recombinant bmp2 r d systems  (R&D Systems)

Journal: Cell systems

Article Title: Ligand-receptor promiscuity enables cellular addressing.

doi: 10.1016/j.cels.2022.03.001

Figure Lengend Snippet: Figure 5. Cell lines preferentially respond to different ligand combinations (A) Responses were measured for, from left to right, NMuMG cells, NMuMG cells with ACVR1 knockdown (KD), NMuMG cells with BMPR2 KD, NMuMG cells with ACVRL1 overexpression (OX), and mESCs, using flow cytometry of an integrated fluorescent protein reporter (STAR Methods: Addressing of cell lines). Each cell line was exposed to a double titration of BMP2 and BMP9, and responses were quantified by taking the mean of at least 3 replicates. For each cell line, fold change is calculated relative to the baseline fluorescence with no added ligand and then normalized by the maximum value. Responses at select ligand words (red circles) are analyzed further in (B). (B) For select ligand words from (A), the responses of each cell line are shown. Error bars indicate SD of at least 3 repeats. Ligand words were chosen by fixing a threshold of 0.5 (gray dashed line) and identifying those ligand combinations yielding unique on- and off-target activation patterns. (C) Data from (B) are summarized by showing the response of each cell type (columns) to each ligand word (rows), illustrating that distinct ligand words can activate different subsets of cell types. (D) Responses of NMuMG, NMuMG with ACVR1 KD, and NMuMG with BMPR2 KD to BMP9 and BMP10 are shown, as in (A). (E) As in (B), the responses of each cell type at selected ligand words are shown. (F) As in (C), the responses of each cell type (columns) to each ligand word (rows) confirm that distinct ligand words preferentially activate distinct groups of cell types. See also Table S1.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Chemicals, Peptides, and Recombinant Proteins BMP2 R&D Systems Cat# 355-BM BMP9 R&D Systems Cat# 5566-BP BMP10 R&D Systems Cat# 6038-BP Fetal Bovine Serum Clontech Cat# 631367 Fetal Bovine Serum, ES Cell Qualified Gibco Cat# 16141 Leukemia Inhibitory Factor MilliporeSigma Cat# ESG1107 Trypsin Gibco Cat# 25200 Accutase Gibco Cat# A11105 Deposited Data Optimization and simulation results This paper https://doi.org/10.22002/D1.1692 Experimental measurements of BMP responses in multiple cell lines This paper https://doi.org/10.22002/D1.1692 Experimental Models: Cell Lines NMuMG sensor line Klumpe et al., 2022 N/A NMuMG sensor line with ACVR1 knockdown Klumpe et al., 2022 N/A NMuMG sensor line with BMPR2 knockdown Klumpe et al., 2022 N/A NMuMG sensor line with ACVRL1 overexpression Klumpe et al., 2022 N/A mESC sensor line Antebi et al., 2017 N/A Software and Algorithms MATLAB MathWorks N/A Python Python Software Foundation N/A Equilibrium Toolkit (EQTK) (Python) Bois, 2020 https://github.com/justinbois/eqtk and https://doi.org/10.22002/D1.1430 PromiSys (Python) This paper https://github.com/christinasu/PromiSys and https://doi.org/10.22002/D1.20047 Simulation and analysis code (Python) This paper https://github.com/christinasu/PromiSys and https://doi.org/10.22002/D1.20047 EasyFlow (MATLAB) Antebi et al., 2017 https://github.com/AntebiLab/easyflow

Techniques: Knockdown, Over Expression, Cytometry, Titration, Activation Assay