{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T17:15:03Z","timestamp":1783358103199,"version":"3.54.6"},"reference-count":81,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Microscopy"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The key advantage of spatial transcriptomics (ST) technologies lies in the spatial domain: these techniques not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stage for integration with other imaging\u2010based modalities. However, how to most effectively exploit spatial context and integrate ST with imaging\u2010based modalities that capture morphological insight remains an open and heavily investigated question. To address this, particularly under real\u2010world experimental constraints such as limited dataset size, class imbalance, and bounding\u2010box\u2010based segmentation, we used a publicly available murine ileum Multiplexed Error\u2010Robust Fluorescence In Situ Hybridisation (MERFISH) dataset to evaluate whether a minimally tuned variational autoencoder (VAE) could extract informative low\u2010dimensional representations from cell crops of spot counts, nuclear stain, membrane stain, or a combination thereof. We assessed the resulting embeddings through PERMANOVA, cross\u2010validated classification, and unsupervised clustering, and compared them to classical image\u2010based feature vectors extracted via CellProfiler. While transcript counts (TC) generally outperformed other feature spaces, the VAE\u2010derived latent spaces (LSs) captured meaningful biological variation and enabled improved label recovery for specific cell types. LS2, in particular, trained solely on morphological input, also exhibited moderate predictive power for a handful of genes in a ridge regression model. Notably, combining TC with LSs improved clustering outcomes in a strategy\u2010dependent manner, yielding gains in either cluster homogeneity or label recovery under graph\u2010 or factor\u2010based clustering approaches, respectively. In contrast, CellProfiler\u2010derived features provided little benefit, highlighting the advantage of learned representations over handcrafted features. Collectively, these findings demonstrate that even under constrained conditions, VAEs can extract biologically meaningful signals from imaging data and constitute a promising strategy for multi\u2010modal integration.<\/jats:p>","DOI":"10.1111\/jmi.70129","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:43:27Z","timestamp":1781617407000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating integrative strategies for incorporating phenotypic features in spatial transcriptomics"],"prefix":"10.1111","author":[{"given":"Levin M","family":"Moser","sequence":"first","affiliation":[{"name":"Imaging Platform Broad Institute of MIT and Harvard  Cambridge Massachusetts USA"},{"name":"Department of Computer Science ETH Zurich  Zurich Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad Kamal","family":"Hamid","sequence":"additional","affiliation":[{"name":"Imaging Platform Broad Institute of MIT and Harvard  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esteban","family":"Miglietta","sequence":"additional","affiliation":[{"name":"Imaging Platform Broad Institute of MIT and Harvard  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nodar","family":"Gogoberidze","sequence":"additional","affiliation":[{"name":"Imaging Platform Broad Institute of MIT and Harvard  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beth A","family":"Cimini","sequence":"additional","affiliation":[{"name":"Imaging Platform Broad Institute of MIT and Harvard  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101740"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010025\u201003574\u20101"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/imcb.12669"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1308\/rcsann.2023.0091"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01409-2"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-022-01075-1"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-0716-4276-4_5"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae719"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad048"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2023.03.008"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43120-6"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbae052"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-024-02173-8"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-35233-1"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-024-02193-4"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-023-05640-1"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad469"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36796-3"},{"key":"e_1_2_8_20_1","doi-asserted-by":"crossref","unstructured":"Huang X. Ma Z. Meng D. Liu Y. Ruan S. Sun Q. Zheng X. &Qiao Z. (2024 September 19).PRAGA: Prototype\u2010aware graph adaptive aggregation for Spatial Multi\u2010modal Omics Analysis.arXiv [q\u2010bio.GN].","DOI":"10.1609\/aaai.v39i1.32010"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02316-4"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2021.09.004"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-022-00515-3"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2022.10.001"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-021-01044-w"},{"key":"e_1_2_8_26_1","unstructured":"Moffitt J. Xu R. Kharchenko P. Petukhov V. Cadinu P. Soldatov R. &Khodosevich K.(2021).MERFISH measurements in the mouse ileum. Dryad.https:\/\/doi.org\/10.5061\/dryad.jm63xsjb2"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2006-7-10-r100"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-021-04344-9"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471142727.mb1417s109"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.4397"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01018-x"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-017-1382-0"},{"key":"e_1_2_8_33_1","doi-asserted-by":"crossref","unstructured":"Comiter C. Vaishnav E. D. Ciampricotti M. Li B. Yang Y. Rodig S. J. Turner M. Pfaff K. L. Jan\u00e9\u2010Valbuena J. Slyper M. Waldman J. Vigneau S. Wu J. Blosser T. R. Segerstolpe \u00c5. Abravanel D. Wagle N. Zhuang X. \u2026Regev A. (2023 March 23).Inference of single cell profiles from histology stains with the Single\u2010Cell omics from Histology Analysis Framework (SCHAF).bioRxivorg. bioRxiv.","DOI":"10.1101\/2023.03.21.533680"},{"key":"e_1_2_8_34_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad642"},{"key":"e_1_2_8_35_1","unstructured":"Kingma D. P. &Welling M. (2013 December 20).Auto\u2010encoding variational Bayes.arXiv [stat.ML]."},{"key":"e_1_2_8_36_1","unstructured":"Kingma D. P. &Ba J.(2014 December 22).Adam: A method for stochastic optimization.arXiv [cs.LG]."},{"key":"e_1_2_8_37_1","unstructured":"Shen C. Roth H. R. Oda H. Oda M. Hayashi Y. Misawa K. &Mori K. (2018 January 17).On the influence of Dice loss function in multi\u2010class organ segmentation of abdominal CT using 3D fully convolutional networks.arXiv [cs.CV]."},{"key":"e_1_2_8_38_1","doi-asserted-by":"crossref","unstructured":"Milletari F. Navab N. &Ahmadi S.\u2010A.(2016).V\u2010net: Fully convolutional neural networks for volumetric medical image segmentation. In2016 Fourth International Conference on 3D Vision (3DV)(pp.565\u2013571).IEEE.","DOI":"10.1109\/3DV.2016.79"},{"key":"e_1_2_8_39_1","doi-asserted-by":"crossref","unstructured":"Lin T.\u2010Y. Goyal P. Girshick R. He K. &Dollar P.(2017).Focal loss for dense object detection. InProceedings of the IEEE International Conference on Computer Vision(pp.2980\u20132988). IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"e_1_2_8_40_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15114-1"},{"key":"e_1_2_8_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2015.12.002"},{"key":"e_1_2_8_42_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-025-02611-8"},{"key":"e_1_2_8_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-20249-2"},{"key":"e_1_2_8_44_1","unstructured":"Scverse Community.Virshup I. Bredikhin D. Heumos L. Palla G. Sturm G. Gayoso A. Kats I. Koutrouli M. Berger B. Pe'er D. Regev A. Teichmann S. A. Finotello F. Wolf F. A. Yosef N. Stegle O. &Theis F. J.(2023).The scverse project provides a computational ecosystem for single\u2010cell omics data analysis.Nature Biotechnology 41 604\u2013606."},{"key":"e_1_2_8_45_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.04371"},{"key":"e_1_2_8_46_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-021-02577-8"},{"key":"e_1_2_8_47_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_2_8_48_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-41695-z"},{"key":"e_1_2_8_49_1","doi-asserted-by":"publisher","DOI":"10.15252\/msb.20178124"},{"key":"e_1_2_8_50_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-021-02577-8"},{"key":"e_1_2_8_51_1","first-page":"2825","article-title":"Scikit\u2010learn: Machine learning in python","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research: JMLR"},{"key":"e_1_2_8_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"e_1_2_8_53_1","unstructured":"Rosenberg A. &Hirschberg J.(2007).V\u2010Measure: A conditional entropy\u2010based external cluster evaluation measure. InProceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning(EMNLP\u2010CoNLL)(pp.410\u2013420). ACL."},{"key":"e_1_2_8_54_1","first-page":"2837","article-title":"Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance","volume":"11","author":"Nguyen X.","year":"2010","journal-title":"Journal of Machine Learning Research: JMLR"},{"key":"e_1_2_8_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01908075"},{"key":"e_1_2_8_56_1","unstructured":"Jaccard P.(1901). \u00c9tude comparative de la distribution florale dans une portion des Alpes et du Jura. Imprimerie Corbaz & Comp.https:\/\/doi.org\/10.5169\/SEALS\u2010266450"},{"key":"e_1_2_8_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAES.2016.140952"},{"key":"e_1_2_8_58_1","doi-asserted-by":"crossref","unstructured":"SciPy 1.0 Contributors.Virtanen P. Gommers R. Oliphant T. E. Haberland M. Reddy T. Cournapeau D. Burovski E. Peterson P. Weckesser W. Bright J. van derWalt S. J. Brett M. Wilson J. Millman K. J. Mayorov N. Nelson A. R. J. Jones E. Kern R. \u2026VanderPlas J.(2020).SciPy 1.0: Fundamental algorithms for scientific computing in Python.Nature Methods 17 261\u2013272.","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_2_8_59_1","unstructured":"Rideout J. R. Caporaso G. Bolyen E. McDonald D. Baeza Y. V. Alastuey J. C. Pitman A. Morton J. Zhu Q. Navas J. Gorlick K. Debelius J. Xu Z. Aton M. llcooljohn Shorenstein J. Luce L. Van Treuren W. \u2026Murray K. D.(2025). scikit\u2010bio\/scikit\u2010bio: scikit\u2010bio 0.6.3. Zenodo."},{"key":"e_1_2_8_60_1","doi-asserted-by":"crossref","unstructured":"Seabold S. &Perktold J.(2010).Statsmodels: Econometric and statistical modeling with python. InProceedings of the Python in Science Conference(pp.92\u201396).SciPy.","DOI":"10.25080\/Majora-92bf1922-011"},{"key":"e_1_2_8_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_8_62_1","unstructured":"Loshchilov I. &Hutter F.(2017).Decoupled weight decay regularization. ArXiv.https:\/\/arxiv.org\/abs\/1711.05101"},{"key":"e_1_2_8_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.08.063"},{"key":"e_1_2_8_64_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2041-210X.2011.00127.x"},{"key":"e_1_2_8_65_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2005.00440.x"},{"key":"e_1_2_8_66_1","doi-asserted-by":"publisher","DOI":"10.1186\/s42523-021-00157-6"},{"key":"e_1_2_8_67_1","doi-asserted-by":"publisher","DOI":"10.1083\/jcb.200901021"},{"key":"e_1_2_8_68_1","unstructured":"Brigato L. &Iocchi L. (2020 March 28).A close look at deep learning with small data.arXiv [cs.LG]."},{"key":"e_1_2_8_69_1","unstructured":"Higgins I. Matthey L. Glorot X. Pal A. Uria B. Blundell C. Mohamed S. &Lerchner A. (2016 June 17).Early visual concept learning with unsupervised deep learning.arXiv [stat.ML]."},{"key":"e_1_2_8_70_1","unstructured":"Taghanaki S. A. Havaei M. Lamb A. Sanghi A. Danielyan A. &Custis T. (2020 May 11).Jigsaw\u2010VAE: Towards balancing features in variational autoencoders.arXiv [cs.LG]."},{"key":"e_1_2_8_71_1","doi-asserted-by":"publisher","DOI":"10.1101\/gr.121947.111"},{"key":"e_1_2_8_72_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2015.12.010"},{"key":"e_1_2_8_73_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.100.022405"},{"key":"e_1_2_8_74_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1461-0248.2006.00926.x"},{"key":"e_1_2_8_75_1","article-title":"Detecting incidental correlation in multimodal learning via latent variable modeling","author":"Makino T.","year":"2023","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_2_8_76_1","first-page":"9938","volume-title":"International conference on machine learning","author":"Javaloy A.","year":"2022"},{"key":"e_1_2_8_77_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-023-02097-9"},{"key":"e_1_2_8_78_1","doi-asserted-by":"publisher","DOI":"10.1002\/cm.20475"},{"key":"e_1_2_8_79_1","doi-asserted-by":"publisher","DOI":"10.3390\/ncrna6030026"},{"key":"e_1_2_8_80_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0605965104"},{"key":"e_1_2_8_81_1","unstructured":"Higgins I. Matthey L. Pal A. Burgess C. Glorot X. Botvinick M. Mohamed S. &Lerchner A.(2017). beta\u2010VAE: Learning basic visual concepts with a constrained variational framework. ICLR."},{"key":"e_1_2_8_82_1","unstructured":"Burgess C. P. Higgins I. Pal A. Matthey L. Watters N. Desjardins G. &Lerchner A.(2018).Understanding disentangling in \u03b2\u2010VAE.arXiv [stat.ML]."}],"container-title":["Journal of Microscopy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jmi.70129","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/jmi.70129","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/jmi.70129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T16:21:26Z","timestamp":1783354886000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/jmi.70129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":81,"alternative-id":["10.1111\/jmi.70129"],"URL":"https:\/\/doi.org\/10.1111\/jmi.70129","archive":["Portico"],"relation":{},"ISSN":["0022-2720","1365-2818"],"issn-type":[{"value":"0022-2720","type":"print"},{"value":"1365-2818","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"2025-07-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-05-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-06-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"jmi.70129"}}