, Paša-Tolić, Ljiljana·2024·Clinical Proteomics
DOI 10.1101/2024.02.13.580062PMC PMC11881370

Preprint — published as 10.1186/s12014-025-09531-x

Open this paper in CoChat — free account

Abstract

Background: The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results: Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions: We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

Overview

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 · 10.1101/2024.02.13.580062

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

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

Self-check

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

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

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 63

  1. The Consortium for Top Down, P., 2013, Proteoform: a single term describing protein complexity, Nature Methods, 10, 186, 10.1038/nmeth.2369 · DOI · Paper page
  2. 2021, The Human Proteoform Project: Defining the human proteome, Sci Adv, 7, eabk0734, 10.1126/sciadv.abk0734 · DOI · Paper page
  3. 10.1038/nature11835 · DOI · Paper page
  4. 2021, Systematic detection of functional proteoform groups from bottom-up proteomic datasets, Nature Communications, 12, 3810, 10.1038/s41467-021-24030-x · DOI · Paper page
  5. 10.1126/science.aaz5284 · DOI · Paper page
  6. 10.3892/ijmm.2017.3036 · DOI
  7. 10.1073/pnas.1503262112 · DOI
  8. 10.1021/acs.analchem.7b04747 · DOI · Paper page
  9. Liao, Y.-C. ; Fulcher, J. M. ; Degnan, D. J. ; Williams, S. M. ; Bramer, L. M. ; Veličković, D. ; Zemaitis, K. J. ; Veličković, M. ; Sontag, R. L. ; Moore, R. J. ; Paša-Tolić, L. ; Zhu, Y. ; Zhou, M. , Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform. Molecular & Cellular Proteomics 2023, 22 (2). · DOI
  10. 2016, Progress in Top-Down Proteomics and the Analysis of Proteoforms, Annual Review of Analytical Chemistry, 9, 499, 10.1146/annurev-anchem-071015-041550 · DOI · Paper page
  11. 2018, Enhanced Ion Transmission Efficiency up to m/ z 241000 for MALDI Protein Imaging Mass Spectrometry, Analytical chemistry, 90, 5090, 10.1021/acs.analchem.7b05105 · DOI
  12. 2022, Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection, Analytical Chemistry, 94, 12604, 10.1021/acs.analchem.2c01034 · DOI
  13. 10.1126/sciadv.abp9929 · DOI
  14. 2023, Nano-DESI Mass Spectrometry Imaging of Proteoforms in Biological Tissues with High Spatial Resolution, Anal Chem, 95, 5214, 10.1021/acs.analchem.2c04795 · DOI
  15. 2021, Native Mass Spectrometry Imaging of Proteins and Protein Complexes by Nano-DESI, Anal Chem, 93, 4619, 10.1021/acs.analchem.0c05277 · DOI
  16. 2019, Direct Mass Spectrometry Analysis of Protein Complexes and Intact Proteins up to >70 kDa from Tissue, Anal Chem, 91, 6962, 10.1021/acs.analchem.9b00971 · DOI
  17. 2020, Native LESA TWIMS-MSI: Spatial, Conformational, and Mass Analysis of Proteins and Protein Complexes, Journal of the American Society for Mass Spectrometry, 31, 873, 10.1021/jasms.9b00122 · DOI
  18. 2022, A reference tissue atlas for the human kidney, Sci Adv, 8, eabn4965, 10.1126/sciadv.abn4965 · DOI
  19. 2021, SpaceM reveals metabolic states of single cells, Nature Methods, 18, 799, 10.1038/s41592-021-01198-0 · DOI
  20. Pei, S. ; Michael, A. R. H. ; Stanislav, R. ; Fatma Ayaloglu, B. ; Joseph, B. G. ; Bryan, P. E. ; Ryan, T. F. ; Michael, A. C. ; Jonathan, V. S. ; Jared, O. K. ; Neil, L. K. , Top-down Proteomics of 10,000 Single Brain Cells. bioRxiv 2023, 2023.05.31.543176.
  21. 2019, Direct sampling mass spectrometry for clinical analysis, Analyst, 144, 1034, 10.1039/C8AN01722K · DOI
  22. 10.1016/J.CBPA.2018.10.023 · DOI
  23. 2019, Imaging of protein distribution in tissues using mass spectrometry: An interdisciplinary challenge, TrAC Trends in Analytical Chemistry, 112, 13, 10.1016/j.trac.2018.12.016 · DOI
  24. 10.1021/acs.analchem.8b05889 · DOI
  25. 10.1074/mcp.M116.065755 · DOI
  26. 2022, Laser capture microdissection-capillary zone electrophoresis-tandem mass spectrometry (LCM-CZE-MS/MS) for spatially resolved top-down proteomics: a pilot study of zebrafish brain, Molecular Omics, 18, 112, 10.1039/D1MO00335F · DOI
  27. 2018, Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells, Nature Communications, 9, 882, 10.1038/s41467-018-03367-w · DOI · Paper page
  28. 2022, Proteomic Profiling of Intra-Islet Features Reveals Substructure-Specific Protein Signatures, Mol Cell Proteomics, 21, 100426, 10.1016/j.mcpro.2022.100426 · DOI
  29. 2023, Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine–Exocrine Signaling Sphere of Influence, Molecular & Cellular Proteomics, 22, 100592, 10.1016/j.mcpro.2023.100592 · DOI
  30. 2020, Sensitive Top-Down Proteomics Analysis of a Low Number of Mammalian Cells Using a Nanodroplet Sample Processing Platform, Anal Chem, 92, 7087, 10.1021/acs.analchem.0c00467 · DOI · Paper page
  31. Zhou, M. ; Fulcher, J. ; Liao, Y.-C. ; L, P.-T., Overall protocol for MicroPOTS LCMS top down proteomics of kidney tissue sections. protocols.io 2022. · DOI
  32. Jain, S. ; Pei, L. ; Spraggins, J. M. ; Angelo, M. ; Carson, J. P. ; Gehlenborg, N. ; Ginty, F. ; Gonçalves, J. P. ; Hagood, J. S. ; Hickey, J. W. ; Kelleher, N. L. ; Laurent, L. C. ; Lin, S. ; Lin, Y. ; Liu, H. ; Naba, A. ; Nakayasu, E. S. ; Qian, W.-J. ; Radtke, A. ; Robson, P. ; Stockwell, B. R. ; Van de Plas, R. ; Vlachos, I. S. ; Zhou, M. ; Ahn, K. J. ; Allen, J. ; Anderson, D. M. ; Anderton, C. R. ; Curcio, C. ; Angelin, A. ; Arvanitis, C. ; Atta, L. ; Awosika-Olumo, D. ; Bahmani, A. ; Bai, H. ; Balderrama, K. ; Balzano, L. ; Bandyopadhyay, G. ; Bandyopadhyay, S. ; Bar-Joseph, Z. ; Barnhart, K. ; Barwinska, D. ; Becich, M. ; Becker, L. ; Becker, W. ; Bedi, K. ; Bendall, S. ; Benninger, K. ; Betancur, D. ; Bettinger, K. ; Billings, S. ; Blood, P. ; Bolin, D. ; Border, S. ; Bosse, M. ; Bramer, L. ; Brewer, M. ; Brusko, M. ; Bueckle, A. ; Burke, K. ; Burnum-Johnson, K. ; Butcher, E. ; Butterworth, E. ; Cai, L. ; Calandrelli, R. ; Caldwell, M. ; Campbell-Thompson, M. ; Cao, D. ; Cao-Berg, I. ; Caprioli, R. ; Caraccio, C. ; Caron, A. ; Carroll, M. ; Chadwick, C. ; Chen, A. ; Chen, D. ; Chen, F. ; Chen, H. ; Chen, J. ; Chen, L. ; Chen, L. ; Chiacchia, K. ; Cho, S. ; Chou, P. ; Choy, L. ; Cisar, C. ; Clair, G. ; Clarke, L. ; Clouthier, K. A. ; Colley, M. E. ; Conlon, K. ; Conroy, J. ; Contrepois, K. ; Corbett, A. ; Corwin, A. ; Cotter, D. ; Courtois, E. ; Cruz, A. ; Csonka, C. ; Czupil, K. ; Daiya, V. ; Dale, K. ; Davanagere, S. A. ; Dayao, M. ; de Caestecker, M. P. ; Decker, A. ; Deems, S. ; Degnan, D. ; Desai, T. ; Deshpande, V. ; Deutsch, G. ; Devlin, M. ; Diep, D. ; Dodd, C. ; Donahue, S. ; Dong, W. ; dos Santos Peixoto, R. ; Duffy, M. ; Dufresne, M. ; Duong, T. E. ; Dutra, J. ; Eadon, M. T. ; El-Achkar, T. M. ; Enninful, A. ; Eraslan, G. ; Eshelman, D. ; Espin-Perez, A. ; Esplin, E. D. ; Esselman, A. ; Falo, L. D. ; Falo, L. ; Fan, J. ; Fan, R. ; Farrow, M. A. ; Farzad, N. ; Favaro, P. ; Fermin, J. ; Filiz, F. ; Filus, S. ; Fisch, K. ; Fisher, E. ; Fisher, S. ; Flowers, K. ; Flynn, W. F. ; Fogo, A. B. ; Fu, D. ; Fulcher, J. ; Fung, A. ; Furst, D. ; Gallant, M. ; Gao, F. ; Gao, Y. ; Gaulton, K. ; Gaut, J. P. ; Gee, J. ; Ghag, R. R. ; Ghazanfar, S. ; Ghose, S. ; Gisch, D. ; Gold, I. ; Gondalia, A. ; Gorman, B. ; Greenleaf, W. ; Greenwald, N. ; Gregory, B. ; Guo, R. ; Gupta, R. ; Hakimian, H. ; Haltom, J. ; Halushka, M. ; Han, K. S. ; Hanson, C. ; Harbury, P. ; Hardi, J. ; Harlan, L. ; Harris, R. C. ; Hartman, A. ; Heidari, E. ; Helfer, J. ; Helminiak, D. ; Hemberg, M. ; Henning, N. ; Herr, B. W. ; Ho, J. ; Holden-Wiltse, J. ; Hong, S.-H. ; Hong, Y.-K. ; Honick, B. ; Hood, G. ; Hu, P. ; Hu, Q. ; Huang, M. ; Huyck, H. ; Imtiaz, T. ; Isberg, O. G. ; Itkin, M. ; Jackson, D. ; Jacobs, M. ; Jain, Y. ; Jewell, D. ; Jiang, L. ; Jiang, Z. G. ; Johnston, S. ; Joshi, P. ; Ju, Y. ; Judd, A. ; Kagel, A. ; Kahn, A. ; Kalavros, N. ; Kalhor, K. ; Karagkouni, D. ; Karathanos, T. ; Karunamurthy, A. ; Katari, S. ; Kates, H. ; Kaushal, M. ; Keener, N. ; Keller, M. ; Kenney, M. ; Kern, C. ; Kharchenko, P. ; Kim, J. ; Kingsford, C. ; Kirwan, J. ; Kiselev, V. ; Kishi, J. ; Kitata, R. B. ; Knoten, A. ; Kollar, C. ; Krishnamoorthy, P. ; Kruse, A. R. S. ; Da, K. ; Kundaje, A. ; Kutschera, E. ; Kwon, Y. ; Lake, B. B. ; Lancaster, S. ; Langlieb, J. ; Lardenoije, R. ; Laronda, M. ; Laskin, J. ; Lau, K. ; Lee, H. ; Lee, M. ; Lee, M. ; Strekalova, Y. L. ; Li, D. ; Li, J. ; Li, J. ; Li, X. ; Li, Z. ; Liao, Y.-C. ; Liaw, T. ; Lin, P. ; Lin, Y. ; Lindsay, S. ; Liu, C. ; Liu, Y. ; Liu, Y. ; Lott, M. ; Lotz, M. ; Lowery, L. ; Lu, P. ; Lu, X. ; Lucarelli, N. ; Lun, X. ; Luo, Z. ; Ma, J. ; Macosko, E. ; Mahajan, M. ; Maier, L. ; Makowski, D. ; Malek, M. ; Manthey, D. ; Manz, T. ; Margulies, K. ; Marioni, J. ; Martindale, M. ; Mason, C. ; Mathews, C. ; Maye, P. ; McCallum, C. ; McDonough, E. ; McDonough, L. ; McDowell, H. ; Meads, M. ; Medina-Serpas, M. ; Ferreira, R. M. ; Messinger, J. ; Metis, K. ; Migas, L. G. ; Miller, B. ; Mimar, S. ; Minor, B. ; Misra, R. ; Missarova, A. ; Mistretta, C. ; Moens, R. ; Moerth, E. ; Hu, B. C. , Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nature Cell Biology 2023. · DOI
  33. 10.1038/s41586-019-1629-x · DOI · Paper page
  34. 10.1021/acs.analchem.2c05244 · DOI
  35. 10.1093/bioinformatics/btw398 · DOI · Paper page
  36. 2019, A comprehensive pipeline for translational top-down proteomics from a single blood draw, Nature Protocols, 14, 119, 10.1038/s41596-018-0085-7 · DOI · Paper page
  37. 2023, TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis, Journal of Proteome Research, 22, 399, 10.1021/acs.jproteome.2c00570 · DOI
  38. 2017, Informed-Proteomics: open-source software package for top-down proteomics, Nature Methods, 14, 909, 10.1038/nmeth.4388 · DOI
  39. 2018, Proteome-wide identification of ubiquitin interactions using UbIA-MS, Nature Protocols, 13, 530, 10.1038/nprot.2017.147 · DOI
  40. 2017, Design and Performance of a Novel Interface for Combined Matrix-Assisted Laser Desorption Ionization at Elevated Pressure and Electrospray Ionization with Orbitrap Mass Spectrometry, Analytical Chemistry, 89, 7493, 10.1021/acs.analchem.7b01168 · DOI
  41. 2023, SMART: A data reporting standard for mass spectrometry imaging, Journal of Mass Spectrometry, 58, e4904, 10.1002/jms.4904 · DOI
  42. Degnan, D. J. ; Zemaitis, K. J. ; Lewis, L. A. ; McCue, L. A. ; Bramer, L. M. ; Fulcher, J. M. ; Veličković, D. ; Paša-Tolić, L. ; Zhou, M. , IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra. Journal of the American Society for Mass Spectrometry 2023. · DOI
  43. Veličković, M. ; Wu, R. ; Gao, Y. ; Thairu, M. W. ; Veličković, D. ; Munoz, N. ; Clendinen, C. S. ; Bilbao, A. ; Chu, R. K. ; Lalli, P. M. ; Zemaitis, K. ; Nicora, C. D. ; Kyle, J. E. ; Orton, D. ; Williams, S. ; Zhu, Y. ; Zhao, R. ; Monroe, M. E. ; Moore, R. J. ; Webb-Robertson, B.-J. M. ; Bramer, L. M. ; Currie, C. R. ; Piehowski, P. D. ; Burnum-Johnson, K. E. , Mapping microhabitats of lignocellulose decomposition by a microbial consortium. Nature Chemical Biology 2024. · DOI
  44. 2017, Mitochondrial energetics in the kidney, Nature Reviews Nephrology, 13, 629, 10.1038/nrneph.2017.107 · DOI
  45. 2023, Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection, Journal of Proteome Research, 22, 2199, 10.1021/acs.jproteome.2c00673 · DOI
  46. 10.1021/jasms.8b05050 · DOI
  47. 10.1016/J.KINT.2021.08.033 · DOI
  48. 10.1021/bi400254f · DOI
  49. Bianchi, M. E. ; Mezzapelle, R. , The Chemokine Receptor CXCR4 in Cell Proliferation and Tissue Regeneration. Frontiers in Immunology 2020, 11. · DOI
  50. 10.1016/j.jmb.2010.12.026 · DOI
  51. 10.1007/BF00304849 · DOI
  52. 2020, Automated mass spectrometry imaging of over 2000 proteins from tissue sections at 100-μm spatial resolution, Nature Communications, 11, 8, 10.1038/s41467-019-13858-z · DOI
  53. 2022, Proteoform-Selective Imaging of Tissues Using Mass Spectrometry**, Angewandte Chemie International Edition, 61, e202200721, 10.1002/anie.202200721 · DOI · Paper page
  54. 2020, Near-Single-Cell Proteomics Profiling of the Proximal Tubular and Glomerulus of the Normal Human Kidney, Front Med (Lausanne), 7, 499
  55. 2022, New Views of Old Proteins: Clarifying the Enigmatic Proteome, Molecular & Cellular Proteomics, 21, 100254, 10.1016/j.mcpro.2022.100254 · DOI
  56. 10.1152/ajplung.00139.2017 · DOI
  57. 2022, NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health, Nature Aging, 2, 1090, 10.1038/s43587-022-00326-5 · DOI · Paper page
  58. 10.1038/s41556-021-00788-6 · DOI
  59. 10.1126/science.1260419 · DOI · Paper page
  60. 2019, Optimization of MALDI-TOF mass spectrometry imaging for the visualization and comparison of peptide distributions in dry-cured ham muscle fibers, Food Chem, 283, 275, 10.1016/j.foodchem.2018.12.126 · DOI
  61. Xiang, P. ; Liyu, A. ; Kwon, Y. ; Hu, D. ; Williams, S. M. ; Veličković, D. ; Markillie, L. M. ; Chrisler, W. B. ; Paša-Tolić, L. ; Zhu, Y. , Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation. ACS Measurement Science Au 2023. · DOI
  62. 2023, Exploring Charge-Detection Mass Spectrometry on Chromatographic Time Scales, Analytical Chemistry, 95, 15118, 10.1021/acs.analchem.3c03325 · DOI
  63. 2022, The Omnitrap Platform: A Versatile Segmented Linear Ion Trap for Multidimensional Multiple-Stage Tandem Mass Spectrometry, Journal of the American Society for Mass Spectrometry, 33, 1990, 10.1021/jasms.2c00214 · DOI

Metadata

Authors
Zemaitis, K. J., Fulcher, J. M., Kumar, R., Degnan, D. J., Lewis, L. A., Liao, Y.-C., Veličković, M., Williams, S. M., Moore, R. J., Bramer, L. M., Veličković, D., Zhu, Y., Zhou, M., & Paša-Tolić, L.
Affiliations
Pacific Northwest National Laboratory (US)
ORCID
Venue
Clinical Proteomics
Publisher
openRxiv
Published
2024-02-13
Volume
22
Issue
1
Pages
9-9
ISSN
1542-6416 (ISSN-L)
DOI
10.1101/2024.02.13.580062
PMC
PMC11881370
OpenAlex
W4408216616
Cited by
15
References
1
Citations by year
2026: 9 · 2025: 4 · 2024: 2
FWCI
3.27
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Clinical Proteomics (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • U.S. Department of Energy (DE-AC05–76RL01830, 76RL01830, AC05-76RL01830, DE-AC05?)
  • Vanderbilt University
  • Northwestern University
  • National Institutes of Health (DE-AC05-76RL01830)
  • Office of Science (DE‐AC05‐76 RL01830)
  • National Cancer Institute (UH3CA256959, UG3CA256959)
  • Biological and Environmental Research (DE-AC05-76 RL01830, AC05-76RL01830, 76RL01830)
  • Pacific Northwest National Laboratory (DEAC0576RL01830, 76RL01830)
  • Common Fund
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
Keywords
proteoforms, human kidney, top-down proteomics, spatial proteomics, mass spectrometry imaging, laser capture microdissection, glomeruli, renal tubules, medullary rays, hemoglobin subunits

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