RNA splicing programs define tissue compartments and cell types at single-cell resolution
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Curated by eLife
Evaluation Summary:
This manuscript describes an analysis of cell type-specific alternative splicing using 10x scRNA-seq data. This work shows that in spite of the challenges associated with the analysis of such datasets, it is possible to identify alternative exons with differential splicing between tissue compartments and to some extent reveal cell types by splicing profiles of single cells. This work is informative regarding what can be done to analyse alternative splicing using 10X data and fills in a gap in the field.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10X Chromium data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24 . These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1 . Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.
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Author Response:
Evaluation Summary:
This manuscript describes an analysis of cell type-specific alternative splicing using 10x scRNA-seq data. This work shows that in spite of the challenges associated with the analysis of such datasets, it is possible to identify alternative exons with differential splicing between tissue compartments and to some extent reveal cell types by splicing profiles of single cells. This work is informative regarding what can be done to analyse alternative splicing using 10X data and fills in a gap in the field.
Thank you very much for this thoughtful distillation of the contributions of this paper; we are grateful that you find this work to be useful in filling the gap of how splicing analysis can be performed on 10X data.
Reviewer #1 (Public Review):
Olivieri, Julia Eve et al., applied their novel …
Author Response:
Evaluation Summary:
This manuscript describes an analysis of cell type-specific alternative splicing using 10x scRNA-seq data. This work shows that in spite of the challenges associated with the analysis of such datasets, it is possible to identify alternative exons with differential splicing between tissue compartments and to some extent reveal cell types by splicing profiles of single cells. This work is informative regarding what can be done to analyse alternative splicing using 10X data and fills in a gap in the field.
Thank you very much for this thoughtful distillation of the contributions of this paper; we are grateful that you find this work to be useful in filling the gap of how splicing analysis can be performed on 10X data.
Reviewer #1 (Public Review):
Olivieri, Julia Eve et al., applied their novel statistical approach, the SpliZ (detailed in a separate manuscript but it's very difficult to judge the approach since we do not really have access to it) to high-throughput single-cell RNA-seq datasets collected using the 10x platform to discover novel insights into cell-level heterogeneity of alternative splicing.
We understand that the SpliZ paper not being published makes it more difficult to review this manuscript. It is currently in review at Nature methods, where it is being re-evaluated by reviewers after an invitation for resubmission. We are happy to share the comments from the reviewers on that manuscript if it would help you make a decision. We have also added a thorough explanation of the SpliZ to the methods in the section called “Explanation of the SpliZ method” on page 12 and added several more sentences of explanation to the main text at the beginning of page 3: “A large negative (resp. positive) SpliZ score for a gene in a cell means that the cell has shorter (resp. longer) introns than average for that gene. In the simplest exon skipping case, the SpliZ reduces to PSI.”
Previous works in the field of single-cell alternative splicing have relied on single-cell technologies that profile a much lower number of cells. The authors validate their findings using the experimental approaches of single-cell PCR and RNA FISH, and validate that their findings can also be found using Smart-seq2 data, on which the gold standard approaches for single-cell alternative splicing analysis have been developed. They demonstrate conservation analysis of single-cell alternative splicing events across species, examples of genes that are spliced in cellular compartment specific and cell-type specific patterns, cell-type specific alternative splicing changes correlated with psuedotime in spermatogenesis, and, importantly, the discovery of new cell-type subpopulations that are defined by splicing changes but are indistinguishable based on gene expression. They show that the SpliZ score correlates well across replicates on a tissue level and cell-type level, which indicate the robustness of the method.
We are glad that you find the analyses of these datasets biologically important, robust, and informative.
The conclusions of the paper are reasonably well supported by the data, and the authors have sufficiently proven that their approach allows for the discovery of novel biological phenomena. The authors provide examples in which the key questions that can be addressed with a single-cell splicing technology are investigated. An important question in the field of cellular heterogeneity is whether or not novel cell populations can be detected by clustering based on splicing events that can be not detected based on gene expression. The authors convincingly demonstrate that subpopulations of the blood classical monocyte cell type can be distinguished by a single splicing event captured by their approach that do not separate by gene expression.
Thank you for highlighting that our conclusions are reasonably well supported by the data, and that we have discovered new biological phenomena including identification of subpopulations differentiated by splicing.
Overall this paper reports some novel biological discoveries. The weakness and limitations of the method should be elaborated to guide future usage. When introducing a new technology, it is important for researchers utilizing these findings to be aware of the known limits.
We agree that a thorough understanding of the weaknesses of a method are important for readers considering using the method themselves. We have now added the following paragraph on page 9 to more clearly outline these weaknesses: “Although the SpliZ method enables biological discovery of splicing differences based on droplet-based sequencing data, droplet-based data still presents major challenges for splicing analysis compared to full-length data. In this study, droplet-based sequencing has much lower sequencing coverage than full-length data, resulting in only 1,416 genes with measurable SpliZ values in the first human individual based on 10X data compared to 9,802 genes with measurable SpliZ values in Smart-Seq2 data. Additionally, current droplet-based data is 3-prime-biased, meaning that some splicing events will never be sequenced by the technology and therefore cannot be analyzed. Despite these challenges, the ubiquity of droplet-based data, its utility for profiling rare cell types, and its unprecedented scale make it a powerful approach to discover regulated splicing.”
Furthermore, evaluation of alternative splicing conservation on a transcriptome-wide scale and reproducibility of splicing change detected on a single cell level are not demonstrated, and could further strengthen the arguments claimed by the authors.
Thank you for pointing out that the comparative analysis between organisms on a transcriptome-wide scale and at a cell-type level would improve the paper. A complete, rigorous analysis was limited by the fact that full maps between cell types of the three organisms were not complete, leaving many cell types in human without corresponding cell type in mouse and/or mouse lemur, and that some of the gene orthologs have not been identified between the organisms. This motivated our decision to use the mouse and mouse lemur data to validate specific biological discoveries rather than perform global analyses. We anticipate all of these difficulties will improve over time, and we hope to incorporate more thorough comparisons in future work.
Reviewer #2 (Public Review):
This manuscript from Salzman and colleagues described interesting attempts to study cell type-specific alternative splicing using 10x scRNA-seq data. Given the strong 3' bias, analysis of splicing using such a dataset is in general challenging. This work provided evidence that alternative exons with differential splicing between tissue compartments can be identified, and cell types can be revealed by splicing profiles of single cells, to some extent. This work is informative regarding what can be done for alternative splicing using 10X data and filled in a gap in the field in this regard.
Thank you very much for this thoughtful distillation of the contributions of this paper; we are grateful that you find this work to be useful in filling the gap of how splicing analysis can be performed on 10X data.
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Evaluation Summary:
This manuscript describes an analysis of cell type-specific alternative splicing using 10x scRNA-seq data. This work shows that in spite of the challenges associated with the analysis of such datasets, it is possible to identify alternative exons with differential splicing between tissue compartments and to some extent reveal cell types by splicing profiles of single cells. This work is informative regarding what can be done to analyse alternative splicing using 10X data and fills in a gap in the field.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Reviewer #1 (Public Review):
Olivieri, Julia Eve et al., applied their novel statistical approach, the SpliZ (detailed in a separate manuscript but it's very difficult to judge the approach since we do not really have access to it) to high-throughput single-cell RNA-seq datasets collected using the 10x platform to discover novel insights into cell-level heterogeneity of alternative splicing. Previous works in the field of single-cell alternative splicing have relied on single-cell technologies that profile a much lower number of cells. The authors validate their findings using the experimental approaches of single-cell PCR and RNA FISH, and validate that their findings can also be found using Smart-seq2 data, on which the gold standard approaches for single-cell alternative splicing analysis have been developed. They demonstrate …
Reviewer #1 (Public Review):
Olivieri, Julia Eve et al., applied their novel statistical approach, the SpliZ (detailed in a separate manuscript but it's very difficult to judge the approach since we do not really have access to it) to high-throughput single-cell RNA-seq datasets collected using the 10x platform to discover novel insights into cell-level heterogeneity of alternative splicing. Previous works in the field of single-cell alternative splicing have relied on single-cell technologies that profile a much lower number of cells. The authors validate their findings using the experimental approaches of single-cell PCR and RNA FISH, and validate that their findings can also be found using Smart-seq2 data, on which the gold standard approaches for single-cell alternative splicing analysis have been developed. They demonstrate conservation analysis of single-cell alternative splicing events across species, examples of genes that are spliced in cellular compartment specific and cell-type specific patterns, cell-type specific alternative splicing changes correlated with psuedotime in spermatogenesis, and, importantly, the discovery of new cell-type subpopulations that are defined by splicing changes but are indistinguishable based on gene expression. They show that the SpliZ score correlates well across replicates on a tissue level and cell-type level, which indicate the robustness of the method.
The conclusions of the paper are reasonably well supported by the data, and the authors have sufficiently proven that their approach allows for the discovery of novel biological phenomena. The authors provide examples in which the key questions that can be addressed with a single-cell splicing technology are investigated. An important question in the field of cellular heterogeneity is whether or not novel cell populations can be detected by clustering based on splicing events that can be not detected based on gene expression. The authors convincingly demonstrate that subpopulations of the blood classical monocyte cell type can be distinguished by a single splicing event captured by their approach that do not separate by gene expression.
Overall this paper reports some novel biological discoveries. The weakness and limitations of the method should be elaborated to guide future usage. When introducing a new technology, it is important for researchers utilizing these findings to be aware of the known limits. Furthermore, evaluation of alternative splicing conservation on a transcriptome-wide scale and reproducibility of splicing change detected on a single cell level are not demonstrated, and could further strengthen the arguments claimed by the authors.
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Reviewer #2 (Public Review):
This manuscript from Salzman and colleagues described interesting attempts to study cell type-specific alternative splicing using 10x scRNA-seq data. Given the strong 3' bias, analysis of splicing using such dataset is in general challenging. This work provided evidence that alternative exons with differential splicing between tissue compartments can be identified, and cell types can be revealed by splicing profiles of single cells, to some extent. This work is informative regarding what can be done for alternative splicing using 10X data and filled in a gap in the field in this regard.
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