Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

Abstract

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAF V600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drugtolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

“…1a, bottom panel). Just like the well-reported phenotypic markers 28,36 , metabolic genes also showed a clear phenotype-dependent expression trend, with associated functions that span different metabolic processes (The representative (top 4 ranked) metabolic genes are shown in the bottom of Fig. 1b, the complete heatmap and list of the top ranked metabolic genes are shown in Supplementary Fig.…”

“…Custom code for the surprisal analysis of Raman spectra has previously been published and deposited on GitHub (https://github.com/mesako/Melanoma-Publication) 36 . Source data are provided with this paper.…”

Cancer Hallmarks Define a Continuum of Plastic Cell States between Small Cell Lung Cancer Archetypes

“…Inhibition of phenotype switching has been suggested as a target for therapy in other cancer types, such as breast cancer and melanoma. 20,71,[101][102][103][104] For SCLC, we point to epigenetic strategies that can be derived from analyses of TF network dynamics, such as the MYC inhibition strategy proposed in Figure 8. Given the primary role of TFs in driving SCLC phenotype, as well as transitions across the phenotypic continuum, SCLC should be a prime candidate for epigenetic therapy targeted to plasticity.…”

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

“…In other words, genes from different cell types may co-vary and therefore reside in the same gene module, thus providing a simplifying insight into how different cell types coordinate their behaviors. To capture such coordinated changes, we utilized Surprisal Analysis (Remacle et al, 2010;Zadran et al, 2014), which has been applied to consolidate multi-omics bulk and single-cell data (Kravchenko-Balasha et al, 2014Remacle et al, 2010;Su et al, 2017Su et al, , 2019Su et al, , 2020. The goal is to condense the changes of millions of correlated genes from different cell types into changes in just one major gene module or axis of expression, which contains the co-varying genes from all major immune cell types.…”