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Sökning: AMNE:(NATURAL SCIENCES Biological Sciences Biochemistry and Molecular Biology) > Nilsson Mats > De novo spatiotempo...

De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks

Marco Salas, Sergio (författare)
Stockholms universitet,Science for Life Laboratory (SciLifeLab),Institutionen för biokemi och biofysik,Stockholm Univ, Sci Life Lab, Dept Biochem & Biophys, Solna, Sweden
Yuan, Xiao (författare)
Uppsala universitet,Institutionen för informationsteknologi,Science for Life Laboratory, SciLifeLab
Sylven, Christer (författare)
Karolinska Inst, Dept Med, Stockholm, Sweden
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Nilsson, Mats (författare)
Stockholms universitet,Science for Life Laboratory (SciLifeLab),Institutionen för biokemi och biofysik,Stockholm Univ, Sci Life Lab, Dept Biochem & Biophys, Solna, Sweden
Wählby, Carolina, professor, 1974- (författare)
Uppsala universitet,Science for Life Laboratory, SciLifeLab,Institutionen för informationsteknologi
Partel, Gabriele, 1988- (författare)
Uppsala universitet,Science for Life Laboratory, SciLifeLab,Institutionen för informationsteknologi,Katholieke Univ Leuven, Lab Multiom Integrat Bioinformat, Dept Human Genet, Leuven, Belgium.;VIB KU Leuven Ctr Brain & Dis Res, Lab Computat Biol, Dept Human Genet, Leuven, Belgium
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 (creator_code:org_t)
2022-08-12
2022
Engelska.
Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 18:8
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Cell- och molekylärbiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Cell and Molecular Biology (hsv//eng)
NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
NATURVETENSKAP  -- Matematik -- Matematisk analys (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Mathematical Analysis (hsv//eng)
NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

Nyckelord

Convolutional neural networks
Cytology
Image resolution
Mammals
Tissue
Cell data
Cell types
Cell typing
Cellular diversity
Heart development
Human heart
Molecular diversity
Probabilistics
Single cells
Spatio-temporal models
Cells
article
cardiac muscle cell
cell differentiation
conception
convolutional neural network
fetus heart
human
human tissue
single cell RNA seq
spatiotemporal analysis
animal
genetics
mammal
metabolism
Animals
Humans
Myocytes
Cardiac
Neural Networks
Computer

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ref (ämneskategori)
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