Biology subjects

Parker, S. C.

Publications and source records attributed to Parker, S. C..

9 recordsLinked to original sources

bioRxiv2026

A single-cell multimodal foundation model enables cross modality analysis between transcriptome and chromatin

Most single-cell studies measure either gene expression or chromatin accessibility, and connecting the two usually requires fitting a model to each study. Here we present MUTUAL (MUltiome Transfer in a Unified Aligned Latent space), which keeps the single-cell foundation models scFoundation and EpiAgent frozen, combines their embeddings with linear summaries of each modality and learns a shared RNA--ATAC space from 3.0 million paired cells of 400 public multiome datasets. Applied without refitting to ten datasets not used for training, MUTUAL matched cells' RNA and ATAC profiles more accurately than methods fitted on other donors of the same dataset, annotated ATAC-only cells more accurately and transferred transcription-factor motif activity to RNA-only cells beyond what cell-type assignment provides. In a maturation series of stem-cell-derived islets, motif activity predicted from RNA largely reproduced the measured stage-wise changes when other libraries of the series served as the reference. MUTUAL makes public multiome data directly usable by single-modality studies.

bioinformatics↗

bioRxiv2026

Integrated Analysis of Skeletal Muscle Transcriptional Networks Characterizes Dysregulation in Pathways and Trait-Associated Regulatory Regions in Type 2 Diabetes

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

bioinformatics↗

bioRxiv2026

Decoding the mystery of ultra-conservation in developmental enhancers: a role for nucleosome positioning, DNA structure and transcription factor binding.

Many human developmental enhancers are characterized by extreme evolutionary constraint in the vertebrate lineage and unique DNA sequence properties, the functional relevance of which is still unknown. Here, we investigate the consequences of their DNA sequence features on three potential aspects important for their function - transcription factor sequence recognition, chromatin accessibility and DNA structure. Using computational predictions in human as well as other vertebrates and invertebrates, we find that conserved non-coding elements (CNEs) are intrinsically nucleosome disfavoring at their core, but favor nucleosome occupancy at their borders driven by distinct nucleotide features conserved over large evolutionary distances. Nevertheless, using genome-wide nucleosome occupancy datasets, we find vertebrate CNEs exhibit higher nucleosome occupancy in comparison to surrounding regions in differentiated cells but a highly accessible conformation in embryonic tissues, suggesting a role for nucleosome positioning in their function. In addition, CNEs are exclusively enriched for homeobox transcription factor motifs, which are found at high density across their sequences. In particular, motifs specifically enriched at the boundary belong to the PBX-HOX, MEIS and POU transcription factor families, known to recognize specific DNA structural features. Consistent with this finding, CNE boundaries are enriched for unusual DNA structural motifs that may constitute a recognition mechanism by transcription factors that bind a narrow minor groove. The finding that extreme nucleotide conservation are likely to be driven by a combination of nucleosome and protein binding constraints provide a potential mechanistic insight into the function of early developmental enhancers.

genomics↗

bioRxiv2026

Genetic demultiplexing and transcript start site identification from nanopore sequencing of 10x Genomics multiome libraries

Short-read Illumina sequencing of 10x Genomics single-nucleus multiome libraries captures only the 3 end of RNA transcripts, losing transcription start site (TSS) information. Here we demonstrate nanopore sequencing of 10x multiome libraries, which enables the profiling of full length transcripts. We show concordance with common short-read sequencing based workflows including successful genetic demultiplexing of nanopore data despite its higher error rate. We compare TSS identified using nanopore sequencing of multiome cDNA to those identified using a short-read 5 assay, and provide an optimized approach for the preprocessing of nanopore reads prior to TSS identification. We find that nanopore sequencing of multiome cDNA captures a median of 63% of the TSS detected by the 5 assay.

bioinformatics↗

bioRxiv2025

Massively parallel reporter assay reveals promoter-, position-, and strand-specific effects in transcription start sites

Massively parallel reporter assays (MPRA) are a high-throughput method of assessing the activity of candidate cis-regulatory sequences, and can be used to detect allelic differences at disease-associated variants. Previous MPRA studies have screened thousands of functional SNPs associated with various complex traits and conditions. Most MPRA libraries utilize a single plasmid configuration, a single minimal promoter to drive expression, and a single-strand orientation, which may fail to capture the context-dependent activity of disease-associated cis-regulatory elements. We interrogate the potential regulatory differences introduced by variable MPRA plasmid promoters and positions. We used an MPRA library to quantify the activity of 1,305 pancreatic islet-derived transcription start sites generated from CAP analysis of gene expression profiling. We cloned fragments upstream or downstream of a reporter gene along with either the human insulin (INS) promoter or a synthetic housekeeping promoter (SCP1). We used elastic net regression to predict position-specific fragment activity based on enrichment of transcription factor binding site motifs, and generalized linear models to predict position-specific fragment activity from tissue-specific chromatin state regulatory annotations. Our results support the use of MPRA strategies that account for context-dependent factors when assaying candidate regulatory elements in pursuit of understanding complex genetic diseases.

genomics↗

bioRxiv2025

Genetic integration with cell-specific nucleosome positioning resolves causal relationships underlying chromatin accessibility profiles

Cell type-specific chromatin accessibility QTL (caQTL) mapping is a promising approach to understand genetic control of chromatin landscapes and identify regulatory mechanisms underlying GWAS associations. However, current caQTL studies lack resolution and do not distinguish nucleosome-free regions (NFR) from positioned nucleosomes. Here, we leverage statistical modeling of fragment position and length to decompose ATAC-seq profiles into NFRs and phased nucleosomes. With single nucleus (sn)ATAC-seq from 281 human muscle biopsies, we map cell type-specific genetic effects on NFRs (76,027 nfrQTLs) and nucleosome occupancy (24,623 nucQTLs) across skeletal muscle cell types. Colocalization and causal inference between nucQTLs and nearby nfrQTLs and show that nfrQTLs are substantially more likely to causally influence nucQTLs and phase adjacent nucleosomes, indicating a causal relationship in shaping chromatin profiles. Hundreds of nfrQTLs colocalize with GWAS signals for muscle-related traits, including grip strength, atrial fibrillation, and fasting insulin, and the majority of colocalizing signals mapped to credible sets overlapping the corresponding nfrPeak. This approach adds mechanistic insights for how variants underlying caQTLs and GWAS signals exert their cis regulatory effects by initially modifying NFR accessibility and subsequently shaping broader chromatin landscapes, nucleosome positioning, gene expression, and ultimately higher-level traits and disease.

genomics↗

bioRxiv2023

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Skeletal muscle, the largest human organ by weight, is relevant in several polygenic metabolic traits and diseases including type 2 diabetes (T2D). Identifying genetic mechanisms underlying these traits requires pinpointing cell types, regulatory elements, target genes, and causal variants. Here, we use genetic multiplexing to generate population-scale single nucleus (sn) chromatin accessibility (snATAC-seq) and transcriptome (snRNA-seq) maps across 287 frozen human skeletal muscle biopsies representing nearly half a million nuclei. We identify 13 cell types and integrate genetic variation to discover >7,000 expression quantitative trait loci (eQTL) and >100,000 chromatin accessibility QTLs (caQTL) across cell types. Learning patterns of e/caQTL sharing across cell types increased precision of effect estimates. We identify high-resolution cell-states and context-specific e/caQTL with significant genotype by context interaction. We identify nearly 2,000 eGenes colocalized with caQTL and construct causal directional maps for chromatin accessibility and gene expression. Almost 3,500 genome-wide association study (GWAS) signals across 38 relevant traits colocalize with sn-e/caQTL, most in a cell-specific manner. These signals typically colocalize with caQTL and not eQTL, highlighting the importance of population-scale chromatin profiling for GWAS functional studies. Finally, our GWAS-caQTL colocalization data reveal distinct cell-specific regulatory paradigms. Our results illuminate the genetic regulatory architecture of human skeletal muscle at high resolution epigenomic, transcriptomic, and cell-state scales and serve as a template for population-scale multi-omic mapping in complex tissues and traits.

genomics↗

bioRxiv2020

Identification of pituitary thyrotrope signature genes and regulatory elements

Pituitary thyrotropes are specialized cells that produce thyroid stimulating hormone (TSH), a critical factor for growth and maintenance of metabolism. The transcription factors POU1F1 and GATA2 have been implicated in thyrotrope fate and transcriptional regulation of the beta subunit of TSH, Tshb, but no transcriptomic or epigenomic analyses of these cells has been undertaken. The goal of this work was to discover key transcriptional regulatory elements that drive thyrotrope fate. We identified the transcription factors and epigenomic changes in chromatin that are associated with differentiation of POU1F1-expressing progenitors into thyrotropes, a process modeled by two cell lines: one that represents an early, undifferentiated Pou1f1 lineage progenitor (GHF-T1) and one that is a committed thyrotrope that produces TSH (TT1). We generated and compared RNA-seq, ATAC-seq, histone modification (including H3K27Ac, H3K4Me1, and H3K27Me3), and transcription factor (POU1F1) binding in these two cell lines to identify regulatory elements and candidate transcriptional regulators. We identified POU1F1 binding sites that were unique to each cell line. POU1F1 binding sites are commonly associated with bZIP transcription factor consensus binding sites in GHF-T1 cells and Helix-Turn-Helix (HTH) or basic Helix-Loop-Helix (bHLH) factors in TT1 cells, suggesting that these classes of transcription factors may recruit or cooperate with POU1F1 binding to unique sites. We validated enhancer function of novel elements we mapped near Cga, Pitx1, Gata2, and Tshb by transfection in TT1 cells. Finally, we confirmed that an enhancer element near Tshb can drive expression in thyrotropes of transgenic mice, and we demonstrate that GATA2 enhances Tshb expression through this element. These results extend the ENCODE multi-omic profiling approach to an organ that is critical for growth and metabolism, which should be valuable for understanding pituitary development and disease pathogenesis.

genomics↗

bioRxiv2019

Influence of genetic variants on gene expression in human pancreatic islets - implications for type 2 diabetes

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

genomics↗