Biology subjects

Spangler, A.

Publications and source records attributed to Spangler, A..

5 recordsLinked to original sources

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

Quantitative comparison of in vitro and in vivo embryogenesis at a single cell resolution

Stem cell engineering is a powerful platform to address numerous challenges in regenerative medicine and developmental biology. Typically, engineered populations are derived by exposing pluripotent stem cells to a series of signaling cues meant to recapitulate developmental milestones, such as the induction of the primitive streak. Morphologic, genetic, transcriptomic, and functional differences between fully differentiated in vivo and engineered populations have long been recognized. However, the correspondence between engineered and native embryonic progenitors has been relatively less well characterized, largely due to their transient and highly heterogenous nature, and therefore many studies have relied on expression of a few canonical markers to ensure that their cells are on the correct path. To address this challenge, we first generated an in vivo gastrulation mouse single cell RNA sequencing (scRNA-seq) reference data set and used it to train a collection of computational tools for comparing cell types, states, regulators, and trajectories across studies. Then we used these tools to evaluate a commonly used protocol designed to promote mesoderm derivation, as well as four previously published biomimetic protocols. Finally, we diversified our toolkits to reach a broader scientific community by implementing our primary analysis tool in Python and as an interactive web application.

developmental biology↗

bioRxiv2021

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification

Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. Computational gene regulatory network (GRN) reconstruction methods aim to map this control by inferring relationships from transcriptomic data. Unfortunately, existing methods are imprecise, may be computationally burdensome, and do not uncover how networks transition from one topology to another. Here we present Epoch, a computational network reconstruction tool that leverages single cell transcriptomics to infer dynamic network structures. Epoch performs favorably when benchmarked using data derived from in vivo, in vitro, and in silico sources. To illustrate the usefulness of Epoch, we applied it to identify the dynamic networks underpinning directed differentiation of mouse embryonic stem cells (ESC) guided by multiple primitive streak induction treatments. Our analysis demonstrates that modulating signaling pathways drives topological network changes that shape cell fate potential. We also find that Peg3 is a central contributor to the rewiring of the pluripotency network to favor mesoderm specification. By integrating signaling pathways with GRN structures, we traced how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively. Finally, we compare the networks established in in vitro directed differentiation of ESCs to those in in vivo gastrulation and mesoderm specification. The methods presented here are available in the R package Epoch, and provide a foundation for future work in understanding the biological implications of dynamic regulatory structures.

systems biology↗

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↗

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↗