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Biology subjects

de Andrea, C.

Publications and source records attributed to de Andrea, C..

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↗

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune pathways related to tissue injury

Severe lung damage in COVID-19 involves complex interactions between diverse populations of immune and stromal cells. In this study, we used a spatial transcriptomics approach to delineate the cells, pathways and genes present across the spectrum of histopathological damage in COVID-19 lung tissue. We applied correlation network-based approaches to deconvolve gene expression data from areas of interest within well preserved post-mortem lung samples from three patients. Despite substantial inter-patient heterogeneity we discovered evidence for a common immune cell signaling circuit in areas of severe tissue that involves crosstalk between cytotoxic lymphocytes and pro-inflammatory macrophages. Expression of IFNG by cytotoxic lymphocytes was associated with induction of chemokines including CXCL9, CXCL10 and CXCL11 which are known to promote the recruitment of CXCR3+ immune cells. The tumour necrosis factor (TNF) superfamily members BAFF (TNFSF13B) and TRAIL (TNFSF10) were found to be consistently upregulated in the areas with severe tissue damage. We used published spatial and single cell SARS-CoV-2 datasets to confirm our findings in the lung tissue from additional cohorts of COVID-19 patients. The resulting model of severe COVID-19 immune-mediated tissue pathology may inform future therapeutic strategies. One Sentence SummarySpatial analysis identifies IFN{gamma} response signatures as focal to severe alveolar damage in COVID-19 pneumonitis.

immunology↗