Lovelace J. Luquette

The paper investigates somatic copy number variants (CNVs) in human neurons across development, finding that perinatal elimination of neurons with aberrant genomes reduces CNV prevalence and targets dosage-sensitive genes.

The paper compares somatic mutation rates and transcriptomic changes in neurons across six mammalian species, finding higher mutation accumulation and distinct age-related dysregulation in human neurons.

The paper identifies a shared TOP1-mediated mutagenic mechanism in ALS, FTD, and AD through single-cell genomic analysis and experimental validation.

This study benchmarks six duplex sequencing technologies to evaluate their performance in detecting somatic mutations across different genomic contexts and tissue types.

The paper uses single-cell DNA sequencing to analyze somatic mosaicism in postmortem lung and colon tissues, revealing mutation heterogeneity, chromosomal abnormalities, and cellular ancestry.

HiScanner detects high-resolution copy number alterations in single cells, outperforming existing methods and revealing distinct CNA patterns in neurons and oligodendrocytes.

The paper compares somatic mutation rates in neurons and oligodendrocytes across aging human brains, finding faster mutation accumulation in oligodendrocytes and distinct genomic distribution patterns.

The paper introduces PTA for single-cell DNA sequencing and SCAN2 to detect somatic SNVs and indels in single neurons, finding age-related increases in both with indels having greater functional impact.

This study identifies somatic SNVs in non-cancerous human brain tissues and establishes best practices for detecting low-frequency variants.

This paper challenges the predictive value of tumor mutational burden (TMB) for immunotherapy response, finding little evidence of its association and suggesting previous studies had methodological flaws.

The paper analyzes somatic mutations in autistic and control brains, finding higher mutation rates in ASD brains, particularly in enhancer regions.

The paper introduces LiRA, a method to distinguish true mutations from artifacts in single-cell DNA sequencing data by phasing somatic variants with germline variants.

The paper identifies age-related accumulation of somatic mutations in human neurons and links them to neurodegenerative diseases.