N Nature Methods · Oct 27, 2025 Improved reconstruction of single-cell developmental potential with CytoTRACE 2 While single-cell RNA sequencing has advanced our understanding of cell fate, identifying molecular hallmarks of potency—a cell’s ability to differentiate into other cell types—remains a challenge. Here we introduce CytoTRACE 2, an interpretable deep learning framework for predicting absolute developmental potential from single-cell RNA sequencing data. Across diverse platforms and tissues, CytoTRACE 2 outperformed previous methods in predicting developmental hierarchies, enabling detailed mapping of single-cell differentiation landscapes and expanding insights into cell potency. Cancer genomics Machine learning Software Stem cells Transcriptomics biology
N Nature Methods · Sep 29, 2025 Highly multiplexed 3D profiling of cell states and immune niches in human tumors Diseases such as cancer involve alterations in cell proportions, states and interactions, as well as complex changes in tissue morphology and architecture. Histopathological diagnosis of disease and most multiplexed spatial profiling relies on inspecting thin (4–5 µm) specimens. Here we describe a high-plex cyclic immunofluorescence method for three-dimensional tissue imaging and use it to show that few, if any, cells are intact in conventional thin tissue sections, reducing the accuracy of cell phenotyping and interaction analysis. However, three-dimensional cyclic immunofluorescence of sections eightfold to tenfold thicker enables accurate morphological assessment of diverse protein markers in intact tumor, immune and stromal cells. Moreover, the high resolution of this confocal approach generates images of cells in a preserved tissue environment at a level of detail previously limited to cell culture. Precise imaging of cell membranes also makes it possible to detect and map cell–cell contacts and juxtracrine signaling complexes in immune cell niches. Cancer Cancer microenvironment Cell signalling Cellular imaging Tumour heterogeneity Cancer Immunology Human Single-cell
N Nature Methods · Sep 15, 2025 Cancer subclone detection based on DNA copy number in single-cell and spatial omic sequencing data Somatic mutations such as copy number alterations accumulate during cancer progression, driving intratumor heterogeneity that impacts therapy effectiveness. Understanding the characteristics and spatial distribution of genetically distinct subclones is essential for unraveling tumor evolution and improving cancer treatment. Here we present Clonalscope, a subclone detection method using copy number profiles, applicable to spatial transcriptomics and single-cell sequencing data. Clonalscope implements a nested Chinese Restaurant Process to identify de novo tumor subclones, which can incorporate prior information from matched bulk DNA sequencing data for improved subclone detection and malignant cell labeling. On single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing data from gastrointestinal tumors, Clonalscope successfully labeled malignant cells and identified genetically different subclones with thorough validations. On spatial transcriptomics data from various primary and metastasized tumors, Clonalscope labeled malignant spots, traced subclones and identified spatially segregated subclones with distinct differentiation levels and expression of genes associated with drug resistance and survival. Cancer genomics Genomics Software Statistical methods Tumour heterogeneity Cancer Single-cell Genomics Machine Learning Drug Development
