Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
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Abstract
Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.
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Tangram integrated multimodal data for mapping and can use supervised learning methods to infer the distribution of cell types in spatial locations from known cell types or gene expression signatures.
“Tangram [129] also integrated multimodal data for mapping, and it can also use supervised learning methods to infer the distribution of cell types in spatial locations from known cell types or gene expression signatures.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [129]
The Results section (page not provided) says Tangram aligns single-cell profiles with spatial gene-expression data and uses the learned mapping to locate cell types; it also reports combining that mapping with existing cell-type annotations to obtain spatial distributions. The Materials and Methods section describes optimizing agreement with measured spatial gene expression, not a supervised cell-type learning method, so that characterization is not supported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDeconvolution methods can infer the proportion of different cell types within each spatial spot when scRNA-seq data from the same tissue are available.
“When scRNA-seq data from the same tissue are available, computational methods such as SPOTlight, robust cell type decomposition (RCTD), Tangram and conditional autoregressive deconvolution (CARD)84–87 deconvolute the spatial transcriptomic data and infer the proportion of different cell types within each spatial spot.”
From Spatial mapping of cellular senescence: emerging challenges and opportunities (2023) · cites this paper as [86]
Projection of publicly available scRNA-seq data of the developing brain onto the reclustered E9.5 brain using Tangram identified the localization of progenitor cell types including secondary organizers.
“Projection of publicly available scRNA-seq data (La Manno et al., 2021) of the developing brain onto the reclustered E9.5 brain using Tangram (Biancalani et al., 2021) identified the localization of progenitor cell types including secondary organizers (e.g., midbrain basal plate, midbrain floor plate, ventricle roof plate, and hindbrain floor plate) (Figure S3E), which are specialized substruct”
From Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays (2022) · cites this paper as Biancalani et al., 2021
Alignment of Stereo-seq data with scRNA-seq data using Tangram confirmed the reported probabilistic spatial profiles for specific cell types in the whole brain.
“To validate our results, we aligned with scRNA-seq data using Tangram (Biancalani et al., 2021), which confirmed the reported probabilistic spatial profile for specific cell types such as choroid plexus, hindbrain glioblasts, forebrain GABAergic neuroblasts, midbrain glutamatergic neuroblasts, cortical and hippocampal glutamatergic neurons, and forebrain GABAergic neurons in the whole brain (La M”
From Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays (2022) · cites this paper as Biancalani et al., 2021
Tangram was used to embed single-nucleus transcriptomes within the common coordinate system of the mouse spinal cord to corroborate the spatial prioritizations assigned by Magellan.
“To corroborate the spatial prioritizations assigned by Magellan, we leveraged Tangram36 to embed single-nucleus transcriptomes within the common coordinate system of the mouse spinal cord.”
From The neurons that restore walking after paralysis (2022) · cites this paper as [36]
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- Authors
- Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C. R., Segerstolpe, Å., Zhang, M., Avraham-Davidi, I., Vickovic, S., Nitzan, M., Ma, S., Subramanian, A., Lipinski, M., Buenrostro, J., Brown, N. B., Fanelli, D., Zhuang, X., Macosko, E. Z., & Regev, A.
- Affiliations
- Broad Institute (US)
- University of Florence (IT)
- Howard Hughes Medical Institute (US)
- Northeastern University (US)
- Corresponding author
- Biancalani, Tommaso
- Regev, Aviv
- ORCID
- Biancalani, Tommaso
- Scalia, Gabriele
- Buffoni, Lorenzo
- Avasthi, Raghav
- Lu, Ziqing
- Sanger, Aman
- Tokcan, Neriman
- Vanderburg, Charles R.
- Segerstolpe, Åsa
- Zhang, Meng
- Vickovic, Sanja
- Nitzan, Mor
- Ma, Sai
- Subramanian, Ayshwarya
- Lipinski, Michal
- Buenrostro, Jason
- Brown, Nik Bear
- Fanelli, Duccio
- Zhuang, Xiaowei
- Macosko, Evan Z.
- Regev, Aviv
- Venue
- Nature Methods
- Published
- 2021-10-28
- Volume
- 18
- Issue
- 11
- Pages
- 1352-1362
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC8566243
- OpenAlex
- W3208940117
- Cited by
- 931
- References
- 50
- Citations by year
- 2026: 241 · 2025: 245 · 2024: 204 · 2023: 133 · 2022: 80 · 2021: 26 · 2020: 1 · 2012: 1
- FWCI
- 35.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Florence Research (University of Florence) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (OT2OD026673, MH114821)
- Klarman Cell Observatory, Broad Institute
- National Institute of Mental Health (U19MH114821, U19MH114830)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- tangram, single-nucleus RNA sequencing, spatial transcriptomics, MERFISH, STARmap, single-molecule fluorescence in situ hybridization, chromatin accessibility, mouse brain atlas, visual cortex, somatomotor cortex
- MeSH terms
- Deep Learning, RNA-Seq, Animals, Brain, Chromatin, Female, Gene Expression Regulation, Male, Mice, Inbred C57BL, Regulatory Sequences, Nucleic Acid, Software, Gene Expression Profiling, Mice, Single-Cell Analysis, Transcriptome
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