Biology subjects

Wamsley, B.

Publications and source records attributed to Wamsley, B..

6 recordsLinked to original sources

bioRxiv2023

Molecular cascades and cell-type specific signatures in ASD revealed by single cell genomics

Understanding how genetic variation exerts its effects on the human brain in health and disease has been greatly informed by functional genomic characterization. Studies over the last decade have demonstrated robust evidence of convergent transcriptional and epigenetic profiles in post-mortem cerebral cortex from individuals with Autism Spectrum Disorder (ASD). Here, we perform deep single nuclear (sn) RNAseq to elucidate changes in cell composition, cellular transcriptomes and putative candidate drivers associated with ASD, which we corroborate using snATAC-seq and spatial profiling. We find changes in cell state composition representing transitions from homeostatic to reactive profiles in microglia and astrocytes, a pattern extending to oligodendrocytes and blood brain barrier cells. We identify profound changes in differential expression involving thousands of genes across neuronal and glial subtypes, of which a substantial portion can be accounted for by specific transcription factor networks that are significantly enriched in common and rare genetic risk for ASD. These data, which are available as part of the PsychENCODE consortium, provide robust causal anchors and resultant molecular phenotypes for understanding ASD changes in human brain. One-Sentence SummaryWe define the molecular cascades and cells disrupted in post-mortem brain in ASD by performing spatial, single nuclear RNA, and epigenetic profiling, and characterize, at unmatched resolution, the functional regulation of cell-type specific signatures underlying the molecular differences and physiology of ASD. Main TextPsychiatric disorders are defined primarily by behavioral and cognitive characteristics and are classically distinguished from neurological disorders by lacking the associated visible histological or macroscopic pathology observed in neurological conditions. However, a growing body of evidence based on genomic profiling reveals consistent molecular differences in brain tissue from specific neuropsychiatric conditions compared with brain tissue from neurotypical individuals (1-5). In Autism Spectrum Disorder (ASD) robust transcriptomic and epigenetic alterations in the cerebral cortex from patients have been documented over the last decade, delineating a reproducible pattern of molecular pathology (5-13). This robust molecular signature obtained primarily from transcriptomic profiling of bulk cortical tissue has identified convergent biological pathways in ASD brain, which is characterized by an upregulation of immune signaling genes, downregulation of specific neuronal markers, synaptic genes, and an attenuation of the typical patterns of gene expression associated with cortical regional identity (6-8,12-14). These genomic data represent an essential lens through which to understand the cellular and physiological changes occurring in the brains of autistic individuals and to describe potential causal mechanisms via their integration with genetic risk variants (1,4,5). However, small sample sizes in the case of single cell analysis (13), or profiling restricted to bulk tissue have limited biological insights as to the differences in laminar, circuit level, and cell-type specific pathways affected in ASD, as well as their underlying gene regulatory mechanisms. To address these limitations, we leveraged improvement in single cell analyses to profile the largest ASD cohort to date, consisting of 64 cases and controls. This resource, generated as a core component of the PsychENCODE consortium (1,2,4,5,8; http://www.psychencode.org), also enables us to characterize underlying candidate regulatory mechanisms and to connect causal drivers with the observed changes at a cell-type specific level in ASD, providing a deeper and more generalizable understanding of the cell types and biological mechanisms that underlie ASD. These data are available via PsychEncode portals for download (http://psychencode.synapse.org) and on the PsychSCREEN browser (in development, http://psychscreen.beta.wenglab.org).

neuroscience↗

bioRxiv2021

Cell Type Hierarchy Reconstruction via Reconciliation of Multi-resolution Cluster Tree

A wealth of clustering algorithms are available for Single-cell RNA sequencing (scRNA-seq), but it remains challenging to compare and characterize the features across different scales of resolution. To resolve this challenge Multi-resolution Reconciled Tree (MRtree), builds a hierarchical tree structure based on multi-resolution partitions that is highly flexible and can be coupled with most scRNA-seq clustering algorithms. MRtree out-performs bottom-up or divisive hierarchical clustering approaches because it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. Application to fetal brain cells yields insight into subtypes of cells that can be reliably estimated.

cell biology↗

bioRxiv2020

Broad transcriptomic dysregulation across the cerebral cortex in ASD

Classically, psychiatric disorders have been considered to lack defining pathology, but recent work has demonstrated consistent disruption at the molecular level, characterized by transcriptomic and epigenetic alterations.1-3 In ASD, upregulation of microglial, astrocyte, and immune signaling genes, downregulation of specific synaptic genes, and attenuation of regional gene expression differences are observed.1,2,4-6 However, whether these changes are limited to the cortical association areas profiled is unknown. Here, we perform RNA-sequencing (RNA-seq) on 725 brain samples spanning 11 distinct cortical areas in 112 ASD cases and neurotypical controls. We identify substantially more genes and isoforms that differentiate ASD from controls than previously observed. These alterations are pervasive and cortex-wide, but vary in magnitude across regions, roughly showing an anterior to posterior gradient, with the strongest signal in visual cortex, followed by parietal cortex and the temporal lobe. We find a notable enrichment of ASD genetic risk variants among cortex-wide downregulated synaptic plasticity genes and upregulated protein folding gene isoforms. Finally, using snRNA-seq, we determine that regional variation in the magnitude of transcriptomic dysregulation reflects changes in cellular proportion and cell-type-specific gene expression, particularly impacting L3/4 excitatory neurons. These results highlight widespread, genetically-driven neuronal dysfunction as a major component of ASD pathology in the cerebral cortex, extending beyond association cortices to involve primary sensory regions.

neuroscience↗

bioRxiv2020

cFIT: Integration and transfer learning of single cell transcriptomes, illustrated by fetal brain cell development

Large, comprehensive collections of scRNA-seq data sets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these data sets or transfer knowledge from one to the other to better understand cellular identity and functions. Here, we present a simple yet surprisingly effective method named cFIT for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various data sets by a common factor space, while allowing for unique distortions and shifts in gene-wise expression in each batch. The model parameters are learned under an iterative non-negative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. In addition, the model enables transferring via low-rank matrix from more informative data to allow for precise identification in data of lower quality. Compared to existing approaches, our method imposes weaker assumptions on the cell composition of each individual data set, however, is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq data sets of developing brain from human and mouse, varying by technologies and developmental stages. The successful integration and transfer uncover the transcriptional resemblance across systems. The study helps establish a comprehensive landscape of brain cell type diversity and provides insights into brain development.

genomics↗

bioRxiv2019

Nova proteins direct synaptic integration of somatostatin interneurons through activity-dependent alternative splicing

Somatostatin interneurons are the earliest born population of cortical inhibitory cells. They are crucial to support normal brain development and function; however, the mechanisms underlying their integration into nascent cortical circuitry are not well understood. In this study, we begin by demonstrating that the maturation of somatostatin interneurons is activity dependent. We then investigated the relationship between activity, alternative splicing and synapse formation within this population. Specifically, we discovered that the Nova family of RNA-binding proteins are activity-dependent and are essential for the maturation of somatostatin interneurons, as well as their afferent and efferent connectivity. Within this population, Nova2 preferentially mediates the alternative splicing of genes required for axonal formation and synaptic function independently from its effect on gene expression. Hence, our work demonstrates that the Nova family of proteins through alternative splicing are centrally involved in coupling developmental neuronal activity to cortical circuit formation.

neuroscience↗

bioRxiv2018

Rbfox1 mediates cell-type-specific splicing in cortical interneurons

Cortical interneurons display a remarkable diversity in their morphology, physiological properties and connectivity. Elucidating the molecular determinants underlying this heterogeneity is essential for understanding interneuron development and function. We discovered that alternative splicing differentially regulates the integration of somatostatin- and parvalbumin-expressing interneurons into nascent cortical circuits through the cell-type specific tailoring of mRNAs. Specifically, we identified a role for the activity-dependent splicing regulator Rbfox1 in the development of cortical interneuron subtype specific efferent connectivity. Our work demonstrates that Rbfox1 mediates largely non-overlapping alternative splicing programs within two distinct but related classes of interneurons.

neuroscience↗