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Updated: Aug 6, 2026
Spatially Resolved, Integrated Single-Cell Multiomic Profiling of the Transcriptome and Epigenomic Targets in Frozen Tissue Sections
Published on: June 12, 2026
Whole-transcriptome-scale isoform-resolved spatial imaging of single cells in tissues
Limor Cohen1, Aaron R Halpern1, Timothy R Blosser1
1Howard Hughes Medical Institute, Harvard University, Cambridge, MA 02138, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA; Department of Physics, Harvard University, Cambridge, MA 02138, USA.
Researchers developed a new spatial transcriptomics method to analyze RNA at the whole-transcriptome scale in single cells. This technique reveals cell-type-specific gene usage and communication within intact tissues.
Area of Science:
- Molecular Biology
- Genomics
- Neuroscience
Background:
- Cell and tissue functions depend on complex gene interactions.
- Understanding these requires analyzing gene isoforms within single cells at high spatial resolution.
Purpose of the Study:
- To develop a method for whole-transcriptome-scale, isoform-resolved spatial transcriptomics in intact tissues.
- To enable detailed analysis of gene expression and cell communication within the complex cellular environment.
Main Methods:
- Developed an in situ RNA amplification technique integrated with multiplexed error-robust fluorescence in situ hybridization (MERFISH).
- Applied the method to image approximately 33,000 distinct RNAs (genes and isoforms) in the mouse brain.
Main Results:
- Enabled systematic analysis of region- and cell-type-specific gene programs and cell-cell communications.
- Revealed significant spatial diversity and cell-type specificity in RNA isoform usage across the mouse brain.
- Identified brain structures with high isoform specificity.
Conclusions:
- The new method provides unprecedented insights into the molecular and cellular basis of tissue function.
- Facilitates discovery in cell and organismal biology by unlocking isoform-resolved spatial transcriptomics.
- Broad applications anticipated for characterizing complex biological systems.
