Statistical analysis supports pervasive RNA subcellular localization and alternative 3’ UTR regulation
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eLife assessment
This paper describes an important, well-organized study into an under-exploited area of spatial transcriptomics. The limitations of the approach are generally made clear, but there is insufficient orthogonal validation to demonstrate the biological significance of the results, which leads to the evidence for the claims being currently incomplete. Nevertheless, the tools presented will provide a resource to researchers wishing to characterise spatial patterning of mRNAs, and the paper will be of interest to researchers studying cell biology, RNA biology, and method development for spatial transcriptomics/proteomics.
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Abstract
Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Further, these studies link localization to RNA isoform expression, especially 3’ Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enable the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3’ UTR processing and detected examples of significant correlations between 3’ UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the brain highly correlated with regulated 3’ UTR processing that includes use of unannotated, but highly conserved, 3’ ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3’ UTRs for functional prediction and study. SPRAWL predicts 3’ UTR regulation of subcellular localization may be more pervasive than currently known.
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eLife assessment
This paper describes an important, well-organized study into an under-exploited area of spatial transcriptomics. The limitations of the approach are generally made clear, but there is insufficient orthogonal validation to demonstrate the biological significance of the results, which leads to the evidence for the claims being currently incomplete. Nevertheless, the tools presented will provide a resource to researchers wishing to characterise spatial patterning of mRNAs, and the paper will be of interest to researchers studying cell biology, RNA biology, and method development for spatial transcriptomics/proteomics.
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Reviewer #1 (Public Review):
Bierman et al. have developed a set of metrics for measuring the spatial patterning of mRNAs in high-throughput fluorescence in situ hybridisation experiments and applied these to identify a subset of mRNAs whose spatial patterning correlates with 3'UTR length. A strength of the study is the clarity and honesty with which the authors have outlined the strengths and weaknesses of their own approach and reported negative results. A key benefit of the tool is that the methodological choices allow wide applicability to existing datasets. However, these choices also feed into a limitation of the method, which is the difficulty in interpreting the biology underpinning the metrics - raising the question of how users will understand the output of the tool.
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Reviewer #2 (Public Review):
The authors develop SPRAWL (Subcellular Patterning Ranked Analysis With Labels), a statistical framework to identify cell-type specific subcellular RNA localization from multiplexed imaging datasets. The tool is able to assign to each gene and in each annotated cell type, a score (with a p-value) that measures:
- Peripheral/central localization of RNAs within the cell, based on a previous segmentation step defining cell boundaries and the centroid coordinate.
- Radial/punctuate localization of RNAs within the cellThe method is applied to three multiplexed imaging datasets, identifying defined and cell-type specific patterns for several transcripts.
In the second part of the manuscript, the authors couple SPRAWL with ReadZS, a computational tool developed by the same group and recently published (Meyer et …
Reviewer #2 (Public Review):
The authors develop SPRAWL (Subcellular Patterning Ranked Analysis With Labels), a statistical framework to identify cell-type specific subcellular RNA localization from multiplexed imaging datasets. The tool is able to assign to each gene and in each annotated cell type, a score (with a p-value) that measures:
- Peripheral/central localization of RNAs within the cell, based on a previous segmentation step defining cell boundaries and the centroid coordinate.
- Radial/punctuate localization of RNAs within the cellThe method is applied to three multiplexed imaging datasets, identifying defined and cell-type specific patterns for several transcripts.
In the second part of the manuscript, the authors couple SPRAWL with ReadZS, a computational tool developed by the same group and recently published (Meyer et al, 2022). Starting from single-cell datasets, ReadZS is able to quantify 3'UTR length in each cell type. The authors find a subset of genes showing a positive, or negative correlation between the predicted localization and the predicted 3'UTR length across cell types.
Strengths:
As the authors state in the introduction, the study of subcellular RNA localization, with the characterization of organizational principles and of molecular regulation mechanisms, is extremely relevant. The authors develop a strategy to detect statistically significant and non-random patterns of RNA sub-cellular localization in MERFISH and SeqFISH+ datasets, i.e. emerging platforms producing spatially resolved maps of hundreds of transcripts with cellular resolution.Weaknesses:
Although the method and the presented results have strengths in principle, the main weakness of the paper is that these strengths are not directly demonstrated. That is, insufficient validations are performed to show the biological significance of the results and to fully support the key claims in the manuscript by the data presented.In particular, the authors imply that their tool is unique and not comparable to any other method. Therefore there is no comparison of SPRAWL with any other method. For example, a comparison could be made with Baysor (Petukhov, V et al. Nat Biotechnol. https://doi.org/10.1038/s41587-021-01044-w). According to the authors, this method is able to identify "small molecular neighbourhoods with stereotypical transcriptional composition" and provides a "General approach for statistical labeling of spatial data".
The authors claim that SPRAWL is able to identify spatial patterns of localization and generated relevant hypotheses to be tested, yet the manuscript contains little proof that the results have biological significance (for example association of RNAs with specific subcellular compartments) and there is no experimental validation for the results obtained applying this method.
The correlation between localization scores and 3'UTR length across cell types for certain genes is also not experimentally validated: results are based on inference from single-cell or imaging data, with no complementary experimental validation.
It is therefore very difficult to assess the biological relevance of the results produced by SPRAWL.
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Reviewer #3 (Public Review):
Bierman et al. present a novel statistical framework for examining the subcellular localisation of RNA molecules. Subcellular Patterning Ranked Analysis With Labels, SPRAWL, uses the data available in multiplexed single-cell imaging datasets to assign four metrics of localisation patterns to RNA at a gene per cell level. These easy-to-understand scores, ranging from -1 to 1, can be averaged to detect cell-type specific spatial patterns or used in tandem with tools for RNA 3' UTR length or splicing state to determine the correlation between subcellular localisation and RNA isoforms. Such quantitive association between RNA isoforms and localisation provides a useful tool to determine candidate genes for future studies.
The peripheral and central scores indicate the proximity of RNA molecules to the cell …
Reviewer #3 (Public Review):
Bierman et al. present a novel statistical framework for examining the subcellular localisation of RNA molecules. Subcellular Patterning Ranked Analysis With Labels, SPRAWL, uses the data available in multiplexed single-cell imaging datasets to assign four metrics of localisation patterns to RNA at a gene per cell level. These easy-to-understand scores, ranging from -1 to 1, can be averaged to detect cell-type specific spatial patterns or used in tandem with tools for RNA 3' UTR length or splicing state to determine the correlation between subcellular localisation and RNA isoforms. Such quantitive association between RNA isoforms and localisation provides a useful tool to determine candidate genes for future studies.
The peripheral and central scores indicate the proximity of RNA molecules to the cell boundary and centre of the cell respectively in relation to other RNA present in the cell. Whilst understanding whether a gene tends to be localised to the cellular membrane is important, it is unclear what biological benefits the central metric gives compared to high "anti-peripheral" scores considering that no single organelle (eg. the nucleus) is located specifically at the centre of the cell in all cell-types.
The punctate and radial patterning scores provide information on the spatial aggregation of RNA molecules of a given gene within a cell. Whilst the punctate score is easy to understand as simply the distance between RNA, the radial score, the angle between RNA, is harder to understand from the main text and would benefit from a schematic showing how this is in respect to the cell-boundary centroid.
Despite endeavouring to create a robust statistical measure of RNA subcellular localisation, this paper is full of inconsistencies. Values (eg. Pearson correlation coefficient values, number of significant genes, number of total genes) and names (eg. cell types, gene names) stated throughout the main text and figures/table do not match repeatedly and without fixing these disparities, the conclusions from this paper are hard to believe.
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