Protocol for inferring epithelial-to-mesenchymal transition trajectories from single-cell RNA sequencing data using R.

The epithelial-to-mesenchymal transition (EMT) provides crucial insights into the metastatic process and possesses prognostic value within the cancer context. Here, we present COMET, an R package for inferring EMT trajectories and inter-state transition rates from single-cell RNA sequencing data. We describe steps for finding the optimal number of EMT genes for a specific context, estimating EMT-related trajectories, optimal fitting of continuous-time Markov chain to inferred trajectories, and estimating inter-state transition rates.

Population dynamics of EMT elucidates the timing and distribution of phenotypic intra-tumoral heterogeneity.

The Epithelial-to-Mesenchymal Transition (EMT) is a hallmark of cancer metastasis and morbidity. EMT is a non-binary process, and cells can be stably arrested en route to EMT in an intermediate hybrid state associated with enhanced tumor aggressiveness and worse patient outcomes. Understanding EMT progression in detail will provide fundamental insights into the mechanisms underlying metastasis.

Bispectral EEG (BSEEG) quantifying neuro-inflammation in mice induced by systemic inflammation: A potential mouse model of delirium.

Most of the animal studies using inflammation-induced cognitive change have relied on behavioral testing without objective and biologically solid methods to quantify the severity of cognitive disturbances. We have developed a bispectral EEG (BSEEG) method using a novel algorithm in clinical study. This method effectively differentiates between patients with and without delirium, and predict long-term mortality.