Sökning: WFRF:(Anton Petre Mihail 1989) >
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
-
- Anton, Petre Mihail, 1989 (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)
- Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences
-
- Kerkhoven, Eduard, 1985 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
-
- Robinson, Jonathan, 1986 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,BioInnovation Institute (BII)
-
- Nielsen, Jens B, 1962 (författare)
- BioInnovation Institute (BII),Chalmers tekniska högskola,Chalmers University of Technology
-
visa färre...
-
(creator_code:org_t)
- 2023-01-31
- 2023
- Engelska.
-
Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 120:6
- Relaterad länk:
-
https://research.cha... (primary) (free)
-
visa fler...
-
https://doi.org/10.1...
-
https://research.cha...
-
https://gup.ub.gu.se...
-
visa färre...
Abstract
Ämnesord
Stäng
- Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but 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 minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues 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. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. 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)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Nyckelord
- RNA-Seq
- GEM
- modeling
- single-cell
- GEM
- single-cell
- RNA-Seq
- modeling
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas