AI Insight
SPARC is a new computational framework that reconstructs cellular trajectories from single-cell RNA sequencing data while respecting biological ordering constraints such as developmental time or disease progression. Unlike existing methods that either ignore experimental ordering or only compare adjacent timepoints, SPARC performs global optimization across all conditions simultaneously using graph-based shortest-path algorithms. Applied to osteosarcoma metastasis, SPARC revealed that lung metastases establish a bone-like microenvironment with osteoclastogenic signaling, creating a remodeling niche that promotes osteoclast activity within the lung tissue.
Why it matters
This method addresses a fundamental limitation in single-cell trajectory analysis by preventing biologically impossible transitions between cell states. The osteosarcoma findings suggest that metastatic tumors may recreate their tissue of origin's microenvironment in distant organs, which could inform targeted therapeutic strategies for preventing or treating metastatic disease.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Single-cell transcriptomics has enabled systematic profiling of cellular states across ordered biological contexts, including developmental stages, treatment phases, disease progression, and anatomical compartments. A central challenge is to reconstruct trajectories that respect the directionality imposed by biology or experimental design. Existing trajectory inference methods reconstruct cell-state progressions from latent-space geometry but do not enforce external biological ordering during graph construction, yielding biologically inadmissible transitions. An emerging paradigm of optimal-transport (OT) approaches partially addresses this limitation by incorporating experimental ordering into probabilistic state-to-state correspondences, yet their pairwise formulation cannot resolve whether a given state is an intermediate state or a terminal state along a multi-step progression. In multi-timepoint settings, OT typically estimates couplings only betweenadjacent timepoints and then chains these locally solved couplings to approximate long-range trajectories without a global optimization across all conditions simultaneously. Here we present SPARC, a graph-based optimization framework that quantifies similarity in a shared high-dimensional latent space and reconstruct directional trajectories under biological constraints. Global shortest-path optimization over this graph yields progression routes, from which SPARC derives path-based pseudotime identifies bottlenecks clusters, and detects gene temporal behavior. SPARC was evaluated across three complementary settings representing distinct trajectory-inference challenges. Its application to paired primary and lung metastatic osteosarcoma samples allows us to be the first to propose a cross-organ bone-like microenvironment hypothesis, in which osteoclastogenic signaling establishes a bone-like remodeling niche within the pulmonary metastatic lesion that promotes osteoclast differentiation and activity. The findings are independently recoverable in human osteosarcoma Visium HD spatial transcriptomics.