Consensus tissue domain detection in spatial omics data using multiplex image labeling with regional morphology (MILWRM).
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
03
02
2024
accepted:
02
05
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Spatially resolved molecular assays provide high dimensional genetic, transcriptomic, proteomic, and epigenetic information in situ and at various resolutions. Pairing these data across modalities with histological features enables powerful studies of tissue pathology in the context of an intact microenvironment and tissue structure. Increasing dimensions across molecular analytes and samples require new data science approaches to functionally annotate spatially resolved molecular data. A specific challenge is data-driven cross-sample domain detection that allows for analysis within and between consensus tissue compartments across high volumes of multiplex datasets stemming from tissue atlasing efforts. Here, we present MILWRM (multiplex image labeling with regional morphology)-a Python package for rapid, multi-scale tissue domain detection and annotation at the image- or spot-level. We demonstrate MILWRM's utility in identifying histologically distinct compartments in human colonic polyps, lymph nodes, mouse kidney, and mouse brain slices through spatially-informed clustering in two different spatial data modalities from different platforms. We used tissue domains detected in human colonic polyps to elucidate the molecular distinction between polyp subtypes, and explored the ability of MILWRM to identify anatomical regions of the brain tissue and their respective distinct molecular profiles.
Identifiants
pubmed: 39478141
doi: 10.1038/s42003-024-06281-8
pii: 10.1038/s42003-024-06281-8
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1295Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : U2CCA233291
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : U54CA274367
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : P50CA236733
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases)
ID : R01DK103831
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases)
ID : U01DK133766
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)
ID : P01AI139449
Organisme : U.S. Department of Health & Human Services | NIH | National Eye Institute (NEI)
ID : U54EY032442
Informations de copyright
© 2024. The Author(s).
Références
Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).
pubmed: 34215862
pmcid: 8647621
doi: 10.1038/s41596-021-00556-8
Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalinfixed, paraffin-embedded cancer tissue. Proc. Natl. Acad. Sci. USA 110, 11982–11987 (2013).
pubmed: 23818604
pmcid: 3718135
doi: 10.1073/pnas.1300136110
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
pubmed: 35273392
doi: 10.1038/s41592-022-01409-2
Schapiro, D. et al. MITI minimum information guidelines for highly multiplexed tissue images. Nat. Methods 19, 262–267 (2022).
pubmed: 35277708
pmcid: 9009186
doi: 10.1038/s41592-022-01415-4
Ruddle, N. H. High endothelial venules and lymphatic vessels in tertiary lymphoid organs: characteristics, functions, and regulation. Front Immunol. 7, 491 (2016).
Sipos, F. & Muzes, G. Isolated lymphoid follicles in colon: Switch points between inflammation and colorectal cancer? World J. Gastroenterol. 17, 1666–1673 (2011).
pubmed: 21483625
pmcid: 3072629
doi: 10.3748/wjg.v17.i13.1666
Hickey, J. W. et al. Organization of the human intestine at single-cell resolution. Nature. 619, 572–584 (2023).
McKinley, E. T. et al. MIRIAM: a machine and deep learning single-cell segmentation and quantification pipeline for multi-dimensional tissue images. Cytom. Part A 101, 521–528 (2022).
doi: 10.1002/cyto.a.24541
Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555 (2022).
pubmed: 34795433
doi: 10.1038/s41587-021-01094-0
Liu, C. C. et al. Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering. Nat. Commun. 14, 4618 (2023).
Kim, J. et al. Unsupervised discovery of tissue architecture in multiplexed imaging. Nat. Methods. 19, 1653–1661 (2022).
Chen, Z., Soifer, I., Hilton, H., Keren, L. & Jojic, V. Modeling multiplexed images with spatial-lda reveals novel tissue microenvironments. J. Comput. Biol. 27, 1204 (2020).
pubmed: 32243203
pmcid: 7415889
doi: 10.1089/cmb.2019.0340
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2021).
pubmed: 33603203
pmcid: 8606190
doi: 10.1038/s41587-021-00830-w
Andersson, A. et al. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun. Biol. 3, 1–8 (2020).
doi: 10.1038/s42003-020-01247-y
Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).
pubmed: 30193111
pmcid: 6132072
doi: 10.1016/j.cell.2018.08.039
Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).
pubmed: 32763154
pmcid: 7479520
doi: 10.1016/j.cell.2020.07.005
Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12, 1–14 (2021).
doi: 10.1038/s41467-021-26271-2
Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).
pubmed: 31959985
doi: 10.1038/s41586-019-1876-x
Lin, J.-R. et al. Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer. Cell. 186, 363–381 (2023).
Piccinini, F. et al. Advanced cell classifier: user-friendly machine-learning-based software for discovering phenotypes in high-content imaging data. Cell Syst. 4, 651–655.e5 (2017).
pubmed: 28647475
doi: 10.1016/j.cels.2017.05.012
Amitay, Y. et al. CellSighter: a neural network to classify cells in highly multiplexed images. Nat. Commun.14, 4302 (2022).
Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6, 1435–1448 (2022).
pubmed: 36357512
doi: 10.1038/s41551-022-00951-w
Alexandrov, T. & Kobarg, J. H. Efficient spatial segmentation of large imaging mass spectrometry datasets with spatially aware clustering. Bioinformatics 27, i230–i238 (2011).
pubmed: 21685075
pmcid: 3117346
doi: 10.1093/bioinformatics/btr246
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).
pubmed: 34083791
pmcid: 8763026
doi: 10.1038/s41587-021-00935-2
Townes, F. W. & Engelhardt, B. E. Nonnegative spatial factorization. Nat. Methods. https://doi.org/10.48550/arxiv.2110.06122 (2021).
Liu, W. et al. Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nat. Commun. 14, 1–18 (2023).
Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1–19 (2023).
doi: 10.1038/s41467-023-36796-3
Li, Z. & Zhou, X. BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome Biol. 23, 168 (2022).
Greenacre, M. J. (eds) Theory and Applications of Correspondence Analysis (Academic Press, 1984). 10.3/JQUERY-UI.JS.
Chen, B. et al. Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps. Cell 184, 6262–6280.e26 (2021).
pubmed: 34910928
pmcid: 8941949
doi: 10.1016/j.cell.2021.11.031
McKinley, E. T. et al. Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity. JCI Insight 2, e93487 (2017).
Herring, C. A. et al. Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternative tuft cell origins in the gut. Cell Syst. 6, 37–51.e9 (2018).
pubmed: 29153838
doi: 10.1016/j.cels.2017.10.012
Tytgat, K. M. A. J. et al. Biosynthesis of human colonic mucin: Muc2 is the prominent secretory mucin. Gastroenterology 107, 1352–1363 (1994).
pubmed: 7926500
doi: 10.1016/0016-5085(94)90537-1
Allen, A., Hutton, D. A. & Pearson, J. P. The MUC2 gene product: a human intestinal mucin. Int. J. Biochem. Cell Biol. 30, 797–801 (1998).
pubmed: 9722984
doi: 10.1016/S1357-2725(98)00028-4
Karlsson, N. G. et al. Molecular characterization of the large heavily glycosylated domain glycopeptide from the rat small intestinal Muc2 mucin. Glycoconj. J. 13, 823–831 (1996).
pubmed: 8910009
doi: 10.1007/BF00702346
Vega, P. N. et al. Cancer-associated fibroblasts and squamous epithelial cells constitute a unique microenvironment in a mouse model of inflammation-induced colon cancer. Front Oncol. 12, 878920 (2022).
Takeuchi, A. et al. A distinct subset of fibroblastic stromal cells constitutes the cortex-medulla boundary subcompartment of the lymph node. Front Immunol. 9, 414794 (2018).
doi: 10.3389/fimmu.2018.02196
Roozendaal, R. & Carroll, M. C. Complement receptors CD21 and CD35 in humoral immunity. Immunol. Rev. 219, 157–166 (2007).
pubmed: 17850488
doi: 10.1111/j.1600-065X.2007.00556.x
Rodig, S. J., Shahsafaei, A., Li, B. & Dorfman, D. M. The CD45 isoform B220 identifies select subsets of human B cells and B-cell lymphoproliferative disorders. Hum. Pathol. 36, 51–57 (2005).
pubmed: 15712182
doi: 10.1016/j.humpath.2004.10.016
Shiota, T. et al. The clinical significance of CD169-positive lymph node macrophage in patients with breast cancer. PLoS One 11, e0166680 (2016).
pubmed: 27861544
pmcid: 5115774
doi: 10.1371/journal.pone.0166680
Schmidt, D. & von Hochstetter, A. R. The use of CD31 and collagen IV as vascular markers a study of 56 vascular lesions. Pathol. Res. Pract. 191, 410–414 (1995).
pubmed: 7479359
doi: 10.1016/S0344-0338(11)80727-2
Willard-Mack, C. L. Normal structure, function, and histology of lymph nodes. Toxicol. Pathol. 34, 409–424 (2006).
pubmed: 17067937
doi: 10.1080/01926230600867727
Neumann, E. K. et al. Highly multiplexed immunofluorescence of the human kidney using co-detection by indexing. Kidney Int. 101, 137–143 (2022).
pubmed: 34619231
doi: 10.1016/j.kint.2021.08.033
Jain, S. et al. Advances and prospects for the Human Biomolecular Atlas Program (HuBMAP). Nat. Cell Biol. 25, 1089–1100 (2023).
pubmed: 37468756
pmcid: 10681365
doi: 10.1038/s41556-023-01194-w
Consortium, H. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187–192 (2019).
doi: 10.1038/s41586-019-1629-x
Gueutin, V., Deray, G. & Isnard-Bagnis, C. [Renal physiology]. Bull. Cancer 99, 237–249 (2012).
pubmed: 22157516
doi: 10.1684/bdc.2011.1482
Agarwal, S., Sudhini, Y. R., Polat, O. K., Reiser, J. & Altintas, M. M. Renal cell markers: lighthouses for managing renal diseases. Am. J. Physiol. Ren. Physiol. 321, F715–F739 (2021).
doi: 10.1152/ajprenal.00182.2021
Aoki, R. et al. Foxl1-expressing mesenchymal cells constitute the intestinal stem cell niche. Cell Mol. Gastroenterol. Hepatol. 2, 175 (2016).
pubmed: 26949732
doi: 10.1016/j.jcmgh.2015.12.004
Shoshkes-Carmel, M. et al. Subepithelial telocytes are an important source of Wnts that supports intestinal crypts. Nature 557, 242 (2018).
pubmed: 29720649
pmcid: 5966331
doi: 10.1038/s41586-018-0084-4
Becker, W. R. et al. Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer. Nat. Genet. 54, 985–995 (2022).
pubmed: 35726067
pmcid: 9279149
doi: 10.1038/s41588-022-01088-x
Sakamoto, N. et al. BRAFV600E cooperates with CDX2 inactivation to promote serrated colorectal tumorigenesis. Elife 6, e20331 (2017).
Leow, C. C., Romero, M. S., Ross, S., Polakis, P. & Gao, W. Q. Hath1, down-regulated in colon adenocarcinomas, inhibits proliferation and tumorigenesis of colon cancer cells. Cancer Res. 64, 6050–6057 (2004).
pubmed: 15342386
doi: 10.1158/0008-5472.CAN-04-0290
Yang, K. et al. Interaction of Muc2 and Apc on Wnt signaling and in intestinal tumorigenesis: potential role of chronic inflammation. Cancer Res. 68, 7313 (2008).
pubmed: 18794118
pmcid: 2698434
doi: 10.1158/0008-5472.CAN-08-0598
Femia, A. P. et al. Frequent mutation of apc gene in rat colon tumors and mucin-depleted foci, preneoplastic lesions in experimental colon carcinogenesis. Cancer Res. 67, 445–449 (2007).
pubmed: 17234750
doi: 10.1158/0008-5472.CAN-06-3861
Pretlow, T. P. & Pretlow, T. G. Mutant KRAS in aberrant crypt foci (ACF): Initiation of colorectal cancer? Biochim Biophys. Acta 1756, 83–96 (2005).
pubmed: 16219426
Femia, A. P., Dolara, P. & Caderni, G. Mucin-depleted foci (MDF) in the colon of rats treated with azoxymethane (AOM) are useful biomakers for colon carcinogenesis. Carcinogenesis 25, 277–281 (2004).
pubmed: 14604897
doi: 10.1093/carcin/bgh005
Blache, P. et al. SOX9 is an intestine crypt transcription factor, is regulated by the Wnt pathway, and represses the CDX2 and MUC2 genes. J. Cell Biol. 166, 37–47 (2004).
pubmed: 15240568
pmcid: 2172132
doi: 10.1083/jcb.200311021
Mizoshita, T. et al. Loss of MUC2 expression correlates with progression along the adenoma-carcinoma sequence pathway as well as de novo carcinogenesis in the colon. Histol. Histopathol. 22, 251–260 (2007).
pubmed: 17163399
Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996 (2013).
pubmed: 23193282
doi: 10.1093/nar/gks1042
Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).
Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).
pubmed: 33558695
pmcid: 8095368
doi: 10.1038/s41593-020-00787-0
Warchol, S. et al. Visinity: visual spatial neighborhood analysis for multiplexed tissue imaging data. IEEE Trans. Vis. Comput. Graph. https://doi.org/10.1109/TVCG.2022.3209378 (2022).
Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).
pubmed: 34711970
doi: 10.1038/s41592-021-01255-8
Gao, J., Zhang, F., Hu, K. & Cui, X. Hexagonal convolutional neural network for spatial transcriptomics classification. In Proc. 2022 IEEE International Conference on Bioinformatics and Biomedicine, 200–205 (BIBM, 2022) https://doi.org/10.1109/BIBM55620.2022.9995701 .
Raykov, Y. P., Boukouvalas, A., Baig, F. & Little, M. A. What to do when K-means clustering fails: a simple yet principled alternative algorithm. PLoS One 11, e0162259 (2016).
pubmed: 27669525
pmcid: 5036949
doi: 10.1371/journal.pone.0162259
Harris, C. R. et al. Quantifying and correcting slide-to-slide variation in multiplexed immunofluorescence images. Bioinformatics 38, 1700–1707 (2022).
pubmed: 34983062
pmcid: 8896603
doi: 10.1093/bioinformatics/btab877
Graf, J. et al. FLINO: a new method for immunofluorescence bioimage normalization. Bioinformatics 38, 520–526 (2022).
pubmed: 34601553
doi: 10.1093/bioinformatics/btab686
Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. Elife 8, e43803 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).
pubmed: 35102346
pmcid: 8828470
doi: 10.1038/s41592-021-01358-2
Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
doi: 10.1016/0377-0427(87)90125-7
Gosline, S. J. C. et al. Proteome mapping of the human pancreatic Islet microenvironment reveals endocrine- exocrine signaling sphere of influence. Mol. Cell. Proteomics 22, 100592 (2023).
NHPatterson/wsireg: multimodal whole slide image registration in a graph structure. https://github.com/NHPatterson/wsireg .
Halekoh, U., Højsgaard, S. & Yan, J. The R package geepack for generalized estimating equations. J. Stat. Softw. 15, 1–11 (2006).
doi: 10.18637/jss.v015.i02
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
doi: 10.1111/j.2517-6161.1995.tb02031.x
Vandekar, S., Tao, R. & Blume, J. A robust effect size index. Psychometrika 85, 232 (2020).
pubmed: 32232646
pmcid: 7186256
doi: 10.1007/s11336-020-09698-2
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 1–5 (2018).
doi: 10.1186/s13059-017-1382-0
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
pubmed: 25867923
pmcid: 4430369
doi: 10.1038/nbt.3192