Search for: All records

Abstract Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package:https://github.com/jranek/delve. 

Deep-sea methane seeps host highly diverse microbial communities whose biological diversity is distinct from other marine habitats. Coupled with microbial community analysis, untargeted metabolomics of environmental samples using high resolution tandem mass spectrometry provides unprecedented access to the unique specialized metabolisms of these chemosynthetic microorganisms. In addition, the diverse microbial natural products are of broad interest due to their potential applications for human and environmental health and well-being. In this exploratory study, sediment cores were collected from two methane seeps (-1000 m water depth) with very different gross geomorphologies, as well as a non-seep control site. Cores were subjected to parallel metabolomic and microbial community analyses to assess the feasibility of representative metabolite detection and identify congruent patterns between metabolites and microbes. Metabolomes generated using high resolution liquid chromatography tandem mass spectrometry were annotated with predicted structure classifications of the majority of mass features using SIRIUS and CANOPUS. The microbiome was characterized by analysis of 16S rRNA genes and analyzed both at the whole community level, as well as the small subgroup of Actinobacteria, which are known to produce societally useful compounds. Overall, the younger Dagorlad seep possessed a greater abundance of metabolites while there was more variation in abundance, number, and distribution of metabolites between samples at the older Emyn Muil seep. Lipid and lipid-like molecules displayed the greatest variation between sites and accounted for a larger proportion of metabolites found at the older seep. Overall, significant differences in composition of the microbial community mirrored the patterns of metabolite diversity within the samples; both varied greatly as a function of distance from methane seep, indicating a deterministic role of seepage. Interdisciplinary research to understand microbial and metabolic diversity is essential for understanding the processes and role of ubiquitous methane seeps in global systems and here we increase understanding of these systems by visualizing some of the chemical diversity that seeps add to marine systems.