Abstract
Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Several methods have been developed for reconstructing developmental trajectories from single-cell transcriptomic data, but efforts on analyzing single-cell epigenomic data and on trajectory visualization remain limited. Here we present STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data. We have tested STREAM on several synthetic and real datasets generated with different single-cell technologies. We further demonstrate its utility for understanding myoblast differentiation and disentangling known heterogeneity in hematopoiesis for different organisms. STREAM is an open-source software package.
Highlights
Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions
This study focused on the mouse hematopoietic system, on the consequences of cell-fate determination within the granulocyte monocyte progenitors (GMP) population after the knockout of important master regulators
We present below how STREAM can be used to infer trajectories from single-cell epigenomic data and show its application to a recently published dataset, where a total of 3072 cells were profiled from the human bone marrow and isolated by FACS into nine different cellular populations, including hematopoietic stem cells (HSCs), MPP, CMP, CLP, lymphoid multipotent progenitors (LMPP), GMP, MEP, mono and plasmacytoid dendritic cells[44]
Summary
Single-cell transcriptomic assays have enabled the de novo reconstruction of lineage differentiation trajectories, along with the characterization of cellular heterogeneity and state transitions. Single-cell sequencing protocols have been developed to measure different molecular layers, including transcriptomics[1,2,3,4,5,6], epigenomics[7,8,9,10], and proteomics[11,12,13] The combination of these powerful measurements makes it possible to study important biological processes such as gene regulation on a multi-omics scale. No trajectory inference method provides the possibility to map new cells to previously obtained reference trajectories without pooling cells and re-computing trajectories To fill these gaps, we have developed STREAM (Single-cell Trajectories Reconstruction, Exploration And Mapping), a comprehensive single-cell trajectory analysis pipeline, which can robustly reconstruct complex trajectories along with accurate pseudotime estimation from both single-cell transcriptomic data and chromatin-accessibility data. In STREAM, the ElPiGraph was further developed to integrate a new heuristic graph structure seeding to learn principal graphs in high dimensions with several problem-specific topological graph grammar rules optimized for single-cell trajectory inference (Methods, Supplementary Fig. 1)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
