%0 Journal Article %1 Kuritz2020 %A Kuritz, Karsten %A Stöhr, Daniela %A Maichl, Daniela Simone %A Pollak, Nadine %A Rehm, Markus %A Allgöwer, Frank %D 2020 %I Nature Publishing Group %J Scientific Reports %K PN2 EXC2075 selected %N 1 %P 3619 %R 10.1038/s41598-020-60400-z %T Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities %U http://www.nature.com/articles/s41598-020-60400-z %V 10 %X Modern cytometry methods allow collecting complex, multi-dimensional data sets from heterogeneous cell populations at single-cell resolution. While methods exist to describe the progression and order of cellular processes from snapshots of such populations, these descriptions are limited to arbitrary pseudotime scales. Here we describe MAPiT, an universal transformation method that recovers real-time dynamics of cellular processes from pseudotime scales by utilising knowledge of the distributions on the real scales. As use cases, we applied MAPiT to two prominent problems in the flow-cytometric analysis of heterogeneous cell populations: (1) recovering the kinetics of cell cycle progression in unsynchronised and thus unperturbed cell populations, and (2) recovering the spatial arrangement of cells within multi-cellular spheroids prior to spheroid dissociation for cytometric analysis. Since MAPiT provides a theoretic basis for the relation of pseudotime values to real temporal and spatial scales, it can be used broadly in the analysis of cellular processes with snapshot data from heterogeneous cell populations.

@article{Kuritz2020, abstract = {Modern cytometry methods allow collecting complex, multi-dimensional data sets from heterogeneous cell populations at single-cell resolution. While methods exist to describe the progression and order of cellular processes from snapshots of such populations, these descriptions are limited to arbitrary pseudotime scales. Here we describe MAPiT, an universal transformation method that recovers real-time dynamics of cellular processes from pseudotime scales by utilising knowledge of the distributions on the real scales. As use cases, we applied MAPiT to two prominent problems in the flow-cytometric analysis of heterogeneous cell populations: (1) recovering the kinetics of cell cycle progression in unsynchronised and thus unperturbed cell populations, and (2) recovering the spatial arrangement of cells within multi-cellular spheroids prior to spheroid dissociation for cytometric analysis. Since MAPiT provides a theoretic basis for the relation of pseudotime values to real temporal and spatial scales, it can be used broadly in the analysis of cellular processes with snapshot data from heterogeneous cell populations.}, added-at = {2024-03-26T11:56:34.000+0100}, author = {Kuritz, Karsten and St{\"{o}}hr, Daniela and Maichl, Daniela Simone and Pollak, Nadine and Rehm, Markus and Allg{\"{o}}wer, Frank}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2d66f040fb48167c55cf68bc13a9e5226/exc2075}, doi = {10.1038/s41598-020-60400-z}, interhash = {19692d5c33e2d2b062704c42ee6d8d71}, intrahash = {d66f040fb48167c55cf68bc13a9e5226}, issn = {2045-2322}, journal = {Scientific Reports}, keywords = {PN2 EXC2075 selected}, month = {12}, number = 1, pages = 3619, publisher = {Nature Publishing Group}, timestamp = {2024-03-26T11:56:34.000+0100}, title = {{Reconstructing temporal and spatial dynamics from single-cell pseudotime using prior knowledge of real scale cell densities}}, url = {http://www.nature.com/articles/s41598-020-60400-z}, volume = 10, year = 2020 }