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. Despite increasingly available single-cell RNA sequencing (scRNA-seq) data that enable in-depth analyses of EMT at the single-cell resolution, current inferential approaches are limited to bulk microarray data. There is thus a great need for computational frameworks to systematically infer and predict the timing and distribution of EMT-related states at single-cell resolution. Here, we develop a computational framework for reliable inference and prediction of EMT-related trajectories from scRNA-seq data. Our model can be utilized across a variety of applications to predict the timing and distribution of EMT from single-cell sequencing data.

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. In the present study, we aimed to apply our bispectral EEG (BSEEG) method, which can detect patients with delirium, to a mouse model of delirium with systemic inflammation induced by lipopolysaccharides (LPS) injection. We recorded EEG after LPS injection using wildtype early adulthood mice (2~3-month-old) and aged mice (18-19-month-old). Animal EEG recordings were converted for power spectral density to calculate BSEEG score using the similar BSEEG algorithm previously developed for our human study. The BSEEG score was relatively stable and slightly high during the day. Alternatively, the BSEEG score was erratic and low in average during the night. LPS injection increased the BSEEG score dose-dependently and diminished the diurnal changes. The mean BSEEG score increased much more in the aged mice group as dosage increased. Our results suggest that BSEEG method can objectively "quantify" level of neuro-Inflammation induced by systemic inflammation (LPS), and that this BSEEG method can be useful as a model of delirium in mice.