Biology subjects

Brown, D. E.

Publications and source records attributed to Brown, D. E..

6 recordsLinked to original sources

bioRxiv2025

Development of the Early Childhood Duodenum across Ancestry, Geography and Environment

During early childhood, the proximal small intestinal mucosa plays a central role in growth, metabolism, immune priming, and neuronal development. Yet the cellular architecture and environmental responsiveness of the human small intestinal mucosa during this period remain poorly defined. Here, we generate a comprehensive cellular and spatial map of the duodenum from 87 children aged 6 months to 13 years, representing diverse ancestries and geographic contexts. This atlas integrates single-cell transcriptomic and spatial profiling with data on diet, social drivers of health, and environmental exposures. Using these data, we define mucosal cellular composition and chart its developmental trajectory in early childhood. Comparative analyses of children residing in the United States (US) and Pakistan reveal a differentiated enterocyte subset expressing the aquaglyceroporin, AQP10 (AQP10+ enterocyte), that is enriched in children from the US. We show that emergence of this enterocyte state depends on lipid exposure to intestinal stem cells and correlates with dietary fat intake. We also identify a previously-undescribed thyrotropin-releasing hormone (TRH+) enteroendocrine cell and provide evidence for a local endocrine-epithelial-lymphocyte circuit. Our work establishes a detailed framework for pediatric duodenal mucosal development and illuminates how intestinal cellular dynamics are shaped by age and environment.

developmental biology↗

bioRxiv2023

Joint representation learning for retrieval and annotation of genomic interval sets

MotivationAs available genomic interval data increases in scale, we require fast systems to search it. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but these approaches lead to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. ResultsHere, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string; suggesting new labels for database region sets; and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

bioinformatics↗

bioRxiv2023

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

MotivationData from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower-dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. ResultsWe implemented our approach in scEmbed, an unsupervised machine learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, pre-trained models on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. AvailabilityscEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

genomics↗

bioRxiv2023

The functionally plastic rod photoreceptors in the simplex retina of Little skate (Leucoraja erinacea) exhibit a hybrid rod-cone morphology and enhanced synaptic connectivity.

The retinas of the vast majority of vertebrate species are termed "duplex" - that is, they contain both rod and cone photoreceptor neurons in different ratios. The retina of Little skate (Leucoraja erinacea) is a rarity among vertebrates because it contains only rod photoreceptors and is thus "simplex". This unique retina provides us with an important comparative model and an exciting opportunity to study vertebrate rod circuitry within the context of a functional, evolutionarily optimized system, all without the concern about artifacts from genetically modified rod-only mouse models. Perhaps even more importantly, the Leucoraja retina is able to function under both scotopic and photopic ranges of illumination with a single complement of photoreceptors. It is currently unknown what structural characteristics mediate this remarkable functional plasticity. To address this question, we performed serial block-face electron microscopy imaging and examined the structure of rods and their post-synaptic partners. We find that skate rods exhibit ultrastructural characteristics that are either common to rods or cones in other vertebrates (e.g., outer segment architecture, synaptic ribbon number, terminal extensions), or are uniquely in-between those of a typical vertebrate rod or cone (e.g., number of invaginating contacts, clustering of multiple ribbons over a single synaptic invagination). We therefore hypothesize that the unique hybrid rod-cone structure of skate rods and their post-synaptic partners is correlated with the ability of the skate visual system to function across scotopic and photopic ranges of illumination. These findings have the potential to reveal as yet undescribed principles of vertebrate retinal design. Significance statementThe vast majority of vertebrate retinas are duplex and have mixed rod-cone populations of photoreceptors in varying ratios. The processing of visual information in a duplex retina tends to be separated between rod and cone systems, which mediate function under scotopic and photopic lighting conditions, respectively. However, the cartilaginous fish Little skate (Leucoraja erinacea) has a simplex retina, comprised solely of rod photoreceptors. Skate rods are also unusual because they have the ability to retain function over a full range of lighting conditions. We have little knowledge about the ultrastructural anatomy of the skate retina, and we hypothesize that this functional plasticity can be traced back to morphological adaptations at the level of individual photoreceptors and the downstream retinal circuitry, thus illuminating new pathways for the processing of visual information among vertebrates.

neuroscience↗

bioRxiv2021

Embeddings of genomic region sets capture rich biological associations in lower dimensions

MotivationGenomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. ResultsWe propose a new method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. We compared our approach to two simpler methods based on interval unions or term frequency-inverse document frequency and evaluated the methods in three ways: First, by classifying the cell line, antibody, or tissue type of the region set; second, by assessing whether similarity among embeddings can reflect simulated random perturbations of genomic regions; and third, by testing robustness of the proposed representations to different signal thresholds for calling peaks. Our word2vec-based region set embeddings reduce dimensionality from more than a hundred thousand to 100 without significant loss in classification performance. The vector representation could identify cell line, antibody, and tissue type with over 90% accuracy. We also found that the vectors could quantitatively summarize simulated random perturbations to region sets and are more robust to subsampling the data derived from different peak calling thresholds. Our evaluations demonstrate that the vectors retain useful biological information in relatively lower-dimensional spaces. We propose that vector representation of region sets is a promising approach for efficient analysis of genomic region data. Availabilityhttps://github.com/databio/regionset-embedding

bioinformatics↗

bioRxiv2020

Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. There is a rich history of using local field potentials (LFP), to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time and have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative HVC of freely behaving male zebra finches during song production, to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative HVC LFP is qualitatively similar to well established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight to common computational principles for learning and/or generating complex motor-vocal behaviors. Author SummaryVocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

neuroscience↗