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In this Comment, Agustín Robles-Remacho and Mats Nilsson highlight the opportunities and challenges of using spatial transcriptomics to detect and localize microRNAs in biological tissues, and advocate for the increased development of existing spatial transcriptomics methods.
Reflecting on the core values of early data sharing agreements, the Bermuda Principles and the Fort Lauderdale Agreement, Kathryn E. Holt and Michael Inouye emphasize the need to reaffirm our commitment to genomic data sharing to shape the future of science.
In this Tools of the Trade article, Ankit Agrawal introduces the computational framework NiCo (Niche Covariation), which integrates spatial transcriptomics with single-cell RNA-sequencing data to study cell–cell communication.
In this Journal Club, Minoli Perera reflects on a 2005 sequencing study by Cohen et al., who discovered two common loss-of-function mutations with large effects on plasma cholesterol levels thanks to the inclusion of African American study participants.
In this Journal Club, Maanasa Raghavan recalls a 1984 paper by Higuchi et al. that demonstrated how sequencing ancient DNA provides unique evolutionary insights.
In this Review, James et al. provide an overview of approaches for planning, constructing, and fine-tuning synthetic genomes and describe their potential applications.
Loss of Y chromosome (LOY), the most commonly occurring post-zygotic (somatic) mutation in male individuals, affects immune activity and is associated with cancer, neurodegeneration, cardiovascular disease and infection. LOY is dynamic over time and has cell-type-specific effects, suggesting its potential as a biomarker and therapeutic target.
Studying germline variants and somatic mutations in cancer using omics technologies helps identify both heritable traits and molecular features of cancer genomes. Population-specific cancer genomics can reduce disparities and ensure equity across racial and ethnic groups for personalized medicine and public health approaches.
Epigenetic clocks based on DNA methylation data are machine learning tools used to estimate chronological and biological age. The authors review computational and statistical challenges that must be addressed for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.
