Quantifying subcellular organization from localization to co-occurrence with InSpace

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.