Biology subjects

Tseng, A. M.

Publications and source records attributed to Tseng, A. M..

5 recordsLinked to original sources

bioRxiv2024

Decoding sequence determinants of gene expression in diverse cellular and disease states

Sequence-to-function models that predict gene expression from genomic DNA sequence have proven valuable for many biological tasks, including understanding cis-regulatory syntax and interpreting non-coding genetic variants. However, current state-of-the-art models have been trained largely on bulk expression profiles from healthy tissues or cell lines, and have not learned the properties of precise cell types and states that are captured in large-scale single-cell transcriptomic datasets. Thus, they lack the ability to perform these tasks at the resolution of specific cell types or states across diverse tissue and disease contexts. To address this gap, we present Decima, a model that predicts the cell type- and condition- specific expression of a gene from its surrounding DNA sequence. Decima is trained on single-cell or single-nucleus RNA sequencing data from over 22 million cells, and successfully predicts the cell type-specific expression of unseen genes based on their sequence alone. Here, we demonstrate Decimas ability to reveal the cis-regulatory mechanisms driving cell type-specific gene expression and its changes in disease, to predict non-coding variant effects at cell type resolution, and to design regulatory DNA elements with precisely tuned, context-specific functions.

genomics↗

bioRxiv2020

Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics

Deep learning models can accurately map genomic DNA sequences to associated functional molecular readouts such as protein-DNA binding data. Base-resolution importance (i.e. "attribution") scores inferred from these models can highlight predictive sequence motifs and syntax. Unfortunately, these models are prone to overfitting and are sensitive to random initializations, often resulting in noisy and irreproducible attributions that obfuscate underlying motifs. To address these shortcomings, we propose a novel attribution prior, where the Fourier transform of input-level attribution scores are computed at training-time, and high-frequency components of the Fourier spectrum are penalized. We evaluate different model architectures with and without attribution priors trained on genome-wide binary or continuous molecular profiles. We show that our attribution prior dramatically improves models stability, interpretability, and performance on held-out data, especially when training data is severely limited. Our attribution prior also allows models to identify biologically meaningful sequence motifs more sensitively and precisely within individual regulatory elements. The prior is agnostic to the model architecture or predicted experimental assay, yet provides similar gains across all experiments. This work represents an important advancement in improving the reliability of deep learning models for deciphering the regulatory code of the genome.

bioinformatics↗

bioRxiv2019

Differential chromatin accessibility in developing projection neurons is correlated with transcriptional regulation of cell fate

We are only just beginning to catalog the vast diversity of cell types in the cerebral cortex. Such categorization is a first step toward understanding how diversification relates to function. All cortical projection neurons arise from a uniform pool of progenitor cells that lines the ventricles of the forebrain. It is still unclear how these progenitor cells generate the more than fifty unique types of mature cortical projection neurons defined by their distinct gene expression profiles. Here we compare gene expression and chromatin accessibility of two subclasses of projection neurons with divergent morphological and functional features as they develop in the mouse brain between embryonic day 13 and postnatal day 5 in order to identify transcriptional networks that diversity neuron cell fate. We find groups of transcription factors whose expression is correlated with chromatin accessibility, transcription factor binding motifs, and lncRNAs that define each subclass and validate the function of a family of novel candidate genes in vitro. Our multidimensional approach reveals that subclass-specific chromatin accessibility is significantly correlated with gene expression, providing a resource for generating new specific genetic drivers and revealing regions of the genome that are particularly susceptible to harmful genetic mutations by virtue of their correlation with important developmental genes.

developmental biology↗

bioRxiv2019

Structural and Functional Characterization of G Protein-Coupled Receptors with Deep Mutational Scanning

In humans, the 813 G protein-coupled receptors (GPCRs) are responsible for transducing diverse chemical stimuli to alter cell state, and are the largest class of drug targets. Their myriad structural conformations and various modes of signaling make it challenging to understand their structure and function. Here we developed a platform to characterize large libraries of GPCR variants in human cell lines with a barcoded transcriptional reporter of G-protein signal transduction. We tested 7,800 of 7,828 possible single amino acid substitutions to the beta-2 adrenergic receptor ({beta}2AR) at four concentrations of the agonist isoproterenol. We identified residues specifically important for {beta}2AR signaling, mutations in the human population that are potentially loss of function, and residues that modulate basal activity. Using unsupervised learning, we resolve residues critical for signaling, including all major structural motifs and molecular interfaces. We also find a previously uncharacterized structural latch spanning the first two extracellular loops that is highly conserved across Class A GPCRs and is conformationally rigid in both the inactive and active states of the receptor. More broadly, by linking deep mutational scanning with engineered transcriptional reporters, we establish a generalizable method for exploring pharmacogenomics, structure and function across broad classes of drug receptors.

molecular biology↗

bioRxiv2018

Maternal Circulating MicroRNAs Control the Placental Response to Alcohol

Prenatal Alcohol exposure (PAE), like other pregnancy complications, can result in placental insufficiency and fetal growth restriction, though the linking causal mechanisms are unclear. We previously identified 11 gestationally-elevated maternal circulating miRNAs that predicted infant growth deficits following PAE. Here, we investigated whether these HEamiRNAs contribute to the pathology of PAE, by inhibiting trophoblast epithelial-mesenchymal transition (EMT), a pathway critical for placental development. We now report for the first time, that PAE inhibits expression of placental pro-EMT pathway members in both rodents and primates, and that HEamiRNAs collectively, but not individually, mediate placental EMT inhibition. HEamiRNAs collectively, but not individually, also inhibited cell proliferation and the EMT pathway in cultured trophoblasts, while inducing cell stress, and following trophoblast syncytialization, aberrant endocrine maturation. Moreover, a single intra-vascular administration of the pooled murine-expressed HEamiRNAs, to pregnant mice, decreased placental and fetal growth and inhibited expression of pro-EMT transcripts in placenta. Our data suggests that HEamiRNAs collectively interfere with placental development, contributing to the pathology of PAE, and perhaps also, to other causes of fetal growth restriction. SummaryMaternal gestational circulating microRNAs, predictive of adverse infant outcomes including growth deficits, following prenatal alcohol exposure, contribute to placental pathology by impairing the EMT pathway in trophoblasts.

developmental biology↗