What happened
Single-cell multi-omics technologies simultaneously capture the transcriptome and TCR sequence of T cells, providing an opportunity to study the relationship between transcriptional states and clonal architectures. However, jointly modeling the relationships between transcriptional states and TCR sequences while preserving modality-specific information remains challenging. Here, we present ImmuneLens, an interpretable multimodal representation learning framework designed for paired single-cell transcriptome and TCR sequence data. ImmuneLens supports the construction of a transferable multi-cohort immune reference atlas and enables unsupervised mapping of external query data. The complementarity between GEX and TCR information improves the stability of antigen-specificity prediction. In neoadjuvant immunotherapy cohorts, ImmuneLens resolves response-associated T cell heterogeneity and reveals links between clonal expansion and CD8 T cell functional states. Overall, ImmuneLens provides a Update (2026-09-18): EM3DFold: accurate de novo protein and nucleic acid model building for cryo-EM maps using language model-powered deep learning . Cryo-electron microscopy (cryo-EM) has become one of the most powerful techniques for macromolecular structure determination. However, accurate model building from cryo-EM maps remains challenging, particularly for nucleic acids. Here, we present EM3DFold, a unified de novo model-building framework for accurate structure determination of proteins, nucleic acids, and protein-nucleic acid complexes from cryo-EM maps using a density-aware, large language model-powered three-track attention (TTA) network. The TTA network effectively integrates sequence, density, and structural information to enable accurate all-atom model building. EM3DFold was extensively evaluated on independent benchmarks of 298 experimental cryo-EM maps at < 4.0 [A] resolutions, and achieves an unprecedentedly high median accuracy of 75% completeness (88% coverage and 94% sequence accuracy) for 178 nucleic acid maps, 90% completeness (95% coverage and 96% sequence accuracy) for 124 protein-nucleic acid complexes, and 95% completeness (97% coverage and 98% sequen Update (2026-09-24): FlowMap: Geometry Dynamics Consistent Embedding of RNA Velocity for Interpretable Cellular Trajectories . Single-cell RNA sequencing reveals how cells vary across states, but these measurements capture only static snapshots of dynamic biological processes. RNA velocity addresses this limitation by estimating how gene expression is changing over time, providing directional information about cell state transitions. However, current approaches treat cellular state and dynamics separately, leading to representations that violate fundamental geometric consistency and obscure biological interpretation. Here, we present FlowMap, a framework that jointly models cellular states and their dynamics within a unified geometric representation. FlowMap simultaneously reconstructs a smooth low-dimensional manifold of gene expression and constrains RNA velocity to follow its local geometry, producing coherent and denoised representations of cellular trajectories. Across simulated and real datasets, FlowMap recovers interpretable dynamical patterns, including continuous progressions, branching events, cyclic behaviors, and stable-like states. It highlights key genes and ge Update (2026-09-24): Extracting interpretable single-cell metabolic states with graph-guided representation learning . Metabolism shapes cellular function and state, yet measuring single-cell metabolic states at scale remains a challenge. We present Metabolic Representation Net (MeRN), a graph-guided variational autoencoder that leverages prior metabolic knowledge as a topology graph to learn latent representations of metabolic state and reaction activity from single-cell transcriptomes. MeRN's scalable estimation of reaction activity enables the definition of data-driven pathways (DDPs): context-specific metabolic modules supported by transcriptomic evidence and agnostic of standard pathway definitions. Using DDPs, we introduce the weakest link analysis to identify metabolic network rewiring. MeRN recovers metabolic zonation in the mouse intestine, links a folate deficiency-induced break in de novo purine synthesis to embryonic neural tube defects, shows cytokines with similar non-metabolic effects can elicit divergent T cell metabolism, and identifies metabolic drivers of T cell exhaustion and therapy response in human cancers. Our results establish MeRN as a unified method Update (2026-09-24): Lossless compression of protein databases for efficient and accurate metagenomic sequence classification with Centrifuger . We present a lossless compression algorithm for indexing a protein database while supporting fast taxonomic classification in the method Centrifuger. The algorithm is a new scheme of the previously proposed run-block compression algorithm to reduce the size of the Ferragina-Manzini (FM) index, and it scales better with alphabet size than the original. On the RefSeq prokaryotic and viral protein sequences, Centrifuger reduces the memory footprint by over a third compared to the method Kaiju that builds on a plain FM-index, while having comparable running time. Furthermore, the compressed FM-index is lossless and can locate matches of arbitrary length, which helps Centrifuger achieve greater accuracy than Kraken2, a k-mer-based taxonomic classification method. We leverage the computational efficiency of Centrifuger to create an index of size 182 GB for classifying the reads against the full nr database that contains about 250 billion amino acid characters. Using this index, Centrifuger reveals different SARS-CoV-2 infection states and