Molecule-resolved inference reveals the spatial organization of transcriptional programs
Abstract
Transcriptional programs encode cellular identity and physiological activity, linking gene regulation to cellular phenotypes. Although spatial transcriptomics measures individual RNA molecules across intact tissues, most analyses aggregate them into cells or pixels, obscuring molecular level functional dynamics. We introduce FICTURE2, a statistical model that infers the transcriptional program from which each individual molecule is generated, using gene identity and spatial context alone without segmentation or binning. FICTURE2 learns programs with distinct spatial distributions, ranging from millimeter-scale continuous transition to micron-scale subcellular localization, the coexisting patterns of which then reveal both cellular identity and physiological activity with unified interpretations. FICTURE2 supports both unsupervised and reference-guided inference and is orders of magnitude more efficient than alternative approaches, enabling systematic analysis of large-scale spatial datasets as we demonstrate in data across diverse tissue types profiled by various spatial technologies.
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BibTeX
@misc{si2026moleculeresolved,
title = {{Molecule-resolved inference reveals the spatial organization of transcriptional programs}},
author = {Yichen Si and ChangHee Lee and Weiqiu Cheng and Michael Sweeney and Jeong H. Yun and Myungjin Kim and Goo Jun and Jun Hee Lee and Hyun Min Kang},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.762960},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.762960v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - Molecule-resolved inference reveals the spatial organization of transcriptional programs
AU - Yichen Si
AU - ChangHee Lee
AU - Weiqiu Cheng
AU - Michael Sweeney
AU - Jeong H. Yun
AU - Myungjin Kim
AU - Goo Jun
AU - Jun Hee Lee
AU - Hyun Min Kang
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.762960
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.762960v1
AB - Transcriptional programs encode cellular identity and physiological activity, linking gene regulation to cellular phenotypes. Although spatial transcriptomics measures individual RNA molecules across intact tissues, most analyses aggregate them into cells or pixels, obscuring molecular level functional dynamics. We introduce FICTURE2, a statistical model that infers the transcriptional program from which each individual molecule is generated, using gene identity and spatial context alone without segmentation or binning. FICTURE2 learns programs with distinct spatial distributions, ranging from millimeter-scale continuous transition to micron-scale subcellular localization, the coexisting patterns of which then reveal both cellular identity and physiological activity with unified interpretations. FICTURE2 supports both unsupervised and reference-guided inference and is orders of magnitude more efficient than alternative approaches, enabling systematic analysis of large-scale spatial datasets as we demonstrate in data across diverse tissue types profiled by various spatial technologies.
SN - 2692-8205
ER -