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Background Single-cell RNA sequencing is a powerful tool for characterizing cellular heterogeneity in gene expression. However, high variability and a large number of zero counts present challenges for analysis and interpretation. There is substantial controversy over the origins and proper treatment of zeros and no consensus on whether zero-inflated count distributions are necessary or even useful. While some studies assume the existence of zero inflation due to technical artifacts and attempt to impute the missing information, other recent studies argue that there is no zero inflation in scRNA-seq data. Results We apply a Bayesian model selection approach to unambiguously demonstrate zero inflation in multiple biologically realistic scRNA-seq datasets. We show that the primary causes of zero inflation are not technical but rather biological in nature. We also demonstrate that parameter estimates from the zero-inflated negative binomial distribution are an unreliable indicator of zero inflation. Conclusions Despite the existence of zero inflation in scRNA-seq counts, we recommend the generalized linear model with negative binomial count distribution, not zero-inflated, as a suitable reference model for scRNA-seq analysis.

Little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand these changes, we measured mRNA and protein levels in genetically diverse mice at different ages. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.

Enhlink is a computational tool for scATAC-seq data analysis, facilitating precise interrogation of enhancer function at the single-cell level. It employs an ensemble approach incorporating technical and biological covariates to infer condition-specific regulatory DNA linkages. Enhlink can integrate multi-omic data for enhanced specificity, when available. Evaluation with simulated and real data, including multi-omic datasets from the mouse striatum and novel promoter capture Hi-C data, demonstrate that Enhlink outperfoms alternative methods. Coupled with eQTL analysis, it identified a putative super-enhancer in striatal neurons. Overall, Enhlink offers accuracy, power, and potential for revealing novel biological insights in gene regulation.

Background Age is the principal risk factor for neurodegeneration in both the retina and brain. The retina and brain share many biological properties; thus, insights into retinal aging and degeneration may shed light onto similar processes in the brain. Genetic makeup strongly influences susceptibility to age-related retinal disease. However, studies investigating retinal aging have not sufficiently accounted for genetic diversity. Therefore, examining molecular aging in the retina across different genetic backgrounds will enhance our understanding of human-relevant aging and degeneration in both the retina and brain—potentially improving therapeutic approaches to these debilitating conditions. Methods Transcriptomics and proteomics were employed to elucidate retinal aging signatures in nine genetically diverse mouse strains (C57BL/6J, 129S1/SvlmJ, NZO/HlLtJ, WSB/EiJ, CAST/EiJ, PWK/PhK, NOD/ShiLtJ, A/J, and BALB/cJ) across lifespan. These data predicted human disease-relevant changes in WSB and NZO strains. Accordingly, B6, WSB, and NZO mice were subjected to human-relevant in vivo examinations at 4, 8, 12, and/or 18M, including: slit lamp, fundus imaging, optical coherence tomography, fluorescein angiography, and pattern/full-field electroretinography. Retinal morphology, vascular structure, and cell counts were assessed ex vivo . Results We identified common molecular aging signatures across the nine mouse strains, which included genes associated with photoreceptor function and immune activation. Genetic background strongly modulated these aging signatures. Analysis of cell type-specific marker genes predicted age-related loss of photoreceptors and retinal ganglion cells (RGCs) in WSB and NZO, respectively. Fundus exams revealed retinitis pigmentosa-relevant pigmentary abnormalities in WSB retinas and diabetic retinopathy (DR)-relevant cotton wool spots and exudates in NZO retinas. Profound photoreceptor dysfunction and loss were confirmed in WSB. Molecular analyses indicated changes in photoreceptor-specific proteins prior to loss, suggesting photoreceptor-intrinsic dysfunction in WSB. In addition, age-associated RGC dysfunction, loss, and concomitant microvascular dysfunction were observed in NZO mice. Proteomic analyses revealed an early reduction in protective antioxidant processes, which may underlie increased susceptibility to DR-relevant pathology in NZO. Conclusions Genetic context is a strong determinant of retinal aging, and our multi-omics resource can aid in understanding age-related diseases of the eye and brain. Our investigations identified and validated WSB and NZO mice as improved preclinical models relevant to common retinal neurodegenerative diseases.

An amendment to this paper has been published and can be accessed via the original article.

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions have previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here, we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic–epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic–epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF-binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of 3D chromatin structure.

We determined that extrachromosomal 2μ plasmid was present in 67 of the Saccharomyces cerevisiae 100-genome strains; in addition to variation in the size and copy number of 2μ, we identified three distinct classes of 2μ. We identified 2μ presence/absence and class associations with populations, clinical origin and nuclear genotypes. We also screened genome sequences of S. paradoxus, S. kudriavzevii, S. uvarum, S. eubayanus, S. mikatae, S. arboricolus and S. bayanus strains for both integrated and extrachromosomal 2μ. Similar to S. cerevisiae, we found no integrated 2μ sequences in any S. paradoxus strains. However, we identified part of 2μ integrated into the genomes of some S. uvarum, S. kudriavzevii, S. mikatae and S. bayanus strains, which were distinct from each other and from all extrachromosomal 2μ. We identified extrachromosomal 2μ in one S. paradoxus, one S. eubayanus, two S. bayanus and 13 S. uvarum strains. The extrachromosomal 2μ in S. paradoxus, S. eubayanus and S. cerevisiae were distinct from each other. In contrast, the extrachromosomal 2μ in S. bayanus and S. uvarum strains were identical with each other and with one of the three classes of S. cerevisiae 2μ, consistent with interspecific transfer. We show extensive 2μ sequence variation in Saccharomyces cerevisiae and in other species of Saccharomyces and 2μ associations with S. cerevisiae populations, clinical origin and genotypes.

Data from human and model organism studies suggest that genetic background influences susceptibility and resilience to Alzheimer's Disease (AD) neuropathology. We previously showed that, wild-derived PWK/PhJ (PWK) mice carrying the APP/PS1 transgene (PWK.APP/PS1) exhibit cognitive and synaptic resilience compared to traditionally-studied B6.APP/PS1 inbred mice in presence of amyloid beta (Aβ) plaque deposition. PWK.APP/PS1 mice also contain different proportions of transcriptionally-defined microglia compared to B6.APP/PS1. The precise molecular mechanisms underlying these differences between strains remain unknown. In this study, we aim to identify and characterize cell populations in the hippocampi of genetically distinct B6 and PWK mouse strains, and investigate cell-type specific gene expression patterns associated with cognitive resilience in presence of amyloid deposition. We generated cohorts of B6.APP/PS1 and PWK.APP/PS1 transgenic female mice and wild-type (WT) littermates as controls (n = 4 per strain/genotype group). At 8 months of age mice were euthanized, hippocampi collected, and single nuclear RNA sequencing (snRNAseq) was performed using 10x Genomics Chromium platform. Seurat R package (version 5.0.1) was used for major data analysis. We identified subpopulations of microglial cells characterized by varying expression patterns of a set of genes including Hk2, Itgam and Dock8. Neuronal subtypes were clustered by top marker genes Plk5 and Adarb2. Oligodendrocytes, oligodendrocyte progenitor cells, and endothelial cells were marked by expression of Mog, Neu4 and Abcc9, respectively. Differential expression and pathway enrichment analysis revealed strain-specific immune and cell signaling pathways altered in distinct cell populations in response to amyloid pathogenesis. Cross-species mapping of these transcriptomic signatures with human hippocampal gene modules identified AD-relevant molecular mechanisms observed in mouse models of AD at the cell-type level. This study suggests that cell-type specific transcriptomic response to amyloid is modulated by genetic background and our findings re-iterate the importance of incorporating genetic diversity to model phenotypic and molecular heterogeneity in AD. Since the nuclei enriched in this dataset are enriched for neurons, future interrogations will provide evidence for specific neuronal populations and molecular mechanisms that differentiate cognitively resilient PWK from susceptible B6 mice, providing insights into mechanisms that can be leveraged to promote AD resilience.

Systems genetics exploits natural genetic variation to associate molecular variation with complex traits. It is often impossible to measure complex traits and molecular intermediates on the same individuals and independent cohorts are used... Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

Genetic variation leads to phenotypic variability in pluripotent stem cells that presents challenges for regenerative medicine. Although recent studies have investigated the impact of genetic variation on pluripotency maintenance and differentiation capacity, less is known about how genetic variants affecting the pluripotent state influence gene regulation later in development. Here, we characterized expression of 12,000 genes in a large panel of donor-matched Diversity Outbred mouse embryonic stem cell and mouse neural progenitor cell lines. QTL mapping identified 4,060 expression QTLs in mouse neural progenitor cells, including 2,998 local and 1,062 distant expression QTLs. In a comparison of mouse neural progenitor cell and mouse embryonic stem cell expression QTLs, we found that local expression QTLs were more likely than distant expression QTL to be detected in both cell types. Distant expression QTLs were largely unique to 1 cell type, and we mapped 3 mouse neural progenitor cell–specific expression QTL hotspots on chromosomes 1, 10, and 11. Mediation analysis of the chromosome 1 hotspot identified Rnf152 as the best candidate mediator expressed in mouse neural progenitor cells, while cross-cell-type mediation using mouse embryonic stem cell gene expression along with partial correlation analysis strongly implicated genetic variant(s) affecting Pign expression in the mouse embryonic stem cell state as regulating the mouse neural progenitor cell chromosome 1 hotspot. These findings highlight that local mouse neural progenitor cell expression QTLs are more likely than distant expression QTLs to be shared with mouse embryonic stem cells; distant mouse neural progenitor cell expression QTLs are numerous but largely unique to that cell type, with many colocalizing to mouse neural progenitor cell–specific hotspots; and mediation analysis across cell types suggests that expression of Pign in mouse embryonic stem cells shapes the transcriptome of the more specialized mouse neural progenitor cell state.