Results 251 to 260 of about 71,340 (293)

A technical comparison of spatial transcriptomics platforms across six cancer types. [PDF]

Genome Biol
Cervilla S, Grases D, Perez E, Real FX, Musulen E, Aprea J, Esteller M, Porta-Pardo E.   +7 more
europepmc   +1 more source

​​Integrating machine learning and spatial transcriptomics uncovers shared immunomodulatory deubiquitinases in MAFLD and HCC. [PDF]

Hum Genet
Han YX, Li H, Xia W, Ma J, Zhang J, Li Y.   +5 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Spatially resolved transcriptomics and beyond

Nature Reviews Genetics, 2014
Considerable progress in sequencing technologies makes it now possible to study the genomic and transcriptomic landscape of single cells. However, to better understand the complexity of multicellular organisms, we must devise ways to perform high-throughput measurements while preserving spatial information about the tissue context or subcellular ...
Crosetto N, Bienko M, van Oudenaarden A
openaire   +6 more sources

Spatial transcriptomics

Cancer Cell, 2022
Spatial transcriptomics, with other spatial technologies, has enabled scientists to dissect the organization and interaction of different cell types within the tumor microenvironment. We asked experts to discuss some aspects of this technology from revealing the tumor microenvironment and heterogeneity, to tracking tumor evolution, to guiding tumor ...
Ana C, Anderson, Itai, Yanai, Lucy R, Yates, Linghua, Wang, Alexander, Swarbrick, Peter, Sorger, Sandro, Santagata, Wolf H, Fridman, Qiang, Gao, Livnat, Jerby, Benjamin, Izar, Lulu, Shang, Xiang, Zhou   +12 more
openaire   +2 more sources

Expansion Spatial Transcriptomics

Nature Methods, 2022
AbstractCapture array-based spatial transcriptomics methods have been widely used to resolve gene expression in diverse tissue contexts, however, their spatial resolution is limited by array density. We present Expansion Spatial Transcriptomics (Ex-ST) to overcome this limitation by clearing and expanding tissue prior to capturing the entire ...
Yuhang Fan, Žaneta Andrusivová, Yunming Wu, Chew Chai, Ludvig Larsson, Mengxiao He, Liqun Luo, Joakim Lundeberg, Bo Wang   +8 more
openaire   +2 more sources

SnapShot: Spatial transcriptomics

Cell, 2022
Spatially resolved transcriptomics methodologies using RNA sequencing principles have and will continue to contribute to decode the molecular landscape of tissues. Linking quantitative sequencing data with tissue morphology empowers profiling of cellular morphology and transcription over time and space in health and disease. To view this SnapShot, open
Larsson, Ludvig, Bergenstråhle, Ludvig, He, Mengxiao, Andrusivova, Zaneta, Lundeberg, Joakim   +4 more
openaire   +2 more sources

Spatial epigenome–transcriptome comapping technology

Trends in Cell Biology, 2023
Spatial omics facilitate an in-depth understanding of cell states and cell interactions. Recent work by Zhang et al. simultaneously seizes spatial epigenetic priming, differentiation, and gene regulation at nearly single-cell resolution by developing an epigenome-transcriptome comapping technology.
Ming-Zhu Jin, Wei-Lin Jin
openaire   +2 more sources

New adventures in spatial transcriptomics

Developmental Cell, 2022
Complex dynamic processes such as development involve the deployment of gene regulatory pathways that transform the spatial arrangement of cells. Disentangling these genetic programs is at the core of many biological problems. Stereo-seq is a promising spatial transcriptomics method, as demonstrated by three papers in this issue of Developmental Cell ...
Maayan, Pour, Itai, Yanai
openaire   +2 more sources