Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

Moncada, Reuben; Barkley, Dalia; Wagner, Florian; Chiodin, Marta; Devlin, Joseph C.; Baron, Maayan; Hajdu, Cristina; Simeone, Diane M.; Yanai, Itai

doi:10.1038/s41587-019-0392-8

Cited by 642 publications

(695 citation statements)

References 64 publications

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“…This approach, where half of a tumor sample was subjected to scRNA-seq and the other half to ST, was recently demonstrated on pancreatic tumor samples. [54] Marker genes found in the scRNA-seq data were used to deconvolute cell type compositions of different tissue regions containing multiple cell types.…”

Section: Using a Reference Mapmentioning

confidence: 99%

Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

2020

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Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method-specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue-type dependence, and limited capacity to obtain detailed single-cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.

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Section: Using a Reference Mapmentioning

confidence: 99%

Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

2020

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“…Several methods to integrate spatial and scRNA-seq data have been proposed, and success has been shown upon applying these techniques to spatial cancer data. [21] Most of these methods rely on correlation or elevated expression of a select set of marker genes; however, we decided to use a method that takes advantage of the full expression profiles from both data modalities. In short, the method we used ( stereoscope ) decomposes the expression observed in each spatial location -a mixture of transcripts from multiple cells -into contributions from different cell types defined by the single cell data using a probabilistic model.…”

Section: Hla-d{qb1rarb1}mentioning

confidence: 99%

“…[18] Cell interactions and spatial context are key components of the tumor ecosystem, however this space is inhabited by a diverse population of complex cell types that cannot be defined by a few marker genes or surface receptors; hence the benefits of using a technique like ST. [19,20] Although ST does not provide single-cell resolution, this issue can be addressed by leveraging information from scRNA-seq, spatially mapping cell types or clusters by integration of the two data modalities. [21,22] In this study, we used ST to survey the spatial patterns of gene expression and cell types in 36 samples collected from eight HER2-positive individuals. Intra-and inter-patient heterogeneity was examined using a number of different methods, including expression-based clustering and single cell data integration.…”

Section: Introductionmentioning

confidence: 99%

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

Andersson

Larsson

Stenbeck

et al. 2020

Preprint

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In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra-and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.

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“…molecular alterations, clinical chemistry, survival, etc. ), via corresponding immunohistochemistry stains (IHC), 5,6 and mutational panels of known oncological driver mutations (among others) [7][8][9] . Furthermore, generative techniques have been developed to computationally translate one histological stain (e.g.…”

Section: Introductionmentioning

confidence: 99%

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

Levy

Jackson

Haudenschild

et al. 2020

Preprint

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Image registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments' orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

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Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas

Cited by 642 publications

References 64 publications

Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

Spatially Resolved Transcriptomes—Next Generation Tools for Tissue Exploration

Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

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