Biology subjects

Demaree, B.

Publications and source records attributed to Demaree, B..

3 recordsLinked to original sources

bioRxiv2025

The Immunophenotype and Proviral Landscape of HIV-infected CD4 T Cells During Antiretroviral Therapy

In individuals on effective antiretroviral therapy, integrated HIV proviruses persist within CD4 T cells, forming a viral reservoir that rebounds if treatment is stopped. Identifying and targeting these rare, infected cells is critical for advancing therapies, but methods to study reservoir cells are limited and their unique properties remain largely unknown. We applied DAb-seq, a high-throughput method that combines single-cell DNA and surface protein sequencing, to profile over five hundred and twenty thousand CD4 T cells from the blood of six individuals on ART. Infected cells were unequally distributed in T cell subsets, and differential protein expression between infected and uninfected cells revealed significant heterogeneity across cell subsets. Attempts to identify surface markers that differentiate infected from uninfected cells found antigens that mirrored the enrichment of HIV in central memory subsets. However, while central memory T cells harbored the majority of HIV, cells with intact provirus were enriched relative to their defective counterparts in Naive and Regulatory T cell subsets, suggesting that they differentially maintain intact proviruses. In summary, we developed DAb-seq as an open-source platform for linking the proviral landscape to diverse cellular phenotypes, revealing heterogeneity in surface protein expression and provirus maintenance across infected subsets.

genomics↗

bioRxiv2023

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters genome by sequence similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

bioengineering↗

bioRxiv2020

Joint profiling of proteins and DNA in single cells reveals extensive proteogenomic decoupling in leukemia

Studies of acute myeloid leukemia rely on DNA sequencing and immunophenotyping by flow cytometry as primary tools for disease characterization. However, leukemia tumor heterogeneity complicates integration of DNA variants and immunophenotypes from separate measurements. Here we introduce DAb-seq, a novel technology for simultaneous capture of DNA genotype and cell surface phenotype from single cells at high throughput, enabling direct profiling of proteogenomic states in tens of thousands of cells. To demonstrate the approach, we analyze the disease of three patients with leukemia over multiple treatment timepoints and disease recurrences. We observe complex genotype-phenotype dynamics that illustrate the subtlety of the disease process and the degree of incongruity between blast cell genotype and phenotype in different clinical scenarios. Our results highlight the importance of combined single-cell DNA and protein measurements to fully characterize the heterogeneity of leukemia.

cancer biology↗