TY - JOUR
T1 - SimiC enables the inference of complex gene regulatory dynamics across cell phenotypes
AU - Peng, Jianhao
AU - Serrano, Guillermo
AU - Traniello, Ian M.
AU - Calleja-Cervantes, Maria E.
AU - Chembazhi, Ullas V.
AU - Bangru, Sushant
AU - Ezponda, Teresa
AU - Rodriguez-Madoz, Juan Roberto
AU - Kalsotra, Auinash
AU - Prosper, Felipe
AU - Ochoa, Idoia
AU - Hernaez, Mikel
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.
AB - Single-cell RNA-Sequencing has the potential to provide deep biological insights by revealing complex regulatory interactions across diverse cell phenotypes at single-cell resolution. However, current single-cell gene regulatory network inference methods produce a single regulatory network per input dataset, limiting their capability to uncover complex regulatory relationships across related cell phenotypes. We present SimiC, a single-cell gene regulatory inference framework that overcomes this limitation by jointly inferring distinct, but related, gene regulatory dynamics per phenotype. We show that SimiC uncovers key regulatory dynamics missed by previously proposed methods across a range of systems, both model and non-model alike. In particular, SimiC was able to uncover CAR T cell dynamics after tumor recognition and key regulatory patterns on a regenerating liver, and was able to implicate glial cells in the generation of distinct behavioral states in honeybees. SimiC hence establishes a new approach to quantitating regulatory architectures between distinct cellular phenotypes, with far-reaching implications for systems biology.
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U2 - 10.1038/s42003-022-03319-7
DO - 10.1038/s42003-022-03319-7
M3 - Article
C2 - 35414121
AN - SCOPUS:85128133533
SN - 2399-3642
VL - 5
JO - Communications biology
JF - Communications biology
IS - 1
M1 - 351
ER -