Trajectory inference across multiple conditions with condiments: differential topology, progression, differentiation, and expression

Abstract

In single-cell RNA-sequencing (scRNA-seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolutions. In such dynamic biological systems, grouping cells into discrete groups is not reflective of the biology. Cellular states rather form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present a method for the estimation and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.

Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates

“…3E) in injured lungs. We combined pseudotime inference ( 46 ) and differential trajectory analysis ( 53 ) to directly evaluate the impact of LPS injury on developmental trajectories. In the epithelium, the AT1 differentiation trajectory is notably disrupted by LPS, with loss of AT1 progenitors and fully differentiated AT1 cells (Fig.…”

Single-nucleus RNA-seq reveals disrupted cell maturation by chorioamnionitis in the preterm cerebellum of nonhuman primates

“…We then analyzed cerebellar myelination by Western blotting for MBP, which showed increased MBP in the cerebellum of LPS exposed fetuses ( figure 4e ). To further validate those findings we performed pseudotime analysis on Monocle3 27 and Slingshot 28 followed by differential topology between conditions 29 . On both analyses we observed an increased proportion of mature oligodendrocytes in the chorioamnionitis animals ( figure 4f ).…”

“…Genes induced in granule cells of LPS-exposed fetuses were associated with synapse transmission and hippo signaling pathways, while suppressed gene were associated with cerebellum development, and Purkinje cell – granule cell precursor signaling involved in granule cell precursor proliferation ( Figure 2f ). To further explore the effect of chorioamnionitis on granule cell development we performed pseudotime analyses on Monocle3 27 and Slingshot 28 followed by differential topology between conditions 29 ( Figure 2g ). Both pseudotime analysis methods showed an increased proportion of the most mature granule cells suggesting that chorioamnionitis accelerates cerebellar granule cell maturation.…”

Single-cell RNA sequencing reveals a role for reactive oxygen species and peroxiredoxins in fatty-acid-induced rat ß-cell proliferation

“…These trajectories branched within the UPR/ER-stress-related Beta 3 subpopulation, leading to the secretory Beta 1 (lineage 1), the more differentiated insulin-expressing Beta 4 (lineage 2), or the proliferative Beta 2 (lineage 3) subpopulations. Although no lineage was unique to a particular condition, comparing the mean cell weight of each lineage calculated using Condiments (De Bézieux et al, 2021) across the 3 conditions showed that oleate favored the progression towards the proliferative path (lineage 3 has significant greater weights for oleate); that vehicle-treated islets differentiated preferentially through lineage 1; and that palmitate-treated islet cells differentiated preferentially through lineage 2 (Figure 5D). These results confirm that oleate conducts more cells towards proliferation.…”

“…A single lineage represents a path in a cellular trajectory (from a start point to end point) and the cells belonging to the lineage are then ordered by the pseudotime calculated by Slingshot. The Condiments R/Bioconductor package (De Bézieux et al, 2021), which implements a statistical workflow for modeling cell trajectories across multiple experimental conditions was used to refit a slingshot trajectory for each experimental condition and to map the Cell weights, the probability that a cell belongs to a particular lineage in the trajectory, to each experimental condition.…”