THESIS
2023
1 online resource (xvii, 126 pages) : color illustrations
Abstract
The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing
our understanding of tissue spatial architecture and their biology. Spatial
transcriptomics (ST) technologies enable the measurement of transcriptomes while
retaining spatial information, which offers an unprecedented chance to uncover
transcriptomic landscapes on tissues. These transcriptomics datasets have provided
new insights into tissue composition/function and accelerated the capacity
to elucidate the development of healthy tissue and tumor microenvironment of
cancers.
Current ST technologies based on either next-generation sequencing (seq-based
approaches) or fluorescence in situ hybridization (image-based approaches), while
providing hugely informative insights, remain unable to provide spatial c...[
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The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing
our understanding of tissue spatial architecture and their biology. Spatial
transcriptomics (ST) technologies enable the measurement of transcriptomes while
retaining spatial information, which offers an unprecedented chance to uncover
transcriptomic landscapes on tissues. These transcriptomics datasets have provided
new insights into tissue composition/function and accelerated the capacity
to elucidate the development of healthy tissue and tumor microenvironment of
cancers.
Current ST technologies based on either next-generation sequencing (seq-based
approaches) or fluorescence in situ hybridization (image-based approaches), while
providing hugely informative insights, remain unable to provide spatial characterization
at transcriptome-wide single-cell resolution, limiting their usage in resolving
detailed tissue structure and detecting cellular communications. Seq-based approaches,
such as 10x Visium [1] and Slide-seq [2], can detect transcriptome-wide
gene expression within spatial spots, but each spot often contains multiple cells.
Therefore, the resolution of present seq-based approaches do not achieve single-cell resolution, which limits their usage in resolving detailed tissue structure and in
characterizing cellular communications (e.g., identifying ligand-receptor interactions
[3]). Image-based approaches such as seqFISH [4] and MERFISH [5] achieve
single-cell resolution but are limited to profiling panels of tens to hundreds of
genes per sample, leaving the majority of the transcriptome unmeasured. Users
of these image-based methods need well-defined biological hypotheses to design
an appropriate and useful gene panel, and it is unlikely to generate incidental
discoveries in this scenario.
On the other hand, single-cell RNA sequencing (scRNA-seq) characterizes the
whole transcriptome of individual cells within a given organ, providing remarkable
opportunities for broad and deep biological investigations of diverse cellular
behaviors [6, 7, 8]. However, scRNA-seq does not capture the spatial distribution
of cells due to samples having to undergo tissue dissociation [9]. As spatial
information is so critical to understanding communication between cells, many
related scientific questions related to cellular communication cannot be fully
addressed by scRNA-seq alone [10].
Ideally, the integration of single-cell and ST data should allow us to characterize
the spatial distribution of the whole transcriptome at single-cell resolution by
combining their complementary information. However, existing integration methods
are far from satisfactory in real data analysis [11]. Deconvolution methods
are applied to seq-based ST data, they only estimate the proportions of cell types
in each spatial spot but cannot achieve single-cell resolution. For image-based ST
data, methods developed to infer unmeasured gene expressions are not sufficiently
accurate, especially when ST expression data are sparse [11].
In this thesis, we propose a unified framework, SpaitalScope, to integrate scRNA-seq
reference data and ST data, resulting in the characterization of the spatial
distribution of the whole transcriptome at single-cell resolution. By leveraging the deep generative model to approximate the distribution of gene expressions
accurately from the scRNA-seq reference data, SpatialScope can resolve the spot-level
data composed of multiple cells to single-cell resolution when it is applied to
seq-based ST data, corrects low-accuracy genes for high resolution spatial data,
such as Slide-seq and infers transcriptome-wide expression levels for image-based
ST data. We demonstrate the utility of SpatialScope through comprehensive
simulation studies and then apply it to real data from both seq-based and imagebased
ST approaches. SpatialScope provides a spatial characterization of tissue
structures at transcriptome-wide single-cell resolution, greatly facilitating the
downstream analysis of ST data, such as detection of cellular communication by
identifying ligand-receptor interactions from seq-based ST data, localization of
cellular subtypes, and detection of spatially differently expressed genes.
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