Neuroinflammation drives neurodegeneration and cognitive decline in aging, but the signals that spread this inflammation across the CNS remain unclear. Here, we show in mice that advanced age and experimental autoimmune encephalomyelitis both result in elevated cytosolic mtDNA in microglia.
Microbial genomes encode essential ncRNAs and nucleic-acid-interacting proteins. However, discovery of these systems remains based on similarity to known sequences. Here, we introduce Minerva, a framework for coevolutionary mining that uses genome language models to predict pairwise interactions directly from sequence.
Tahoe-100M, generated by Tahoe, is a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform.
Here, we introduce scBaseCount, a database that leverages an AI agent to automate discovery and metadata extraction and standardize data processing. It is the largest public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues.
The mechanism underlying the role of ENPP1 in metabolic disease remains unsolved. Using a 2′3′-cyclic GMP-AMP (cGAMP)-hydrolysis-deficient mouse (Enpp1H362A), we show that selective loss of this activity exacerbates high-fat diet (HFD)-induced weight gain and insulin resistance.
The DNA damage response (DDR) is a sophisticated network of cellular pathways whose perturbation leads to genome instability and is a key hallmark of oncogenesis. Here, we present data from 32 genome-scale loss-of-function CRISPR interference chemical–genetic screens with inhibitors targeting core constituents of the DDR machinery.
his review summarizes respiratory and fermentative electron acceptors across eukaryotes, focusing on the evolution of anaerobic pathways. We highlight how ancient metabolic strategies have been conserved and modified to support organisms’ adaptation to diverse environmental niches and to sustain biological resilience.
While machine learning models offer potential for predicting transcriptomic effects of perturbation, they currently struggle to generalize across cellular contexts. Here, we introduce State, a machine learning model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments.
The Virtual Cell Challenge returns in 2026 with a more demanding test of biological generalization: zero-shot prediction across multiple independent cellular contexts. Participants will build models to predict gene knockdown responses in a new Arc-generated dataset comprising unseen cell lines.
Three-dimensional genome organization shapes transcriptional regulation, yet measuring its spatial coordination in situ within intact tissues remains challenging. We present Spatial Hi-C-RNA, a multimodal platform that simultaneously maps genome-wide chromatin contacts and transcriptomes from the same tissue section at near-single-cell resolution.