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

Maynard, Kristen R.; Collado‐Torres, Leonardo; Weber, Lukas M.; Uytingco, Cedric; Barry, Brianna K.; Williams, Stephen R.; Catallini, Joseph L.; Tran, Matthew N.; Besich, Zachary; Tippani, Madhavi; Chew, Jennifer; Yin, Yifeng; Kleinman, Joel E.; Hyde, Thomas M.; Rao, Nikhil; Hicks, Stephanie; Jaffe, Andrew E.

doi:10.1101/2020.02.28.969931

Cited by 137 publications

(336 citation statements)

References 85 publications

(156 reference statements)

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“…Case study 2: We also tested SMEclust on ST data from all twelve human brain tissue sections 10 and compared the results to those generated with another ST clustering algorithm, SpatialLIBD, which was applied in the original publication of this data. We tested five options that SpatialLIBD used to select gene markers: SpatialDE genes, SpatialDE pooled genes, highly variable genes (HVG), pseudo-bulk, and known markers.…”

Section: Supplementary Figs 1-4)mentioning

confidence: 99%

“…1. b, Clustering analysis for human dorsolateral prefrontal cortex data 10 . For each of 12 tissue sections, we compared SMEclust to spatialLIBD run with 20 parameter combinations (10 ways of selecting gene features for clustering and two ways for dimensionality reduction).…”

Section: Implementation Of Softwarementioning

confidence: 99%

“…As with scRNAseq, ST analysis tool development has lagged behind rapid technological and data generation advances. To find cell types, existing clustering algorithms for ST data use only gene expression information, such as in Spaniel 17 , Seurat (V3.2) 18 , spatialLIBD 10 , and SpatialCpie 19 . Current available methods to find differentially expressed genes 20,21 or spatially variable genes 22 , can integrate gene expression and spatial distance information but not tissue-image information.…”

Section: Introductionmentioning

confidence: 99%

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stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

Pham

Tan

et al. 2020

Preprint

214

263

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Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues. 14/18

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Section: Supplementary Figs 1-4)mentioning

confidence: 99%

Section: Implementation Of Softwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

Pham

Tan

et al. 2020

Preprint

214

263

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“…Furthermore, recent studies successfully implemented ST to define the spatial topography of the human dorsolateral prefrontal cortex and its association with schizophrenia and autism 6 .…”

Section: Introductionmentioning

confidence: 99%

SPOTlight: Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes

Elosua

Nieto

Mereu

et al. 2020

Preprint

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The integration of orthogonal data modalities greatly supports the interpretation of transcriptomic landscapes in complex tissues. In particular, spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes, and nonnegative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Using synthetic spots, simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. We trained the NMF regression model with sample-matched or external datasets, resulting in accurate and sensitive spatial predictions.SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections into healthy and cancerous areas, and further fine-mapped normal and neoplastic cell states. Trained on an external pancreatic tumor immune reference, we charted the localization of clinical-relevant and tumor-specific immune cell states. Using SPOTlight to detect regional enrichment of immune cells and their co-localization with tumor and adjacent stroma provides an illustrative example in its flexible application spectrum and future potential in digital pathology.The SPOTlight code and the analysis notebooks to reproduce the aforementioned analysis are hosted at HH and ME designed the study. ME developed SPOTlight and performed ST and scRNA-seq data analyses. PN compiled the PDAC scRNA-seq data and performed clustering and immune cell annotation.EM and IG supported the data analysis. HH and ME wrote the manuscript. All authors read and approved the final version.

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“…Such spatially resolved transcriptomic profiling allows analyses to be made within the context of the biological tissue. Studies done on the Spatial Transcriptomics (ST) platform and the closely related Visium platform have already generated insights into diverse areas such as tumor heterogeneity 2,3 , brain function 4 , and the pathophysiology of sepsis 5 . The primary technological limitation of these spatial gene expression platforms is resolution, with the unit of observation being spots that are 100μm in diameter on the ST platform and 55μm in diameter on the Visium platform.…”

Section: Introductionmentioning

confidence: 99%

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Zhao

Stone

Ren

et al. 2020

Preprint

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Recently developed spatial gene expression technologies such as the Spatial Transcriptomics and Visium platforms allow for comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing methods for analyzing spatial gene expression data often do not efficiently leverage the spatial information and fail to address the limited resolution of the technology. Here, we introduce BayesSpace, a fully Bayesian statistical method for clustering analysis and resolution enhancement of spatial transcriptomics data that seamlessly integrates into current transcriptomics analysis workflows. We show that BayesSpace improves the identification of transcriptionally distinct tissues from spatial transcriptomics samples of the brain, of melanoma, and of squamous cell carcinoma. In particular, BayesSpace's improved resolution allows the identification of tissue structure that is not detectable at the original resolution and thus not recovered by other methods. Using an in silico dataset constructed from scRNA-seq, we demonstrate that BayesSpace can spatially resolve expression patterns to near single-cell resolution without the need for external single-cell sequencing data. In all, our results illustrate the utility BayesSpace has in facilitating the discovery of biological insights from a variety of spatial transcriptomics datasets.

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

Cited by 137 publications

References 85 publications

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

SPOTlight: Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

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