Biology subjects

Maynard, K. R.

Publications and source records attributed to Maynard, K. R..

35 records · Page 2Linked to original sources

bioRxiv2024

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex

BackgroundCellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. ResultsA rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. ConclusionsBisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.

neuroscience↗

bioRxiv2023

Spatially-resolved transcriptomics of human dentate gyrus across postnatal lifespan reveals heterogeneity in markers for proliferation, extracellular matrix, and neuroinflammation

The dentate gyrus of the hippocampus is important for many cognitive functions, including learning, memory, and mood. Here, we investigated age-associated changes in transcriptome-wide spatial gene expression in the human dentate gyrus across the lifespan. Genes associated with neurogenesis and the extracellular matrix were enriched in infants, while gene markers of inhibitory neurons and cell proliferation showed increases and decreases in post-infancy, respectively. While we did not find evidence for neural proliferation post-infancy, we did identify molecular signatures supporting protracted maturation of granule cells. We also identified a wide-spread hippocampal aging signature and an age-associated increase in genes related to neuroinflammation. Our findings suggest major changes to the putative neurogenic niche after infancy and identify molecular foci of brain aging in glial and neuropil enriched tissue.

neuroscience↗

bioRxiv2023

Influence of Alzheimer's disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex

Neuropathological lesions in the brains of individuals affected with neurodegenerative disorders are hypothesized to trigger molecular and cellular processes that disturb homeostasis of local microenvironments. Here, we applied the 10x Genomics Visium Spatial Proteogenomics (Visium-SPG) platform, which measures spatial gene expression coupled with immunofluorescence protein co-detection, in post-mortem human brain tissue from individuals with late-stage Alzheimers disease (AD) to investigate changes in spatial gene expression with respect to amyloid-{beta} (A{beta}) and hyperphosphorylated tau (pTau) pathology. We identified A{beta}-associated transcriptomic signatures in the human inferior temporal cortex (ITC) during late-stage AD, which we further investigated at cellular resolution with combined immunofluorescence and single molecule fluorescent in situ hybridization (smFISH) co-detection technology. We present a workflow for analysis of Visium-SPG data and demonstrate the power of multi-omic profiling to identify spatially-localized changes in molecular dynamics that are linked to pathology in human brain disease. We provide the scientific community with web-based, interactive resources to access the datasets of the spatially resolved AD-related transcriptomes at https://research.libd.org/Visium_SPG_AD/.

neuroscience↗

bioRxiv2023

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex

The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture. Here we used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex (DLPFC). Integration with paired single nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we map the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains. Finally, we provide resources for the scientific community to explore these integrated spatial and single cell datasets at research.libd.org/spatialDLPFC/. SummaryGeneration of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease.

neuroscience↗

bioRxiv2023

Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments

High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com.

bioinformatics↗

bioRxiv2022

Activity-regulated gene expression across cell types of the mouse hippocampus

Activity-regulated gene (ARG) expression patterns in the hippocampus (HPC) regulate synaptic plasticity, learning, and memory, and are linked to both risk and treatment response for many neuropsychiatric disorders. The HPC contains discrete classes of neurons with specialized functions, but cell type-specific activity-regulated transcriptional programs are not well characterized. Here, we used single-nucleus RNA-sequencing (snRNA-seq) in a mouse model of acute electroconvulsive seizures (ECS) to identify cell type-specific molecular signatures associated with induced activity in HPC neurons. We used unsupervised clustering and a priori marker genes to computationally annotate 15,990 high-quality HPC neuronal nuclei from N=4 mice across all major HPC subregions and neuron types. Activity-induced transcriptomic responses were divergent across neuron populations, with dentate granule cells being particularly responsive to activity. Differential expression analysis identified both upregulated and downregulated cell type-specific gene sets in neurons following ECS. Within these gene sets, we identified enrichment of pathways associated with varying biological processes such as synapse organization, cellular signaling, and transcriptional regulation. Finally, we used matrix factorization to reveal continuous gene expression patterns differentially associated with cell type, ECS, and biological processes. This work provides a rich resource for interrogating activity-regulated transcriptional responses in HPC neurons at single-nuclei resolution in the context of ECS, which can provide biological insight into the roles of defined neuronal subtypes in HPC function.

neuroscience↗

bioRxiv2022

The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics

Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimers and Parkinsons disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.

neuroscience↗

bioRxiv2022

Data-driven Identification of Total RNA Expression Genes (TREGs) for Estimation of RNA Abundance in Heterogeneous Cell Types

Next-generation sequencing technologies have facilitated data-driven identification of gene sets with different features including genes with stable expression, cell-type specific expression, or spatially variable expression. Here, we aimed to define and identify a new class of "control" genes called Total RNA Expression Genes (TREGs), which correlate with total RNA abundance in heterogeneous cell types of different sizes and transcriptional activity. We provide a data-driven method to identify TREGs from single cell RNA-sequencing (RNA-seq) data, available as an R/Bioconductor package at https://bioconductor.org/packages/TREG. We demonstrated the utility of our method in the postmortem human brain using multiplex single molecule fluorescent in situ hybridization (smFISH) and compared candidate TREGs against classic housekeeping genes. We identified AKT3 as a top TREG across five brain regions, especially in the dorsolateral prefrontal cortex.

bioinformatics↗

bioRxiv2021

VistoSeg: a MATLAB pipeline to process, analyze and visualize high resolution histology images for Visium spatial transcriptomics data

BackgroundSpatial transcriptomics is a next-generation sequencing technology that combines the strengths of transcriptome-wide RNA-sequencing with histological imaging to generate spatial maps of gene expression in intact tissue sections. The 10x Genomics Visium and Visium-Immunofluorescence (Visium-IF) platforms are widely available commercial technologies for quantifying spatially-resolved gene expression. These technologies directly couple gene expression with high resolution histological or immunofluorescence images that contain rich morphological information about the tissue section. However, extracting and integrating image features with gene expression data remains challenging. ResultsUsing MATLAB, we developed VistoSeg, which is a pipeline to process, analyze, and interactively visualize the high-resolution images from the 10x Genomics Visium and Visium-IF platforms. The output from VistoSeg can then be integrated with the spatial-molecular information in downstream analyses using common programming languages, such as R or Python. ConclusionVistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially-resolved gene expression data. This integrated approach can advance our understanding of the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/. Impact StatementTechnologies for measuring gene activity levels, referred to as gene expression, have been evolving over decades and are the core of the transcriptomics subfield within genomics. The first report describing individual cell gene expression is from 2009 and as a method it became commercially available in 2014. While single cell transcriptomics increased our resolution beyond homogenate tissue, the advent of spatial transcriptomics technologies and commercial availability of spatial gene expression platforms, such as Visium, has facilitated studying gene expression in anatomical context. Visium measures local gene expression within the histological organization of single 6.5 mm2 cryosection of tissue. Spatially-resolved transcriptomics provides a new challenge: integrating spatial gene expression with high resolution tissue images (brightfield histology or fluorescent antibody staining). VistoSeg image processing software is compatible with both Visium and Visium-IF from 10x Genomics, which are spatially-resolved transcriptomics assays employing histological and immunofluorescent images, respectively. From these images, the number of cells, identity of cell types, and other image-derived markers can be obtained for thousands of 2,375 {micro}m2 spots, where genome-wide gene expression is also measured. VistoSeg provides tools that enable processing these images in the context of gene expression maps to integrate these two high dimensional data types, and thus help unlock the new frontier in transcriptomics.

bioinformatics↗

bioRxiv2021

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

MotivationSpatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissuespecific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. ResultsWe describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the users computer or through a web application. AvailabilityspatialLIBD is available at bioconductor.org/packages/spatialLIBD. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

SUFI: An automated approach to spectral unmixing of fluorescent biological images

Multispectral fluorescence imaging coupled with linear unmixing is a form of image data collection and analysis that uses multiple fluorescent dyes - each measuring a specific biological signal - that are simultaneously measured and subsequently "unmixed" to provide a read-out for each individual signal. This strategy allows for measuring multiple signals in a single data capture session - for example, multiple proteins or RNAs in tissue slices or cultured cells, but can often result in mixed signals and bleed-through problems across dyes. Existing spectral unmixing algorithms are not optimized for challenging biological specimens such as postmortem human brain tissue, and often require manual intervention to extract spectral signatures. We therefore developed an intuitive, automated, and flexible package called SUFI: spectral unmixing of fluorescent images (https://github.com/LieberInstitute/SUFI). This package unmixes multispectral fluorescence images by automating the extraction of spectral signatures using Vertex Component Analysis, and then performs one of three unmixing algorithms derived from remote sensing. We demonstrate these remote sensing algorithms performance on four unique biological datasets and compare the results to unmixing results obtained using ZEN Black software (Zeiss). We lastly integrate our unmixing pipeline into the computational tool dotdotdot that is used to quantify individual RNA transcripts at single cell resolution in intact tissues and perform differential expression analysis of smFISH data, and thereby provide a one-stop solution for multispectral fluorescence image analysis and quantification. In summary, we provide a robust, automated pipeline to assist biologists with improved spectral unmixing of multispectral fluorescence images.

bioinformatics↗

bioRxiv2021

Decoding shared versus divergent transcriptomic signatures across cortico-amygdala circuitry in PTSD and depressive disorders

Post-traumatic stress disorder (PTSD) is a debilitating neuropsychiatric disease with a projected lifetime risk of 8.7%. PTSD is highly comorbid with depressive disorders including major depressive disorder (MDD) and bipolar disorder (BD). It is hypothesized that the overlap in symptoms stems from partially shared underlying neurobiological mechanisms. To better understand shared and unique transcriptional patterns of PTSD and MDD we performed RNA-sequencing in the postmortem brain of two prefrontal cortex (PFC) regions and two amygdala (AMY) regions, from neurotypical donors (N=109) as well as donors with diagnoses of PTSD (N=107) or MDD (N=109) across 1285 RNA-seq samples. We identified a small number of differentially expressed genes (DEGs) specific to PTSD, mostly in the cortex compared to amygdala. PTSD-specific DEGs were preferentially enriched in cortistatin-expressing cells, a subpopulation of somatostatin interneurons. These PTSD DEGs also showed strong enrichment for gene sets associated with immune-related pathways and microglia, largely driven by decreased expression of these genes in PTSD donors. While we identified a greater number of DEGs for MDD, there were only a few that were specific to MDD as they showed high overlap with PTSD DEGs. Finally, we used weighted gene co-expression network analysis (WGCNA) as an orthogonal approach to confirm the observed cellular and molecular associations. These findings highlight the sub-population of cortistatin-expressing interneurons as having potential functional significance in PTSD and provide supporting evidence for dysregulated neuroinflammation and immune signaling in MDD and PTSD pathophysiology.

neuroscience↗

bioRxiv2020

Single-nucleus transcriptome analysis reveals cell type-specific molecular signatures across reward circuitry in the human brain

Single cell/nucleus technologies are powerful tools to study cell type-specific expression in the human brain, but most large-scale efforts have focused on characterizing cortical brain regions and their constituent cell types. However, additional brain regions - particularly those embedded in basal ganglia and limbic circuits - play important roles in neuropsychiatric disorders and addiction, suggesting a critical need to better understand their molecular characteristics. We therefore created a single-nucleus RNA-sequencing (snRNA-seq) resource across five human brain regions (hippocampus, HPC; dorsolateral prefrontal cortex, DLPFC; subgenual anterior cingulate cortex, sACC; nucleus accumbens, NAc; and amygdala, AMY), with emphasis on the NAc and AMY, given their involvement in reward signaling and emotional processing. We identified distinct and potentially novel neuronal subpopulations, which we validated by smFISH for various subclasses of NAc interneurons and medium spiny neurons (MSNs). We additionally benchmarked these datasets against published datasets for corresponding regions in rodent models to define cross-species convergence and divergence across analogous cell subclasses. We characterized the transcriptomic architecture of regionally-defined neuronal subpopulations, which revealed strong patterns of similarities in specific neuronal subclasses across the five profiled regions. Finally, we measured genetic associations between risk for psychiatric disease and substance use behaviors with each of the regionally-defined cell types. This analysis further supported NAc and AMY involvement in risk for psychiatric illness by implicating specific neuronal subpopulations, and highlighted potential involvement of an MSN population associated with stress signaling in genetic risk for substance use.

neuroscience↗

bioRxiv2019

dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

Multiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.

neuroscience↗

bioRxiv2019

Human cortical neural stem cells generate regional organizer states in vitro before committing to excitatory neuronal fates

Better understanding the progression of neural stem cells (NSCs) in the developing cerebral cortex is important for modeling neurogenesis and defining the pathogenesis of neuropsychiatric disorders. Here we used RNA-sequencing, cell imaging and lineage tracing of mouse and human in vitro NSCs to model the generation of cortical neuronal fates. We show that conserved signaling mechanisms regulate the acute transition from proliferative NSCs to committed glutamatergic excitatory neurons. As human telencephalic NSCs developed from pluripotency in vitro, they first transitioned through organizer states that spatially pattern the cortex before generating glutamatergic precursor fates. NSCs derived from multiple human pluripotent lines varied in these early patterning states leading differentially to dorsal or ventral telencephalic fates. This work furthers systematic analysis of the earliest patterning events that generate the major neuronal trajectories of the human telencephalon.

neuroscience↗

bioRxiv2019

Bdnf promoter IV-expressing cells in the hippocampus modulate fear expression and hippocampal-prefrontal synchrony in mice

Brain-derived neurotrophic factor (BDNF) signaling regulates synaptic plasticity in the hippocampus (HC) and prefrontal cortex (PFC), and has been extensively linked with fear memory expression in rodents. Notably, disrupting BDNF production from promoter IV-derived transcripts enhances fear expression in mice, and decreases fear-associated HC-PFC synchrony, suggesting that Bdnf transcription from promoter IV plays a key role in HC-PFC function during fear memory retrieval. To understand how promoter IV-derived BDNF affects fear expression and HC-PFC connectivity, we generated a viral construct that selectively targets cells expressing promoter IV-derived Bdnf transcripts ("p4-cells") for tamoxifen-inducible Cre-mediated recombination (AAV8-p4Bdnf-ERT2CreERT2-PEST). Using this construct, we found that ventral (vHC) p4-cells are recruited during fear expression, and that activation of these cells causes exaggerated fear expression that co-occurs with disrupted vHC-PFC synchrony in mice. Our data highlight how this novel construct can be used to interrogate genetically-defined cell types that selectively contribute to BDNF-dependent behaviors.

neuroscience↗

bioRxiv2018

BDNF-TrkB signaling in oxytocin neurons contributes to maternal behavior

Brain-derived neurotrophic factor (Bdnf) transcription is controlled by several promoters, which drive expression of multiple transcripts encoding an identical protein. We previously reported that BDNF derived from promoters I and II is highly expressed in hypothalamus and is critical for regulating aggression in male mice. Here we report that BDNF loss from these promoters causes reduced sexual receptivity and impaired maternal care in female mice, which is concomitant with decreased oxytocin (Oxt) expression during development. We identify a novel link between BDNF signaling, oxytocin, and maternal behavior by demonstrating that ablation of TrkB selectively in OXT neurons partially recapitulates maternal care impairments observed in BDNF-deficient females. Using translating ribosome affinity purification and RNA-sequencing we define a molecular profile for OXT neurons and delineate how BDNF signaling impacts gene pathways critical for structural and functional plasticity. Our findings highlight BDNF as a modulator of sexually-dimorphic hypothalamic circuits that govern female-typical behaviors.

neuroscience↗