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DestVI identifies continuums of cell types in spatial transcriptomics data
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Abstract
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools (https://scvi-tools.org). DestVI models continuous cell states in spatial transcriptomics data.
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“DestVI proposed a framework that jointly learned the latent representations of scRNA-seq datasets and spatial spot expression datasets to deconvolute cell-type proportions and related transcriptional states in single spots [126].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [126]
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Srivatsan, S. R., Regier, M. C., Barkan, E. R., Franks, J. M., Packer, J. S., Grosjean, P., Duran, M., Saxton, S. H., Ladd, J. J., Spielmann, M., et al. (2021) · Science · doi:10.1126/science.abb9536 · Cited by 266 · cited 2×
“Using a dataset of mouse embryo cortex [35], we apply our full battery of tests from the simulation benchmarking, and report our results in Supplementary Figure 5.”
MethodDSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 229 · cited 1×
“The convention for analyzing such pairs of datasets (as implemented by all existing pipelines, including NMFReg [14], RCTD [16], SPOTLight [17], Stereoscope [18], DSTG [19], and cell2location [20]) is to apply a two-step process.”
MethodMultimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.043 · Cited by 215
Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis
Arnol, D., Schapiro, D., Bodenmiller, B., Sáez-Rodríguez, J., & Stegle, O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.077 · Cited by 214
SpaGE: Spatial Gene Enhancement using scRNA-seq
Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa740 · Cited by 207
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190 · cited 1×
“This idea was introduced in the context of scRNA-seq imputation by the SAVER-X method, in which an autoencoder alongside the high-dimensional expression data is used to perform accurate data imputation [64].”
MethodRobust decomposition of cell type mixtures in spatial transcriptomicsPreprint
Cable, D. M., Murray, E. M., Zou, L. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.07.082750 · Cited by 154
Robust linear dimensionality reduction
Koren, Y., & Carmel, L. (2004) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2004.17 · Cited by 152
Space-time logic of liver gene expression at sub-lobular scale
Droin, C., Kholtei, J. E., Bahar Halpern, K., Hurni, C., Rozenberg, M., Muvkadi, S., Itzkovitz, S., & Naef, F. (2021) · Nature Metabolism · doi:10.1038/s42255-020-00323-1 · Cited by 148 · cited 1×
“Turning to hepatocytes, we used DestVI to explore how hepatocyte-specific gene expression pertains to liver zonation [77].”
MethodMacrophage arginase-1 controls bacterial growth and pathology in hypoxic tuberculosis granulomas
Duque-Correa, M. A., Kühl, A. A., Rodriguez, P. C., Zedler, U., Schommer-Leitner, S., Rao, M., Weiner, J., Hurwitz, R., Qualls, J. E., Kosmiadi, G. A., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408839111 · Cited by 146 · cited 1×
“This observation is also consistent with previous work, as cells with similar phenotypes have been observed to congregate in hypoxic tissue niches (cancerous [60] and non-cancerous [61]).”
ResultSimulating multiple faceted variability in single cell RNA sequencing
Zhang, X., Xu, C. A., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-10500-w · Cited by 124 · cited 1×
“A similar approach was taken in the design of the Simsym simulator for scRNA-seq data [84].”
MethodSelf-citationKumar, S., Sharife, H., Kreisel, T., Mogilevsky, M., Bar-Lev, L., Grunewald, M., Aizenshtein, E., Karni, R., Paldor, I., Shlomi, T., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.04.003 · Cited by 122 · cited 1×
“Furthermore, our spatial analysis identified a discrete sub-population of immunosuppressive macrophages, expressing an hypoxia activation signature, within the tumor core [25].”
BackgroundLeal, J. M., Huang, J. Y., Kohli, K., Stoltzfus, C. R., Lyons‐Cohen, M. R., Olin, B. E., Gale, M., & Gerner, M. Y. (2021) · Science Immunology · doi:10.1126/sciimmunol.abb9435 · Cited by 109
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · cited 1×
“In general, we suggest that users of DestVI prepare their single-cell RNA sequencing data according to power analysis tools [69].”
MethodGarraud, O., Borhis, G., Badr, G., Degrelle, S. A., Pozzetto, B., Cognasse, F., & Richard, Y. (2012) · BMC Immunology · doi:10.1186/1471-2172-13-63 · Cited by 105 · cited 1×
“As expected, the first striking pattern is the organization of the lymph node sections into the B cell follicles (Cd79a; external area) and a T cell compartment (CD8; internal area) (Figure 3E; Supplementary Figure 11) [42,43].”
BackgroundJoint cell segmentation and cell type annotation for spatial transcriptomics
Littman, R., Hemminger, Z., Foreman, R., Arneson, D. V., Zhang, G., Gómez‐Pinilla, F., Yang, X., & Wollman, R. (2021) · Molecular Systems Biology · doi:10.15252/msb.202010108 · Cited by 94 · cited 1×
“However, these methods rely on cell-segmentation algorithms [8,9].”
MethodSubcapsular Sinus Macrophages: The Seat of Innate and Adaptive Memory in Murine Lymph Nodes
Moran, I., Grootveld, A. K., Nguyen, A., & Phan, T. G. (2018) · Trends in Immunology · doi:10.1016/j.it.2018.11.004 · Cited by 92
Lymph node conduits transport virions for rapid T cell activation
Reynoso, G. V., Weisberg, A. S., Shannon, J. P., McManus, D. T., Shores, L. S., Americo, J. L., Stan, R. V., Yewdell, J. W., & Hickman, H. D. (2019) · Nature Immunology · doi:10.1038/s41590-019-0342-0 · Cited by 90
Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Lomakin, A., Kedlian, V. R., Jain, M. S., Park, J. S., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.15.378125 · Cited by 88 · cited 3×
“15 ) and is expected, as these embedding methods (for example, scVI) do not explicitly consider that spatial spots may include a mixture of cell types.”
BackgroundEnhancing scientific discoveries in molecular biology with deep generative models
Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · cited 2×
“Unlike other methods, DestVI learns both discrete cell-type-specific profiles and continuous sub-cell-type latent variations using a conditional deep generative model 18 .”
BackgroundMethodSelf-citationHampton, H. R., & Chtanova, T. (2016) · Seminars in Immunology · doi:10.1016/j.smim.2016.03.008 · Cited by 78 · cited 1×
“As expected, the first striking pattern is the organization of the lymph node sections into the B cell follicles (Cd79a; external area) and a T cell compartment (CD8; internal area) (Figure 3E; Supplementary Figure 11) [42,43].”
BackgroundSpatial detection of fetal marker genes expressed at low level in adult human heart tissue
Asp, M., Salmén, F., Ståhl, P. L., Vicković, S., Felldin, U., Löfling, M., Navarro, J. F., Maaskola, J., Eriksson, M. J., Persson, B., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-13462-5 · Cited by 74 · cited 1×
“In particular, this emerging technology helped study the organization of complex tissues such as the mouse brain [2] and the human heart [3].”
BackgroundCharacterization of Kupffer cells in livers of developing mice
Lopez, B. G., Tsai, M. S., Baratta, J. L., Longmuir, K. J., & Robertson, R. T. (2011) · Comparative Hepatology · doi:10.1186/1476-5926-10-2 · Cited by 73
Neuropathology for the neuroradiologist: palisades and pseudopalisades.
Wippold, F. J., Lämmle, M., Anatelli, F., Lennerz, J. K., & Perry, A. (2006) · PubMed Central · Cited by 70
Wei, S., Shreiner, A. B., Takeshita, N., Chen, L., Zou, W., & Chang, A. E. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-6598 · Cited by 67 · cited 1×
“MCA205 is a weakly immunogenic fibrosarcoma-derived cell line which is a common model used to study the immune responses to tumors and for developing immunotherapies [51].”
Backgroundscvi-tools: a library for deep probabilistic analysis of single-cell omics dataPreprint
Gayoso, A., Lopez, R., Xing, G. K., et al. (2021) · bioRxiv · doi:10.1101/2021.04.28.441833 · Cited by 66 · cited 1×
“DestVI is implemented in the scvi-tools package 21 and is readily available as open source, along with the accompanying tutorials.”
MethodSelf-citationCoombes, J. L., Han, S., van Rooijen, N., Raulet, D. H., & Robey, E. A. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.06.001 · Cited by 64
Boyeau, P., Regier, J., Gayoso, A., Jordan, M. I., Lopez, R., & Yosef, N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209124120 · Cited by 56
Self-citationMassi, D., Marconi, C., Franchi, A., Bianchini, F., Paglierani, M., Ketabchi, S., Miracco, C., Santucci, M., & Calorini, L. (2007) · Human Pathology · doi:10.1016/j.humpath.2007.02.018 · Cited by 53 · cited 1×
“We found that the tumor-infiltrating myeloid suppressive cells (Mreg and TAM) were mostly abundant in the inner layers of the MCA205 tumor, consistent with previous observations in human tumors [55] and murine models [54].”
ResultWrit large: Genomic dissection of the effect of cellular environment on immune response
Yosef, N., & Regev, A. (2016) · Science · doi:10.1126/science.aaf5453 · Cited by 52 · cited 1×
“Spatial transcriptomics is a promising approach for unravelling cell-cell interactions [78] and other forms of cellular communication and function in a tissue [79].”
BackgroundSelf-citationBlecher‐Gonen, R., Bost, P., Hilligan, K. L., David, E., Salame, T. M., Roussel, E., Connor, L. M., Mayer, J. U., Halpern, K. B., Tóth, B., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.01.001 · Cited by 51 · cited 19×
“This finding is consistent with our previous work, which showed that, after immunization with MS, NK cells are recruited to the lymph nodes and produce IFN γ .”
BackgroundMethodResultInfluentialSelf-citationBiancalani, T., Scalia, G., Buffoni, L., et al. (2020) · bioRxiv · doi:10.1101/2020.08.29.272831 · Cited by 38
Decision-Making with Auto-Encoding Variational BayesPreprint
Lopez, R., Boyeau, P., Yosef, N., Jordan, M. I., & Regier, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.07217 · Cited by 30
Self-citationGeneric Inference in Latent Gaussian Process ModelsPreprint
Bonilla, E. V., Krauth, K., & Dezfouli, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.00577 · Cited by 24 · cited 1×
“A plethora of computational techniques, based on black-box inference techniques [65,66] for Gaussian processes [67] is likely to make this possible.”
BackgroundSpatially resolved transcriptomics reveals the architecture of the tumor/microenvironment interfacePreprint
Hunter, M. V., Moncada, R. M., Weiss, J. M., Yanai, I., & White, R. M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.05.368753 · Cited by 24 · cited 8×
“For scRNA-seq data generation, we refer to the recent review on deconvolution 47 and provide more specific recommendations in Supplementary Note 5.”
MethodInfluentialBayesian segmentation of spatially resolved transcriptomics dataPreprint
Petukhov, V., Soldatov, R., Khodosevich, K., & Kharchenko, P. V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.05.326777 · Cited by 21 · cited 1×
“However, these methods rely on cell-segmentation algorithms [8,9].”
MethodSpatialDE - Identification of spatially variable genesPreprint
Svensson, V., Teichmann, S. A., & Stegle, O. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/143321 · Cited by 17
Steinberg, J. D., Raju, A., Chandrasekharan, P., et al. (2014) · EJNMMI Research · doi:10.1186/2191-219x-4-15 · Cited by 14 · cited 2×
“To build the spatial transcriptomics data, we first constructed a regular grid of dimension 40 × 40.”
BackgroundMethodDeep Generative Models for Detecting Differential Expression in Single CellsPreprint
Boyeau, P., Lopez, R., Regier, J., Gayoso, A., Jordan, M. I., & Yosef, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/794289 · Cited by 13 · cited 2×
“In our previous work, we used a purely Bayesian approach to differential expression [62,63].”
MethodSelf-citationIdentifying Informative Gene Modules Across Modalities of Single Cell GenomicsPreprint
DeTomaso, D., & Yosef, N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.06.937805 · Cited by 13
Self-citationComparison and evaluation of statistical error models for scRNA-seqPreprint
Choudhary, S., & Satija, R. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.07.451498 · Cited by 8
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Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700
An introduction to spatial transcriptomics for biomedical research
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A Python library for probabilistic analysis of single-cell omics data
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Self-citationSpatially informed cell-type deconvolution for spatial transcriptomics
Ma, Y., & Zhou, X. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01273-7 · Cited by 525
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Self-citation
Metadata
- Authors
- Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., Ergen, C., Addadi, Y., Golani, O., Ronchese, F., Jordan, M. I., Amit, I., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Weizmann Institute of Science (IL)
- Malaghan Institute of Medical Research (NZ)
- Ragon Institute of MGH, MIT and Harvard (US)
- Corresponding author
- Amit, Ido
- Yosef, Nir
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2022-04-21
- Volume
- 40
- Issue
- 9
- Pages
- 1360-1369
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC9756396
- OpenAlex
- W4224218779
- Semantic Scholar
- d88d598ef4cdd976986d65e6f9849e5b7b223ecb
- Cited by
- 276
- References
- 98
- Citations by year
- 2026: 63 · 2025: 67 · 2024: 71 · 2023: 40 · 2022: 25
- FWCI
- 17.06
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U19 AI090023)
- Chan-Zuckerberg BioHub
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, cell type deconvolution, variational inference, single-cell RNA sequencing, gene expression estimation, lymph node infection, mouse tumor model, cell organization
- MeSH terms
- Exome Sequencing, Animals, Neoplasms, Software, Gene Expression Profiling, Mice, Single-Cell Analysis, Transcriptome
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