Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
Preprint, 2022

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.

Single-cell RNA-Seq

metabolism

neurons

Genome-scale metabolic modeling

cancer

normalization

Författare

Johan Gustafsson

Chalmers, Biologi och bioteknik, Systembiologi

Jonathan Robinson

Chalmers, Biologi och bioteknik, Systembiologi

Fariba Roshanzamir

Chalmers, Biologi och bioteknik, Systembiologi

Rebecka Jörnsten

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Eduard Kerkhoven

Chalmers, Biologi och bioteknik, Systembiologi

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

Ämneskategorier

Cellbiologi

Cell- och molekylärbiologi

Bioinformatik (beräkningsbiologi)

Styrkeområden

Hälsa och teknik

Livsvetenskaper och teknik (2010-2018)

DOI

10.1101/2022.04.25.489379

Mer information

Skapat

2022-05-03