Analysis of single cell and spatially-resolved data
Recent technologies allow us to profile cells at the single-cell level, opening new venues to study cellular regulation. To extract the most of these data, new tools, or adaptation of the tools for bulk data, are required.
Extracting mechanistic insights from single-cell RNA data.
Given the limitations of single-cell RNA, such as the lower coverage, when compared to bulk, we benchmark how our methods to extract mechanistic features performed on these conditions. We found that they are reasonably robust [1], and we thus use them to estimate the activity of pathways and transcription factors in different contexts, for example to study kidney fibrosis [2].
We have also expanded our OmniPath, our meta-database that includes over 100 resources, to include information from multiple databases that provide information on cell-cell interactions, that can be used to study cellular communication from single-cell RNA [3].
OmniPath serve as a one-stop resource for multiple analyses of single-cell data
Analysis of spatially-resolved data.
We also work on extracting knowledge from spatially resolved single-cell data. In collaboration with the group of Oliver Stegle at EMBL/DKFZ we developed Spatial Variance Component Analysis (SVCA). SVCA can, for example, show how much the cell signaling can be explained by intracellular effects and also by the phosphorylation state of surrounding cells [4]. We have extended this approach with MISTY, a machine learning pipeline to identify relationships between proteins from different signaling contexts, e.g., intracrine, juxtacrine and paracrine [5]. The identification of these connections facilitates the downstream mechanistic modeling and the integration of knowledge from literature and databases.
Modelling cell-to-cell interactions with MISTY
Dynamic models from single-cell data
Mass cytometry (CyTOF) allows us to measure up to 40 phosphorylation sites of key proteins in the signaling network on single cell level. We are working on novel mechanistic modeling frameworks building on our general methodologies for logic modeling in our tool CellNOpt [6]. We build Prior Knowledge Networks (PKN) of molecular interactions from our tool OmniPath [7] and we translate PKNs into logic models [8]. These models are then fitted to single cell signaling (e.g. CyTOF) data upon perturbation.
As examples, we are applying the above approaches in collaboration with Bernd Bodenmiller’s group to a kinase overexpression study (Lun et al. 2017). and to understand heterogeneity in breast cancer and predict drug response [9].
The pipeline for modelling single cell, suspension mass cytometry data using CellNOpt.
You can watch here a summary of our activities in this area.
References:
- Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E, et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol. 2020;21: 36. doi:10.1186/s13059-020-1949-z
- Kuppe C, Ibrahim MM, Kranz J, Zhang X, Ziegler S, Perales-Patón J, et al. Decoding myofibroblast origins in human kidney fibrosis. Nature. 2021;589: 281–286. doi:10.1038/s41586-020-2941-1
- 3. Türei D, Valdeolivas A, Gul L, Palacio-Escat N, Ivanova O, Gábor A, et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. doi:10.1101/2020.08.03.221242
- Arnol D, Schapiro D, Bodenmiller B, Saez-Rodriguez J, Stegle O. Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis. 2018. doi:10.1101/265256
- Tanevski J, Gabor A, Flores ROR, Schapiro D, Saez-Rodriguez J. Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data. doi:10.1101/2020.05.08.084145
- Gjerga E, Trairatphisan P, Gabor A, Koch H, Chevalier C, Ceccarelli F, et al. Converting networks to predictive logic models from perturbation signalling data with CellNOpt. Bioinformatics. 2020. doi:10.1093/bioinformatics/btaa561
- Türei D, Korcsmáros T, Saez-Rodriguez J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat Methods. 2016;13: 966–967. doi:10.1038/nmeth.4077
- MacNamara A, Terfve C, Henriques D, Bernabé BP, Saez-Rodriguez J. State-time spectrum of signal transduction logic models. Phys Biol. 2012;9: 045003. doi:10.1088/1478-3975/9/4/045003
- Tognetti M, Gabor A, Yang M, Cappelletti V, Windhager J, Charmpi K, et al. Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction. doi:10.1101/2020.01.21.907691