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Regardless, the key assumption in these idealized models is that the diffusion rate is consistent across various tissue landscapes and that all responding cells interpret a particular morphogen concentration in the same way. Despite many pioneers in the field, however, it is still not clear how cells interpret positional information to make decisions about its growth and differentiation.

Do intrinsic cellular variations such as the cell cycle state, 69 biophysical forces, 70 morphology, 71 and cell—cell interactions play a role? Figure 8. The location awareness of cells is critical to understanding the origin of cancer as well. The most important benefit of in situ sequencing is that one can develop methods to capture other types of genetic data, including the somatic variation, the methylation signature, the DNA accessibility, and the chromatin topology. Given these considerations, quantitatively surveying large tissues using multiplexed smFISH will likely be limited to a relatively small number of less abundant genes.

While FISSEQ is promising for investigating cell—cell and cell—microenvironmental interactions in situ , it is too premature to say whether the method will be practical or informative in a wide range of cell and tissue types. Currently, the low sensitivity of FISSEQ limits its application for the targeted detection of specific RNA transcripts, including those bearing genetic barcode sequences.

In addition, it could allow for tracking RNA processing, transport, and compartmentalization as well as discriminating multiple odorant receptors and their connectivity based on the transcript sequence. Finally, finding a way of out of the maze of new technologies can be challenging.

For example, given that complex traits and cancer clonal evolution involve spatial mosaics of gene expression and somatic mutations, will cheaper sequencing of a large patient population be sufficient to map causative genetic elements? In the long run, we believe that such scientists may be best prepared to utilize emerging technologies, recognize their deficiencies, and build tools necessary to tackle many of the unaddressed questions in biology.

Volume 9 , Issue 2. The full text of this article hosted at iucr. If you do not receive an email within 10 minutes, your email address may not be registered, and you may need to create a new Wiley Online Library account. If the address matches an existing account you will receive an email with instructions to retrieve your username. Focus Article Open Access.

Quantitative approaches for investigating the spatial context of gene expression

Lee Corresponding Author E-mail address: jlee cshl. Conflict of interest: The author has declared no conflicts of interest for this article.

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Share full text access. Please review our Terms and Conditions of Use and check box below to share full-text version of article. Figure 1 Open in figure viewer PowerPoint. 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.

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 Open in figure viewer PowerPoint. Quantitative methods for detecting multiple RNA molecules in situ. Figure 3 Open in figure viewer PowerPoint.

Spatial versus temporal genetic barcoding for multiplexed RNA detection. Figure 4 Open in figure viewer PowerPoint. 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.

Transcriptome Analysis of Single Cells

Figure 5 Open in figure viewer PowerPoint. Here, the practical sampling number limits the spatial resolution as the specimen size increases along multiple dimensions. Figure 6 Open in figure viewer PowerPoint. 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.

Figure 7 Open in figure viewer PowerPoint. Figure 8 Open in figure viewer PowerPoint. Focusing technology development around a central question enables multiple creative approaches necessary to measure specific elements, rather than simply scaling up existing technologies.

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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.

Spatially resolved transcriptomics and beyond.

Nat Rev Genet , 16 , 57 — Google Scholar. Nat Rev Genet , 14 : — Citing Literature. Figures References Related Information. Close Figure Viewer. Browse All Figures Return to Figure. Previous Figure Next Figure. Relative levels of let-7i-5p, let-7a-5p, miRb-5p, miRa-3p, and U6 were determined relative to a spike-in control synthetic miR Each dot represents one K cell. Each data point reflects the average measurements from two technical replicates. Error bars present standard deviation. The levels of miRNA are shown in log2 scale.

The dashed lines and NC negative control indicates the detection range of water samples. Roughly 40, cells were used from each clone. Each dot represents one K clone. Each data point reflects the average from two technical replicates. U6 was measured as a control for normalization. The levels of miRNAs are shown in log scale.

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  • Of note, initial tests with even splitting in vol of single-cell lysate prepared with standard cell lysis protocol did not yield even splitting of miRNAs in two half-cell samples. Interestingly, we observed selective enrichment or depletion of a group of miRNAs in one half-cell sample presumably due to the binding to intracellular proteins.

    This remains to be further investigated. We further examined the validity of this half-cell approach using different cell types and another measurement technique. Small RNA libraries were generated from two halves of a single murine hematopoietic BaF3 cell, or single K and T cells, and measured with a Luminex bead array assay. Upon validating the consistency of the half-cell approach to capture single-cell miRNA profiles, we turned to measure intercellular variability by sequencing 20 half-cell K miRNA samples.

    The other halves of the same cells were processed for RNAseq to be described in next section. The one that failed the quality assessment had very low levels of mappable reads in both miRNA and mRNA data presumably due to the poor quality of this cell itself e. As expected, low expression level miRNAs showed inherently large standard deviations in log2 data. The variation of high abundance miRNAs gradually decreased as the expression level increased. MiRa-3p was detected as the most abundant miRNA in our K cell sample, in agreement with previous literature These results showed the direct evidence of intercellular miRNA expression heterogeneity, which differed substantially for individual miRNAs.