Jamie L. Marshall

The paper defines the chromatin landscape of healthy and injured kidney cells using multi-omics data, identifying regulatory elements and transcription factor networks involved in injury response.

The paper creates a high-resolution cellular atlas of healthy and injured kidney cell states and niches using single-cell and spatial technologies, identifying molecular signatures and pathways related to kidney injury.

This study evaluates background noise in single-cell RNA-seq data and assesses methods for its removal, finding that CellBender performs best for marker gene detection.

This study uses Slide-seqV2 to identify disease-specific cell neighborhoods and pathways in human and mouse kidneys.

The paper identifies obesity-related TREM2high macrophages in diabetic mouse and human kidney through single-cell transcriptomics and cross-species comparison.

HyPR-seq sensitively quantifies up to 100 chosen genes in single cells, achieving high sensitivity for individual transcripts and reducing costs compared to whole-transcriptome scRNA-seq.

The paper introduces CISI, a method that uses compressed sensing to efficiently recover spatial gene expression data from composite measurements, achieving high efficiency and scalability.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper improves Slide-seq technology to enhance mRNA capture sensitivity, enabling high-resolution spatial transcriptomics in mouse tissues.

The paper uses single-cell RNA-Seq to assess kidney organoid reproducibility across iPSC lines and shows transplantation reduces off-target cells.

This paper introduces Linked-Reads as a method to comprehensively analyze genome variation, showing it can detect both small and large variants and reconstruct haplotypes more effectively than short-read sequencing.

Transcriptome sequencing improves the diagnostic rate for Mendelian disease in patients for whom genetic analysis has not returned a diagnosis.

This paper systematically surveys X chromosome inactivation across human tissues using transcriptomic and genomic data, finding that XCI is generally uniform but shows heterogeneity and identifies new escape genes with sex-biased expression.