Biology subjects

Conrad, D. N.

Publications and source records attributed to Conrad, D. N..

5 recordsLinked to original sources

bioRxiv2026

Single cell RNA-sequencing reveals an association between testosterone treatment and reduced hormone signaling in the human mammary gland

Testosterone is the most studied androgen in hormone replacement therapy in postmenopausal women. Many transgender men also receive long-term testosterone replacement therapy (TRT) and have high serum testosterone levels compared to the low levels seen in cisgender women. Compared to other sex hormones such as estrogen and progesterone, the effects of testosterone on the mammary gland have been relatively understudied and there is little data regarding the long term safety of this treatment. Comparison of mammary glands from transgender men on TRT and cisgender women can reveal the effects of testosterone treatment on mammary gland biology and provide critical information regarding the long-term effects of TRT on patient health and disease outcomes. In this study, we performed single-cell RNA sequencing of breast tissues from a demographics-matched cohort of cisgender women and transgender men on TRT. Surprisingly, participants on TRT had unchanged serum levels of estradiol compared to controls. Among the observed transcriptional differences for participants on TRT were a dramatically reduced expression of genes downstream of estrogen signaling pathways in hormone receptor positive (HR+) luminal epithelial cells, as well as a decreased overall menstrual cycle-related hormone signaling. We confirmed this finding experimentally by showing reduced expression of progesterone receptor a/b, a prominent marker of estrogen signaling, in donors on TRT. Our results support the hypothesis that high levels of testosterone in transgender men on TRT suppress sex hormone signaling in the breast as seen on their impact on HR+ mammary epithelial cells, with implications for TRT as an antagonist of estrogen signaling and protection against breast cancer.

cell biology↗

bioRxiv2025

Reducing batch effects in single cell chromatin accessibility measurements by pooled transposition with MULTI-ATAC

Large-scale scATAC-seq experiments are challenging because of their costs, lengthy protocols, and confounding batch effects. Several sample multiplexing technologies aim to address these challenges, but do not remove batch effects introduced when performing transposition reactions in parallel. We demonstrate that sample-to-sample variability in nuclei-to-Tn5 ratios is a major cause of batch effects and develop MULTI-ATAC, a multiplexing method that pools samples prior to transposition, as a solution. MULTI-ATAC provides high accuracy in sample classification and doublet detection while eliminating batch effects associated with variable nucleus-to-Tn5 ratio. We illustrate the power of MULTI-ATAC by performing a 96-plex multiomic drug assay targeting epigenetic remodelers in a model of primary immune cell activation, uncovering tens of thousands of drug-responsive chromatin regions, cell-type specific effects, and potent differences between matched inhibitors and degraders. MULTI-ATAC therefore enables batch-free and scalable scATAC-seq workflows, providing deeper insights into complex biological processes and potential therapeutic targets.

molecular biology↗

bioRxiv2024

Selection promotes age-dependent degeneration of the mitochondrial genome

Somatic mutations in mitochondrial genomes (mtDNA) accumulate exponentially during aging. Using single cell sequencing, we characterize the spectrum of age-accumulated mtDNA mutations in mouse and human liver and identify directional forces that accelerate the accumulation of mutations beyond the rate predicted by a neutral model. "Driver" mutations that give genomes a replicative advantage rose to high cellular abundance and carried along "passenger" mutations, some of which are deleterious. In addition, alleles that alter mtDNA-encoded proteins selectively increased in abundance overtime, strongly supporting the idea of a "destructive" selection that favors genomes lacking function. Overall, this combination of selective forces acting in hepatocytes promotes somatic accumulation of mutations in coding regions of mtDNA that are otherwise conserved in evolution. We propose that these selective processes could contribute to the population prevalence of mtDNA mutations, accelerate the course of heteroplasmic mitochondrial diseases and promote age-associated erosion of the mitochondrial genome.

genomics↗

bioRxiv2023

Demultiplex2: robust sample demultiplexing for scRNA-seq

Single-cell sample multiplexing technologies function by associating sample-specific barcode tags with cell-specific barcode tags, thereby increasing sample throughput, reducing batch effects, and decreasing reagent costs. Computational methods must then correctly associate cell-tags with sample-tags, but their performance deteriorates rapidly when working with datasets that are large, have imbalanced cell numbers across samples, or are noisy due to cross-contamination among sample tags - unavoidable features of many real-world experiments. Here we introduce deMULTIplex2, a mechanism-guided classification algorithm for multiplexed scRNA-seq data that successfully recovers many more cells across a spectrum of challenging datasets compared to existing methods. deMULTIplex2 is built on a statistical model of tag read counts derived from the physical mechanism of tag cross-contamination. Using generalized linear models and expectation-maximization, deMULTIplex2 probabilistically infers the sample identity of each cell and classifies singlets with high accuracy. Using Randomized Quantile Residuals, we show the model fits both simulated and real datasets. Benchmarking analysis suggests that deMULTIplex2 outperforms existing algorithms, especially when handling large and noisy single-cell datasets or those with unbalanced sample compositions.

bioinformatics↗

bioRxiv2018

MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices

We describe MULTI-seq: A rapid, modular, and universal scRNA-seq sample multiplexing strategy using lipid-tagged indices. MULTI-seq reagents can barcode any cell type from any species with an accessible plasma membrane. The method is compatible with enzymatic tissue dissociation, and also preserves viability and endogenous gene expression patterns. We leverage these features to multiplex the analysis of multiple solid tissues comprising human and mouse cells isolated from patient-derived xenograft mouse models. We also utilize MULTI-seqs modular design to perform a 96-plex perturbation experiment with human mammary epithelial cells. MULTI-seq also enables robust doublet identification, which improves data quality and increases scRNA-seq cell throughput by minimizing the negative effects of Poisson loading. We anticipate that the sample throughput and reagent savings enabled by MULTI-seq will expand the purview of scRNA-seq and democratize the application of these technologies within the scientific community.

genomics↗