Abstract
Spatial transcriptomics (ST) enables gene expression profiling while preserving the spatial architecture of intact tissue. Analyzing ST data often proceeds by first extracting cell-level information, typically through cell segmentation or cell-type deconvolution. However, a critical oversight has been that a substantial portion of molecular expression is systematically lost or unannotated by these methods. This lost expression can arise from diverse and biologically important sources like fragile or underrepresented cell types, subcellular structures like neurites, and extracellular expression. These omissions can result in biased analyses and incorrect or incomplete biological interpretations. We describe a new computational method, RESCUE, that can recover the unattributed spatial expression patterns missed by existing ST analysis methods and enable robust inference even when reference is incomplete. We validate RESCUE using MERFISH data from the honey bee brain and apply it to multiple ST datasets to demonstrate how it can reveal novel insights into complex tissue biology.
Similar content being viewed by others
Data availability
The honey bee brain MERFISH data used in this study is deposited in our GitHub repository: https://github.com/brunoyjlee/RESCUE/tree/main/data. The honey bee brain scRNA-seq data48 is available at https://doi.org/10.6084/m9.figshare.16832518. The human breast cancer dataset5 is available via GEO with accession ID GSE243280. The human DLPFC dataset60 is available at https://github.com/LieberInstitute/spatialLIBD. The cichlid fish brain dataset61 is available via GEO with accession ID GSE217619. Source data are provided with this paper.
Code availability
RESCUE is available as an R package at https://github.com/brunoyjlee/RESCUE. Codes for the version of RESCUE used in this paper are also deposited at Zenodo88 (https://doi.org/10.5281/zenodo.18965449).
References
Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science https://doi.org/10.1126/science.aaa6090 (2015).
Nagendran, M. et al. 1457 Visium HD enables spatially resolved, single-cell scale resolution mapping of FFPE human breast cancer tissue. J. Immunother. Cancer 11, A1620–A1620 (2023).
Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 14, 8353 (2023).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Petukhov, V. et al. Cell segmentation in imaging-based spatial transcriptomics. Nat. Biotechnol. 40, 345–354 (2022).
Polański, K. et al. Bin2cell reconstructs cells from high resolution Visium HD data. Bioinformatics 40, btae546 (2024).
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).
Ma, Y. & Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat. Biotechnol. 40, 1349–1359 (2022).
Zhou, Z., Zhong, Y., Zhang, Z. & Ren, X. Spatial transcriptomics deconvolution at single-cell resolution using Redeconve. Nat. Commun. 14, 7930 (2023).
Liu, Z., Wu, D., Zhai, W. & Ma, L. SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics. Nat. Commun. 14, 4727 (2023).
Si, Y. et al. FICTURE: scalable segmentation-free analysis of submicron-resolution spatial transcriptomics. Nat. Methods 21, 1843–1854 (2024).
Mitchel, J., Gao, T., Petukhov, V., Cole, E. & Kharchenko, P. V. Impact of segmentation errors in analysis of spatial transcriptomics. Nat. Genet. 58, 434–444 (2026).
Nguyen, H., Nguyen, H., Tran, D., Draghici, S. & Nguyen, T. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges. Nucleic Acids Res. 52, 4761–4783 (2024).
Garmire, L. X. et al. Challenges and perspectives in computational deconvolution of genomics data. Nat. Methods 21, 391–400 (2024).
Gaspard-Boulinc, L. C., Gortana, L., Walter, T., Barillot, E. & Cavalli, F. M. G. Cell-type deconvolution methods for spatial transcriptomics. Nat. Rev. Genet. 1–19 https://doi.org/10.1038/s41576-025-00845-y (2015).
Ament, S. A. & Poulopoulos, A. The brain’s dark transcriptome: sequencing RNA in distal compartments of neurons and glia. Curr. Opin. Neurobiol. 81, 102725 (2023).
Ivich, A. et al. Missing cell types in single-cell references impact deconvolution of bulk data but are detectable. Genome Biol. 26, 86 (2025).
Han, J. et al. Single-cell sequencing unveils key contributions of immune cell populations in cancer-associated adipose wasting. Cell Discov. 8, 122 (2022).
Fetit, R. et al. Characterizing neutrophil subtypes in cancer using scRNA sequencing demonstrates the importance of IL1β/CXCR2 axis in generation of metastasis-specific neutrophils. Cancer Res. Commun. 4, 588–606 (2024).
Sutton, M. A. et al. Miniature neurotransmission stabilizes synaptic function via tonic suppression of local dendritic protein synthesis. Cell 125, 785–799 (2006).
Cajigas, I. J. et al. The local transcriptome in the synaptic neuropil revealed by deep sequencing and high-resolution imaging. Neuron 74, 453–466 (2012).
Hafner, A.-S., Donlin-Asp, P. G., Leitch, B., Herzog, E. & Schuman, E. M. Local protein synthesis is a ubiquitous feature of neuronal pre- and postsynaptic compartments. Science 364, eaau3644 (2019).
Glock, C. et al. The translatome of neuronal cell bodies, dendrites, and axons. Proc. Natl. Acad. Sci. USA 118, e2113929118 (2021).
von Kügelgen, N. & Chekulaeva, M. Conservation of a core neurite transcriptome across neuronal types and species. WIREs RNA 11, e1590 (2020).
Lee, K.-W., Kim, H.-J., Sung, J.-J., Park, K.-S. & Kim, M. Defective neurite outgrowth in aphidicolin/cAMP-induced motor neurons expressing mutant Cu/Zn superoxide dismutase. Int. J. Dev. Neurosci. 20, 521–526 (2002).
Markmiller, S. et al. Persistent mRNA localization defects and cell death in ALS neurons caused by transient cellular stress. Cell Rep. 36, 109685 (2021).
Buxbaum, A. R., Haimovich, G. & Singer, R. H. In the right place at the right time: visualizing and understanding mRNA localization. Nat. Rev. Mol. Cell Biol. 16, 95–109 (2015).
Gandalovičová, A., Vomastek, T., Rosel, D. & Brábek, J. Cell polarity signaling in the plasticity of cancer cell invasiveness. Oncotarget 7, 25022–25049 (2016).
Backlund, M. et al. Plasticity of nuclear and cytoplasmic stress responses of RNA-binding proteins. Nucleic Acids Res. 48, 4725–4740 (2020).
Apicco, D. J. et al. Reducing the RNA binding protein TIA1 protects against tau-mediated neurodegeneration in vivo. Nat. Neurosci. 21, 72–80 (2018).
Wolozin, B. & Ivanov, P. Stress granules and neurodegeneration. Nat. Rev. Neurosci. 20, 649–666 (2019).
Ridder, K. et al. Extracellular vesicle-mediated transfer of functional RNA in the tumor microenvironment. OncoImmunology 4, e1008371 (2015).
Li, I. & Nabet, B. Y. Exosomes in the tumor microenvironment as mediators of cancer therapy resistance. Mol. Cancer 18, 32 (2019).
O’Brien, K., Breyne, K., Ughetto, S., Laurent, L. C. & Breakefield, X. O. RNA delivery by extracellular vesicles in mammalian cells and its applications. Nat. Rev. Mol. Cell Biol. 21, 585–606 (2020).
Landry, F. J. & Findlay, S. R. Purification of human basophils by negative selection. J. Immunol. Methods 63, 329–336 (1983).
Hansel, T. T. et al. Purification of human blood eosinophils by negative selection using immunomagnetic beads. J. Immunol. Methods 122, 97–103 (1989).
Hansel, T. T. et al. An improved immunomagnetic procedure for the isolation of highly purified human blood eosinophils. J. Immunol. Methods 145, 105–110 (1991).
Munoz, N. M. & Leff, A. R. Highly purified selective isolation of eosinophils from human peripheral blood by negative immunomagnetic selection. Nat. Protoc. 1, 2613–2620 (2006).
She, Y. & Owen, A. B. Outlier detection using nonconvex penalized regression. J. Am. Stat. Assoc. 106, 626–639 (2011).
Lee, Y., MacEachern, S. N. & Jung, Y. Regularization of case-specific parameters for robustness and efficiency. Stat. Sci. 27, 350–372 (2012).
Kong, D., Bondell, H. D. & Wu, Y. Fully efficient robust estimation, outlier detection and variable selection via penalized regression. Stat. Sin. 28, 1031–1052 (2018).
Traniello, I. M. et al. Single-cell dissection of aggression in honeybee colonies. Nat. Ecol. Evol. 7, 1232–1244 (2023).
Lin, L. I.-K. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268 (1989).
Kumar, A. et al. Intracellular spatial transcriptomic analysis toolkit (InSTAnT). Nat. Commun. 15, 7794 (2024).
Kim, Y. et al. SpaceExpress enables differential spatial transcriptomics with natural coordinate systems. 2024.12.19.628720 Preprint at https://doi.org/10.1101/2024.12.19.628720 (2025).
Yeo, S., Schrader, A. W., Lee, J., Asadian, M. & Han, H.-S. Spot-based global registration for subpixel stitching of single-molecule resolution images for tissue-scale spatial transcriptomics. Anal. Chem. 96, 6517–6522 (2024).
Wang, G. et al. Spatial organization of the transcriptome in individual neurons. 2020.12.07.414060 Preprint at https://doi.org/10.1101/2020.12.07.414060 (2020).
Moffitt, J. R. et al. High-performance multiplexed fluorescence in situ hybridization in culture and tissue with matrix imprinting and clearing. Proc. Natl. Acad. Sci. USA 113, 14456–14461 (2016).
Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).
Khaledian, B., Thibes, L. & Shimono, Y. Adipocyte regulation of cancer stem cells. Cancer Sci. 114, 4134–4144 (2023).
Hu, X. et al. Decorin-mediated suppression of tumorigenesis, invasion, and metastasis in inflammatory breast cancer. Commun. Biol. 4, 72 (2021).
Cai, R. et al. Primary breast tumor induced extracellular matrix remodeling in premetastatic lungs. Sci. Rep. 13, 18566 (2023).
de Freitas Oliveira-Tore, C. et al. Non-canonical extracellular complement pathways and the complosome paradigm in cancer: a scoping review. Front. Immunol. 16, 1519465 (2025).
Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).
Johnson, Z. V. et al. Cellular profiling of a recently-evolved social behavior in cichlid fishes. Nat. Commun. 14, 4891 (2023).
Lake, B. B. et al. A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci. Rep. 7, 6031 (2017).
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
DeFelipe, J. et al. Cortical white matter: beyond the pale remarks, main conclusions and discussion. Front. Neuroanat. 4, 1692 (2010).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Sakers, K. et al. Astrocytes locally translate transcripts in their peripheral processes. Proc. Natl. Acad. Sci. USA 114, E3830–E3838 (2017).
Jiang, T. et al. The lncRNA MALAT1/miR-30/Spastin Axis Regulates Hippocampal Neurite Outgrowth. Front. Cell. Neurosci. 14, 555747 (2020).
Overly, C. C., Rieff, H. I. & Hollenbeck, P. J. Organelle motility and metabolism in axons vs dendrites of cultured hippocampal neurons. J. Cell Sci. 109, 971–980 (1996).
Harris, J. J., Jolivet, R. & Attwell, D. Synaptic energy use and supply. Neuron 75, 762–777 (2012).
Than-Trong, E. & Bally-Cuif, L. Radial glia and neural progenitors in the adult zebrafish central nervous system. Glia 63, 1406–1428 (2015).
Miranda-Negrón, Y. & García-Arrarás, J. E. Radial glia and radial glia-like cells: Their role in neurogenesis and regeneration. Front. Neurosci. 16, 1006037 (2022).
Zhu, J., Sun, S. & Zhou, X. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biol. 22, 184 (2021).
Abramsson, A. et al. The zebrafish amyloid precursor protein-b is required for motor neuron guidance and synapse formation. Dev. Biol. 381, 377–388 (2013).
Newman, M., Ebrahimie, E. & Lardelli, M. Using the zebrafish model for Alzheimer’s disease research. Front. Genet. 5, 189 (2014).
Haenisch, C. et al. The neuronal growth and regeneration associated Cntn1 (F3/F11/Contactin) gene is duplicated in fish: expression during development and retinal axon regeneration. Mol. Cell. Neurosci. 28, 361–374 (2005).
Eiraku, M. et al. DNER acts as a neuron-specific Notch ligand during Bergmann glial development. Nat. Neurosci. 8, 873–880 (2005).
Díaz-Ramos, À., Roig-Borrellas, A., García-Melero, A. & López-Alemany, R. α-Enolase, a multifunctional protein: its role on pathophysiological situations. BioMed. Res. Int. 2012, 156795 (2012).
Zhou, Z., Li, X., Wright, J., Candès, E. & Ma, Y. Stable principal component pursuit. in 2010 IEEE International Symposium on Information Theory 1518–1522 https://doi.org/10.1109/ISIT.2010.5513535 (2010).
Mateos, G. & Giannakis, G. B. Robust PCA as bilinear decomposition with outlier-sparsity regularization. Trans. Sig. Proc. 60, 5176–5190 (2012).
Manabe, R. et al. Transcriptome-based systematic identification of extracellular matrix proteins. Proc. Natl. Acad. Sci. USA 105, 12849–12854 (2008).
Giannakis, G. B., Mateos, G., Farahmand, S., Kekatos, V. & Zhu, H. USPACOR: Universal sparsity-controlling outlier rejection. in 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 1952–1955 https://doi.org/10.1109/ICASSP.2011.5946891 (2011).
Huang, P., Cai, M., Lu, X., McKennan, C. & Wang, J. Accurate estimation of rare cell-type fractions from tissue omics data via hierarchical deconvolution. Ann. Appl. Stat. 18, 1178–1194 (2024).
Zhong, Y. & Liu, Z. Gene expression deconvolution in linear space. Nat. Methods 9, 8–9 (2012).
Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19, 562–578 (2018).
Townes, F. W., Hicks, S. C., Aryee, M. J. & Irizarry, R. A. Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model. Genome Biol. 20, 295 (2019).
Mu, X. et al. Single-nucleus and spatial transcriptomics identify brain landscape of gene regulatory networks associated with behavioral maturation in honeybees. Nat. Commun. 16, 3343 (2025).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Lee, Y. J. et al. RESCUE: recovery of unattributed expression patterns in spatial transcriptomics. Zenodo https://doi.org/10.5281/zenodo.18965449.
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS A J. Integr. Biol. 16, 284–287 (2012).
Acknowledgements
We thank Dr. Tina Barbasch on valuable discussions on the analysis of fish brain data. Funding: This work was supported by the National Institutes of Health (R21HG013180 to H.-S.H. and S.D.Z., R35GM147420 to H.-S.H. and R01AT013189 to S.D.Z.).
Author information
Authors and Affiliations
Contributions
Y.J.L., H.-S.H., and S.D.Z. conceptualized the problem and developed the methodology. Y.J.L. performed the numerical studies, interpreted the results, and developed the software. S.Y. and A.W.S. performed data processing and cell segmentation on honey bee MERFISH data. J.L. and M.A. generated honey bee MERFISH dataset. J.L. and I.M.T. selected the gene panel for honey bee MERFISH experiments. I.M.T. and A.C.A. collected honey bee brains. Y.J.L., H.-S.H., and S.D.Z. wrote the manuscript with feedback from G.E.R. H.-S.H. and S.D.Z. supervised the project.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks Guoqiang Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Lee, Y.J., Yeo, S., Schrader, A.W. et al. RESCUE: recovery of unattributed expression patterns in spatial transcriptomics. Nat Commun (2026). https://doi.org/10.1038/s41467-026-71720-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-026-71720-5
