Maigan A. Brusko

The paper profiles chromatin accessibility in post-sepsis MDSCs, showing divergent epigenetic trajectories between rapid recovery and chronic critical illness, with MHC class II repression in CCI.

This study uses spatial transcriptomics to identify inflammation signatures in pancreas and lymph node tissues of T1D patients, highlighting LTB and other genes involved in disease pathogenesis.

The paper uses 3D imaging to analyze pancreatic islets in healthy and type 1 diabetes (T1D) individuals, finding that islet size and composition influence their loss in T1D.

This study examines age and sex effects on MDSCs in sepsis using single-cell RNA sequencing, finding differences in expansion, transcriptome, and metabolism.

This study identifies aberrant immune regulation and stem-like CD8+ T cells in pancreatic lymph nodes during type 1 diabetes development using mass cytometry and single-cell RNA sequencing.

This paper describes the construction and usage of the Human Reference Atlas (HRA) v2.0, which integrates multiscale data across 36 organs and provides tools for analysis and visualization.

The paper uses imaging mass cytometry to analyze human pancreatic samples and identifies early β-cell dysfunction and immune changes in type 1 diabetes progression.

This study compares transcriptional profiles of memory T cells in infants and adults to identify age-specific regulatory mechanisms.

The paper introduces CRAWDAD, a tool for analyzing cell-type spatial relationships across scales in spatially resolved omics data, and demonstrates its application on simulated and real datasets.

This study shows that blocking CD226 reduces diabetes in NOD mice by enhancing Treg function and reducing effector T cell activity.

The paper examines islet and acinar cell secretion in different pancreatic regions across diabetes stages, finding reduced insulin secretion and beta-cell volume in T1D with normal enzyme secretion.

The paper introduces Escort, a framework to evaluate dataset suitability for trajectory inference and quantify trajectory properties, reducing analysis uncertainty through data-driven assessments.

geneBasis selects an optimal gene panel from scRNA-seq data by iteratively adding genes that maximize manifold distance, outperforming existing methods in resolving cell types and states.