- Reviewing EditorEduardo EyrasAustralian National University, Canberra, Australia
- Senior EditorKevin StruhlHarvard Medical School, Boston, United States of America
Reviewer #1 (Public Review):
In this study, the authors set out to investigate spatial RNA processing events, specifically alternative splicing and 3' UTR usage, in mouse brain and kidney tissues using ReadZS and SpliZ methodologies on spatial transcriptomics data. The research contributes to understanding tissue-specific gene expression regulation from a spatial perspective. The study introduces a novel approach for analyzing spatial transcriptomics data, allowing for the identification of RNA processing and regulation patterns directly from 10X Visium data. The authors present convincing evidence supporting the identification of novel RNA processing patterns using their methodology, which holds significant implications for researchers in the field of spatial transcriptomics and the study of alternative splicing and 3' UTR usage
The conclusions of the study are mostly well-supported by the data; however, certain aspects could be improved to strengthen the findings.
- The conclusions of this study would be strengthened by conducting a more extensive tissue sample analysis and including biological replicates. Additionally, appropriate batch effect corrections should be applied when dealing with biological replicates.
- The 3' UTR usage and alternative splicing should be compared among clearly labeled clusters for a more comprehensive analysis.
- The authors should clarify their rationale for choosing ReadZS and SpliZ approaches and provide comparisons with other methods to demonstrate the advantages and potential limitations of their chosen methodologies.
Reviewer #2 (Public Review):
The authors applied existing ReadZS and the SpliZ methods, previously developed to analyze RNA process in scRNA-seq data, to Visium data to study spatial splicing and RNA processing events in tissues by Moran's I. The authors showed several example genes in mouse brain and kidney, whose processing are spatially regulated, such as Rps24, Myl6, Gng13.
The paper touches on an important question in RNA biology about how RNA processing is regulated spatially. Both experimental and computational challenges remain to address it. Despite some potentially interesting findings, most claims remain to be validated by orthogonal methods such as RNA FISH and simulations. In addition, the percentage of spatial processing events (splicing in 0.8-2.2% of detected genes, i.e. 8-17 genes and RNA processing in 1.1-5.5% of detected genomic windows, i.e. 57-161 windows) discovered is low. Does it suggest that most of RNA processing events were not spatially regulated across the tissue? Or does it question the assumption of treating spatial transcriptomics data similar to scRNA-seq data? The unique features for ST data, such as mixture of neighboring cells, different capture biases and much smaller number of spots (pseudo cells here), may have significant effects on the power of scRNA-seq based methods, but it is not discussed in the manuscript. The lack of careful evaluation and low discovery rates could limit application of the approach to other tissues and subcellular data.