Ying Xin1, Pin Lyu1, Junyao Jiang2, Fengquan Zhou3,4, Jie Wang2, Seth Blackshaw1,4, Jiang Qian1. 1. Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. 2. CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China. 3. Department of Orthopedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. 4. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.
Abstract
MOTIVATION: Intercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration, and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops. RESULTS: Here we describe LRLoop, a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other, and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis, and/or cell fate specification. AVAILABILITY: An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figureshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figureshare (https://doi.org/10.6084/m9.figshare.20126021.v1). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Intercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration, and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops. RESULTS: Here we describe LRLoop, a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other, and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis, and/or cell fate specification. AVAILABILITY: An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figureshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figureshare (https://doi.org/10.6084/m9.figshare.20126021.v1). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Thomas V Johnson; Nicholas W DeKorver; Victoria A Levasseur; Andrew Osborne; Alessia Tassoni; Barbara Lorber; Janosch P Heller; Rafael Villasmil; Natalie D Bull; Keith R Martin; Stanislav I Tomarev Journal: Brain Date: 2013-10-30 Impact factor: 13.501
Authors: Mathias Uhlén; Linn Fagerberg; Björn M Hallström; Cecilia Lindskog; Per Oksvold; Adil Mardinoglu; Åsa Sivertsson; Caroline Kampf; Evelina Sjöstedt; Anna Asplund; IngMarie Olsson; Karolina Edlund; Emma Lundberg; Sanjay Navani; Cristina Al-Khalili Szigyarto; Jacob Odeberg; Dijana Djureinovic; Jenny Ottosson Takanen; Sophia Hober; Tove Alm; Per-Henrik Edqvist; Holger Berling; Hanna Tegel; Jan Mulder; Johan Rockberg; Peter Nilsson; Jochen M Schwenk; Marica Hamsten; Kalle von Feilitzen; Mattias Forsberg; Lukas Persson; Fredric Johansson; Martin Zwahlen; Gunnar von Heijne; Jens Nielsen; Fredrik Pontén Journal: Science Date: 2015-01-23 Impact factor: 47.728