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Hansen, K. D.

Publications and source records attributed to Hansen, K. D..

At least 19 recordsLinked to original sources

Orchestrating Spatial Transcriptomics Analysis with Bioconductor

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

bioinformatics↗

Shared molecular consequences of epigenetic machinery disruption in neuronal progenitors

The Mendelian disorders of the epigenetic machinery (MDEMs) are an emerging cause of intellectual disability and growth abnormalities, which commonly disrupt hippocampal function. To investigate consequences of epigenetic machinery (EM) disruption during neurodevelopment, we systematically knocked out (KO) EM factors in neuronal progenitors isolated from the murine hippocampus and established a neurodifferentiation model to interrogate their functions. We then profiled gene expression and DNA methylation (DNAm) in the EM-KOs using RNA sequencing and Nanopore long-read DNA sequencing. While Dnmt1-KO induced extensive DNAm alterations, Kmt2a-KO had little effect on methylation. Nevertheless, the disruption of Kmt2a and Dnmt1 produced strikingly convergent transcriptional changes. Loss of either EM factor led to premature neuronal differentiation, partially explaining this convergence, and MYC appeared as a shared regulatory node linked to downregulation of cell-cycle programs in these cells. Extending our methylation analysis to 46 EM genes, we found that loss of DNA methyltransferases induced the strongest DNAm changes, whereas other EM-KOs had subtle or negligible effects. However, clustering of EM-KOs based on promoter DNAm levels revealed three distinct EM subgroups, of which two were enriched for interactions with the DNAm machinery. Allele-specific analysis of DNAm further identified a single differentially methylated region shared across the 46 EM-KOs, localized to the FVB allele over the Zic4 3UTR. Furthermore, Zic4 overexpression appears to maintain the neuronal progenitor state, suggesting functional relevance of this locus. Taken together, our results reveal both gene-specific and convergent effects across diverse EM-KOs and provide new insight into the molecular etiology of the MDEMs.

genomics↗

Sample-specific CpG loci are important for accurate long-read methylation analysis

Methylated CpG loci mutate at high rates, but it remains challenging to identify sample-specific non-reference CpG loci for methylation analyses. Here, we present a likelihood-based method that identifies sample-specific CpG loci at high precision in long-read sequencing data from Nanopore and PacBio platforms. Inclusion of non-reference CpG loci increased the number of loci and reduced reference bias, and is important for accurate estimation of global methylation levels across samples.

genomics↗

Single Cell Spatial Transcriptomics Reveals Immunotherapy-Driven Bone Marrow Niche Remodeling in AML

Given the successful graft-versus-leukemia cell treatment effect observed with allogeneic hematopoietic stem cell transplant for patients with refractory or relapsed acute myeloid leukemia, immunotherapies have also been investigated in the nontransplant setting. Here, we use a multi-omic approach to investigate spatiotemporal interactions in the bone marrow niche between leukemia cells and immune cells in patients with refractory or relapsed acute myeloid leukemia treated with a combination of the immune checkpoint inhibitor pembrolizumab and hypomethylating agent decitabine. We derived precise segmentation data by extensively training nuclear and membrane cell segmentation models, which enabled accurate transcript assignment and deep learning-feature-based image analysis. To overcome read-depth limitations, we integrated the single-cell RNA sequencing data with single-cell-resolution spatial transcriptomic data from the same sample. Quantifying cell-cell distances between cell edges rather than cell centroids allowed us to conduct a more accurate downstream analysis of the tumor microenvironment, revealing that multiple cell types of interest had global enrichment or local enrichment proximal to leukemia cells after pembrolizumab treatment, which could be associated with their clinical responses. Furthermore, ligand-receptor analysis indicated a potential increase in TWEAK signaling between leukemia cells and immune cells after pembrolizumab treatment. HighlightsO_LISpatial transcriptomic analysis of R-AML bone marrow niches provides detailed information about intercellular interactions in the tumor microenvironment. C_LIO_LIImmunotherapy shifts the cell composition of the leukemia neighborhood. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/634753v1_ufig1.gif" ALT="Figure 1"> View larger version (93K): org.highwire.dtl.DTLVardef@955273org.highwire.dtl.DTLVardef@158234corg.highwire.dtl.DTLVardef@175837borg.highwire.dtl.DTLVardef@b0a38_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Spatially-resolved molecular sex differences at single cell resolution in the adult human hypothalamus

The hypothalamus contains multiple regions, including the ventromedial hypothalamus (VMH) and arcuate (ARC), which are responsible for sex-differentiated functions such as endocrine signaling, metabolism, and reproductive behaviors. While molecular, anatomic, and sex-differentiated features of rodent hypothalamus are well-established, much less is known about these regions in humans. Here we provide a spatially-resolved single cell atlas of sex-differentially expressed (sex-DE) genes in human VMH and ARC. We identify neuronal populations governing hypothalamus-specific functions, define their spatial distributions, and show increased retinoid pathway gene expression compared to rodents. Within VMH and ARC, we find correlated autosomal expression differences localized to ESR1/TAC3-expressing and CRHR2-expressing neurons, and extensive sex-DE of genes linked to sex-biased disorders including autism, depression, and schizophrenia. Our molecular mapping of disease associations to hypothalamic cell types with established roles in sex-divergent physiology and behavior provides insights into mechanistic bases of sex bias in neurodevelopmental and neuropsychiatric disorders.

genomics↗

Multi-sample non-negative spatial factorization

Analyzing multi-sample spatial transcriptomics data requires accounting for biological variation. We present multi-sample non-negative spatial factorization (mNSF), an alignment-free framework extending single-sample spatial factorization (NSF) to multi-sample datasets. mNSF incorporates sample-specific spatial correlation modeling and extracts low-dimensional data representations. Through simulations and real data analysis, we demonstrate mNSFs efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSFs performance is comparable to alignment-based methods when alignment is feasible, while enabling analysis in scenarios where spatial alignment is unfeasible. mNSF shows promise as a robust method for analyzing spatially resolved transcriptomics data across multiple samples.

genomics↗

Genotype prediction of 336,463 samples from public expression data

Tens of thousands of RNA-sequencing experiments comprising hundreds of thousands of individual samples have now been performed. These data represent a broad range of experimental conditions, sequencing technologies, and hypotheses under study. The Recount project has aggregated and uniformly processed hundreds of thousands of publicly available RNA-seq samples. Most of these samples only include RNA expression measurements; genotype data for these same samples would enable a wide range of analyses including variant prioritization, eQTL analysis, and studies of allele specific expression. Here, we developed a statistical model based on the existing reference and alternative read counts from the RNA-seq experiments available through Recount3 to predict genotypes at autosomal biallelic loci in coding regions. We demonstrate the accuracy of our model using large-scale studies that measured both gene expression and genotype genome-wide. We show that our predictive model is highly accurate with 99.5% overall accuracy, 99.6% major allele accuracy, and 90.4% minor allele accuracy. Our model is robust to tissue and study effects, provided the coverage is high enough. We applied this model to genotype all the samples in Recount3 and provide the largest ready-to-use expression repository containing genotype information. We illustrate that the predicted genotype from RNA-seq data is sufficient to unravel the underlying population structure of samples in Recount3 using Principal Component Analysis.

genetics↗

Growth retardation in a mouse model of Kabuki syndrome 2 bears mechanistic similarities to Kabuki syndrome 1

Growth retardation is a characteristic feature of both Kabuki syndrome 1 (KS1) and Kabuki syndrome 2 (KS2), Mendelian disorders of the epigenetic machinery with similar phenotypes but distinct genetic etiologies. We previously described skeletal growth retardation in a mouse model of KS1 and further established that a Kmt2d-/- chondrocyte model of KS1 exhibits precocious differentiation. Here we characterized growth retardation in a mouse model of KS2, Kdm6atm1d/+. We show that Kdm6atm1d/+ mice have decreased femur and tibia length compared to controls and exhibit abnormalities in cortical and trabecular bone structure. Kdm6atm1d/+ growth plates are also shorter, due to decreases in hypertrophic chondrocyte size and hypertrophic zone height. Given these disturbances in the growth plate, we generated Kdm6a-/- chondrogenic cell lines. Similar to our prior in vitro model of KS1, we found that Kdm6a-/- cells undergo premature, enhanced differentiation towards chondrocytes compared to Kdm6a+/+ controls. RNA-seq showed that Kdm6a-/- cells have a distinct transcriptomic profile that indicates dysregulation of cartilage development. Finally, we performed RNA-seq simultaneously on Kmt2d-/-, Kdm6a-/-, and control lines at Days 7 and 14 of differentiation. This revealed surprising resemblance in gene expression between Kmt2d-/- and Kdm6a-/- at both time points and indicates that the similarity in phenotype between KS1 and KS2 also exists at the transcriptional level.

genetics↗

Neuron-specific chromatin disruption at CpG islands and aging-related regions in Kabuki syndrome mice

Many Mendelian developmental disorders caused by coding variants in epigenetic regulators have now been discovered. Epigenetic regulators are broadly expressed, and each of these disorders typically exhibits phenotypic manifestations from many different organ systems. An open question is whether the chromatin disruption - the root of the pathogenesis - is similar in the different disease-relevant cell types. This is possible in principle, since all these cell-types are subject to effects from the same causative gene, that has the same kind of function (e.g. methylates histones) and is disrupted by the same germline variant. We focus on mouse models for Kabuki syndrome types 1 and 2, and find that the chromatin accessibility abnormalities in neurons are mostly distinct from those in B or T cells. This is not because the neuronal abnormalities occur at regulatory elements that are only active in neurons. Neurons, but not B or T cells, show preferential chromatin disruption at CpG islands and at regulatory elements linked to aging. A sensitive analysis reveals that the regions disrupted in B/T cells do exhibit chromatin accessibility changes in neurons, but these are very subtle and of uncertain functional significance. Finally, we are able to identify a small set of regulatory elements disrupted in all three cell types. Our findings reveal the cellular-context-specific effect of variants in epigenetic regulators, and suggest that blood-derived "episignatures" may not be well-suited for understanding the mechanistic basis of neurodevelopment in Mendelian disorders of the epigenetic machinery.

genomics↗

Novel mouse model of Weaver syndrome displays overgrowth and excess osteogenesis reversible with KDM6A/6B inhibition

Weaver syndrome is a Mendelian disorder of the epigenetic machinery (MDEM) caused by germline pathogenic variants in EZH2, which encodes the predominant H3K27 methyltransferase and key enzymatic component of Polycomb repressive complex 2 (PRC2). Weaver syndrome is characterized by striking overgrowth and advanced bone age, intellectual disability, and distinctive facies. We generated a mouse model for the most common Weaver syndrome missense variant, EZH2 p.R684C. Ezh2R684C/R684C mouse embryonic fibroblasts (MEFs) showed global depletion of H3K27me3. Ezh2R684C/+ mice had abnormal bone parameters indicative of skeletal overgrowth, and Ezh2R684C/+ osteoblasts showed increased osteogenic activity. RNA-seq comparing osteoblasts differentiated from Ezh2R684C/+and Ezh2+/+ bone marrow mesenchymal stem cells (BM-MSCs) indicated collective dysregulation of the BMP pathway and osteoblast differentiation. Inhibition of the opposing H3K27 demethylases Kdm6a/6b substantially reversed the excessive osteogenesis in Ezh2R684C/+ cells both at the transcriptional and phenotypic levels. This supports both the ideas that writers and erasers of histone marks exist in a fine balance to maintain epigenome state, and that epigenetic modulating agents have therapeutic potential for the treatment of MDEMs.

genetics↗

Precision pharmacological reversal of genotype-specific diet-induced metabolic syndrome in mice informed by transcriptional regulation

Diet-related metabolic syndrome is the largest contributor to adverse health in the United States. However, the study of gene-environment interactions and their epigenomic and transcriptomic integration is complicated by the lack of environmental and genetic control in humans that is possible in mouse models. Here we exposed three mouse strains, C57BL/6J (BL6), A/J, and NOD/ShiLtJ (NOD), to a high-fat high-carbohydrate diet, leading to varying degrees of metabolic syndrome. We then performed transcriptomic and genomic DNA methylation analyses and found overlapping but also highly divergent changes in gene expression and methylation upstream of the discordant metabolic phenotypes. Strain-specific pathway analysis of dietary effects reveals a dysregulation of cholesterol biosynthesis common to all three strains but distinct regulatory networks driving this dysregulation. This suggests a strategy for strain-specific targeted pharmacologic intervention of these upstream regulators informed by transcriptional regulation. As a pilot study, we administered the drug GW4064 to target one of these genotype-dependent networks, the Farnesoid X receptor pathway, and found that GW4064 exerts genotype-specific protection against dietary effects in BL6, as predicted by our transcriptomic analysis, as well as increased inflammatory-related gene expression changes in NOD. This pilot study demonstrates the potential efficacy of precision therapeutics for genotype-informed dietary metabolic intervention, and a mouse platform for guiding this approach.

genetics↗

Massively parallel single molecule tracking of sequence-dependent DNA mismatch repair in vivo

Whether due to mutagens or replication errors, DNA mismatches arise spontaneously in vivo. Unrepaired mismatches are sources of genetic variation and point mutations which can alter cellular phenotype and cause dysfunction, diseases, and cancer. To understand how diverse mismatches in various sequence contexts are recognized and repaired, we developed a high-throughput sequencing-based approach to track single mismatch repair outcomes in vivo and determined the mismatch repair efficiencies of 5682 distinct singly mispaired sequences in E. coli. We found that CC mismatches are always poorly repaired, whereas local sequence context is a strong determinant of the hypervariable repair efficiency of TT, AG, and CT mismatches. Single molecule FRET analysis of MutS interactions with mismatched DNA showed that well-repaired mismatches have a higher effective rate of sliding clamp formation. The hypervariable repair of TT mismatches can cause selectively enhanced mutability if a failure to repair would result in synonymous codon change or a conservative amino acid change. Sequence-dependent repair efficiency in E. coli can explain the patterns of substitution mutations in mismatch repair-deficient tumors, human cells, and C. elegans. Comparison to biophysical and biochemical analyses indicate that DNA physics is the primary determinant of repair efficiency by its impact on the mismatch recognition by MutS.

biophysics↗

Modbamtools: Analysis of single-molecule epigenetic data for long-range profiling, heterogeneity, and clustering

The advent of long-read sequencing methods provides new opportunities for profiling the epigenome - especially as the methylation signature comes for "free" when native DNA is sequenced on either Oxford Nanopore or Pacific Biosciences instruments. However, we lack tools to visualize and analyze data generated from these new sources. Recent efforts from the GA4GH consortium have standardized methods to encode modification location and probabilities in the BAM format. Leveraging this standard format, we developed a technology-agnostic tool, modbamtools to visualize, manipulate and compare base modification/methylation data in a fast and robust way. modbamtools can produce high quality, interactive, and publication-ready visualizations as well as provide modules for downstream analysis of base modifications. Modbamtools comprehensive manual and tutorial can be found at https://rrazaghi.github.io/modbamtools/.

bioinformatics↗

Pumping the brakes on RNA velocity -- understanding and interpreting RNA velocity estimates

RNA velocity analysis of single cells promises to predict temporal dynamics from gene expression. Indeed, in many systems, it has been observed that RNA velocity produces a vector field that qualitatively reflects known features of the system. Despite this observation, the limitations of RNA velocity estimates are poorly understood. Using real data and simulations, we dissect the impact of different steps in the RNA velocity workflow on the estimated vector field. We find that the process of mapping RNA velocity estimates into a low-dimensional representation, such as those produced by UMAP, has a large impact on the result. The RNA velocity vector field strongly depends on the k-NN graph of the data. This dependence leads to significant estimator errors when the k-NN graph is not a faithful representation of the true data structure, a feature that cannot be known for most real datasets. Finally, we establish that RNA velocity estimates expression speed neither at the gene nor cellular level. We propose that RNA velocity is best considered a smoothed interpolation of the observed k-NN structure, as opposed to an extrapolation of future cellular states, and that the use of RNA velocity as a validation of latent space embedding structures is circular.

genomics↗

recountmethylation enables flexible analysis ofpublic blood DNA methylation array data

Thousands of DNA methylation (DNAm) array samples from human blood are publicly available on the Gene Expression Omnibus (GEO), but they remain underutilized for experiment planning, replication, and cross-study and cross-platform analyses. To facilitate these tasks, we augmented our recountmethylation R/Bioconductor package with 12,537 uniformly processed EPIC and HM450K blood samples on GEO as well as several new features. We subsequently used our updated package in several illustrative analyses, finding (1) study ID bias adjustment increased variation explained by biological and demographic variables, (2) most variation in autosomal DNAm was explained by genetic ancestry and CD4+ T-cell fractions, and (3) the dependence of power to detect differential methylation on sample size was similar for each of peripheral blood mononuclear cells (PBMC), whole blood, and umbilical cord blood. Finally, we used PBMC and whole blood to perform independent validations, and we recovered 40-46% of differentially methylated probes (DMPs) between sexes from two previously published epigenome-wide association studies (EWAS).

bioinformatics↗

Quantification and statistical modeling of Chromium-based single-nucleus RNA-sequencing data

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudo-negative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system, we found that snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

genomics↗

nnSVG: scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG.

genomics↗

recount3: summaries and queries for large-scale RNA-seq expression and splicing

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

genomics↗