The expanding vistas of spatial transcriptomics

Tian, Luyi; Chen, Fei; Macosko, Evan Z.

doi:10.1038/s41587-022-01448-2

Cited by 202 publications

(122 citation statements)

References 89 publications

Supporting

Mentioning

122

Contrasting

Order By: Relevance

“…sci-rna-seq [30]), provided that N c and q can be estimated separately. Because of the modular and flexible nature of memento, we further anticipate that our modeling framework could be extended to alternative scRNA-seq workflows that use hybridization instead of reverse transcription [54] and spatial transcriptomics data [55,56]. Analyses of emerging multimodal workflows (e.g., ATAC-seq and CITE-seq) should also be possible by modifying the method-of-moments estimators to correctly capture sources of technical variation unique to each assay.…”

Section: Discussionmentioning

confidence: 99%

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

Kim

Gate

Lee

et al. 2022

Preprint

View full text Add to dashboard Cite

Differential expression analysis of scRNA-seq data is central for characterizing how experimental factors affect the distribution of gene expression. However, it remains challenging to distinguish biological and technical sources of cell-cell variability and to assess the statistical significance of quantitative comparisons between groups of cells. We introduce memento to address these limitations and enable accurate and efficient differential expression analysis of the mean, variability, and gene correlation from scRNA-seq. We used memento to analyze 70,000 tracheal epithelial cells to identify interferon response genes with distinct variability and correlation patterns, 160,000 T cells perturbed with CRISPR-Cas9 to reconstruct gene-regulatory networks that control T cell activation, and 1.2 million PMBCs to map cell-type-specific cis expression quantitative trait loci (eQTLs). In all cases, memento identified more significant and reproducible differences in mean expression but also identified differences in variability and gene correlation that suggest distinct modes of transcriptional regulation imparted by cytokines, genetic perturbations, and natural genetic variation. These results demonstrate memento as a first-in-class method for the quantitative comparisons of scRNA-seq data scalable to millions of cells and thousands of samples.

show abstract

Section: Discussionmentioning

confidence: 99%

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

Kim

Gate

Lee

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…35 This is essential for the understanding on how cellular neighborhoods and disease pathology influence gene expression, cell states, cell abundance and to monitor the molecular interactions between tissue components. 35 Over the past decade spatial transcriptomics have evolved rapidly in sensitivity, multiplexing, and throughput. They can be divided into 2 different technologies: sequencing-based technologies and imaging-based technologies.…”

Section: Spatial Omics In Cardiovascular Researchmentioning

confidence: 99%

“…The barcoded mRNA can then be transcribed to cDNA, amplified, and sequenced using next-generation sequencing. 35,38 Imaging-based spatial transcriptomics use microscope techniques to simultaneously image several mRNA transcripts within a tissue. They use either in situ sequencing 39,40 or fluorescence in situ hybridization.…”

Section: Spatial Omics In Cardiovascular Researchmentioning

confidence: 99%

Multiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy

Schumacher

Kramann

2023

ATVB

View full text Add to dashboard Cite

Ischemic heart disease including myocardial infarction is still the leading cause of death worldwide. Although the survival early after myocardial infarction has been significantly improved by the introduction of percutaneous coronary intervention, long-term morbidity and mortality remain high. The elevated long-term mortality is mainly driven by cardiac remodeling processes triggering ischemic heart failure and electric instability. Despite the new developments in pharmaco-therapy of heart failure, we still lack targeted therapies for cardiac remodeling and fibrosis. Single-cell and genomic technologies allow us to map the human heart at unprecedented resolution and allow to gain insights into cellular and molecular heterogeneity. However, these technologies rely on digested tissue and isolated cells or nuclei and thus lack spatial information. Spatial information is critical to understand tissue homeostasis and disease and can be utilized to identify disease-driving cell populations and mechanisms including cellular cross-talk. Here, we discuss recent advances in single-cell and spatial genomic technologies that give insights into cellular and molecular mechanisms of cardiac remodeling after injury and can be utilized to identify novel therapeutic targets and pave the way toward new therapies in heart failure.

show abstract

“…Spatial information of cells is lost when constructing scRNA‐seq libraries, including cell distribution and identities of the neighboring microenvironment, thus hampering the in‐depth investigation of the biological process of a tissue or organ in situ. To this end, new spatial transcriptomics technologies developed recently stand out to be a solution, such as microdissection‐based cell capture and sequencing laser capture microdissection (LCM, Geo‐seq), high throughput in situ hybridization (smFISH, MERFISH), in situ sequencing (STARmap), and in situ capturing technologies (Stereo‐seq, Slide‐seq), 5 some of which (Stereo‐seq, Seq‐Scope, and sci‐Space) reach a single‐cell or subcellular resolution. Combining the accurate information of the transcript coordinate and hundreds of genes detected in a single spot on the chip, scientists can not only obtain the physical location and transcription profile of each cell on the section, but also redefine the cell types and even tissue anatomy of a given organ at the molecular level.…”

Section: Figurementioning

confidence: 99%

Re‐exploring tissue regeneration by novel spatial transcriptomics technologies

Wei

et al. 2022

Clinical & Translational Med

View full text Add to dashboard Cite

One of the ultimate goals of regenerative medicine is to replace damaged tissues using natural growth and repair mechanisms, which barely remain in adult human body. In contrast, certain lower vertebrates, such as salamanders and zebrafish, exhibit an incredible ability to regenerate various damaged body parts both structurally and functionally. These animals with such natural gifts have therefore drawn the long-term attention of biologists, who are dedicated to uncovering the molecular mechanisms that direct programmed regeneration and how regenerative medicine practice would learn from fundamental scientific discoveries. While early efforts focused on one or a few molecules/pathways/cell types have extensively substantiated our understanding in this field, 1 the gap between Xiaoyu Wei and Hanbo Li contributed equally to this work.

show abstract

The expanding vistas of spatial transcriptomics

Cited by 202 publications

References 89 publications

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

Multiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy

Re‐exploring tissue regeneration by novel spatial transcriptomics technologies

Contact Info

Product

Resources

About