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Biology subjects

Pio, R.

Publications and source records attributed to Pio, R..

2 recordsLinked to original sources

RNA and protein are not interchangeable in single-cell spatial multi-omics

Spatial transcriptomics is increasingly used to infer cellular phenotypes, molecular interactions, and functional tissue organization from RNA, although most of these biological processes are ultimately mediated by proteins. Here, we directly tested the interchangeability of spatial RNA and protein measurements using paired Xenium profiling of 21 matched targets across ~1.8 million cells from primary lung adenocarcinoma and metastatic castration-resistant prostate cancer. After accounting for marker- and dataset-specific protein background, we quantified concordance at multiple levels, including single-cell detection, expression magnitude, cell-type distribution, patient-level abundance, and spatial organization. Agreement at single-cell level was strongly marker-dependent. Canonical lineage markers generally showed concordant RNA and protein patterns, whereas several molecules, including in our study PD-1, PD-L1, and LAG-3, showed little concordance across modalities. Importantly, spatial organization remained substantially more discordant between modalities than overall abundance, indicating that spatial biomarkers are not directly transferable across modalities. Together, our results show that spatial RNA and protein measurements provide largely overlapping information for lineage markers and overall abundance, but can diverse for specific molecules and for higher-order spatial features, where the two layers provide complementary rather than equivalent readouts. These findings provide practical guidance on when spatial transcriptomic readouts can be interpreted as proxies for protein and when orthogonal protein validation is advisable.

cancer biology↗

Comparison of RNA-seq and Microarray Platforms for Splice Event Detection using a Cross-Platform Algorithm

RNA-seq is a reference technology for determining alternative splicing at genome-wide level. Exon arrays remain widely used for the analysis of gene expression, but show poor validation rate with regard to splicing events. Commercial arrays that include probes within exon junctions have been developed in order to overcome this problem.\n\nWe compare the performance of RNA-seq (Illumina HiSeq) and junction arrays (Affymetrix Human Transcriptome array) for the analysis of transcript splicing events. Three different breast cancer cell lines were treated with CX-4945, a drug that severely affects splicing. To enable a direct comparison of the two platforms, we adapted EventPointer, an algorithm that detects and labels alternative splicing events using junction arrays, to work also on RNA-seq data. Common results and discrepancies between the technologies were validated and/or resolved by over 200 PCR experiments.\n\nAs might be expected, RNA-seq appears superior in cases where the technologies disagree, and is able to discover novel splicing events beyond the limitations of physical probe-sets. We observe a high degree of coherence between the two technologies, however, with correlation of EventPointer results over 0.90. Through decimation, the detection power of the junction arrays is equivalent to RNA-seq with up to 60 million reads. Our results suggest, therefore, that exon-junction arrays are a viable alternative to RNA-seq for detection of alternative splicing events when focusing on well-described transcriptional regions.

bioinformatics↗