Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex

By K. E. Maynard, L. Collado-Torres, Lukas M. Weber, C. Uytingco, B. Barry, S. Williams, J. L. Catallini, M. N. Tran, Z. Besich, M. Tippani and 8 more. Nature Neuroscience, 2020

1,067 citations recorded by the source corpus.

We used the 10x Genomics Visium platform to define the spatial topography of gene expression in the six-layered human dorsolateral prefrontal cortex. We identified extensive layer-enriched expression signatures and refined associations to previous laminar markers. We overlaid our laminar expression signatures on large-scale single nucleus RNA-sequencing data, enhancing spatial annotation of expression-driven clusters. By integrating neuropsychiatric disorder gene sets, we showed differential layer-enriched expression of genes associated with schizophrenia and autism spectrum disorder, highlighting the clinical relevance of spatially defined expression. We then developed a data-driven framework to define unsupervised clusters in spatial transcriptomics data, which can be applied to other tissues or brain regions in which morphological architecture is not as well defined as cortical laminae. Last, we created a web application for the scientific community to explore these raw and summarized data to augment ongoing neuroscience and spatial transcriptomics research (http://research.libd.org/spatialLIBD). This study defined spatial gene expression in the human dorsolateral prefrontal cortex. It reveals layer-enriched expression of genes associated with schizophrenia and autism, highlighting the clinical relevance of spatially defined expression.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/86ad0695623e8964d06d97dd88b8958b566751de

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In Genetics

1. Lior S. Pachter
2. Raphael Gottardo

In Molecular Biology

1. Min Wu
2. Roser Vento-Tormo
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Citations in the corpus

• DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, methodology): “Maynard et al. (23) manually annotated the cortical layers (L1–L6) and white matter (WM) of 12 slides of the dorsolateral prefrontal cortex (DLPFC) by gene marker and cytoarchitecture (Figure 2A), which is a publicly available 10 × Visium ST benchmarking dataset.”
• Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST (2023, positive): “We fi rstassessedGraphST ’ sspatialclusteringperformanceontheLIBD human dorsolateral prefrontal cortex (DLPFC) dataset 36 .”
• Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, neutral): “We downloaded the count matrix, annotation and spatial data from Maynard et al. 11 [https://github.com/LieberInstitute/HumanPilot].”
• Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder (2021, methodology): “Maynard et al. 16 has manually annotated DLPFC layers and white matter (WM) based on the morphological features and gene markers (Fig.”
• stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, methodology): “Human Breast Cancer (Block A Section 1) spatial data, publicly available from 10x Genomics, was used.”
• An introduction to spatial transcriptomics for biomedical research (2022, methodology): “However, it is limited in practice by optical crowding if too many transcripts are profiled [8].”
• Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “We used the 16 integration methods to combine the spatial and single-cell transcriptomics data, and then compared their performance for (1) predicting the spatial distribution of RNA transcripts and (2) deconvoluting cell types of a histological spot.”
• Museum of spatial transcriptomics (2020, positive): “In terms of tissues analyzed, multiplexed current era techniques have been used widely to characterize human tissues [140], tumors [53] (especially breast tumors and squamous cell carcinoma), and pathological tissues that don’t necessarily have a stereotypical structure [141] (Figure 3B, C).”
• Spatial transcriptomics at subspot resolution with BayesSpace (2021, methodology): “Following the methodology of Maynard et al. 4 , we use the adjusted Rand index (ARI) to quantify similarity between cluster labels and manual annotations, which are considered the ground truth.”