Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior

Voukantsis, Dimitris; Kahn, Ken; Hadley, Martin J.; Wilson, Rowan; Buffa, Francesca M.

doi:10.1093/gigascience/giz010

Cited by 16 publications

(19 citation statements)

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“…[ 131 ] Another interesting application is that single cell data can help to build artificial intelligence models that can predict cellular behaviour. [ 5 ]…”

Section: Discussionmentioning

confidence: 99%

“…[131] Another interesting application is that single cell data can help to build artificial intelligence models that can predict cellular behaviour. [5] Thus, scRNA-seq is the key technique to reveal new dependencies, rare populations and drug targets. With further developments, it has a potential to bring the vision of personalised medicine a reality by providing an in-time analysis of the heterogeneity present in the resected patient tumour that could help providing the right treatment or combinations of treatments to the patients.…”

Section: Discussionmentioning

confidence: 99%

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Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Elshenawy

Sheldon

et al. 2022

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Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ cannot outright be used as representative of the ‘average cell’. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

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“…[ 131 ] Another interesting application is that single cell data can help to build artificial intelligence models that can predict cellular behaviour. [ 5 ]…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Elshenawy

Sheldon

et al. 2022

BioEssays

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“…BGRNs are increasingly used also in combination within more complex, multi-scale models, such as our recently published framework microC (Voukantsis et al 2019). These are models where each cell (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ingres: from single-cell RNA-seq data to single-cell probabilistic Boolean networks

Victori

Buffa

2022

Preprint

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Motivation: The current explosion of omics data has provided scientists with an unique opportunity to elucidate the inner workings of biological processes that remained opaque. For this, computational models are essential. Gene regulatory networks (GRN) have long been used as a way to integrate heterogeneous data into a discrete model, and are very useful to generate actionable hypotheses on the mechanisms governing these biological processes. Boolean networks are particularly popular for this kind of discrete models. When working with single-cell RNA-seq datasets a main focus of analysis is the differential expression between subpopulations of cells. Boolean networks are limited in this task, since they cannot easily represent different levels of expression, confined as they are to binary states. We set out to develop an algorithm that can fit Boolean networks with this kind of data, maintaining both the heterogeneity of the data and the simplicity and computational efficiency of these type of networks. Results: Here we present Ingres (Inferring Probabilistic Boolean Networks of Gene Regulation Using Protein Activity Enrichment Scores) an open-source tool that uses single-cell sequencing data and prior knowledge GRNs to produce a probabilistic Boolean network (PBN) per each cell and/or cluster of cells in the dataset. Ingres allows to better capture the differences between cell phenotypes, using a continuous measure of protein activity while still confined to the simplicity of a GRN. We believe Ingres will be useful to better understand the heterogeneous makeup of cell populations, to gain insight into the specific circuits that drive certain phenotypes, and to use expression and other omics to infer computational cellular models in bulk or single-cell data. Availability and implementation: Ingres has been implemented as an R package, and it is publicly available at https://github.com/CBigOxf/ingres. It is currently being submitted to the public repository CRAN too.

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“…Voukantsis et al [ 32 ] proposed a multicellular model for tumor growth in which cells are placed in a lattice. Each cell is endowed with a Boolean network that controls cellular actions, such as proliferation and apoptosis, that are key for tumor growth.…”

Section: Introductionmentioning

confidence: 99%

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

et al. 2020

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Background Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. Results By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type–specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. Conclusions The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.

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Modeling genotypes in their microenvironment to predict single- and multi-cellular behavior

Cited by 16 publications

References 50 publications

Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Ingres: from single-cell RNA-seq data to single-cell probabilistic Boolean networks

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

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