Cited by 1,059Single-cell and spatial transcriptomicsBiotin and Related StudiesGenomics and Chromatin Dynamics

The DOI matches its Crossref record; 193 of the 193 references it cites were found in a registry or an index.

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Open access

Longo, Sophia K., Guo, Margaret G., Ji, Andrew L., Khavari, Paul A.·2021·Nature Reviews Genetics

DOI 10.1038/s41576-021-00370-8PMC PMC9888017

Open this paper in CoChat — free account

Abstract

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41576-021-00370-8

Cites

193 of 193 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881
Inference and analysis of cell-cell communication using CellChat
Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572

Show all 183 references

Research works printed but not linked: 3

Cho, C.-S. et al. Seq-Scope: submicrometer-resolution spatial transcriptomics for single cell and subcellular studies. bioRxiv https://doi.org/10.1101/2021.01.25.427807 (2021). · DOI
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015). · DOI · Paper page
Bishop, C. M. Pattern Recognition and Machine Learning. Oxidation Communications Vol. 27 (Springer, 2004).

Sorted by citations

Items 901–999 of 999

Additional file 3 of scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis
Fu, Y. C., Das, A., Wang, D., Braun, R., & Yi, R. (2024) · Figshare · doi:10.6084/m9.figshare.26738433
Additional file 2 of aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph
Li, J., Shyr, Y., & Liu, Q. (2024) · Figshare · doi:10.6084/m9.figshare.26461890
Additional file 1 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Hu, Y., Xie, M., Li, Y., Rao, M., Shen, W., Luo, C., Qin, H., Baek, J., & Zhou, X. M. (2024) · Figshare · doi:10.6084/m9.figshare.26531073
Additional file 20 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658805
Additional file 14 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658784
Additional file 9 of Integrated transcriptomics uncovers an enhanced association between the prion protein gene expression and vesicle dynamics signatures in glioblastomas
Boccacino, J. M., dos Santos Peixoto, R., Fernandes, C. F. D. L., Cangiano, G., Sola, P. R., Coelho, B. P., Prado, M. B., Melo-Escobar, M. I., de Sousa, B. P., Ayyadhury, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26680220
Additional file 1 of Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications
Tan, F., Xuan, Y., Long, L., Yu, Y., Zhang, C., Liang, P., Wang, Y., Chen, M., Wen, J., & Chen, G. (2023) · Figshare · doi:10.6084/m9.figshare.24077039
Additional file 3 of Integrating single-nucleus RNA sequencing and spatial transcriptomics to elucidate a specialized subpopulation of astrocytes, microglia and vascular cells in brains of mouse model of lipopolysaccharide-induced sepsis-associated encephalopathy
Zhu, Y., Zhang, Y., He, S., Yi, S., Feng, H., Xia, X., Fang, X., Gong, X., & Zhao, P. (2024) · Figshare · doi:10.6084/m9.figshare.26743052
Additional file 10 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658772
Additional file 2 of Integrated transcriptomics uncovers an enhanced association between the prion protein gene expression and vesicle dynamics signatures in glioblastomas
Boccacino, J. M., dos Santos Peixoto, R., Fernandes, C. F. D. L., Cangiano, G., Sola, P. R., Coelho, B. P., Prado, M. B., Melo-Escobar, M. I., de Sousa, B. P., Ayyadhury, S., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25210867
Additional file 4 of Integrating single-nucleus RNA sequencing and spatial transcriptomics to elucidate a specialized subpopulation of astrocytes, microglia and vascular cells in brains of mouse model of lipopolysaccharide-induced sepsis-associated encephalopathy
Zhu, Y., Zhang, Y., He, S., Yi, S., Feng, H., Xia, X., Fang, X., Gong, X., & Zhao, P. (2024) · Figshare · doi:10.6084/m9.figshare.26743055
Additional file 11 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658775
Additional file 1 of aKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph
Li, J., Shyr, Y., & Liu, Q. (2024) · Figshare · doi:10.6084/m9.figshare.26461839
Additional file 21 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658808
Additional file 18 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658799
Additional file 8 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658766
Additional file 5 of Integrated single-nucleus sequencing and spatial architecture analysis identified distinct injured-proximal tubular types in calculi rats
Wang, Z., Deng, Q., Gu, Y., Li, M., Chen, Y. K., Wang, J., Zhang, Y., Zhang, J., Hu, Q., Shenping, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22978169
Additional file 1 of Integrating single-nucleus RNA sequencing and spatial transcriptomics to elucidate a specialized subpopulation of astrocytes, microglia and vascular cells in brains of mouse model of lipopolysaccharide-induced sepsis-associated encephalopathy
Zhu, Y., Zhang, Y., He, S., Yi, S., Feng, H., Xia, X., Fang, X., Gong, X., & Zhao, P. (2024) · Figshare · doi:10.6084/m9.figshare.26170542
Additional file 3 of Integrated single-nucleus sequencing and spatial architecture analysis identified distinct injured-proximal tubular types in calculi rats
Wang, Z., Deng, Q., Gu, Y., Li, M., Chen, Y. K., Wang, J., Zhang, Y., Zhang, J., Hu, Q., Shenping, Z., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22978163
Additional file 10 of Integrating single-nucleus RNA sequencing and spatial transcriptomics to elucidate a specialized subpopulation of astrocytes, microglia and vascular cells in brains of mouse model of lipopolysaccharide-induced sepsis-associated encephalopathy
Zhu, Y., Zhang, Y., He, S., Yi, S., Feng, H., Xia, X., Fang, X., Gong, X., & Zhao, P. (2024) · Figshare · doi:10.6084/m9.figshare.26743073
Additional file 9 of Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes
Fisher, J. L., Clark, A. D., Jones, E. F., & Lasseigne, B. N. (2024) · Figshare · doi:10.6084/m9.figshare.26658769
Practices for shrimp disease detection, control, management, and genetics improvement
Campa‐Córdova, Á. I., Mazón-Suástegui, J. M., López‐Carvallo, J. A., Bernal, M. R. G., Cruz‐Flores, R., Dhar, A. K., Peña-Navarro, N., Sánchez-Ortiz, A. C., & López‐Torres, M. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-27358-2.00005-1
Hydrogel array patterning using 3D-printed microfluidic inserts to control cell-cell and cell-ECM interactionsPreprint
Poskus, M. D., Eskandarisani, M., & Zervantonakis, I. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.21.695682
HiSTaR: identifying spatial domains with hierarchical spatial transcriptomics variational autoencoder
Yu, J., Yuan, J., Yi, Q., Ye, Z., Xu, P., & Liu, W. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07404-3
Spatial domain recognition for multi-slice spatial transcriptomics based on self encoder adversarial training
Zhang, X., Peng, X., Zhu, H., Ding, W., Zhou, Y., & Wu, Z. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109414
Single-Cell Genomics
Balcı, M. A., Kuralay, S. C., Aksel, E. G., Shahpar, Z., Kayalar, Ö., & Eldem, V. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_38
Single-Cell Analysis and Cancer Research: Implications for Diagnosis and Treatment
Biswas, P., Rahim, M. A., Chatterjee, D., Ali, M. S., Tabia, J. N., Siam, M. K. S., & Shohan, M. U. S. (2026) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-95-3612-2_3
Disulfidptosis-based cancer therapy: Opportunity and challenge
Wang, B., Liu, Z., Li, J., Xu, J., Guo, Y., Zhang, J., Chu, T., Feng, Z., Jiang, Q., & Wu, D. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2025.102028 · Cited by 1
Single‐Cell Transcriptomics and Integrated Bioinformatic Analysis Reveal Critical Biomarkers and Immune Infiltration Characteristics in Osteoarthritis
Gao, T., Yang, C., Bi, Y., Zou, P., Wan, M., Lan, S., Song, Y., & Xu, Y. (2026) · Genetics Research · doi:10.1155/genr/1174568
CellMap: precision mapping of cellular landscape in spatial transcriptomics
Liu, H., Li, H., Sharma, A., Tang, G., Xie, Z., Shen, Y., Li, Q., Chen, G., Sun, X. S., Luo, K., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1484
ULMnet: inferring physical cell-cell communication networks from scRNAseq data using univariate linear models
Hameed, S. A., Iglesias-Martinez, L. F., Kölch, W., & Zhernovkov, V. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1722504
stRGAT: identifying spatial domains in spatial transcriptomics via a relational graph attention network
Zhang, Z., Wang, J., Ren, J., Zhao, Y., Xu, Y., Bai, J., Liu, S., & Liu, Y. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-025-07676-9 · Cited by 1
GxP Single-cell RNA-seq and Spatial Transcriptomics end-to-end pipeline for clinical researchPreprint
Zaratiegui, A., Burfield, T., Povlsen, H. R., Solá, M. E. G., Czaban, A., Soh, K., & Das, V. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.23.701261
Uncovering EMT-Associated Molecular Mechanisms Through Integrative Transcriptomic and Machine Learning Analyses
Büyükkılıç, Ş., Alotaibi, H., Georgakilas, A. G., & Pavlopoulou, A. (2026) · Frontiers in Bioscience-Landmark · doi:10.31083/fbl48085
PRISM: Niche-informed Deciphering of Incomplete Spatial Multi-Omics DataPreprint
Mu, S., Wang, Z., Liao, Y., Liang, J., Zhang, D., Wang, C., Xie, J., Sheng, X., Zhang, T. Q., Huang, W., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.03.703456
Intratumoral heterogeneity in microsatellite instability status at single-cell resolution
Anthony, H., & Seoighe, C. (2026) · iScience · doi:10.1016/j.isci.2026.114860
Attention-guided enhanced deconvolution enables reference-free cell type estimation in spatial transcriptomics
Yang, X., Wang, Y., & Chen, X. (2026) · Scientific Reports · doi:10.1038/s41598-026-39703-0
Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAPPreprint
Jiang, S., Coleman, K., Chen, Z., Jin, K., Liu, Y., Lee, D. H., Hwang, T. H., Xiao, R., Jin, J., Walsh, C. A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.21.707167 · Cited by 1
SpatialCompassV (SCOMV): De novo cell and gene spatial pattern classification and spatially differential gene identificationPreprint
Nomura, R., Sakai, S. A., Kageyama, S., Tsuchihara, K., & Yamashita, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.707418
Computational analysis of multi-omics data reveals CXCL10+ DC-Treg interaction drives immunosuppressive microenvironment in AFP-positive hepatocellular carcinoma
Luo, Z.-H., Chen, W.-M., Yang, X.-M., Zhao, J., Zhang, Y.-L., Huang, Q., Zhang, M.-Y., Wang, N., Wang, F., & Zhou, J. (2026) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-026-06167-4
FOXP3+ Cells in Tertiary Lymphoid Structures Have Adverse Impact on Overall Survival in Patients with Gastric Cancer
Karaman, A. P., Ivanović, T., Mustapić, K., Vukojević, K., Minarik, L., Durdov, M. G., & Đolonga, P. (2026) · Medical Sciences · doi:10.3390/medsci14010145
Co-localization analysis of spatial transcriptomics in ligand-receptor pairs from tumor microenvironment
Fan, X., Yue, L., Gong, Y., Wang, B., & Liu, P. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105288
SA2E: spatial-aware auto-encoder for cell type deconvolution of spatial transcriptomics data
Ma, Y., Dou, Z., Zha, Y., & Ma, X. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag133
STGNET: extending panel coverage in imaging-based spatial transcriptomics using deep generative adversarial networks
Wang, T., Wang, B., Shu, H., Zhen, P., Hu, J., Wang, Y., Peng, J., Shang, X., Wu, Z., Xiao, B., et al. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag122
Modern Understanding of Epigenetic Inheritance Mechanisms and Their Role in the Evolutionary Process
Toshchakova, V. A., Noskov, S. A., Khazieva, K. R., Erofeeva, T. V., & Toshchakov, S. V. (2025) · Molecular Genetics Microbiology and Virology · doi:10.3103/s0891416825700272
Aging in 3D: Organoid Systems as Models to Uncover Cellular Senescence and Therapeutic Targets Across Diseases
Bisht, S., Varshney, P., & Gupta, A. (2026) · Targets · doi:10.3390/targets4020012
Zero-shot reconstruction of mutant spatial transcriptomes
Okochi, Y., Matsui, T., Sakaguchi, S., Kondo, T., & Naoki, H. (2026) · Patterns · doi:10.1016/j.patter.2026.101521
KGLAR: Deconvoluting Spatial Transcriptomics Data with Single-cell Transcriptomes through Knowledge-guided NMF and Least Angle Regression
Peng, L., Wang, F., Wu, W., Peng, X., Xiong, W., Jiang, Y., Nie, L., & Chen, X. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-026-00815-w
Global scientific growth and thematic development of spatial transcriptomics in oncology research
Sweileh, M. W. (2026) · Discover Oncology · doi:10.1007/s12672-026-04937-x
INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
Han, B., Qi, C., Musialski, P., & Wei, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38186-6_12 · Cited by 1
circPVT1 regulates EMT and induces macrophage polarization to promotes the progression of renal cell carcinoma
Zhang, M., He, A., Xu, Z. S., Chen, M., Zhang, H., & Mao, W. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1760058
Reverse aging through immune driven signaling
Kumar, U., Simran, Tyagi, L., Mishra, S., Gupta, M., & Sohal, J. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-33159-6.00016-3
Decoding cellular composition changes: State-of-the-art differential abundance analysis in single-cell transcriptomics
Xiao, Y., Sun, J., Hao, Y., Xie, L., Duo, H., Zou, Y., Li, Y., Qin, D., Yang, Q., & Li, B. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.118880
GR2ST: spatial transcriptomics prediction based on graph-enhanced multimodal contrastive learning
Zhou, J., Li, S. L., Han, R., Wang, X., Wang, Y., & Li, J. W. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag209
Benchmarking cell-type-specific spatially variable gene detection methods
Yao, H., Mu, S., He, F., & Fang, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag190
Periostin Safeguards EGFR‐Driven Genomic Instability and Sustains the Immune‐Suppressive Niche in Glioblastoma
Liu, H., Tan, S., Qi, J., Du, Z., You, J., Muhammad, S., Tang, X., & Li, J. (2026) · Human Mutation · doi:10.1155/humu/9501906
spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics AnalysisPreprint
Das, A., Zhang, K., Song, J., Han, M., Chen, A., Meng, W., Galloway, H., Chen, P.-Y., Jo, S., Liu, Z., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.29.721735 · Cited by 1
Deep learning localizes focal biology implicated in therapeutic resistance within routine histologyPreprint
Goncalves, T., Pulido-Arias, D., Perrino, C. M., Lomphithak, T., Cleveland, M. C., Dalca, A. V., Gerstner, E. R., Hipp, J., Patel, J., Rosen, B. R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.05.723013
InSituPREP enables 3D single-cell mapping of interaction-associated gene programs in the breast cancer tumor microenvironment
Goldberg, T., Danino-Levi, M., Safra, M., Copeland, Y., Weizman, E., Konforti, N., Ishon, T., Ben-Ari, N., Brand, Y., Kruger, B., et al. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag406
Decoding murine corneal epithelial specification and homeostasis by single-cell spatial transcriptomics with scRNA-seq enrichmentPreprint
Javidjam, D., Vattulainen, M., Lagali, N., & Moustardas, P. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.08.723186
Identification of Mitochondrial Dysfunction-Related Candidate Biomarkers and Analysis of the Immune Cell Infiltration in Epilepsy
Wu, Z., Pei, H., Huang, R., Huang, W., Xiong, A., Dong, Z., Li, S., Chen, Y., Wang, W., Yu, H., et al. (2026) · Molecular Neurobiology · doi:10.1007/s12035-026-05858-0
Deep Learning for Cross-Domain Spatial Transcriptomic Modeling of Tissue RepairPreprint
Pham, T. D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.13.724803 · Cited by 1
Decoding spatial transcriptomics across multicellular and subcellular resolutions
Zhao, C., Liu, T., Miller, L. M., Li, H., Zhao, H., Barnett, D., Yuan, R., Zhang, H., Wang, Q., Eddens, T. J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72872-0
ConMGIN: Interpretable multilayer GIN-Bayesian framework for spatial domain analysis
Li, J., Liu, F., Lv, H., Ren, S., Gao, X., & Yu, B. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109155
RECOMBINE identifies recurrent composite markers of cell types and statesPreprint
Li, X., Nguyen, J., & Korkut, A. (2026) · Genome Research · doi:10.1101/gr.280817.125
Spatial Omics Revisits Cancer Immunity and Reshapes Immunotherapy
Chu, H., Shi, Y., Tian, Z., Ma, J. Y., Bian, X. W., & ZHU, B. (2026) · Fundamental Research · doi:10.1016/j.fmre.2026.04.033
Integrating development, metabolism, immunity, and inflammation in aortic dissection: Insights from single-cell and spatial omics
Tian, Y., Liang, Y., & Zheng, H. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.177599
Deciphering skeletal muscle development: cellular heterogeneity and molecular regulatory networks from single-cell and spatial transcriptomic perspectives
Wang, X., Zhang, Y., Wei, H., Zhao, G., & Mu, R. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1827120
Integrative single-cell RNA-seq and transcriptomic analysis uncovers cellular heterogeneity and immunotherapy resistance mechanisms in colorectal cancer
Wang, Y., Zhang, Y., Mao, Z., Du, W., Cao, X., Jiang, M., Zhang, Q., Zhan, S., & Chen, Z. (2026) · Discover Oncology · doi:10.1007/s12672-026-05284-7
Cycle-consistent deep generative modeling unifies cellular states across unpaired spatial and single-cell modalitiesPreprint
Zhang, H.-R., Quinn, J., & Tansey, W. (2026) · bioRxiv · doi:10.64898/2026.05.25.727736
Cell Communication Analysis of Invasive Ductal Carcinoma of the Breast Based on Spatial Domain Recognition
汪, 宇. (2026) · Hans Journal of Biomedicine · doi:10.12677/hjbm.2026.163050
Proteomic insights into azoospermia: protein differences in testicular tissue between non-obstructive and obstructive azoospermia patients
Wang, Z., Li, X., Shao, W.-M., Zheng, D.-X., Xiang, X.-Q., An, H.-Q., & Yang, J. (2026) · Asian Journal of Andrology · doi:10.4103/aja2025114
ScSpTITH: a rank-correlation framework for robust quantification of multi-dimensional tumor heterogeneity
Luo, J., Lu, Q., Zhang, X., & Wang, X. (2026) · Human Genetics · doi:10.1007/s00439-026-02841-6
Decoding Hierarchical Cell–Cell Communication in Spatial Multi-Omics with CellSTICPreprint
Wang, S., Wang, J., Wang, J., Yuan, Z., & Xu, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728114
From the brain to the liver: A pharmacokinetic dilemma and safety barriers in levodopa-based antifibrotic therapy
Yin, D.-Y., Liu, X.-Y., Li, A., & Ren, Z.-G. (2026) · World Journal of Gastroenterology · doi:10.3748/wjg.117873
The molecular asynchrony of single cellsPreprint
Fu, B., Tan, R., Cao, Z., Bai, X., Bai, D., Song, J., & Zhu, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.02.729594
Well-ST-seq: Cost-Effective and Near-Cellular Spatial Transcriptomics Using Deterministic Barcoded Bead Arrays
Yu, N., Jin, Z., Zhu, S., Hu, Z., Tang, X., Liang, C., Zhang, J., & Yang, B. (2026) · Analytical Chemistry · doi:10.1021/acs.analchem.6c01070 · Cited by 2
The relevance of m6A RNA modifications in cancer stem cells
Monteiro, A., Pádua, D., Gonçalves, C., Mesquita, P., & Almeida, R. (2026) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-026-06525-6
ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics
Wang, X., Xia, L. C., LIU, H. J., Du, C., Chen, L., Li, Z., Du, Y., Li, Y., & Ai, D. (2026) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-026-00850-7
HistoMap: Reconstructing Spatially Resolved Single-Cell Profiles from Bulk RNA-Seq to Decipher the Immune-Excluded Microenvironment in Colon Cancer
He, J., Cao, Y., Liu, Y., Zhang, X., Ji, J., Wang, H., Song, Y., Zhang, Q., & Cao, L. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27125259
Reverse Genetics of Orthoflaviviruses: Strategies for Constructing Functional or Infectious cDNA
Lee, Y.-M. (2026) · Journal of Medical Virology · doi:10.1002/jmv.71007
Hidden architecture of resistance: The extracellular matrix in melanoma’s immune landscape
Guan, Y., Edny, G., Chandra, J., Souza-Fonseca-Guimarães, F., & Haass, N. K. (2026) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2026.06.004
SpatialSyn: A synergistic graph framework for spatial domain identification in multi-omics
Zeng, P., Li, R., Meng, Y., Zhang, Z., Cui, F., Tang, X., Yang, J., Hu, W., & Xu, J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133296
EMD-HVG: a normalization-independent method for highly variable gene selection based on Earth mover’s distance
Peng, C., Li, G., Wu, J., Tan, F., & Guo, X. (2026) · BMC Bioinformatics · doi:10.1186/s12859-026-06527-8
Multimodal Human Scalp Atlas Defines Cell Landscape and Lineage Architecture In SituPreprint
Li, E. B., Stephens, C. M., Klay, M., Carcamo, A., Han, J., Quan, V. L., Colavincenzo, M. L., Pearlman, R. L., Yoo, S. S., Wang, D., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.29.735239
Glutathione metabolism-linked ferroptosis in human seminoma: a spatial multi-omics mapping study
Deng, C., Li, J., Guo, Z., Zhu, J., Zhao, Q., 方子水, Zheng, J., Ran, L., Li, R., Li, X., et al. (2026) · Redox Biology · doi:10.1016/j.redox.2026.104284 · Cited by 1
Single-cell transcriptomic insights into the intrinsic cardiac nervous system: diversity, development, and neuro-cardiac interactions
Zheng, J., Wu, R., Gu, C., Chen, C., & Shi, L. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1833404
Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights
Huang, R., & Goods, B. A. (2026) · Reproduction · doi:10.1093/reprod/xaag079 · Cited by 1
AI-Driven Signal Processing for Single-Cell Data Analysis: A Case Study in Cancer Research
Gangwar, S., Banday, M., Anjum, Z., Khanna, K., Khan, N. A., & Raza, K. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-19543-2_8
From Biomaterials to Biological State Engineering: Reframing Advanced Wound Dressings as Adaptive Therapeutic Interfaces in Translational Medicine
Urbanowicz, T. K., Cielecka‐Piontek, J., Filipiak, K. J., Witkowska, A., Grywalska, E., Rahnama, M., & Krasiński, Z. (2026) · Cells · doi:10.3390/cells15131230
BDNF and GSK-3β signaling in depression: molecular mechanisms underlying neural plasticity dysfunction
Al‐kuraishy, H. M., Abass, S. A., Mahana, H. A. M., Elbanna, O. A., Shahin, I., Radwan, A. A., MohamedMohamed, O. E.-S., Baktoot, A. M. L., & Batiha, G. E. (2026) · Molecular Biology Reports · doi:10.1007/s11033-026-12110-4
Spatial Transcriptomics for Dissecting Cellular and Molecular Heterogeneity in the Aging and Diseased Brain
차세은, Kim, J., Kim, J., Kim, D., Song, H., Lim, K. S., & Chae, S. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27146149 · Cited by 1
Multi-omics-guided dynamic precision medicine in leukemia: An AML-centered framework integrating genotype, cellular state, bone marrow pathology, and the immune ecosystem
Li, Y., Fan, L., Cheng, Y., & Chen, J. (2026) · Pathology - Research and Practice · doi:10.1016/j.prp.2026.156623
MIAT: An important long non-coding RNA regulating cardiovascular and cerebrovascular diseases
Pan, Y., Yu, W. J., Han, Y., & Shao, Y. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1791657
An organotypic ectocervix mucosa model to study host-pathogen interaction of sexually transmitted infections
Mehling, H., Rousseau, C., Krammer, T., Leipold, A. M., Boyny, A., Herbert, S.-L., Wulff, C., Saliba, A., Dölken, L., Groeber-Becker, F., et al. (2026) · Materials Today Bio · doi:10.1016/j.mtbio.2026.103454
Natural Killer Cell Immunotherapy in Solid Tumors: Microenvironmental Obstacles and Translational 3D Models
Palazzo, G., Tinnirello, V., Bivona, G., Ghersi, G., & Campora, S. (2026) · Biology · doi:10.3390/biology15141167
Spatial Omics Technologies in Glioblastoma Research: Principles, Applications, and Best Practices
Vanmechelen, M., Caprioli, C., Clément, P. M., Hoeben, A., & Smet, F. D. (2026) · Genes · doi:10.3390/genes17070822
CTMAP: an adversarial cross-modal learning framework for accurate and robust cell-type annotation in single-cell resolution spatial transcriptomics
Wang, Y., Zhao, J., Guan, M., & Sun, D. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag391
SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis
Lan, W., Deng, X., Ling, T., He, G., Yan, X., Zheng, R., Li, M. M., Pan, S., & Pan, Y. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag524

Authors: Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A.
Affiliations: Stanford Cancer Institute (US)
VA Palo Alto Health Care System (US)
Corresponding author: Khavari, Paul A.
ORCID: Longo, Sophia K.
Guo, Margaret G.
Ji, Andrew L.
Khavari, Paul A.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2021-06-18
Volume: 22
Issue: 10
Pages: 627-644
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/s41576-021-00370-8
PMC: PMC9888017
OpenAlex: W3171692328
Cited by: 1,059
References: 183
Citations by year: 2026: 233 · 2025: 203 · 2024: 242 · 2023: 196 · 2022: 157 · 2021: 27
FWCI: 16.92
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Cancer Institute (R01CA142635)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR049737, R01AR043799, R01AR076965)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords: single-cell RNA sequencing, spatial transcriptomics, data integration, intercellular communication, tissue dynamics, in situ sequencing, cell localization
MeSH terms: Animals, Cell Communication, Humans, Software, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks