Biology subjects

Rumker, L.

Publications and source records attributed to Rumker, L..

10 recordsLinked to original sources

bioRxiv2025

Estimating the cis-heritability of gene expression using single cell expression profiles controls false positive rate of eGene detection

For gene expression traits, cis-genetic heritability can quantify the strength of genetic regulation in particular cell types, elucidating the cell-type-specificity of disease variants and genes. To estimate gene expression heritability, standard models require a single gene expression value per individual, forcing data from single cell RNA-sequencing (scRNA-seq) experiments to be "pseudobulked". Here, we show that applying standard heritability models to pseudobulk data overestimates gene expression heritability and produces inflated false positive rates for detecting cis-heritable genes. Therefore, we introduce a new method called scGeneHE (single cell Gene expression Heritability Estimation), a Poisson mixed-effects model that quantifies the cis-genetic component of gene expression using individual cellular profiles. In simulations, scGeneHE has a consistently well-calibrated false positive rate for eGene detection and unbiasedly estimates cis-heritability at many parameter settings. We applied scGeneHE to scRNA-seq data from 969 individuals, 11 immune cell types, and 822,552 cells from the OneK1K cohort to infer cell-type-specificity of genetic regulation at risk genes for immune-mediated diseases and trace the fluctuation of cis-heritability across cellular populations of varying resolution. In summary, we developed a new statistical method that resolves the analytical challenge of estimating gene expression cis-heritability from native scRNA-seq data.

genetics↗

bioRxiv2025

Powerful and accurate case-control analysis of spatial molecular data with deep learning-defined tissue microniches

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches typically make restrictive assumptions such as representing tissue regions by local abundances of manually typed, discrete cell types, or representing samples in terms of abundances of manually called, discrete spatial structures; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of data-dependent "microniches" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then meta-analyzes across the autoencoders to identify microniches whose abundance correlates with case-control status while controlling for multiple testing. We show in simulations that VIMA is well calibrated. We then apply VIMA to spatial datasets spanning three different diseases and spatial modalities: a 7-marker immunofluorescence (IF) microscopy dataset in rheumatoid arthritis (RA), a 52-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 140-gene spatial transcriptomics dataset in dementia. In each case, we recapitulate known biology and identify novel spatial features of disease that were not discoverable with current state-of-the-art methods.

genomics↗

bioRxiv2024

Modeling heterogeneity in single-cell perturbation states enhances detection of response eQTLs

Identifying response expression quantitative trait loci (reQTLs) can help to elucidate mechanisms of disease associations. Typically, such studies model the effect of perturbation as discrete conditions. However, perturbation experiments usually affect perturbed cells heterogeneously. We demonstrated that modeling of per-cell perturbation state enhances power to detect reQTLs. We use public single-cell peripheral blood mononuclear cell (PBMC) data, to study the effect of perturbations with Influenza A virus (IAV), Candida albicans (CA), Pseudomonas aeruginosa (PA), and Mycobacterium tuberculosis (MTB) on gene regulation. We found on average 36.9% more reQTLs by accounting for single cell heterogeneity compared to the standard discrete reQTL model. For example, we detected a decrease in the eQTL effect of rs11721168 for PXK in IAV. Furthermore, we found that on average of 25% reQTLs have cell-type-specific effects. For example, in IAV the increase of the eQTL effect of rs10774671 for OAS1 was stronger in CD4+T and B cells. Similarly, in all four perturbation experiments, the reQTL effect for RPS26 was stronger in B cells. Our work provides a general model for more accurate reQTL identification and underscores the value of modeling cell-level variation.

genetics↗

bioRxiv2023

Identifying genetic variants that influence the abundance of cell states in single-cell data

Introductory ParagraphTo understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals,1 GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96x10-11) associates with increased abundance of NK cells expressing TNF- response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

genetics↗

bioRxiv2023

The T cell receptor sequence influences the likelihood of T cell memory formation

T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired {beta}TCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2, that promotes positive selection in the thymus as well as transition from naive to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.

immunology↗

bioRxiv2022

A statistical genetics guide to identifying HLA alleles driving complex disease

The human leukocyte antigen (HLA) locus is associated with more human complex diseases than any other locus. In many diseases it explains more heritability than all other known loci combined. Investigators have now demonstrated the accuracy of in silico HLA imputation methods. These approaches enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and infectious diseases, such as HIV infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine-mapping. Here, we present comprehensive statistical genetics guide to impute HLA alleles from genotype data. We provide detailed protocols, including standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids including a web-based Michigan Imputation Server. We updated the HLA imputation reference panel representing global populations (African, East Asian, European and Latino) available at the Michigan Imputation Server (n = 20,349) and achived high imputation accuracy (mean dosage correlation r = 0.981). We finally offer best practice recommendations to conduct association tests in order to define the alleles, amino acids, and haplotypes affecting human traits. This protocol will be broadly applicable to the large-scale genotyping data and contribute to defining the role of HLA in human diseases across global populations.

genetics↗

bioRxiv2022

Cellular deconstruction of inflamed synovium defines diverse inflammatory phenotypes in rheumatoid arthritis

Rheumatoid arthritis (RA) is a prototypical autoimmune disease that causes destructive tissue inflammation in joints and elsewhere. Clinical challenges in RA include the empirical selection of drugs to treat patients, inadequate responders with incomplete disease remission, and lack of a cure. We profiled the full spectrum of cells in inflamed synovium from patients with RA with the goal of deconstructing the cell states and pathways characterizing pathogenic heterogeneity in RA. Our multicenter consortium effort used multi-modal CITE-seq, RNA-seq, and histology of synovial tissue from 79 donors to build a >314,000 single-cell RA synovial cell atlas with 77 cell states from T, B/plasma, natural killer, myeloid, stromal, and endothelial cells. We stratified tissue samples into six distinct cell type abundance phenotypes (CTAPs) individually enriched for specific cell states. These CTAPs demonstrate the striking diversity of RA synovial inflammation, ranging from marked enrichment of T and B cells (CTAP-TB) to a congregation of specific myeloid, fibroblast, and endothelial cells largely lacking lymphocytes (CTAP-EFM). Disease-relevant cytokines, histology, and serology metrics are associated with certain CTAPs. This comprehensive RA synovial atlas and molecular, tissue-based CTAP stratification reveal new insights into RA pathology and heterogeneity, which could lead to novel targeted-treatment approaches in RA.

genomics↗

bioRxiv2021

Axes of inter-sample variability among transcriptional neighborhoods reveal disease associated cell states in single-cell data

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes like clinical phenotypes. Current statistical approaches typically map cells to cell-type clusters and examine sample differences through that lens alone. Here we present covarying neighborhood analysis (CNA), an unbiased method to identify cell populations of interest with greater flexibility and granularity. CNA characterizes dominant axes of variation across samples by identifying groups of very small regions in transcriptional space--termed neighborhoods--that covary in abundance across samples, suggesting shared function or regulation. CNA can then rigorously test for associations between any sample-level attribute and the abundances of these covarying neighborhood groups. We show in simulation that CNA enables more powerful and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, redefines monocyte populations expanded in sepsis, and identifies a previously undiscovered T-cell population associated with progression to active tuberculosis.

bioinformatics↗

bioRxiv2020

Efficient design of maximally active and specific nucleic acid diagnostics for thousands of viruses

Diagnostics, particularly for rapidly evolving viruses, stand to benefit from a principled, measurement-driven design that harnesses machine learning and vast genomic data--yet the capability for such design has not been previously built. Here, we develop and extensively validate an approach to designing viral diagnostics that applies a learned model within a combinatorial optimization framework. Concentrating on CRISPR-based diagnostics, we screen a library of 19,209 diagnostic-target pairs and train a deep neural network that predicts, from RNA sequence alone, diagnostic signal better than contemporary techniques. Our model then makes it possible to design assays that are maximally sensitive over the spectrum of a viruss genomic variation. We introduce ADAPT (https://adapt.guide), a system for fully-automated design, and use ADAPT to design optimal diagnostics for the 1,933 vertebrate-infecting viral species within 2 hours for most species and 24 hours for all but 3. We experimentally show ADAPTs designs are sensitive and specific down to the lineage level, including against viruses that pose challenges involving genomic variation and specificity. ADAPTs designs exhibit significantly higher fluorescence and permit lower limits of detection, across a viruss entire variation, than the outputs of standard design techniques. Our model-based optimization strategy has applications broadly to viral nucleic acid diagnostics and other sequence-based technologies, and, paired with clinical validation, could enable a critically-needed, proactive resource of assays for surveilling and responding to pathogens.

genomics↗

bioRxiv2020

Efficient and precise single-cell reference atlas mapping with Symphony

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony (https://github.com/immunogenomics/symphony), an algorithm for building integrated reference atlases of millions of cells in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony by (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of human fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

bioinformatics↗