Nature Methods最新文献

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer's disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor-tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems.

High-density microelectrode arrays have opened new possibilities for systems neuroscience, but brain motion relative to the array poses challenges for downstream analyses. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from action potential data, DREDge enables automated, high-temporal-resolution motion tracking in local field potential data. In human intraoperative recordings, DREDge's local field potential-based tracking reliably recovered evoked potentials and single-unit spike sorting. In recordings of deep probe insertions in nonhuman primates, DREDge tracked motion across centimeters of tissue and several brain regions while mapping single-unit electrophysiological features. DREDge reliably improved motion correction in acute mouse recordings, especially in those made with a recent ultrahigh-density probe. Applying DREDge to recordings from chronic implantations in mice yielded stable motion tracking despite changes in neural activity between experimental sessions. These advances enable automated, scalable registration of electrophysiological data across species, probes and drift types, providing a foundation for downstream analyses of these rich datasets.

RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.