Biology subjects

Hallgrimsdottir, I. B.

Publications and source records attributed to Hallgrimsdottir, I. B..

7 recordsLinked to original sources

bioRxiv2026

A Unified Comparison of 10X Genomics and Parse Biosciences scRNA-seq Technologies

Droplet-based and split-pool barcoding are the two dominant high-throughput single-cell RNA-seq paradigms, yet their systematic biases remain incompletely characterized. We compared seven matched 10X Genomics Chromium and Parse Biosciences Evercode WT sample pairs from five studies spanning multiple tissues and species focusing on gene counts and compositional differences rather than cell-type resolution. By separating Parse reads by priming strategy, we distinguished biases arising from priming chemistry from platform-level effects. These effects were largely independent, with platform-level differences substantially larger. Priming strategy introduced a GC-content bias within Parse, whereas the two platforms showed a consistent compositional shift across priming strategies, supported by differential capture of nuclear- and cytoplasmic-enriched transcripts. We interpret this shift as a relative enrichment of nuclear RNA in Parse. Finally, using contamination measures that do not assume a droplet-specific model, we found background RNA contamination in Parse to be comparable in magnitude to that in 10X, contrary to claims of substantially lower contamination. Together, these results indicate that expression measurements and cell-type composition are not directly interchangeable between the two platforms.

genomics↗

bioRxiv2026

Hybrid crosses reveal a cell-type-specific landscape of mouse regulatory variation

Understanding the genetic architecture of gene expression is fundamental to evolutionary biology and medicine. As part of the IGVF Consortium, we present a single-nucleus RNA-seq resource of 5.3 million nuclei across eight tissue groups, featuring seven F1 hybrids from C57BL/6J dams crossed with the other Collaborative Cross founder strains for comparison against parental strains. We identify 25,864 genes (91% of those detected) exhibiting non-conserved regulatory behavior in at least one of 92 cell types in one or more crosses. Our results show that while cis-acting variation primarily drives divergence, trans-acting effects are substantially more cell-type specific and sensitive to tissue environment. Notably, bulk tissue analyses frequently mask these signals, particularly in smaller populations such as astrocytes. Furthermore, increasing genetic divergence primarily expands the landscape of cis-acting variation, while trans-acting effects remain stable across genetic distances within species. This atlas establishes a foundational framework for decoding the complex interplay between genetic variation and cell-type-specific regulation across the mammalian body.

genomics↗

bioRxiv2026

Single-Cell Genomics Decontamination with CellSweep

Single-cell genomics technologies enable high-throughput cell profiling, but technical contamination remains an obstacle to accurate downstream analysis. Free-floating ambient molecules released from lysed cells and global bulk contamination introduced during library preparation can distort molecular profiles. These artifacts can obscure cellular identities and reduce the reliability of differential analysis or clustering results. We present an efficient and effective approach to removing ambient and bulk contamination that can be applied to data generated from a wide variety of technologies. We show that our tool, CellSweep, outperforms other methods to remove artifacts using numerous benchmarks.

bioinformatics↗

bioRxiv2025

Systematic cell-type resolved transcriptomes of 8 tissues in 8 lab and wild-derived mouse strains captures global and local expression variation

Mapping the impact of genomic variation on gene expression facilitates an understanding of the molecular basis of complex phenotypic traits and disease predisposition. Mouse models provide a controlled and reproducible framework for capturing the breadth of genomic variation observed in different genotypes across a wide variety of tissues. As part of the IGVF consortiums effort to catalog the effects of genetic variation, we uniformly characterized the transcriptomes of eight tissues from each mouse founder strain used to derive the Collaborative Cross strains, comprising five classical laboratory inbred strains and three wild-derived inbred strains. We sequenced samples from four male and four female replicates per tissue using single-nucleus RNA-seq to generate an "8-cube" dataset of 5.2 million nuclei across 106 cell types and cell states. As expected, the overall extent of transcriptome variation correlates positively with genetic divergence across the strains with the greatest differential between PWK/PhJ and CAST/EiJ. At the individual tissue level, heart and brain are relatively more similar across strains compared with gonads, adrenal, skeletal muscle, kidney, and liver. Further analyses revealed substantial strain variation, often concentrated in a few cell types as well as cell-state signatures that especially reflect strain-associated immune and metabolic trait differences. The founder 8-cube dataset provides rich transcriptome variation signatures to help explain strain-specific phenotypic traits and disease states, as illustrated by examples in tissue-resident immune cells, muscle degeneration, kidney sex differences, and the hypothalamicpituitary-adrenal axis. This data further provides a systematic foundation for the analysis of these tissues in the founder strains as well as the Collaborative Cross.

genomics↗

bioRxiv2024

Estimating cis and trans contributions todifferences in gene regulation

We describe a coordinate system and associated hypothesis testing framework for determining whether cis or trans regulation is responsible for differences in gene expression between two homozygous strains or species. We apply our framework to data from single replicate studies on yeast strains and human-chimpanzee hybrid cells, as well as to data from a mouse study with replicates, showing marked differences between our gene regulatory assignments and those previously reported. We also show how our multi-sample framework can determine the context dependency of cis and trans effects as well as explicitly model different hypotheses regarding the underlying mechanism of trans regulation.

genetics↗

bioRxiv2023

Direct androgen receptor regulation of sexually dimorphic gene expression in the mammalian kidney

Mammalian organs exhibit distinct physiology, disease susceptibility and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA-seq data demonstrated sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR) mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation while analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, and disease and metabolic linkage, of sexually dimorphic gene activity.

developmental biology↗

bioRxiv2022

Depth normalization for single-cell genomics count data

Genomics data analysis requires normalization of feature counts that stabilizes technical variance, accounts for variable cell sequencing depth, and preserves monotonicity of within-cell feature abundances. We show that normalization via an optimal variance stabilizing transform for negative binomial count data followed by a proportional fitting step (PFlog) is the only feature-relabeling-equivariant method satisfying the three desiderata. We demonstrate superior performance of this method, which is equivalent to a shifted centered-log ratio transform, in comparison to other normalizations on numerous benchmarks across hundreds of single-cell RNA-seq datasets. We further show that both the shifted-log scale and centered-log ratio geometry are important for preserving PCA and k-NN structure.

bioinformatics↗