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Developers have married gene-modulating oligonucleotides with the targeted precision of antibodies, and the first filings using such conjugates in Duchenne muscular dystrophy are imminent.
Comprehensively resolving the cell state landscape requires integrating single-cell omics data from diverse studies. We developed CONCORD, a contrastive learning framework that leverages principled mini-batch sampling to learn denoised, batch-integrated and high-resolution representations of cells, capturing intricate structures such as differentiation trajectories and cell-cycle loops across numerous biological contexts.
Our understanding of the genetic mechanisms underlying rare diseases has rapidly advanced over the past decade, largely because of technological innovations. Yet clinical practice still has a strong monogenic focus, leaving many individuals undiagnosed. This Comment outlines how technological advances such as long-read sequencing should be adopted to increase multivariant testing in the clinic.
RNA’s dynamic nature and complex physiochemical properties make it difficult to structurally resolve. This Perspectives examines how integrating experimental and computational approaches to structure determination can address this challenge.
Although base editing has potential as a gene therapy tool, bystander edits limit its clinical use for many pathogenic mutations. This work uses directed evolution to optimize adenine base editors and 3′-extended guide RNAs to enhance targeting, producing more precise editors with reduced bystander effects.
