Spatial protein and RNA analysis on the same tissue section using MICS technology
Neil, E.; Park, D.; Hennessey, R. C.; DiBiasio, E. C.; DiBuono, M.; Lafayette, H.; Lloyd, E.; Lo, H.; Femel, J.; Makrigiorgos, A.; Soliman, S.; Mangiardi, D.; Praveen, P.; Rüberg, S.; Staubach, F.; Hindman, R.; Rothmann, T.; Meyer, H.; Wantenaar, T.; Wang, J.; Müller, W.; Pinard, R.; Bosio, A.
Show abstract
Spatial Biology has evolved from the molecular characterization of microdissected cells to high throughput spatial RNA and protein expression analysis at scale. The main limitation of spatial technologies so far is the inability to resolve protein and RNA information in the same histological section. Here, we report for the first time the integration of highly multiplexed RNA and protein detection on the same tissue section. We developed a new, automated, spatial RNA detection method (RNAsky), which is based on targeted rolling circle amplification and iterative staining. We combine RNAsky with MACSima Imaging Cyclic Staining (MICS) based protein analysis and show compatibility with subsequent standard hematoxylin and eosin (H&E) staining. Using both, open-source tools and our recently developed software suite MACS(R) iQ View, we demonstrate our multiomics MICS workflow by characterizing key immune-oncology markers at subcellular resolution across normal and diseased tissues.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Chrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis 95%
- Full-length single-cell BCR sequencing paired with RNA sequencing reveals convergent responses to vaccination 95%
- Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics 94%
Similar papers in this journal
- Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data 95%
- LINE-1 Retrotransposon expression in cancerous, epithelial and neuronal cells revealed by 5'-single cell RNA-Seq 95%
- Disentangling single-cell omics representation with a power spectral density-based feature extraction 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.