JSTA: joint cell segmentation and cell type annotation for spatial transcriptomics

Littman, Russell; Hemminger, Zachary; Foreman, Robert T.; Arneson, Douglas; Zhang, Guanglin; Gómez‐Pinilla, Fernando; Yang, Xia; Wollman, Roy

doi:10.1101/2020.09.18.304147

Cited by 6 publications

(10 citation statements)

References 47 publications

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“…Although these may not correspond to true physical boundaries, but rather to the limit between cells, they accomplish the task of assigning each mRNA to a cell. Alternatively, the data analysis can begin at the level of individual pixels, and incorporate the gene expression data to delineate cells [84][85][86] .…”

Section: Imaging-based Approachesmentioning

confidence: 99%

Exploring tissue architecture using spatial transcriptomics

Rao¹,

Barkley²,

França³

et al. 2021

Nature

911

610

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Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. Recent technological advancements in next-generation sequencing-based and imaging based approaches have established the potential of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insight in neuroscience, development, plant biology, and a range of diseases including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here, we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs comparing timepoints or conditions -including genetic or environmental perturbations. Finally, spatial transcriptomic data is naturally amenable to integration with other data modalities providing an expandable framework for insight into tissue organization.Many of the notable discoveries in the life sciences followed from the recognition that cellular organization within tissues is intimately linked to biological function. In developmental biology, central topics such as symmetry-breaking between daughter cells and cell fate decisions are based on spatial relationships between cells 1 . In clinical settings, histopathology is often used as a conclusive diagnostic, precisely because diseases are characterized by abnormal spatial organization within tissues 2 . Infectious and inflammatory processes can drastically change the cellular organization of tissues 3 . These discoveries were supported by methods in molecular biology -including in situ hybridization 4 (ISH) and immunohistochemistry 5 -that provided the ability to visualize biological processes more directly by mapping DNA, RNA and protein within tissues. However, these methods limit analysis to at most a handful of genes or proteins at a time.

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Section: Imaging-based Approachesmentioning

confidence: 99%

Exploring tissue architecture using spatial transcriptomics

Rao¹,

Barkley²,

França³

et al. 2021

Nature

911

610

View full text Add to dashboard Cite

show abstract

“…More information about a cell is therefore imperative to know in order to understand how a cell makes decisions. Recent work identified more than 40 genes in the mouse hippocampus to be cell-subtype-specific spatial differentially expressed genes (spDEGs) (Littman et al, 2020). These results suggest that a spatial position can explain much of the heterogeneity seen using dissociative approaches.…”

Section: Spatial Contextmentioning

confidence: 97%

“…The first challenge is the computational and data complexity. Despite the differences in methods, many computational steps, such as spot calling and cell segmentation (Littman et al, 2020), are shared across approaches. Development of standards and mature computational libraries that can allow the separation of the computational analysis from data acquisition will allow more cross-fertilization in this field.…”

Section: Challenges Are Truly Opportunitiesmentioning

confidence: 99%

“…In situ measurement technologies are well suited to generate and utilize cell classification systems. MERFISH and seqFISH were used to categorize the organization of pre-defined cell types within the brain (Chen et al, 2015;Littman et al, 2020;Shah et al, 2016). Other in situ technologies, such as in situ sequencing leverage the existing cell-type taxonomies to overcome low RNA detection efficiency and still provide key celltype information (Qian et al, 2020).…”

Section: Cell Types: the Building Blocks Of Tissuesmentioning

confidence: 99%

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Bridging scales: From cell biology to physiology using in situ single-cell technologies

et al. 2021

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“…For example, methods based on fluorescence imaging (MERFISH [6], osmFISH [2,6], seqFISH [7]) have near single-transcript resolution. However, these methods rely on cell-segmentation algorithms [8,9]. Additionally, these studies are dependent on pre-selected marker genes and are not genome-wide and hence require imputation of the missing gene to avoid overlooking critical information [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

Lopez

Keren‐Shaul

et al. 2021

Preprint

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The function of mammalian cells is largely influenced by their tissue microenvironment. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function by enabling a transcriptome-wide evaluation of gene expression in situ. A critical limitation of the current technologies, however, is that their resolution is limited to niches (spots) of sizes well beyond that of a single cell, thus providing measurements for cell aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single-cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyses are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states that cannot be clearly demarcated but reflects important differences in the way cells function and interact with their surroundings. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi-resolution analysis for spatial transcriptomics that explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods and that it can estimate gene expression by every cell type inside every spot. We then introduce an automated pipeline that uses DestVI for analysis of single tissue slices and comparison between tissues. We apply this pipeline to study the immune crosstalk within lymph nodes to infection and explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a high resolution and accurate spatial characterization of the cellular organization of these tissues, and that it is capable of identifying important cell-type-specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software package in the scvi-tools codebase (https://scvi-tools.org).

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JSTA: joint cell segmentation and cell type annotation for spatial transcriptomics

Cited by 6 publications

References 47 publications

Exploring tissue architecture using spatial transcriptomics

Exploring tissue architecture using spatial transcriptomics

Bridging scales: From cell biology to physiology using in situ single-cell technologies

Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation

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