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 three dimensions. Now in situ sequencing technologies, such as fluorescent in situ sequencing (FISSEQ), are attempting to visualize the genetic signature directly in microscope images. This article 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. WIREs Syst Biol Med 2017, 9:e1369. doi: 10.1002/wsbm.1369 For further resources related to this article, please visit the WIREs website.

Figure 1

Molecular or cellular taxonomy alone is insufficient for understanding the functional dynamics of genetic and phenotypic evolution, as it requires analyzing how the selection pressure from the environment changes the phenotype from a common ancestor. To properly address this question, one needs to compare multiple cell lineages, cell states/types, and microenvironments in parallel. Traditionally, the genetic material was isolated from pulverized tissues, masking the cellular heterogeneity as well as their spatial context. Methods now exist to sample randomly chosen cells or from spatially defined regions; however, they all lack the precise spatial resolution for understanding cell–cell or cell–environment interactions.

Figure 2

Quantitative methods for detecting multiple RNA molecules in situ. (a) The Nilsson method uses target‐specific reverse transcription (RT) primers (typically locked nucleic acid (LNA) derivatives) to make cDNA molecules in situ used for padlock probe‐based T4 DNA ligation.17 The intramolecular ligation reaction here is less efficient and specific than the sequencing‐by‐ligation reaction kinetics. The circular padlock probe is then amplified using rolling circle amplification (RCA) that increases the number of barcode‐binding sites by 100‐fold or more for robust imaging; however, it is not known how the physical constraints or molecular crowding around individual transcripts in tissues affect the RCA bias that is observed in fluorescent in situ sequencing (FISSEQ).19 (b) Compared to the previous method that involves long customized probes and multiple enzymatic steps, single‐molecule fluorescence in situ hybridization (smFISH) offers the unmatched sensitivity, spatial resolution, ease of use, affordability, and scalability, as long as one can optically resolve individual signals under a microscope.7 Because smFISH is so sensitive, this can be challenging for most abundantly expressed transcripts, especially at low magnification.31 In addition, smFISH does not have the single‐nucleotide specificity of the Nilsson method.

Figure 3

Spatial versus temporal genetic barcoding for multiplexed RNA detection. (a) Genetic barcoding to multiplex transcript detection was first demonstrated in situ by Singer and coworkers.6 Hood and coworkers then popularized this concept and made it commercially successful for single‐molecule RNA quantification (Nanostring, Seattle, WA).39 Here, the target RNA serves as a splint that pulls down a complementary probe with a spatial barcode composed of fluorescent nucleic acid segments (~1–2 µm). Optically resolving various color sequences‐associated each RNA molecule enables target identification and quantification. (b) Large barcodes cannot be used for multiplexing in single cells; however, Cai and coworkers showed that targeting the same loci repeatedly with single‐molecule fluorescence in situ hybridization (smFISH) but using different colors generates a temporal barcode.8 Using four‐color imaging and seven hybridization cycles, one could interrogate over 16,000 genes in theory, despite a number of practical challenges due to the diffraction limit of optical microscopy and the imaging time required for super‐resolution microscopy.

Figure 4

Single‐molecule fluorescence in situ hybridization (smFISH) requires spatially resolving individual molecules for quantification and multiplexing.8, 9 The addition of multiple small hybridization probes to cells and tissues generates significant nonspecific fluorescence;7 however, the co‐localization of many independent probe sequences on the same transcript can be detected as a diffraction‐limited spot, resulting in a high signal‐to‐noise ratio (SNR). But when the transcript density is too high or the imaging magnification is too low, it becomes difficult to discriminate signal from noise, rendering smFISH largely qualitative and challenging for a high degree of multiplexing.31

Figure 5

Location‐aware sampling methods for next‐generation sequencing (NGS). For unbiased profiling of transcriptome‐wide gene expression, NGS is currently the only wide available method. Tissue samples can be dissociated into random or sorted single cells and virtually reconstructed later using the known spatial patterns of gene expression (single‐cell RNA‐seq, scRNA‐seq).4, 5 Alternatively, they can be spatially dissected [i.e., laser capture microdissection (LCM), transcriptome in vivo analysis (TIVA)],12 sectioned (RNA tomography),13 or systematically subsampled (i.e., RNA capture array)52 for RNA‐seq. Here, the practical sampling number limits the spatial resolution as the specimen size increases along multiple dimensions. Practically, only a small fraction of all possible sampling points are used for multiplexed RNA‐seq with a lower sequencing depth. In addition, the sampling noise requires pooling multiple regions or single cells together to detect subtle variations, further handicapping NGS‐based methods from achieving the single‐cell spatial resolution based on less abundant and possibly more tissue‐specific transcripts.

Figure 6

Fluorescent in situ sequencing (FISSEQ) can utilize the programmable signal density to acquire a similar amount of information from multiplexed RNA detection regardless of the optical magnification or the transcript density.19, 28 In this way, small and large biological patterns can be observed in the same specimen across a range of spatial scales, similar to how modern maps display the similar density of geographical information regardless of their resolution, making the map useful at all scales.

Figure 7

Fluorescent in situ sequencing (FISSEQ) converts endogenous RNA molecules in fixed cells or tissues into short cDNA fragments in situ using random hexamer‐primed reverse transcription (RT). (a) Each cDNA fragment contains a common sequencing adapter, which is then circularized prior to rolling circle amplification (RCA) in situ. RCA amplicons are then crosslinked to generate a stable 3D matrix of DNA molecules for in situ next‐generation sequencing (NGS) reactions. FISSEQ then generates 3D images containing NGS reads at each pixel for data analysis.19, 28 (b) Currently, the efficiency of RCA is not uniform across subcellular compartments, especially across different cell types.28 We hypothesize that molecular crowding or liquid droplet phase transition may contribute to such observations; however, it is not clear whether such phenomenon is responsible for the relative paucity of housekeeping genes in FISSEQ.

Figure 8

Focusing technology development around a central question enables multiple creative approaches necessary to measure specific elements, rather than simply scaling up existing technologies. For example, positional information in developmental biology has been investigated under the assumption of idealized morphogen gradients without considering cellular and environmental variations. If true, any fluctuations in the signal strength due to environmental factors can make precise tissue patterning difficult over a global scale. To understand what makes interpreting positional information robust, our laboratory is developing three distinct in situ sequencing methods capable of measuring single‐cell variations, microenvironmental heterogeneity, and cell lineages. In the future, it should be possible to perform selective knockdown of genetic pathways or optogenetic induction of morphogen signaling in vivo and quantify how intrinsic, extrinsic, and lineage‐specific factors drive location‐specific cell fate commitment and differentiation.