Exploration of Cell Development Pathways through High-Dimensional Single Cell Analysis in Trajectory Space

Denis Dermadi; Michael Bscheider; Kristina Bjegovic; Nicole H Lazarus; Agata Szade; Husein Hadeiba; Eugene C Butcher

doi:10.1016/j.isci.2020.100842

Exploration of Cell Development Pathways through High-Dimensional Single Cell Analysis in Trajectory Space

iScience. 2020 Feb 21;23(2):100842. doi: 10.1016/j.isci.2020.100842. Epub 2020 Jan 16.

Authors

Denis Dermadi¹, Michael Bscheider², Kristina Bjegovic³, Nicole H Lazarus², Agata Szade², Husein Hadeiba³, Eugene C Butcher⁴

Affiliations

¹ Laboratory of Immunology and Vascular Biology, Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA; The Center for Molecular Biology and Medicine, Veterans Affairs Palo Alto Health Care System and the Palo Alto Veterans Institute for Research (PAVIR), Palo Alto, CA 94304, USA. Electronic address: ddermadi@stanford.edu.
² Laboratory of Immunology and Vascular Biology, Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA; The Center for Molecular Biology and Medicine, Veterans Affairs Palo Alto Health Care System and the Palo Alto Veterans Institute for Research (PAVIR), Palo Alto, CA 94304, USA.
³ The Center for Molecular Biology and Medicine, Veterans Affairs Palo Alto Health Care System and the Palo Alto Veterans Institute for Research (PAVIR), Palo Alto, CA 94304, USA.
⁴ Laboratory of Immunology and Vascular Biology, Department of Pathology, School of Medicine, Stanford University, Stanford, CA 94305, USA; The Center for Molecular Biology and Medicine, Veterans Affairs Palo Alto Health Care System and the Palo Alto Veterans Institute for Research (PAVIR), Palo Alto, CA 94304, USA. Electronic address: ebutcher@stanford.edu.

Abstract

High-dimensional single cell profiling coupled with computational modeling is emerging as a powerful tool to elucidate developmental programs directing cell lineages. We introduce tSpace, an algorithm based on the concept of "trajectory space", in which cells are defined by their distance along nearest neighbor pathways to every other cell in a population. Graphical mapping of cells in trajectory space allows unsupervised reconstruction and exploration of complex developmental sequences. Applied to flow and mass cytometry data, the method faithfully reconstructs thymic T cell development and reveals development and trafficking regulation of tonsillar B cells. Applied to the single cell transcriptome of mouse intestine and C. elegans, the method recapitulates development from intestinal stem cells to specialized epithelial phenotypes more faithfully than existing algorithms and orders C. elegans cells concordantly to the associated embryonic time. tSpace profiling of complex populations is well suited for hypothesis generation in developing cell systems.

Keywords: Developmental Biology; In Silico Biology; Systems Biology.

Abstract

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