Spatial isoform analysis of the neonatal mouse heart reveals transcript-level patterns masked in gene-level analyses
Spatial transcriptomics has advanced the study of gene expression in tissues, but most current approaches rely on 3′-end sequencing and provide limited information on alternative splicing (AS). Spatially resolved isoform analysis can be achieved by combining microtissue sampling with Smart-seq2 RNA sequencing. Using 100-µm microtissues obtained from a single neonatal mouse heart (postnatal day 1; P01), we identified spatially variable transcript isoforms that are not readily detectable by conventional gene-level analyses. Although gene- and transcript-level clustering produced broadly similar spatial patterns, several genes exhibited isoform-level variation. For example, transcripts of Tnni1 and Ckb suggested spatial variation that was less structured than that of Pdlim5 , while Pdlim5 isoforms displayed distinct spatial distributions near the left ventricle. Comparison with postnatal day 7 (P07) hearts provided developmental context, suggesting that these spatial isoform patterns may undergo further remodeling during postnatal maturation. These results demonstrate the technical feasibility of spatial isoform analysis using a Smart-seq2-based workflow and provide a proof-of-principle framework for investigating spatial regulation of transcript isoforms during tissue development. Representative transcript-level observations were independently supported by isoform-specific qPCR. Although the biological findings require validation in independent samples, this approach establishes a practical strategy for transcript-level spatial analysis beyond conventional gene-level studies.
Authors
- Hiroko Matsunaga (ORCID: https://orcid.org/0000-0002-1725-3333)
- Haruko Takeyama (ORCID: https://orcid.org/0000-0002-2058-8185)
- Ryota Wagatsuma (ORCID: https://orcid.org/0000-0001-5400-7521)
- Kaori Sugiyama (ORCID: https://orcid.org/0000-0002-4027-0705)
- Yuki Makino
Institutions
- Waseda University (JP)
Publication Details
- Journal
- Scientific Reports
- Published
- 2026-09-15
- DOI
- https://doi.org/10.1038/s41598-026-71382-9
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00