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Review
Biotechnology & Applied Microbiology
Giovanni Palla et al.
Summary: Methods for profiling RNA and protein expression in a spatially resolved manner have rapidly advanced, but clear articulation of key biological questions and development of computational tools are crucial. Decisions on molecular features and inclusion of cell shape in analysis need to be made by developers. Optimal ways to compare tissue samples at different length scales are still being sought.
NATURE BIOTECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Vitalii Kleshchevnikov et al.
Summary: Cell2location, a Bayesian model, can resolve the spatial distribution of cell types and create comprehensive cellular maps of tissues. By accounting for technical variation and borrowing statistical strength, cell2location has higher sensitivity and resolution than existing tools. Our results demonstrate that cell2location is a versatile analysis tool for mapping tissue architectures in a comprehensive manner.
NATURE BIOTECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Dylan M. Cable et al.
Summary: RCTD is a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures and correct for differences across sequencing technologies. It successfully detects mixtures, identifies cell types, and reproduces cell type localization patterns in simulated datasets and mouse brain data. By enabling spatial mapping of cell types, RCTD defines the spatial components of cellular identity and uncovers new principles of cellular organization in biological tissues.
NATURE BIOTECHNOLOGY
(2022)
Review
Neurosciences
Martin Stacho et al.
Summary: The retrosplenial cortex is a crucial brain structure for spatial navigation and memory, generating diverse neuronal properties and playing an important role in spatial cognition. It supports goal-directed navigation by integrating idiothetic cues, spatial relations, and environmental features. Additionally, its mnemonic functions suggest a possible involvement in autobiographical information storage.
TRENDS IN NEUROSCIENCES
(2022)
Article
Biochemistry & Molecular Biology
Ao Chen et al.
Summary: Spatially resolved transcriptomic technologies enable us to study complex biological processes such as mammalian embryogenesis. However, current methods have limitations in resolution, gene capture, and field of view, which hinders their systematic application to large and three-dimensional mid- and late-gestation embryos. In this study, we developed Stereo-seq, a spatially enhanced resolution omics-sequencing method, by combining DNA nanoball-patterned arrays and in situ RNA capture. We used Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas, MOSTA, which provides single cell resolution and high sensitivity for mapping the kinetics and directionality of transcriptional variation during mouse organogenesis. By utilizing this atlas, we investigated the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues like the dorsal midbrain. Our panoramic atlas will facilitate in-depth research into long-standing questions about normal and abnormal mammalian development.
Article
Neurosciences
Paul M. Klenowski et al.
Summary: Nicotine withdrawal leads to increased activity of IPN GABAergic neurons, resulting in heightened anxiety-like symptoms and somatic behavior in withdrawn mice, highlighting the significant impact of this activity on behavioral expression.
NEUROPSYCHOPHARMACOLOGY
(2022)
Article
Multidisciplinary Sciences
Kangning Dong et al.
Summary: The authors propose a framework called STAGATE, which uses an adaptive graph attention auto-encoder to identify spatial domains by integrating gene expression profiles and spatial information. Validation shows that STAGATE improves the identification accuracy of spatial domains, denoises the data, and preserves spatial expression patterns.
NATURE COMMUNICATIONS
(2022)
Article
Biotechnology & Applied Microbiology
Shanshan He et al.
Summary: Spatial molecular imaging allows measurement of RNA and protein distribution in tissues with subcellular resolution, demonstrating high sensitivity and low error rate. The system generates three-dimensional super-resolution localization of analytes in millions of cells per sample.
NATURE BIOTECHNOLOGY
(2022)
Article
Biotechnology & Applied Microbiology
Robert R. Stickels et al.
Summary: Slide-seqV2 technology achieves an RNA capture efficiency close to 50% of single-cell RNA-seq data, which is 10 times higher than Slide-seq and approaches the detection efficiency of droplet-based single-cell RNA-seq techniques. Leveraging the high detection efficiency of Slide-seqV2, dendritically localized mRNAs in mouse hippocampal neurons were successfully identified, and spatial information was integrated with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex, revealing underlying genetic programs that were poorly sampled with Slide-seq. The combination of near-cellular resolution and high transcript detection efficiency makes Slide-seqV2 applicable across various experimental contexts.
NATURE BIOTECHNOLOGY
(2021)
Editorial Material
Biochemical Research Methods
Ludvig Larsson et al.
Summary: Spatially resolved transcriptomics is a field that has emerged with the advancement of single-cell omics, utilizing experimental and computational methods to map out cell positions and gene expression profiles in space. Current methodologies and applications in genomics research are summarized in this field.
Editorial Material
Biochemical Research Methods
Vivien Marx
Summary: Nature Methods has named spatially resolved transcriptomics as Method of the Year 2020.
Article
Biochemistry & Molecular Biology
Jacob C. Kimmel et al.
Summary: scNym is a semi-supervised adversarial neural network that can transfer cell identity annotations between different experiments by learning rich representations of cell identities from both labeled and unlabeled datasets. It shows superior performance in transferring annotations across experiments and can synthesize information from multiple datasets to improve accuracy. Additionally, scNym models are well calibrated, interpretable, and can be enhanced with saliency methods.
Article
Multidisciplinary Sciences
Zizhen Yao et al.
Summary: Using single-cell transcriptomics, this study generated transcriptomes and epigenomes from over 500,000 individual cells in the mouse primary motor cortex, resulting in a reference atlas containing over 56 highly replicable neuronal cell types. They also discovered thousands of concordant marker genes and gene regulatory elements for these cell types.
Article
Multidisciplinary Sciences
Edward M. Callaway et al.
Summary: This study presents a multimodal cell census and atlas of the mammalian primary motor cortex, integrating various molecular and spatial information to reveal a unified genetic landscape of cortical cell types. The results establish a mechanistic framework of neuronal cell-type organization by linking molecular genetic information with phenotypic properties.
Article
Multidisciplinary Sciences
Meng Zhang et al.
Summary: A single-cell transcriptome-imaging method, MERFISH, was used to create a molecularly defined and spatially resolved cell atlas of the mouse primary motor cortex. Approximately 300,000 cells were profiled, revealing 95 neuronal and non-neuronal cell clusters and a complex spatial map. Integration of MERFISH with retrograde labelling showed that cortical projections from neurons formed a complex network with individual clusters projecting to multiple target regions.
Article
Biochemical Research Methods
Jian Hu et al.
Summary: SpaGCN is a spatially resolved transcriptomics data analysis tool that uses graph convolutional networks to identify spatial domains and spatially variable genes. By integrating gene expression, spatial location, and histology, SpaGCN can detect genes with enriched spatial expression patterns and transferable to other datasets for studying spatial gene expression variation. SpaGCN is computationally fast, platform independent, and ideal for diverse SRT studies.
Article
Biochemical Research Methods
Tommaso Biancalani et al.
Summary: Tangram is a versatile tool that aligns single-cell and single-nucleus RNA-seq data to spatially resolved transcriptomics data using deep learning. By addressing the loss of spatial information in single-cell data and overcoming technical limitations, this method demonstrates genome-wide anatomically integrated spatial mapping at single-cell resolution on healthy mouse brain tissue.
Article
Neurosciences
Kristen R. Maynard et al.
Summary: This study defined spatial gene expression in the human dorsolateral prefrontal cortex, revealing layer-enriched expression of genes associated with schizophrenia and autism, highlighting the clinical relevance of spatially defined expression.
NATURE NEUROSCIENCE
(2021)
Article
Biochemistry & Molecular Biology
Marc Elosua-Bayes et al.
Summary: SPOTlight is a computational tool that integrates spatial transcriptomics with single-cell RNA sequencing data to infer the location of cell types and states within complex tissues. Its high prediction accuracy and flexible application spectrum were demonstrated through applications in mouse brain and human pancreatic cancer.
NUCLEIC ACIDS RESEARCH
(2021)
Review
Neurosciences
Varun M. Bhave et al.
Summary: This review highlights the role of the brainstem's dorsal raphe nucleus (DRN) in regulating energy balance, showing how DRN neurons sense and respond to cues related to energy imbalance to induce appropriate alterations in energy intake and expenditure. Different populations within the DRN play distinct and often opposing roles in controlling energy balance, contributing to the overall extended circuit that regulates energy balance.
TRENDS IN NEUROSCIENCES
(2021)
Article
Cell Biology
Haiqi Chen et al.
Summary: Using Slide-seq technology, this study captures spatial gene expression patterns in the testes of mice and humans, establishing a map of testicular cell types at single-cell resolution. The research reveals differences in testicular cell compositions as a potential mechanism of diabetes-induced male infertility.
Article
Biochemistry & Molecular Biology
Zizhen Yao et al.
Summary: This study provides a comprehensive analysis of cell types in the mammalian isocortex and hippocampal formation, revealing a shared neural circuit organization between the two structures. The research also uncovers large-scale variations in cell types along different dimensions within these brain regions.
Article
Biotechnology & Applied Microbiology
Akira Cortal et al.
Summary: Cell-ID is a clustering-free multivariate statistical method for extracting per-cell gene signatures from single-cell sequencing data. It is reproducible across different donors, tissues, species, and omics technologies, improving biological interpretation at individual cell level and enabling discovery of rare cell types. Cell-ID facilitates the analysis of cell-type heterogeneity and cell identity across multiple samples at the single-cell level.
NATURE BIOTECHNOLOGY
(2021)
Review
Biotechnology & Applied Microbiology
Ricard Argelaguet et al.
Summary: The development of single-cell multimodal assays has provided a powerful tool for investigating cellular heterogeneity in multiple dimensions. Data integration is a key challenge in analyzing single-cell multimodal data, with existing strategies utilizing similar mathematical ideas but having distinct goals and principles.
NATURE BIOTECHNOLOGY
(2021)
Article
Biochemistry & Molecular Biology
David DeTomaso et al.
Summary: The study introduces a general approach named Hotspot for analyzing single-cell RNA-seq data to identify informative gene variations and determine gene organization into modules. By operating directly on a given metric of cell-cell similarity, Hotspot can identify genes that reflect alternative notions of similarity between cells.
Article
Biochemistry & Molecular Biology
Chun-Seok Cho et al.
Summary: Seq-Scope is a spatial barcoding technology with resolution comparable to an optical microscope, allowing visualization of spatial transcriptome heterogeneity at multiple histological scales from tissue sections. The technology works by processing DNA clusters to expose RNA-capture moiety, making cell analysis simpler and more accurate.
Review
Multidisciplinary Sciences
Anjali Rao et al.
Summary: Spatial transcriptomic technologies have revolutionized the field of biological research by systematically measuring gene expression levels throughout tissue space. In addition to generating biological insights, these technologies can be used for exploratory data analysis, hypothesis testing, and integration with other data modalities, providing a framework for understanding tissue organization.
Review
Biochemical Research Methods
Sabrina M. Lewis et al.
Summary: Understanding intratumoral heterogeneity through single-cell analyses has revealed new biomarkers, but lacks information on cellular location within the tumor microenvironment. New technologies enabling detection of cancer subclones in their native spatial context promise to drive the next generation of cancer research, diagnosis, and therapeutic strategies.
Review
Biochemical Research Methods
Peter V. Kharchenko
Summary: Over the past decade, there have been rapid advancements in protocols for sequencing single-cell transcriptomes and computational methods for analyzing such data, revealing increasingly complex aspects of biology. As technology advances, continuous reassessment of underlying models, experimental goals, and data processing volumes is necessary. This review examines key computational steps of single-cell RNA sequencing analysis, highlighting software packages and tools used in executing these analyses.
Review
Biochemical Research Methods
Zoe A. Clarke et al.
Summary: This tutorial provides guidelines for interpreting single-cell transcriptomic maps to identify cell types, states and other biologically relevant patterns, with a recommended three-step workflow including automatic cell annotation, manual cell annotation, and verification. It also discusses frequently encountered challenges, strategies to address them, as well as guiding principles and specific recommendations for software tools and resources.
Review
Genetics & Heredity
Sophia K. Longo et al.
Summary: Integrating single-cell RNA sequencing with spatial transcriptomics allows for the localization of transcriptionally characterized single cells within their native tissue context. While scRNA-seq identifies cell subpopulations within tissue, it does not capture their spatial distribution or reveal local networks of intercellular communication. Recent techniques like multiplexed in situ hybridization and in situ sequencing, defined as high-plex RNA imaging, can help address these limitations. However, the need for approaches to integrate single-cell and spatial data remains, given that no current method provides as complete a scope of the transcriptome as scRNA-seq.
NATURE REVIEWS GENETICS
(2021)
Article
Biochemistry & Molecular Biology
Xin Shao et al.
Summary: scDeepSort is a pre-trained tool for cell-type annotation in single-cell transcriptomics using deep learning and a weighted graph neural network. It demonstrates high performance and robustness across multiple datasets, achieving an accuracy of 83.79%.
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(2021)
Article
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Shiquan Sun et al.
Article
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Jung-Min Oh et al.
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(2020)
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Quanxin Wang et al.
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Michal M. Milczarek et al.
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(2020)
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Devika Agarwal et al.
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Jeremy A. Miller et al.
Review
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Chee-Huat Linus Eng et al.
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Samuel G. Rodriques et al.
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Tim Stuart et al.
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Dvir Aran et al.
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Vladimir Yu Kiselev et al.
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Shiquan Sun et al.
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Daniel Furth et al.
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(2018)
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Xiaoping Han et al.
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Biochemistry & Molecular Biology
Diether Lambrechts et al.
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Vladimir Yu Kiselev et al.
Editorial Material
Biochemical Research Methods
Valentine Svensson et al.
Article
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Amit Zeisel et al.
Article
Cell Biology
Christopher Daniel Green et al.
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(2018)
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Jingtao Guo et al.
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Bosiljka Tasic et al.
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Simone Codeluppi et al.
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Jeffrey R. Moffitt et al.
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