Back

Single-cell X-chromosome inactivation analysis links biased chimerism to differential gene expression and epigenetic erosion

Henning, R. H.; Rust, T. M.; Dijksterhuis, K.; Eggen, B. J. L.; Guryev, V.

2024-08-30 bioinformatics
10.1101/2024.08.29.610317 bioRxiv
Show abstract

Female cells randomly inactivate one X-chromosome, resulting in cellular mosaicism. Determining the bias in X-chromosomal inactivation (XCI) at single cell level may be relevant for understanding diseases prevalent in females. Here, we introduce a computational method that determines XCI profiles at single-cell level using solely sc/snRNA-Seq data. XCI analysis of skin cells from hybrid mice validates our approach and reveals biased inactivation of X-chromosomes among cell types. In human lung and brain cells, XCI status can be determined in 33.8% and 23.6% of cells. Among the patients, cells with opposite inactivation patterns differently express members of specific gene families and pathways. Alzheimers disease patients show reversal of XCI in cortical microglia and regional increase in biallelic expression denoting epigenetic erosion. We provide a robust utility to explore the degree and impact of XCI in single cell expression data.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 0.6%
40.5%
Similar papers in this journal
2
Nature Genetics
○ 286 papers in training set
Top 0.3%
15.4%
Similar papers in this journal
50% of probability mass above
3
Genome Biology
○ 637 papers in training set
Top 2%
6.4%
Similar papers in this journal
4
Nature
○ 645 papers in training set
Top 3%
5.3%
Similar papers in this journal
5
Communications Biology
○ 993 papers in training set
Top 2%
5.0%
Similar papers in this journal
6
Science Advances
● 1243 papers in training set
Top 14%
2.4%
7
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.0%
8
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.5%
9
Genome Medicine
○ 183 papers in training set
Top 3%
1.4%
10
Scientific Reports
○ 3612 papers in training set
Top 61%
1.2%
11
eLife
● 5828 papers in training set
Top 57%
1.2%
12
Cell Genomics
○ 172 papers in training set
Top 3%
1.2%
13
Nature Methods
○ 385 papers in training set
Top 5%
1.2%
14
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 38%
1.0%
15
iScience
○ 1154 papers in training set
Top 31%
1.0%
16
Genome Research
● 468 papers in training set
Top 6%
0.9%
17
Cell Reports
○ 1498 papers in training set
Top 26%
0.9%
18
Nature Structural & Molecular Biology
236 papers in training set
Top 4%
0.7%
19
Nature Cell Biology
○ 118 papers in training set
Top 3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.