Biology subjects

Townes, F. W.

Publications and source records attributed to Townes, F. W..

10 recordsLinked to original sources

bioRxiv2026

Longitudinal single-cell modeling reveals monocyte reprogramming in juvenile systemic sclerosis following autologous stem cell transplantation

Juvenile systemic sclerosis (jSSc) is a rare autoimmune disease marked by skin fibrosis and multi-organ involvement. Autologous stem cell transplantation (ASCT) is an emerging therapy for severe, treatment-refractory jSSc, but its effects on immune cell dynamics remain poorly understood. PBMCs were collected from three patients with jSSc before ASCT and at 6, 12, and 24 months post-ASCT. Patient and healthy control samples were profiled using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). We focused on monocytes, given their role in fibrosis-promoting inflammation. To detect longitudinal trends, pseudobulked gene expression (log scale) was regressed against time since ASCT. This approach identified widespread changes in jSSc monocytes, including decreased expression of systemic sclerosis-linked genes, such as SERPINE1. On the pathway level, NF-{kappa}B-associated inflammatory signaling was elevated in jSSc monocytes at baseline relative to healthy controls and decreased progressively post-ASCT. Genes related to mitochondrial function and oxidative phosphorylation progressively increased in expression after ASCT, suggesting a shift in metabolic state. Compositional changes in monocyte subpopulations were also identified and may have contributed to longitudinal gene expression patterns. Together, these findings characterize the dynamic immune changes in jSSc following ASCT and highlight a widely applicable longitudinal modeling framework for single-cell data.

genomics↗

bioRxiv2022

Expression QTLs in single-cell sequencing data

AO_SCPLOWBSTRACTC_SCPLOWSingle nucleotide polymorphisms (SNPs) are important drivers of gene expression variation and downstream phenotypes including disease risk. Single-cell RNA-sequencing (scRNA-seq) allows an unprecedented exploration of cell-type specific associations between gene expression levels and genotypes, but current methods rely on pseudobulk approaches that use composite expression values across cells and often use summary statistics within cell types, ignoring information across cell types and assuming cell type labels are accurate. Here, we extend our method, telescoping bimodal latent Dirichlet allocation (TBLDA), that identifies covarying genotypes and gene expression values when the matching from samples to cells is not one-to-one in order to allow cell-type label agnostic discovery of eQTLs in noncomposite scRNA-seq data. In particular, we add GPU-compatibility, sparse priors, and amortization to enable fast inference on large-scale scRNA-seq data. We apply single-cell TBLDA (scTBLDA) to 400K cells from 119 individuals with systemic lupus erythematosus and examine properties of features from each modality across the estimated latent factors. We use linked genes and SNPs to identify 205 cis-eQTLS, 66 trans-eQTLs, and 53 cell type proportion QTLs, which we then compare against prior studies of immune-cell eQTLs. Our results demonstrate the ability of scTBLDA to identify genes involved in cell-type specific regulatory processes associated with SNPs in single-cell data.

genomics↗

bioRxiv2022

Alignment of spatial genomics and histology data using deep Gaussian processes

Spatially-resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of the local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals, and technologies. Here, we propose a probabilistic model that aligns a set of spatially-resolved genomics and histology slices onto a known or unknown common coordinate system into which the samples are aligned both spatially and in terms of the phenotypic readouts (e.g., gene or protein expression levels, cell density, open chromatin regions). Our method consists of a two-layer Gaussian process: the first layer maps the observed samples spatial locations into a common coordinate system, and the second layer maps from the common coordinate system to the observed readouts. Our approach also allows for slices to be mapped to a known template coordinate space if one exists. We show that our registration approach enables complex downstream spatially-aware analyses of spatial genomics data at multiple resolutions that are impossible or inaccurate with unaligned data, including an analysis of variance, differential expression across the z-axis, and association tests across multiple data modalities.

bioinformatics↗

bioRxiv2021

Cerebellar contributions to a brainwide network for flexible behavior

The cerebellum regulates nonmotor behavior, but the routes by which it exerts its influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task, acting through a network of diencephalic and neocortical structures. After chemogenetic inhibition of Purkinje cells in lobule VI or crus I, mice could learn a water Y-maze task but were impaired in their ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic regions and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each test group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into well-separated sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of complex whole-body movement revealed deficiencies in across-day adaptation to an open field environment. Neither perturbation affected gait, within-day open-field adaptation, or location preference. Taken together, these experiments reveal brainwide systems for cerebellar influence that can affect multiple flexible responses. Significance statementThe cerebellum, a part of all vertebrate brains, provides feedback to the rest of the brain on a split-second basis in response to unexpected events. The consequent changes can range from minute adjustments in movement to broad changes in behavior. In mice, we investigated cerebellar regions that help guide flexible behavior. Inactivation of these regions prevented mice from responding flexibly to a changing Y-shaped maze. We used light-sheet microscopy of c-fos protein expression to map brainwide effects of different stages of learning and cerebellar inactivation. Inactivation caused changes in thalamocortical activity that were similar in pattern, but opposite in sign, from normal learning. Lobule VI inactivation weakened thalamic functional networks, while crus I inactivation divided sensorimotor from associative neocortical networks. The same inactivation also impaired multiday adaptation to an open arena, as tracked using machine vision. Our work shows how the cerebellums influence over flexible response can be mediated by a widespread brain network.

neuroscience↗

bioRxiv2021

Telescoping bimodal latent Dirichlet allocation to identify expression QTLs across tissues

Expression quantitative trait loci (eQTLs), or single nucleotide polymorphisms (SNPs) that affect average gene expression levels, provide important insights into context-specific gene regulation. Classic eQTL analyses use one-to-one association tests, which test gene-variant pairs individually and ignore correlations induced by gene regulatory networks and linkage disequilibrium. Probabilistic topic models, such as latent Dirichlet allocation, estimate latent topics for a collection of count observations. Prior multi-modal frameworks that bridge genotype and expression data assume matched sample numbers between modalities. However, many data sets have a nested structure where one individual has several associated gene expression samples and a single germline genotype vector. Here, we build a telescoping bimodal latent Dirichlet allocation (TBLDA) framework to learn shared topics across gene expression and genotype data that allows multiple RNA-sequencing samples to correspond to a single individuals genotype. By using raw count data, our model avoids possible adulteration via normalization procedures. Ancestral structure is captured in a genotype-specific latent space, effectively removing it from shared components. Using GTEx v8 expression data across ten tissues and genotype data, we show that the estimated topics capture meaningful and robust biological signal in both modalities, and identify associations within and across tissue types. We identify 53,358 cis-eQTLs and 1,173 trans-eQTLs by conducting eQTL mapping between the most informative features in each topic. Our TBLDA model is able to identify associations using raw sequencing count data when the samples in two separate data modalities are matched one-to-many, as is often the case in biological data.

genomics↗

bioRxiv2020

Identifying Longevity Associated Genes by Integrating Gene Expression and Curated Annotations

Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are best for optimizing classification performance and which algorithms are best suited to this task. Further, performance assessments based on held-out test data are lacking. We systematically compare five popular classification algorithms using gene ontology and gene expression datasets as features to predict the pro-longevity versus anti-longevity status of genes for two model organisms (C. elegans and S. cerevisiae) using the GenAge database as ground truth. We find that elastic net penalized logistic regression performs particularly well at this task. Using elastic net, we make novel predictions of pro- and anti-longevity genes that are not currently in the GenAge database.

genomics↗

bioRxiv2019

Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than alternatives such as census counts. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

genomics↗

bioRxiv2019

An exact, unifying framework for region-based association testing in family-based designs, including higher criticism approaches, SKATs, multivariate and burden tests

Analysis of rare variants in family-based studies remains a challenge. To perform a region/set-based association analysis of rare variants in family-based studies, we propose a general methodological framework that integrates higher criticism, maximum, SKATs, and burden approaches into the family-based association testing (FBAT) framework. Using the haplotype algorithm for FBATs to compute the conditional genotype distribution under the null hypothesis of Mendelian transmissions, virtually any association test statistics can be implemented in our approach and simulation-based or exact p-values can be computed without the need for asymptotic settings. Using simulations, we compare the features of the proposed test statistics in our framework with the existing region-based methodology for family-based studies under various scenarios. The tests of our framework outperform the existing approaches. We provide general guidelines for which scenarios, e.g., sparseness of the signals or local LD structure, which test statistic will have distinct power advantages over the others. We also illustrate our approach in an application to a whole-genome sequencing dataset with 897 asthmatic trios.

genetics↗

bioRxiv2019

Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

Single cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero-inflation. Current normalization pro-cedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We pro-pose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform current practice in a downstream clustering assessment using ground-truth datasets.

genomics↗

bioRxiv2017

Varying-Censoring Aware Matrix Factorization for Single Cell RNA-Sequencing

Single cell RNA-Seq (scRNA-Seq) has become the most widely used high-throughput technology for gene expression profiling of individual cells. The potential of being able to measure cell-to-cell variability at a high-dimensional genomic scale opens numerous new lines of investigation in basic and clinical research. For example, by identifying groups of cells with expression profiles unlike those observed in cells with known phenotypes, new cell types may be discovered. Dimension reduction followed by unsupervised clustering are the quantitative approaches typically used to facilitate such discoveries. However, a challenge for this approach is that most scRNA-Seq datasets are sparse, with the percentages of measurements reported as zero ranging from 35% to 99% across cells, and these zeros are partially explained by experimental inefficiencies that lead to censored data. Furthermore, the observed across-cell differences in the percentages of zeros are partly due to technical artifacts rather than biological differences. Unfortunately, standard dimension reduction approaches treat these censored values as true zeros, which leads to the identification of distorted low-dimensional factors. When these factors are used for clustering, the distortion leads to incorrect identification of biological groups. Here, we propose an approach that accounts for cell-specific censoring with a varying-censoring aware matrix factorization (VAMF) model that permits the identification of factors in the presence of the above described systematic bias. We demonstrate the advantages of our approach on published scRNA-Seq data and confirm these on simulated data.

genomics↗