High-definition spatial transcriptomics for in situ tissue profiling
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
High-definition spatial transcriptomics for in situ tissue profiling
Authors
Keywords
-
Journal
NATURE METHODS
Volume 16, Issue 10, Pages 987-990
Publisher
Springer Science and Business Media LLC
Online
2019-09-10
DOI
10.1038/s41592-019-0548-y
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
- (2019) Samuel G. Rodriques et al. SCIENCE
- ST Spot Detector: a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets
- (2018) Kim Wong et al. BIOINFORMATICS
- SpatialDE: identification of spatially variable genes
- (2018) Valentine Svensson et al. NATURE METHODS
- Three-dimensional intact-tissue sequencing of single-cell transcriptional states
- (2018) Xiao Wang et al. SCIENCE
- Molecular Architecture of the Mouse Nervous System
- (2018) Amit Zeisel et al. CELL
- Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq
- (2018) Mihriban Karaayvaz et al. Nature Communications
- Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections
- (2018) Fredrik Salmén et al. Nature Protocols
- Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations
- (2017) Susanne C van den Brink et al. NATURE METHODS
- The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
- (2017) Ed Lein et al. SCIENCE
- Massively parallel digital transcriptional profiling of single cells
- (2017) Grace X. Y. Zheng et al. Nature Communications
- Dense transcript profiling in single cells by image correlation decoding
- (2016) Ahmet F Coskun et al. NATURE METHODS
- Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
- (2016) P. L. Stahl et al. SCIENCE
- An automated approach to prepare tissue-derived spatially barcoded RNA-sequencing libraries
- (2016) Anders Jemt et al. Scientific Reports
- Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
- (2015) Evan Z. Macosko et al. CELL
- Spatially resolved, highly multiplexed RNA profiling in single cells
- (2015) K. H. Chen et al. SCIENCE
- Single-molecule super-resolution imaging of chromosomes and in situ haplotype visualization using Oligopaint FISH probes
- (2015) Brian J. Beliveau et al. Nature Communications
- HTSeq--a Python framework to work with high-throughput sequencing data
- (2014) S. Anders et al. BIOINFORMATICS
- A mesoscale connectome of the mouse brain
- (2014) Seung Wook Oh et al. NATURE
- Highly Multiplexed Subcellular RNA Sequencing in Situ
- (2014) J. H. Lee et al. SCIENCE
- Neuronal organization of olfactory bulb circuits
- (2014) Shin Nagayama et al. Frontiers in Neural Circuits
- In situ sequencing for RNA analysis in preserved tissue and cells
- (2013) Rongqin Ke et al. NATURE METHODS
- Single-molecule mRNA detection and counting in mammalian tissue
- (2013) Anna Lyubimova et al. Nature Protocols
- TagGD: Fast and Accurate Software for DNA Tag Generation and Demultiplexing
- (2013) Paul Igor Costea et al. PLoS One
- STAR: ultrafast universal RNA-seq aligner
- (2012) Alexander Dobin et al. BIOINFORMATICS
- Fiji: an open-source platform for biological-image analysis
- (2012) Johannes Schindelin et al. NATURE METHODS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now