Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd

Abstract

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.

Cell-to-cell variability of dynamic CXCL12-CXCR4 signaling and morphological processes in chemotaxis

“…However, our group discovered marked heterogeneity in activation of intracellular signaling and chemotaxis among seemingly identical CXCR4-positive cancer cells treated with CXCL12 (4,5). Such heterogeneity is not unique to CXCL12-CXCR4 as prior studies report similar responses to other signaling molecules, such as EGF and EGFR (6)(7)(8)(9). These responses are not simply due to stochasticity but contain governing rules behind (10,11).…”

Application of perturbation gene expression profiles in drug discovery—From mechanism of action to quantitative modelling

“…Also, morphological profiles are generally interpretable for cell biologists, and can be directly connected to perturbation induced cell states. Proteomics and phospho-proteomics are more closely connected to the activity of cellular process than gene expression, so perturbation proteomics datasets can be used to infer compound effects of signalling and compound similarities (Zhao et al, 2020;Gabor et al, 2021). Recently also drug induced metabolomics changes were used to describe cellular phenotype and synergistic drug effect (Lu et al, 2022).…”

A Machine Learning and Bioinformatic Analysis Reveals an Associated between Cell Surface Receptor Transcript Levels with Drug Response of Breast Cancer Cells and the Drug Off-Target Effects

“…The latter is a promising approach that is likely to improve treatment outcomes, as the strategy would not depend on the biological importance of the targeted CSRs within downstream signalling pathways: irrespective of its biological function, any CSR that is expressed in tumour cells but not in healthy cells would be a viable target. This approach to anticancer therapy promises fewer disease remissions and better efficacy than current treatments as non-specifically cytotoxic compounds would not, like current chemotherapeutics, only kill tumour cells with a specific molecular phenotype [34][35][36][37][38]. For as long as the cells in the tumour express the targeted CSR, the non-specific toxin(s) that is(are) delivered to these cells will kill all the cells in their proximity, including those that do not express the CSR.…”