Back

A computational framework for mapping isoform landscape and regulatory mechanisms from spatial transcriptomics data

Su, J.; Qu, Y.; Schertzer, M. D.; Yang, H.; Jiang, J.; Lhakhang, T.; Nelson, T. M.; Park, S.; Lai, Q.; Fu, X.; Choi, S.-W.; Knowles, D. A.; Rabadan, R.

2025-05-08 bioinformatics
10.1101/2025.05.02.651907 bioRxiv
Show abstract

Transcript diversity including splicing and alternative 3 end usage is crucial for cellular identity and adaptation, yet its spatial coordination remains poorly understood. Here, we present SPLISOSM (SpatiaL ISOform Statistical Modeling), a computational framework for detecting isoform-resolution patterns from spatial transcriptomics data. SPLISOSM leverages multivariate testing to account for spot- and isoform-level dependencies, demonstrating robust and theoretically grounded performance on sparse data. In the mouse brain, we identify over 1,000 spatially variable transcript diversity events, primarily in synaptic signaling pathways linked to neuropsychiatric disorders, and uncover both known and novel regulatory relationships with region-specific RNA binding proteins. We further show that these patterns are evolutionarily conserved between mouse and human prefrontal cortex. Analysis of human glioblastoma highlights pervasive transcript diversity in antigen presentation and adhesion genes associated with specific microenvironmental conditions. Together, we present a comprehensive spatial splicing analysis in the brain under normal and neoplastic conditions.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 6%
22.2%
Similar papers in this journal
2
Nature
○ 645 papers in training set
Top 1%
11.8%
Similar papers in this journal
3
Nature Methods
○ 385 papers in training set
Top 2%
6.7%
Similar papers in this journal
4
Genome Biology
○ 637 papers in training set
Top 2%
5.5%
Similar papers in this journal
5
Nature Genetics
○ 286 papers in training set
Top 1%
5.4%
Similar papers in this journal
50% of probability mass above
6
Cell
○ 431 papers in training set
Top 2%
4.8%
7
Cell Genomics
○ 172 papers in training set
Top 1%
4.3%
8
Nature Biotechnology
○ 172 papers in training set
Top 0.9%
4.3%
9
Science Advances
● 1243 papers in training set
Top 11%
3.2%
10
Cell Systems
○ 201 papers in training set
Top 2%
2.6%
11
Cell Reports
○ 1498 papers in training set
Top 15%
2.4%
12
Science
● 477 papers in training set
Top 5%
1.9%
13
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 27%
1.7%
14
Molecular Cell
○ 350 papers in training set
Top 3%
1.7%
15
Nucleic Acids Research
◐ 1281 papers in training set
Top 9%
1.7%
16
Nature Metabolism
○ 69 papers in training set
Top 1%
1.7%
17
eLife
● 5828 papers in training set
Top 55%
1.4%
18
Advanced Science
○ 286 papers in training set
Top 7%
1.1%
19
Nature Cell Biology
○ 118 papers in training set
Top 2%
1.1%
20
Communications Biology
○ 993 papers in training set
Top 22%
1.1%
21
Science Immunology
● 88 papers in training set
Top 2%
0.8%
22
Genome Research
● 468 papers in training set
Top 6%
0.8%
23
Nature Biomedical Engineering
○ 47 papers in training set
Top 2%
0.8%
24
iScience
○ 1154 papers in training set
Top 36%
0.8%
25
Molecular Systems Biology
○ 162 papers in training set
Top 3%
0.8%
26
Nature Chemical Biology
○ 119 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.