Biology subjects

Page, S. C.

Publications and source records attributed to Page, S. C..

28 records · Page 2Linked to original sources

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

Expression of brain-derived neurotrophic factor in basolateral amygdala inputs to lateral septum is necessary for mice to identify socially novel individuals

The lateral septum (LS) is a GABAergic region in the basal forebrain that is implicated in sociability. However, the neural circuits and cell signaling pathways that converge on the LS to mediate social behaviors arent well understood. Multiple lines of evidence suggest that brain-derived neurotrophic factor (BDNF) signaling through its receptor TrkB plays important roles in social behavior. While BDNF is not locally produced in LS, we demonstrate that nearly all GABAergic neurons in LS express TrkB. Local knock-down of TrkB expression from LS neurons decreased sociability and reduced recruitment of social novelty-induced neural activity. Since BDNF is not synthesized in LS, we evaluated which inputs to the LS could serve as potential BDNF sources for controlling sociability. By selectively ablating inputs to LS, we demonstrated that inputs from the basolateral amygdala (BLA), but not ventral CA1 (vCA1), regulate sociability. Moreover, depleting BDNF selectively in BLA-LS projection neurons phenocopied the decreased sociability observed following either local LS TrkB knockdown or ablation of BLA-LS inputs. These data support the hypothesis that BLA-LS projection neurons could serve as a critical source of BDNF for activating TrkB signaling in LS neurons to control sociability.

neuroscience↗

bioRxiv2021

CaPTure: Calcium Peak Toolbox for analysis of in vitro calcium imaging data

BackgroundCalcium imaging is a powerful technique for recording cellular activity across large populations of neurons. However, analysis methods capable of single-cell resolution in cultured neurons, especially for cultures derived from human induced pluripotent stem cells (hiPSCs), are lacking. Existing methods lack scalability to accommodate high-throughput comparisons between multiple lines, across developmental timepoints, or across pharmacological manipulations. ResultsWe developed a scalable, automated Ca2+ imaging analysis pipeline called CaPTure (https://github.com/LieberInstitute/CaPTure). This method detects neurons, classifies and quantifies spontaneous activity, quantifies synchrony metrics, and generates cell- and network-specific metrics that facilitate phenotypic discovery. The method is compatible with parallel processing on computing clusters without requiring significant user input or parameter modification. ConclusionCaPTure allows for rapid assessment of neuronal activity in cultured cells at cellular resolution, rendering it amenable to high-throughput screening and phenotypic discovery. The platform can be applied to both human- and rodent-derived neurons and is compatible with many imaging systems.

neuroscience↗

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

Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance

Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRS) for schizophrenia and a clinical diagnosis of schizophrenia, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures associated with diagnosis that implicated altered Na+ channel function and GABA-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these schizophrenia patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.

neuroscience↗

bioRxiv2021

Induction of Bdnf from promoter I following electroconvulsive seizures contributes to structural plasticity in neurons of the piriform cortex

The efficacy of electroconvulsive therapy (ECT) as a treatment for psychiatric disorders, including major depressive disorder (MDD) is hypothesized to depend on induction of molecular and cellular events that trigger structural plasticity in neurons. Electroconvulsive seizures (ECS) in animal models can help to inform our understanding of how electroconvulsive therapy (ECT) impacts the brain. ECS induces structural plasticity in neuronal dendrites in many brain regions, including the piriform cortex, a highly epileptogenic region that has also been implicated in depression. ECS-induced structural plasticity is associated with differential expression of unique isoforms encoding the neurotrophin, brain-derived neurotrophic factor (BDNF), but the functional significance of these transcripts in dendritic plasticity is not clear. Here, we demonstrate that different Bdnf isoforms are expressed non-stochastically across neurons of the piriform cortex following ECS. Specifically, cells expressing Bdnf exon 1-containing transcripts show a unique spatial recruitment pattern in response to ECS. We further demonstrate that Bdnf Ex1 expression in these cells is necessary for ECS-induced dendritic spine plasticity.

neuroscience↗

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↗

bioRxiv2018

Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs

Human induced pluripotent stem cells (hiPSCs) are a powerful model of neural differentiation and maturation. We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC donor and 13 subclonal lines across nine time points over 5 broad conditions: self-renewal, early neuronal differentiation, neural precursor cells (NPCs), assembled rosettes, and differentiated neuronal cells that were validated using electrophysiology. We identified widespread changes in the expression of individual transcript features and their splice variants, gene networks, and global patterns of transcription. We next demonstrated that co-culturing human NPCs with rodent astrocytes resulted in mutually synergistic maturation, and that cell type-specific expression data can be extracted using only sequencing read alignments without potentially disruptive cell sorting. We lastly developed and validated a computational tool to estimate the relative neuronal maturity of iPSC-derived neuronal cultures and human brain tissue, which were maturationally heterogeneous but contained subsets of cells most akin to adult human neurons.

genomics↗

bioRxiv2017

Defects of myelination are common pathophysiology in syndromic and idiopathic autism spectrum disorders

Autism spectrum disorder (ASD) affects approximately 1:68 individuals and has incalculable burdens on affected individuals, their families, and health care systems. While the genetic contributions to idiopathic ASD are heterogeneous and largely unknown, the causal mutations for syndromic forms of ASD - including truncations and copy number variants - provide a genetic toehold with which to gain mechanistic insights1-3. Models of these syndromic disorders have been used to better characterize the molecular and physiological processes disrupted by these mutations4. Two fundamental questions remain - how biologically similar are the mouse models of syndromic forms of ASD, and how relevant are these mouse models to their human analogs? To address these questions, we performed integrative transcriptomic analyses of seven independent mouse models of three syndromic forms of ASD generated across five laboratories, and assessed dysregulated genes and their pathways in human postmortem brain from patients with ASD and unaffected controls. These cross-species analyses converged on shared disruptions in myelination and axon development across both syndromic and idiopathic ASD, highlighting both the face validity of mouse models for these disorders and identifying novel convergent molecular phenotypes amendable to rescue with therapeutics.

neuroscience↗