Quantitative approaches for investigating the spatial context of gene expression

Abstract

The spatial information associated with gene expression is important for elucidating the context-dependent transcriptional regulation during development. Recently, high-resolution sampling approaches, such as RNA tomography or single cell RNA-seq combined with fluorescence in situ hybridization (FISH), have provided indirect ways to view global gene expression patterns in 3D. Now in situ sequencing technologies, such as florescent in situ sequencing (FISSEQ), are attempting to visualize the genetic signature directly in microscope images. This review will examine the basic principle of modern in situ and single cell genetic methods, hurdles in quantifying intrinsic and extrinsic forces that influence cell decision-making, and technological requirements for making a visual map of gene regulation, form, and function. Successfully addressing these challenges will be essential for investigating the functional evolution of regulatory sequences during growth, development, and cancer progression.

SpatialDE - Identification of spatially variable genes

“…The fixed effect accounts for mean expression level and denotes a spatial covariance matrix, which by default is defined to model general spatial effects using the so called squared exponential covariance function 16 (2) , whereby the covariance between pairs of cells and is modelled to decay exponentially with the squared distance between them in the coordinates. The hyperparameter , also known as the characteristic length scale, determines how rapidly the covariance decays as a function of distance.…”

“…Increased experimental throughput has also fostered new experimental designs, where the spatial context of gene expression variation can now be directly assayed, which is critical for characterizing complex tissue architectures in multicellular organisms. The spatial context of gene expression is crucial for determining functions and phenotypes of cells 1,2 . Spatial expression variation can reflect communication between adjacent cells, or can be caused by cells that migrate to specific locations in a tissue to perform their functions.…”

Reconstruction of cell spatial organization based on ligand-receptor mediated self-assembly

“…However, spatial information of individual cells is lost during the process of tissue dissociation. While it is paramount to investigate the molecular composition of individual cells in the spatial contexture, current methods such as RNA hybridization 3 , in situ sequencing 4 , immunohistochemistry 5 , and purifying predefined subpopulations for subsequent transcriptomic profiling 6 are limited by the throughput and complex experimental procedures that are only accessible by a handful of laboratories. The combination of scRNA-seq with in situ RNA patterns or tissue shapes provides computational solutions for high-throughput mapping the spatial locations of many individual cells [7][8][9][10] , but this type of methods relies on the availability of such spatial references, limiting their wide applications.…”

De Novo Gene Expression Reconstruction in Space

“…3C)[47–49]. Here, fixed cells or tissues are infused with reagents capable of randomly converting RNA molecules into cDNA fragments that are amplified in situ.…”

“…Our laboratory is now using high-speed 3D imaging to scan 4×4-mm regions in just over two hours per cycle. (The entire run requires 32 cycles, or ~2-TB images per run) [47–49]. One of our current goals is to establish the number of arrayable Drosophila embryos required to detect key developmental regulators in early embryo patterning (Fig.…”