Parental Beliefs in Relation to Sexual Minority Young Adults' Critical Consciousness, Mental Health, and Substance Use.

Recent advances in single cell profiling have opened new opportunities to identify developmental trajectories and understand cellular changes underlying dynamic biological processes. Identifying ancestor-descendant relationships allows researchers to gain insight into the shifts in cell state and cell type composition, and the regulatory networks that guide development, differentiation, and activation. In general, these new technologies have opened two approaches to understand ancestor-descendant relations between cells: prospective genetic fate mapping and computational inference based on high-dimensional single cell profiles. In prospective genetic fate mapping, cells are marked in a way that persists upon cell division (e.g., with a transgenic fluorescent reporter or a DNA sequence barcode) such that it is possible to identify cells that share a common ancestor, as in technologies such as scGESTALT, LINNAEUS, and ScarTrace. The advantage of these techniques is that they require minimal inference or assumptions: sets of cells sharing an ancestor can be read out directly. However, with these methods, it is not possible to identify and profile the ancestor cell that differentiated into any given set of labeled descendants or to follow the trajectory at intermediate steps. Further, these methods cannot be applied if performing genetic modification prior to sample collection is not an option. Trajectory methods based on computational inference, on the other hand, do not require manipulation of the sample. For example, RNA velocity methods use observations of newly transcribed versus old transcripts to infer the instantaneous vector of change in gene expression. Alternatively, pseudotime analysis methods like Diffusion Pseudotime or Monocle are commonly used to understand the dynamics of differentiation trajectories. This family of methods take as input a cell-by-gene count table and assigns each cell (or, in some cases, each cell type) a position on a continuous trajectory. In this way, a more complete estimate can be obtained of cell states all along the trajectory of differentiation. Several methods have recently been developed to incorporate spatial information in obtaining pseudotime labels in individual spatial transcriptomics (ST) datasets, including SpaceFlow and the stLearn package’s pseudo-time-space, enabling researchers to study spatial variability in asynchronous development and cell-cell communication’s effects on differentiation. However, popular pseudotime approaches do not incorporate temporal labels into their analysis of time-series data, which can result in incorrect trajectory inferences, especially when compounded with the difficulty of integrating multiple datasets due to batch effects.