Quantifying subcellular organization from localization to co-occurrence with InSpace
- 1The University of Texas MD Anderson Cancer Center, Department of Systems Biology
Abstract
Cell identity and function depend not only on molecular abundance, but also on how molecules are organized within cells. Although spatial transcriptomics now resolves individual RNA molecules within cells, routine computational workflows reduce these measurements to cell-by-gene count matrices, retaining abundance while discarding information about intracellular localization and molecular co-occurrence. This loss of spatial information creates a need for scalable methods that preserve subcellular detail and enable comparisons across diverse cellular morphologies. Therefore, we present InSpace, a scalable framework for quantifying these complementary dimensions of subcellular organization. InSpace-R maps transcripts onto a continuous radial coordinate anchored to nuclear and cell boundaries, while InSpace-C measures distance-dependent co-occurrence or depletion of molecular pairs. Simulations and orthogonal biochemical references support accurate measurements, with scalability demonstrated on datasets containing 275 million transcripts. RNA-RNA and RNA-DNA analyses recover organelle-linked neighbourhoods and associations between nascent transcripts and their genomic origins. Analysis of B and plasma cells across 58 tumour datasets spanning 12 cancer types reveals that reduced MHC-II transcript abundance accompanies more peripheral localization and stronger mutual co-occurrence, separating these transcripts from a program enriched for ER-targeted proteins. InSpace makes intracellular organization accessible alongside abundance, enabling discovery of spatial programs that distinguish cellular states and generate hypotheses about their functional specialization.
Related
- p300 activity and subcellular localization associate with tumor aggressiveness and clinical outcome in triple-negative breast cancer
- Ultrastructural diversity and subcellular organization of nigral Lewy pathology in Parkinson’s disease
- A simple multi-cloning strategy for gene expression and subcellular localization in mycobacteria
- LNK expression, subcellular localization and survival associations in breast, cervical and colorectal cancer
- Irreparable Posterosuperior Rotator Cuff Tears Treated With InSpace Balloon With or Without Arthroscopic Subscapularis Repair: Short‐Term Clinical Outcomes in a Multicenter Retrospective Study
BibTeX
@misc{niu2026quantifying,
title = {{Quantifying subcellular organization from localization to co-occurrence with InSpace}},
author = {Y. Niu and Y. Zheng},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.29.762981},
url = {https://www.biorxiv.org/content/10.64898/2026.10.29.762981v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Quantifying subcellular organization from localization to co-occurrence with InSpace
AU - Y. Niu
AU - Y. Zheng
PY - 2026
DA - 2026/10/29
PB - bioRxiv
DO - 10.64898/2026.10.29.762981
UR - https://www.biorxiv.org/content/10.64898/2026.10.29.762981v1
AB - Cell identity and function depend not only on molecular abundance, but also on how molecules are organized within cells. Although spatial transcriptomics now resolves individual RNA molecules within cells, routine computational workflows reduce these measurements to cell-by-gene count matrices, retaining abundance while discarding information about intracellular localization and molecular co-occurrence. This loss of spatial information creates a need for scalable methods that preserve subcellular detail and enable comparisons across diverse cellular morphologies. Therefore, we present InSpace, a scalable framework for quantifying these complementary dimensions of subcellular organization. InSpace-R maps transcripts onto a continuous radial coordinate anchored to nuclear and cell boundaries, while InSpace-C measures distance-dependent co-occurrence or depletion of molecular pairs. Simulations and orthogonal biochemical references support accurate measurements, with scalability demonstrated on datasets containing 275 million transcripts. RNA-RNA and RNA-DNA analyses recover organelle-linked neighbourhoods and associations between nascent transcripts and their genomic origins. Analysis of B and plasma cells across 58 tumour datasets spanning 12 cancer types reveals that reduced MHC-II transcript abundance accompanies more peripheral localization and stronger mutual co-occurrence, separating these transcripts from a program enriched for ER-targeted proteins. InSpace makes intracellular organization accessible alongside abundance, enabling discovery of spatial programs that distinguish cellular states and generate hypotheses about their functional specialization.
ER -