AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
03 May 2024
03 May 2024
Historique:
received:
10
04
2023
accepted:
25
03
2024
medline:
4
5
2024
pubmed:
4
5
2024
entrez:
3
5
2024
Statut:
epublish
Résumé
Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8
Identifiants
pubmed: 38702321
doi: 10.1038/s41467-024-47334-0
pii: 10.1038/s41467-024-47334-0
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
3744Subventions
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01-CA230800
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01-CA248041
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U01-DK112217
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U01-DK123594
Informations de copyright
© 2024. The Author(s).
Références
Clarke, Z. A. et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat. Protoc. 16, 2749–2764 (2021).
doi: 10.1038/s41596-021-00534-0
pubmed: 34031612
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).
doi: 10.1038/nmeth.4644
pubmed: 29608555
Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16, 983–986 (2019).
doi: 10.1038/s41592-019-0535-3
pubmed: 31501545
pmcid: 6791524
Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20, 264 (2019).
doi: 10.1186/s13059-019-1862-5
pubmed: 31829268
pmcid: 6907144
Wang, T., Bai, J. & Nabavi, S. Single-cell classification using graph convolutional networks. BMC Bioinformatics 22, 364 (2021).
doi: 10.1186/s12859-021-04278-2
pubmed: 34238220
pmcid: 8268184
Lieberman, Y., Rokach, L. & Shay, T. CaSTLe – classification of single cells by transfer learning: harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments. PLoS ONE 13, e0205499 (2018).
doi: 10.1371/journal.pone.0205499
pubmed: 30304022
pmcid: 6179251
Tan, Y. & Cahan, P. SingleCellNet: a computational tool to classify single cell RNA-seq data across platforms and across species. Cell Syst. 9, 207–213 (2019).
doi: 10.1016/j.cels.2019.06.004
pubmed: 31377170
pmcid: 6715530
Schwartz, G. W., Zhou, Y., Petrovic, J., Pear, W. S. & Faryabi, R. B. TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes. Cell Rep. 36, 109575 (2021).
doi: 10.1016/j.celrep.2021.109575
pubmed: 34433064
pmcid: 8409102
Geuenich, M. J. et al. Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data. Cell Syst. 12, 1173–1186.e5 (2021).
doi: 10.1016/j.cels.2021.08.012
pubmed: 34536381
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).
doi: 10.1038/s41587-021-01139-4
pubmed: 35027729
Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).
doi: 10.1038/s41592-021-01358-2
pubmed: 35102346
pmcid: 8828470
Akusok, A., Björk, K.-M., Miche, Y. & Lendasse, A. High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access 3, 1011–1025 (2015).
doi: 10.1109/ACCESS.2015.2450498
Schwartz, G. W. et al. TooManyCells identifies and visualizes relationships of single-cell clades. Nat. Methods 17, 405–413 (2020).
doi: 10.1038/s41592-020-0748-5
pubmed: 32123397
pmcid: 7439807
Zhang, Z. et al. SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples. Genes 10, 531 (2019).
doi: 10.3390/genes10070531
pubmed: 31336988
pmcid: 6678337
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
doi: 10.1038/nmeth.4463
pubmed: 28991892
pmcid: 5937676
Satija, R., Farrell, J., Gennert, D., Schier, A. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
doi: 10.1038/nbt.3192
pubmed: 25867923
pmcid: 4430369
Levine, J. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
doi: 10.1016/j.cell.2015.05.047
pubmed: 26095251
pmcid: 4508757
Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).
doi: 10.1002/cyto.a.22625
pubmed: 25573116
Wang, Y. J. et al. Multiplexed in situ imaging mass cytometry analysis of the human endocrine pancreas and immune system in type 1 diabetes. Cell Metab. 29, 769–783 (2019).
doi: 10.1016/j.cmet.2019.01.003
pubmed: 30713110
pmcid: 6436557
Michels, A. W., Redondo, M. J. & Atkinson, M. A. The pathogenesis, natural history, and treatment of type 1 diabetes: time (thankfully) does not stand still. Lancet Diabetes Endocrinol. 10, 90–92 (2022).
doi: 10.1016/S2213-8587(21)00344-2
pubmed: 34951951
Powers, A. C. Type 1 diabetes mellitus: much progress, many opportunities. J. Clin. Investig. 131, e142242 (2021).
doi: 10.1172/JCI142242
pubmed: 33759815
pmcid: 8262558
Wang, X. et al. Quantitative analysis of pancreatic polypeptide cell distribution in the human pancreas. PLoS ONE 8, e55501 (2013).
doi: 10.1371/journal.pone.0055501
pubmed: 23383206
pmcid: 3561199
Rahier, J. et al. The pancreatic polypeptide cells in the human pancreas: the effects of age and diabetes. J. Clin. Endocrinol. Metab. 56, 441–444 (1983).
doi: 10.1210/jcem-56-3-441
pubmed: 6337179
Gepts, W., De Mey, J. & Marichal-Pipeleers, M. Hyperplasia of “pancreatic polypeptide”-cells in the pancreas of juvenile diabetics. Diabetologia 13, 27–34 (1977).
doi: 10.1007/BF00996324
pubmed: 320079
Brereton, M. F., Vergari, E., Zhang, Q. & Clark, A. Alpha-, Delta- and PP-cells: are they the architectural cornerstones of islet structure and co-ordination? J. Histochem. Cytochem. 63, 575–591 (2015).
doi: 10.1369/0022155415583535
pubmed: 26216135
pmcid: 4530398
Malaisse-Lagae, F., Stefan, Y., Cox, J., Perrelet, A. & Orci, L. Identification of a lobe in the adult human pancreas rich in pancreatic polypeptide. Diabetologia 17, 361–365 (1979).
doi: 10.1007/BF01236270
pubmed: 395002
Stefan, Y. et al. Quantitation of endocrine cell content in the pancreas of nondiabetic and diabetic humans. Diabetes 31, 694–700 (1982).
doi: 10.2337/diab.31.8.694
pubmed: 6131002
Damond, N. et al. A map of human type 1 diabetes progression by imaging mass cytometry. Cell Metab. 29, 755–768 (2019).
doi: 10.1016/j.cmet.2018.11.014
pubmed: 30713109
pmcid: 6821395
Pugliese, A. Advances in the etiology and mechanisms of type 1 diabetes. Discov. Med. 18, 141–150 (2014).
pubmed: 25227755
Campbell-Thompson, M. et al. Insulitis and β-cell mass in the natural history of type 1 diabetes. Diabetes 65, 719–731 (2016).
doi: 10.2337/db15-0779
pubmed: 26581594
Campbell-Thompson, M. L. et al. The diagnosis of insulitis in human type 1 diabetes. Diabetologia 56, 2541–2543 (2013).
doi: 10.1007/s00125-013-3043-5
pubmed: 24006089
Krogvold, L. et al. Insulitis and characterisation of infiltrating T cells in surgical pancreatic tail resections from patients at onset of type 1 diabetes. Diabetologia 59, 492–501 (2016).
doi: 10.1007/s00125-015-3820-4
pubmed: 26602422
Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
doi: 10.1038/nmeth.4391
pubmed: 28783155
pmcid: 5617107
Willcox, A., Richardson, S., Bone, A., Foulis, A. & Morgan, N. Analysis of islet inflammation in human type 1 diabetes. Clin. Exp. Immunol. 155, 173–181 (2009).
doi: 10.1111/j.1365-2249.2008.03860.x
pubmed: 19128359
pmcid: 2675247
La Noce, M., Nicoletti, G. F., Papaccio, G., Del Vecchio, V. & Papaccio, F. Insulitis in human type 1 diabetic pancreas: from stem cell grafting to islet organoids for a successful cell-based therapy. Cells 11, 3941 (2022).
doi: 10.3390/cells11233941
pubmed: 36497199
pmcid: 9740394
Boldison, J. & Wong, F. S. Immune and pancreatic beta cell interactions in type 1 diabetes. Trends Endocrinol. Metab. 27, 856–867 (2016).
doi: 10.1016/j.tem.2016.08.007
pubmed: 27659143
Bair, E. Semi-supervised clustering methods. Wiley Interdiscip. Rev. Comput. Stat. 5, 349–361 (2013).
doi: 10.1002/wics.1270
pubmed: 24729830
pmcid: 3979639
Schwartz, G. W., Petrovic, J., Zhou, Y. & Faryabi, R. B. Differential integration of transcriptome and proteome identifies pan-cancer prognostic biomarkers. Front. Genet. 9, 205 (2018).
doi: 10.3389/fgene.2018.00205
pubmed: 29971090
pmcid: 6018483
Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman and Hall/CRC, 2015).
Stoyan, D., Stoyan, H. & Stoyan, l. Fractals, random shapes and point fields: methods of geometrical statistics in Wiley Series in Probability and Statistics (Wiley, 1994).