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

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Sökning: WFRF:(Jörnsten Rebecka 1971) > Generation and anal...

LIBRIS Formathandbok (Information om MARC21)

Fältnamn	Indikatorer	Metadata
000		04870naa a2200553 4500
001		oai:research.chalmers.se:236d605b-a94b-42f0-aa6a-0e4e6aebd2ce
003		SwePub
008		230210s2023 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
009		oai:gup.ub.gu.se/330092
024	7	a https://doi.org/10.1073/pnas.22178681202 DOI
024	7	a https://research.chalmers.se/publication/5345462 URI
024	7	a https://gup.ub.gu.se/publication/3300922 URI
040		a (SwePub)cthd (SwePub)gu
041		a engb eng
042		9 SwePub
072	7	a art2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Gustafsson, Johan,d 1976u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)gustajo
245	1 0	a Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
264		c 2023-01-31
264	1	b Proceedings of the National Academy of Sciences,c 2023
338		a electronic2 rdacarrier
520		a 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.
650	7	a NATURVETENSKAPx Biologix Cellbiologi0 (SwePub)106042 hsv//swe
650	7	a NATURAL SCIENCESx Biological Sciencesx Cell Biology0 (SwePub)106042 hsv//eng
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng
650	7	a NATURVETENSKAPx Biologix Bioinformatik och systembiologi0 (SwePub)106102 hsv//swe
650	7	a NATURAL SCIENCESx Biological Sciencesx Bioinformatics and Systems Biology0 (SwePub)106102 hsv//eng
653		a RNA-Seq
653		a GEM
653		a modeling
653		a single-cell
653		a GEM
653		a single-cell
653		a RNA-Seq
653		a modeling
700	1	a Anton, Petre Mihail,d 1989u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)petre
700	1	a Roshanzamir, Fariba,d 1986u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)faribar
700	1	a Jörnsten, Rebecka,d 1971u Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences4 aut0 (Swepub:cth)jornsten
700	1	a Kerkhoven, Eduard,d 1985u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)eduardk
700	1	a Robinson, Jonathan,d 1986u Chalmers tekniska högskola,Chalmers University of Technology,BioInnovation Institute (BII)4 aut0 (Swepub:cth)jonrob
700	1	a Nielsen, Jens B,d 1962u BioInnovation Institute (BII),Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)nielsenj
710	2	a Chalmers tekniska högskolab Institutionen för matematiska vetenskaper4 org
773	0	t Proceedings of the National Academy of Sciences of the United States of Americad : Proceedings of the National Academy of Sciencesg 120:6q 120:6x 0027-8424x 1091-6490
856	4	u https://research.chalmers.se/publication/534546/file/534546_Fulltext.pdfx primaryx freey FULLTEXT
856	4 8	u https://doi.org/10.1073/pnas.2217868120
856	4 8	u https://research.chalmers.se/publication/534546
856	4 8	u https://gup.ub.gu.se/publication/330092

Hitta via bibliotek

Proceedings of the National Academy of Sciences of the United States of America (Sök värdpublikationen i LIBRIS)

Till lärosätets databas

Hitta mer i SwePub

Av författaren/redakt...: Gustafsson, Joha ...; Anton, Petre Mih ...; Roshanzamir, Far ...; Jörnsten, Rebeck ...; Kerkhoven, Eduar ...; Robinson, Jonath ...; visa fler...; Nielsen, Jens B, ...; visa färre...

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Biologi; och Cellbiologi

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Bioinformatik

NATURVETENSKAP: NATURVETENSKAP; och Biologi; och Bioinformatik oc ...

Artiklar i publikationen: Proceedings of t ...

Av lärosätet: Chalmers tekniska högskola; Göteborgs universitet

Sök utanför SwePub

Sök vidare i:: Google; Google Book Search; Google Scholar

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.

LIBRIS.kb.se