Transcript-guided targeted cell enrichment for scalable single-nucleus RNA sequencing
Large-scale single-cell atlases have revealed many aging- and disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular analyses. To address this, we developed EnrichSci-a scalable, microfluidics-free platform that combines hybridization chain reaction RNA fluorescence in situ hybridization (FISH) with combinatorial indexing to profile single-nucleus transcriptomes of target cell types with full gene-body coverage. Applied to oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.
Authors
- Jasper Lee
- Zihan Xu (ORCID: https://orcid.org/0000-0002-8647-4287)
- Manolis Maragkakis (ORCID: https://orcid.org/0000-0002-3158-1763)
- Junyue Cao (ORCID: https://orcid.org/0000-0003-4097-489X)
- Andras Sziraki
- Abdulraouf Abdulraouf (ORCID: https://orcid.org/0000-0003-4452-4781)
- Andrew Liao (ORCID: https://orcid.org/0000-0002-4548-9175)
- Ziyu Lu (ORCID: https://orcid.org/0000-0003-4321-2727)
- Abid Rehman (ORCID: https://orcid.org/0000-0002-0577-8538)
- Zehao Zhang (ORCID: https://orcid.org/0000-0003-4140-0572)
- Weirong Jiang
- Wei Zhou (ORCID: https://orcid.org/0000-0003-4236-4091)
- Alia Arya
Institutions
- National Institutes of Health (US)
- Institute on Aging (US)
- National Institute on Aging (US)
- Rockefeller University (US)
Publication Details
- Journal
- Cell Genomics
- Published
- 2025-12-11
- DOI
- https://doi.org/10.1016/j.xgen.2025.101101
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00