SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Kerkhoven Eduard 1985)
 

Sökning: WFRF:(Kerkhoven Eduard 1985) > Generation and anal...

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data

Gustafsson, Johan, 1976 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Robinson, Jonathan, 1986 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Roshanzamir, Fariba, 1986 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
visa fler...
Jörnsten, Rebecka, 1971 (författare)
Göteborgs universitet,University of Gothenburg,Chalmers tekniska högskola,Chalmers University of Technology
Kerkhoven, Eduard, 1985 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Nielsen, Jens B, 1962 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
visa färre...
 (creator_code:org_t)
Cold Spring Harbor Laboratory, 2022
2022
Engelska.
  • Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
Abstract Ämnesord
Stäng  
  • Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

Ämnesord

NATURVETENSKAP  -- Biologi -- Cellbiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Cell Biology (hsv//eng)
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  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

Nyckelord

normalization
Genome-scale metabolic modeling
Single-cell RNA-Seq
cancer
neurons
metabolism

Publikations- och innehållstyp

art (ämneskategori)
vet (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy