Prediction of single-cell gene expression for transcription factor analysis

Ardakani, Fatemeh Behjati; Kattler, Kathrin; Heinen, Tobias; Schmidt, Florian; Feuerborn, David; Gasparoni, Gilles; Lepikhov, Konstantin; Nell, Patrick; Hengstler, Jan G.; Walter, Jörn; Schulz, Marcel H.

doi:10.1093/gigascience/giaa113

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…We then analyzed the levels of relative pathway expression at the single-cell level [ 54 , 55 ], providing a cell-by-cell analysis of all the 24,472 pathways from the Molecular signatures database (MsigDB [ 37 ]) collection (available on the R markdown paragraph “Single-cell GSEA” and associated results). As observed before, the ribosome-associated genes are collectively upregulated in Kelly cells ( Figure 5 C, bottom group; see also Figure 3 B for reference assignment of cell types), but show a noticeable variance in BE2C cells: in these cells, ribosome-associated protein-coding genes appear upregulated in cells in G1 phase (compare Figure 3 D and Figure 5 C).…”

Section: Resultsmentioning

confidence: 99%

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

et al. 2021

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Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. Several cellular models have been implemented to study this disease over the years. Two of these, SK-N-BE-2-C (BE2C) and Kelly, are amongst the most used worldwide as models of MYCN-Amplified human NBL. Here, we provide a transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution. We obtained 1105 Kelly and 962 BE2C unsynchronized cells, with an average number of mapped reads/cell of roughly 38,000. The single-cell data recapitulate gene expression signatures previously generated from bulk RNA-Seq. We highlight low variance for commonly used housekeeping genes between different cells (ACTB, B2M and GAPDH), while showing higher than expected variance for metallothionein transcripts in Kelly cells. The high number of samples, despite the relatively low read coverage of single cells, allowed for robust pathway enrichment analysis and master regulator analysis (MRA), both of which highlight the more mesenchymal nature of BE2C cells as compared to Kelly cells, and the upregulation of TWIST1 and DNAJC1 transcriptional networks. We further defined master regulators at the single cell level and showed that MYCN is not constantly active or expressed within Kelly and BE2C cells, independently of cell cycle phase. The dataset, alongside a detailed and commented programming protocol to analyze it, is fully shared and reusable.

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Section: Resultsmentioning

confidence: 99%

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

et al. 2021

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“…Cellular fate and cell-type-specific gene expression programs are thought to be largely regulated by transcription factors and their corresponding cis -regulatory networks [2] , [4] , [6] . Accordingly, transcription factor expression profiles can be useful in identifying cell types from scRNA-seq data [2] , [7] , [8] . Yet other cellular properties can also vary dynamically, in a cell type-specific manner.…”

Section: Introductionmentioning

confidence: 99%

Extracellular matrix gene expression signatures as cell type and cell state identifiers

Sacher

Feregrino

Tschopp

et al. 2021

Matrix Biology Plus

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“…In addition, their strategy is not able to integrate contact frequencies into TF affinities, nor to derive CS interactions from bulk contact data. There are other tools for predicting gene expression explicitly in individual cells that incorporate TF information, such as SCENIC ( Aibar et al , 2017 ), ACTION ( Mohammadi et al , 2018 ) or TRIANGULATE ( Behjati Ardakani et al , 2020 ), but none of them considers long-range enhancer–gene interactions. A compelling approach would be to combine these expression prediction tools with information on distant enhancers.…”

Section: Discussionmentioning

confidence: 99%

The adapted Activity-By-Contact model for enhancer–gene assignment and its application to single-cell data

et al. 2023

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Motivation Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model (Fulco et al., 2019) which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a gene’s perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC-model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is no implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data. Results We demonstrate that the ABC-score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalised ABC-model (gABC) with TF binding information and illustrate an analysis of a single-cell ATAC-seq data set of the human heart, where we were able to characterise cell type-specific regulatory interactions and predict gene expression based on transcription factor affinities. All executed processing steps are incorporated into our new computational pipeline STARE. Availability The software is available at https://github.com/schulzlab/STARE Supplementary information Supplementary data are available at Bioinformatics online.

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Prediction of single-cell gene expression for transcription factor analysis

Cited by 13 publications

References 37 publications

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

Extracellular matrix gene expression signatures as cell type and cell state identifiers

The adapted Activity-By-Contact model for enhancer–gene assignment and its application to single-cell data

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