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Single-cell and spatial transcriptomics
Research Guide
What is Single-cell and spatial transcriptomics?
Single-cell and spatial transcriptomics is the comprehensive integration and analysis of transcriptomic data at the single-cell level combined with spatial profiling to study cell types, gene expression, cell heterogeneity, lineage tracking, and data integration across conditions, technologies, and species.
This field encompasses 147,380 works focused on single-cell transcriptomics, spatial profiling, and related computational methods for analyzing gene expression and cell heterogeneity. Key advancements include data integration tools like Seurat, demonstrated in "Comprehensive Integration of Single-Cell Data" by Stuart et al. (2019), which has 15,780 citations. Multimodal integration methods, as in "Integrated analysis of multimodal single-cell data" by Hao et al. (2021) with 14,517 citations, enable joint analysis of transcriptomics with other data types.
Topic Hierarchy
Research Sub-Topics
Single-Cell RNA Sequencing
This sub-topic covers high-throughput scRNA-seq technologies like droplet-based and plate-based methods for profiling individual cell transcriptomes. Researchers address noise reduction, batch effects, and scalability for tissue atlas construction.
Single-Cell Data Integration
This sub-topic develops computational methods to harmonize datasets across batches, conditions, modalities, and species. Researchers benchmark anchor-based, graph, and deep learning approaches for cross-study comparability.
Spatial Transcriptomics
This sub-topic explores imaging-based and sequencing-based methods to map gene expression within tissue context. Researchers integrate spatial data with scRNA-seq to reconstruct cellular neighborhoods and signaling.
Cell Type Identification
This sub-topic focuses on unsupervised clustering, marker gene discovery, and reference mapping for automated cell annotation. Researchers build ontologies and train classifiers for reproducible cell typing.
Lineage Tracing
This sub-topic integrates CRISPR barcoding, lineage tracers, and computational inference to reconstruct developmental trajectories. Researchers model branching, convergence, and fate decisions from single-cell data.
Why It Matters
Single-cell and spatial transcriptomics enables detailed mapping of cellular heterogeneity in tissues, with applications in cancer research through intratumour heterogeneity analysis and tumor microenvironment studies, as highlighted in recent preprints like "Integrative Analysis of Single-Cell and Spatial ...". Tools such as Tangram align single-cell data to spatial profiles, supporting investigations into healthy and diseased human lung variations per "Spatial single-cell atlas reveals regional variations in healthy and diseased human lung". In oncology, Complete Genomics' Spatial Xcellerator Grant Program selected four winners from 30 applicants for projects addressing oncology and pediatric applications (2025). Sequencing-free whole-genome spatial transcriptomics via RAEFISH achieves single-molecule resolution, as in Cheng et al.'s work published in Cell (2025), advancing disease understanding.
Reading Guide
Where to Start
"Comprehensive Integration of Single-Cell Data" by Stuart et al. (2019) is the starting point for beginners, as it provides foundational methods for integrating single-cell datasets across experiments using Seurat, with 15,780 citations and broad applicability to cell type identification.
Key Papers Explained
Stuart et al. (2019) "Comprehensive Integration of Single-Cell Data" establishes batch correction via anchor points, which Butler et al. (2018) "Integrating single-cell transcriptomic data across different conditions, technologies, and species" extends to cross-technology and cross-species integration. Hao et al. (2021) "Integrated analysis of multimodal single-cell data" builds on these by adding multimodal support with weighted nearest neighbors. Korsunsky et al. (2019) "Fast, sensitive and accurate integration of single-cell data with Harmony" offers a faster PCA-based alternative, cited 9,243 times.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints focus on 3D spatial profiling, such as Deep-STARmap for thick tissue blocks and RAEFISH for whole-genome imaging at single-molecule resolution (Cheng et al., 2025). Nicheformer introduces transformer-based foundation models for spatial omics (2025). Integration advances like DBiTplus combine imaging and sequencing on the same section, targeting tumor microenvironments and lung atlases.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Comprehensive Integration of Single-Cell Data | 2019 | Cell | 15.8K | ✓ |
| 2 | Integrative genomics viewer | 2011 | Nature Biotechnology | 15.7K | ✕ |
| 3 | Integrated analysis of multimodal single-cell data | 2021 | Cell | 14.5K | ✓ |
| 4 | Integrating single-cell transcriptomic data across different c... | 2018 | Nature Biotechnology | 13.9K | ✓ |
| 5 | Robust enumeration of cell subsets from tissue expression prof... | 2015 | Nature Methods | 13.4K | ✓ |
| 6 | Bioconductor: open software development for computational biol... | 2004 | Genome biology | 12.4K | ✓ |
| 7 | Fast, sensitive and accurate integration of single-cell data w... | 2019 | Nature Methods | 9.2K | ✓ |
| 8 | MultiQC: summarize analysis results for multiple tools and sam... | 2016 | Bioinformatics | 9.2K | ✓ |
| 9 | The UK Biobank resource with deep phenotyping and genomic data | 2018 | Nature | 9.1K | ✓ |
| 10 | deepTools2: a next generation web server for deep-sequencing d... | 2016 | Nucleic Acids Research | 8.4K | ✓ |
In the News
Whole-genome spatial transcriptomic imaging with RAEFISH
### Original article * Cheng, Y. et al. Sequencing-free whole-genome spatial transcriptomics at single-molecule resolution.*Cell***188**, 6953–6970.e12 (2025)
Scalable spatial single-cell transcriptomics and translatomics in 3D thick tissue blocks
and disease. However, most existing spatial profiling techniques are limited to 5–20 µm thin tissue sections. Here, we developed Deep-STARmap and Deep-RIBOmap, which enable 3D in situ quantificatio...
Nicheformer: a foundation model for single-cell and spatial omics
Tissue makeup depends on the local cellular microenvironment. Spatial single-cell genomics enables scalable and unbiased interrogation of these interactions. Here we introduce Nicheformer, a transf...
Complete Genomics Names Four Winners of Spatial ...
****SAN JOSE, Calif., April 15, 2025****– Complete Genomics, a leading innovator in genomic sequencing, today announced four winners from 30 applicants to its Spatial Xcellerator Grant Program. Win...
New cell segmentation methods for better spatial ...
This work was supported by funding from the National Institutes of Health, the Immunotherapy Integrated Research Center, the Cancer Research Institute Irvington Postdoctoral Fellowship (to David Gl...
Code & Tools
LIANA+ is a scalable framework that adapts and extends existing methods and knowledge to study cell-cell communication in single-cell, spatially-re...
Tangram is a Python package, written in PyTorch and based on scanpy , for mapping single-cell (or single-nucleus) gene expression data onto spatial...
Tangram2 is a computational framework for learning cell–cell communication directly from single-cell and spatial transcriptomics data. Tangram2 is ...
## About Profile purification of single-cell spatial transcriptomics data ### Topics r-package deconvolution single-cell-analysis spatial-transcri...
**sc3D**is a Python library to handle 3D spatial transcriptomic datasets. **To access the 3D viewer for sc3D datasets, you can go to the following ...
Recent Preprints
Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus
Spatially mapping the transcriptome and proteome in the same tissue section can profoundly advance our understanding of cellular heterogeneity and function. Here we present Deterministic Barcoding ...
Integrative Analysis of Single-Cell and Spatial ...
Keywords: intratumour heterogeneity ; tumor microenvironment ; single-cell RNA sequencing ; spatial transcriptomics ## 1. Introduction
Single-cell and spatial transcriptomics integration: new frontiers in tumor microenvironment and cellular communication
technology capable of resolving the complexity of cancer landscapes at singlecell resolution. Spatial transcriptomics(ST), as an innovative complementary approach, effectively compensates for the ...
Spatial single-cell atlas reveals regional variations in healthy and diseased human lung
Recent advances in single-cell omics have led to extensive reference datasets of cell types from various human organs, including the lung, harboring the respiratory system with its multitude of cel...
Charting the spatial transcriptome of the human cerebral cortex at single-cell resolution
A high-resolution spatial physiological atlas of cortical neurons serves as an essential reference for studying neurological diseases and is crucial for a comprehensive understanding of the cortica...
Latest Developments
Recent developments in single-cell and spatial transcriptomics research include the availability of high-resolution spatial transcriptomics technology from Illumina in mid-2026, enabling detailed gene expression mapping at single-cell resolution (Illumina). The Wellcome Sanger Institute is actively applying spatial transcriptomics to study tissue cell behavior and disease, such as in brain tumors (Sanger Institute). Additionally, innovative methods like SpaceBar and DBiTplus are advancing imaging-based spatial transcriptomics, allowing clone tracing and integration of imaging with sequencing on the same tissue section (Nature Methods, 2025, 2026). The field continues to evolve rapidly, with new models like Nicheformer and scalable 3D tissue mapping also emerging (Nature Methods, 2025, 2025).
Sources
Frequently Asked Questions
What is the purpose of "Comprehensive Integration of Single-Cell Data"?
Stuart et al. (2019) introduced Seurat v3 for comprehensive integration of single-cell data across experiments, enabling joint analysis of cell types and states. It scales to datasets with hundreds of thousands of cells from different conditions. The method has 15,780 citations and anchors diverse datasets into shared spaces.
How does "Integrated analysis of multimodal single-cell data" work?
Hao et al. (2021) developed a method for joint analysis of multimodal single-cell data, including RNA and protein modalities. It uses weighted nearest neighbor analysis and multimodal neighborhood graphs. The paper has 14,517 citations and applies to datasets like PBMCs.
What methods integrate single-cell data across technologies?
Butler et al. (2018) presented a method in "Integrating single-cell transcriptomic data across different conditions, technologies, and species" using mutual nearest neighbors for batch correction. It handles datasets from droplet-based and plate-based platforms across species. The work has 13,883 citations.
What is Harmony for single-cell data integration?
Korsunsky et al. (2019) introduced Harmony in "Fast, sensitive and accurate integration of single-cell data with Harmony", a fast algorithm correcting batch effects in PCA space. It preserves biological variance while removing technical effects. The paper has 9,243 citations.
How is spatial alignment achieved in single-cell transcriptomics?
Tangram aligns single-cell gene expression to spatial data, as implemented in the broadinstitute/Tangram package based on scanpy and PyTorch. It maps single-cell/nucleus data onto spatial profiles. Genentech/tangram2 extends this to learn cell-cell communication from spatial transcriptomics.
What tools support cell-cell communication analysis?
LIANA+ (saezlab/liana-py) is a framework for cell-cell communication in single-cell and spatial omics data, part of the scverse ecosystem. It adapts methods for scalable analysis. SPLIT (bdsc-tds/SPLIT) purifies profiles in single-cell spatial transcriptomics data.
Open Research Questions
- ? How can imaging-based and sequencing-based spatial omics be integrated on the same tissue section, as explored in DBiTplus?
- ? What are the regional variations in cell types and states between healthy and diseased human lung, per spatial atlases?
- ? How does spatial transcriptomics resolve intratumour heterogeneity and tumor microenvironment interactions?
- ? What molecular characteristics define the spatial transcriptome of the human cerebral cortex at single-cell resolution?
- ? How can foundation models like Nicheformer interrogate spatial cellular microenvironments at scale?
Recent Trends
Recent preprints emphasize 3D and multimodal spatial omics, including Deep-STARmap for scalable transcriptomics in thick tissues and RAEFISH for sequencing-free whole-genome imaging (Cheng et al., Cell 2025).
2025Nicheformer, a transformer foundation model, analyzes spatial single-cell interactions.
2025Grants like Complete Genomics' Spatial Xcellerator awarded four oncology projects from 30 applicants.
2025Tools such as LIANA+, Tangram2, and sc3D support emerging spatial-cell communication and 3D datasets.
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