www.sciencesignaling.org/cgi/content/full/5/235/ra55/DC1
Supplementary Materials for Differential RET Signaling Pathways Drive Development of the Enteric Lymphoid and Nervous Systems Amisha Patel, Nicola Harker, Lara Moreira-Santos, Manuela Ferreira, Kieran Alden, Jon Timmis, Katie Foster, Anna Garefalaki, Panayotis Pachnis, Paul Andrews, Hideki Enomoto, Jeffrey Milbrandt, Vassilis Pachnis, Mark C. Coles, Dimitris Kioussis, Henrique Veiga-Fernandes* *To whom correspondence should be addressed. E-mail:
[email protected] Published 31 July 2012, Sci. Signal. 5, ra55 (2012) DOI: 10.1126/scisignal.2002734
This PDF file includes: Methods Fig. S1. Stroma cell activation, LTi and LTin cell frequencies, chemokine blocking, lymphotoxin modulation, and A test results for chemokine simulations. Fig. S2. RET co-receptors in the gut. Fig. S3. GDNF and GFRα1 signaling in lymphoid organ formation. Fig. S4. Neural projections induced by GFLs. Fig. S5. PP development in GFRα1 cis mice. Fig. S6. Activity diagram of cellular events leading to PP triggering. Fig. S7. State diagram of LTin cells. Fig. S8. State diagram of LTi cells. Fig. S9. State diagram of LTo cells. Fig. S10. State diagram of non-LTo RET ligand producer cells. Fig. S11. Parameter descriptions. Fig. S12. Activity diagram of trans RET signaling events leading to PP formation. Fig. S13. State diagram of LTin cells when RET signaling in trans is used. Fig. S14. State diagram of LTi cells when RET signaling in trans is used. Fig. S15. State diagram of LTo cells when RET signaling in trans is used. Fig. S16. State diagram of non-LTo RET ligand producer cells for in trans RET signaling. Movies S1 to S4 captions Other Supplementary Material for this manuscript includes the following: (available at www.sciencesignaling.org/cgi/content/full/5/235/ra55/DC1)
Movie S1 (.mov format). Motility of hematopoietic GFP+ cells in explanted gut cultures with ARTN-soaked beads. Movie S2 (.mov format). Motility of hematopoietic GFP+ cells in explanted gut cultures with BSA-soaked beads. Movie S3 (.mov format). Motility of hematopoietic cells in silico using RET signaling in cis. Movie S4 (.mov format). Motility of hematopoietic cells in silico using RET signaling in trans. Simulation code
Methods RT-PCR analysis Total RNA was purified using TRIzol reagent (Invitrogen) according to the manufacture instructions. Listed primers are sense and reverse respectively. When a nested-sense primer was used, its sequence is last in the list. Gdnf: GGACGGGACTCTAAGATGAA and TTCCTCTCTCTTCGAGGAAG; Artn: TTCTGGAGCCGAAAGCTATG and TGCACAAATGCGCAGTGTGT; Nrtn: TGCTATCTGTCTGGATGTGC and AGGGAGAAAGTTCTCGAAGC; Pspn: TCTTCAAGAGGCTTCTGTGG and TACCAGACTGTGCTGGGTAT; Gfra1: GACCTGGAAGATTGCCTGAA and GGAGGAGCAGCCATTGATTT; Gfra2: ACACCGAACTATGTGGACTC and GATAGATGTGCAGGTGGTGA; Gfra3: TCTCTGATAGACTGCAGGTG and GTCGTGAAGAGTACACAGCA; Gfra4: CAACTACCTGGACAACGTGA and GTCCACGGTTCATGTTCCTA; Hprt1: TCCCTGGTTAAGCAGTACAG and GCTTTGTATTTGGCTTTTCC; Ccl19: TTCAGCCTGCTGGTTCTCTG, TTCTGGTGCTGTTGCCTTTG and GTGCTAATGATGCGGAAGAC; Ccl21: ATGACTCTGAGCCTCCTTAG, TTGAGGGCTGTGTCTGTTCA and CTACAGTATTGTCCGAGGCT; Cxcl13: AGACTCCGAGCTAAAGGTTG, GTAACCATTTGGCACGAGGA and AATGAGGCTCAGCACAGCAA. Ltb: CGGATTCTACACCAGATCCA, TCCACAACAGGTGTGACTGT and ACTGACCTCAACCCTGAGCT. Primers for full length functional Gfra4 were previously described . Gene sequences were obtained from URL:http://www.ensembl.org. Genetic nomenclature was used according to the International Committee Standardized Genetic Nomenclature for Mice (URL:http://www.informatics.jax.org). Statistical analysis Statistical analysis for Fig. 2G Left panel: BSA n=25; ARTN+GFRα3 (96H) n=25; ARTN+GFRα3 (12H) n=25. Two tailed t-test P values at 96 hours were BSA versus ARTN+GFRα3 (96H), P = 0.00004; BSA versus ARTN+GFRα3 (12H), P = 0.025; ARTN+GFRα3 (96H) versus ARTN+GFRα3 (12H), P = 0.025. Right panel: ARTN and GFRα3 for 96 hours (open circles), with ARTN and GFRα3 for 24 hours (closed circles) or with BSA for 96 hours (open squares). Kinetic analysis was performed up to 96 hours by stereo microscopy. BSA n=20; ARTN+GFRα3 (96H) n=15; ARTN+GFRα3 (12H) n=20. Two tailed t-test P values at 96 hours were BSA versus ARTN+GFRα3 (96H), P = 0.00002; BSA versus ARTN+GFRα3 (24H), P = 0.008; ARTN+GFRα3 (96H) versus ARTN+GFRα3 (24H), P = 0.074. Statistical analysis for Fig. 4A BSA n=35; ARTN n=25; ARTN+GFRα1 n=25; ARTN+GFRα2 n=30; ARTN+GFRα3 n=20. Two tailed t-test P values at 96 hours were BSA versus ARTN, P = 0.0034; BSA versus ARTN+GFRα1, P = 0.0162; BSA versus ARTN+GFRα2, P = 0.0328; BSA versus ARTN+GFRα3, P = 0.000008; ARTN versus ARTN+GFRα1, P = 0.4471; ARTN versus ARTN+GFRα2, P = 0.8206; ARTN versus ARTN+GFRα3, P = 0.0019; ARTN+GFRα1 versus ARTN+GFRα2, P = 0.3794; ARTN+GFRα1 versus ARTN+GFRα3, P = 0.0772; ARTN+GFRα2 versus ARTN+GFRα3, P = 0.0038.
Statistical analysis for Fig. 4B BSA n=25; NRTN n=25; NRTN+GFRα1 n=20; NRTN+GFRα2 n=25; NRTN+GFRα3 n=25. Two tailed t-test P values at 96 hours were BSA versus NRTN, P = 0.0169; BSA versus NRTN+GFRα1, P = 0.0036; BSA versus NRTN+GFRα2, P = 0.00001; BSA versus NRTN+GFRα3, P = 0.0254; NRTN versus NRTN+GFRα1, P = 0.1173; NRTN versus NRTN+GFRα2, P = 0.0001; NRTN versus NRTN+GFRα3, P = 0.3202; NRTN+GFRα1 versus NRTN+GFRα2, P = 0.0019; NRTN+GFRα1 versus NRTN+GFRα3, P = 0.9205; NRTN+GFRα2 versus NRTN+GFRα3, P = 0.0101. Statistical analysis for Fig. 4C BSA n=45; GDNF n=40; GDNF+GFRα1 n=25; GDNF+GFRα2 n=25; GDNF+GFRα3 n=25. Two tailed t-test P values at 96 hours were BSA versus GDNF, P = 0.3399; BSA versus GDNF+GFRα1, P = 0.000003; BSA versus GDNF+GFRα2, P = 0.5330; BSA versus GDNF+GFRα3, P = 0.9849; GDNF versus GDNF+GFRα1, P = 0.0073; GDNF versus GDNF+GFRα2, P = 0.6263; GDNF versus GDNF+GFRα3, P = 0.4432; GDNF+GFRα1 versus GDNF+GFRα2, P = 0.00005; GDNF+GFRα1 versus GDNF+GFRα3, P = 0.00008; GDNF+GFRα2 versus GDNF+GFRα3, P = 0.5795.
Fig. S1. Stroma cell activation, LTi and LTin cell frequencies, chemokine blocking, lymphotoxin modulation, and A test results for chemokine simulations. (A) E15.5 enteric CD45-GP38+ mesenchymal cells were purified by flow cytometry and stimulated with BSA or ARTN for 24 hours. Data show flow cytometry analysis for VCAM1 expression. (B) Cell suspensions were prepared from hCD2-GFP E14.5 intestines. hCD2-GFP E17.5 PPs were micro dissected under a fluorescent stereo microscope. CD45+ cells were analyzed by flow cytometry for the CD4 LTi and CD11c LTin cells. (C) Percentage of LTi (black) and LTin (white) cells are depicted. Left: E14.5 n = 6; Right: E17.5 (PP) n = 3. Two tailed t-test P values are indicated. Error bars show SD. (D) Explant organ cultures of E15.5 intestines were incubated for 24 hours with agarose beads impregnated with ARTN in the presence of blocking antibodies to CCL19, CCL21 and CXCL13 (5 µg/ml each) or goat IgG isotype control. CD11c (green) and VCAM1 (red). Similar results were obtained in three independent experiments. Scale bar: 100 µm. (E) E15.5 enteric LTin cells were purified by flow cytometry and stimulated with BSA or with ARTN and GFR3 (500 ng/ml) for 24 hours. Results show quantitative RTPCR analysis of Ltb expression. (F) Explant organ cultures of E15.5 intestines were incubated for 24 hours with agarose beads impregnated with ARTN in the presence or absence of LTRIg (10 µg/ml). CD11c (green) and VCAM1 (red). Scale bar: 100 µm. Similar results were obtained in three independent experiments. (G) Results show chemokine lower probability and chemokine upper probability. Points in the graph are averages of 500 independent runs of the simulation, and for each parameter that was varied the remaining parameter values were kept constant (leave-one-out test).
Fig. S2. RET co-receptors in the gut. (A) Expression of RET co-receptors and GFLs in embryonic guts. RNA from E12.5 to E15.5 small intestines was purified and RT-PCR analysis was performed. (B) Nonhematopoietic E15.5 cells were purified by FACS. RET– CD31–GP38–Ret–UEA– (smooth muscle cells), RET–CD31+GP38– (blood endothelial cells), RET–CD31+GP38+ (lymph endothelial cells), and RET–CD31–GP38+Ret–UEA+ (enterocytes). Results were normalized to Hprt1. (C) RNA from E12.5 to E15.5 small intestines, brain, and kidney were analyzed for full-length Gfra4 (59). Results show expression of a predicted GPI linked isoform of Gfra4 . (D) PPs in combined ARTN and NRTN null mice. Left: PP numbers in E17.5 intestines, whole-mount stained for VCAM-1 and analyzed by microscopy. Artn+/- Nrtn+/- n = 3; Artn-/- Nrtn+/- n = 5; Artn+/Nrtn-/- n = 3; Artn-/- Nrtn-/- n = 5. ANOVA analysis was used to determine equality of means, P = 0.824. Error bars show SEM. Right: PP numbers in adult intestines. Artn+/Nrtn+/+ n = 10; Artn-/- Nrtn+/+ n = 3; Artn+/+ Nrtn+/- n = 6; Artn+/+ Nrtn-/- n = 3; Artn+/- Nrtn-/n = 3; Artn-/- Nrtn-/- n = 5. ANOVA analysis was used to determine equality of means, P = 0.182. Error bars show SEM.
Fig. S3. GDNF and GFRα1 signaling in lymphoid organ formation. (A) Explant organ cultures of E15.5 intestines from hCD2-GFP mice were incubated with agarose beads impregnated with GDNF and GFRα1. Kinetic analysis was performed for up to 96 hours by stereo microscopy. Agarose beads impregnated with BSA (left) or GDNF and GFRα1 recombinant proteins (right). Arrows indicate GFP+ cell clusters. Similar results were obtained in five independent experiments. (B) E15.5 intestines were treated for 8 hours with BSA or PIPLC. Intestines were then cultured with GDNF-impregnated agarose microspheres. CD11c (green), VCAM1 (red). Scale bar: 100 µm. Similar results were obtained in three independent experiments. (C) E15.5 intestines from WT embryos were cultured for 48 hours in serum-free medium. Supernatants were collected, clarified, concentrated, and analyzed by SDS-PAGE and Western blotting. Lane1: 18 guts; Lane 2: 9 guts. 150 µg of protein per lane were analyzed with anti-GFRα2 and anti-GFRα3 antibodies. GFRα2: ~75 kD (glycosylated form) (60); GFRα3: 43 to 62 kD according to different glycosylation levels (61).
Fig. S4. Neural projections induced by GFLs. (A) Explant organ cultures of E15.5 intestines from Gfra1-/- or WT littermate controls were incubated with agarose beads impregnated with GDNF for 96 hours and immunostained for TUJ1 (red). Results show that in the absence of neural crest cells (Gfra1-/-), no other neuronal cell types (TUJ1+) responded to GDNF-impregnated beads. (B) Explant organ cultures of E15.5 intestines from hCD2-GFP mice were incubated for 96 hours with agarose beads impregnated with PSPN. They were then immunostained for GFP (green) and TUJ1 (red) and analyzed by confocal microscopy. (C) Explant organ cultures were incubated for 96 hours with agarose beads impregnated with ARTN and GFRα3 or with NTRN and GFRα2 recombinant proteins at 1 µg/µl (40 µM). Samples were then immunostained for TUJ1 (red) and analyzed by confocal microscopy.
Fig. S5. PP development in GFR1 cis mice. (A) Intestines from adult WT littermate controls and GFR1 cis mice were analyzed by stereo microscopy and Peyer’s patches counted. WT n = 10; GFR1 cis n = 18. Two tailed t-test P value was P = 0.9873. Error bars show SEM.
Fig. S6. Activity diagram of cellular events leading to PP triggering. To test the hypotheses arising from the PP-triggering events that were observed in explant organ cultures, we built an agent-based model of enteric cell movement and interactions. We investigated, using a stochastic individual cell-based model, how the effect of RET signaling on LTin cells could lead to PP triggering, as observed in explant cultures. This diagram provides a description of the cell types and steps used to create the model. We assumed that hematopoietic single cells (LTin or LTi) acted as random entities, which initially migrate into the gut and then move on an enteric two-dimensional matrix. We considered LTo cells as stationary entities that may produce RET ligands, and we also included other RET ligand producer cells that do not have an LTo potential. Cell movements were simulated with a stochastic cell motility algorithm. We assumed that contacts between LTin and RET ligand–producing LTo cells initially depended only on the cell density in the area into which the cells move and are later mediated by adhesion molecules, such as VCAM, whereas LTi motility is modulated by adhesion molecules and chemokine gradients. Random motility leads to initial contact between LTin and LTo RET ligand–producing cells, which results in the maturation of the LTo cells and their production of adhesion molecules. We assumed that enhanced amounts of adhesion molecules would mediate prolonged contacts between LTo cells and LTi cells that in turn would lead to chemokine production by the LTo cells.
Fig. S7. State diagram of LTin cells. This diagram provides a description of LTin states in which this cell type might be found at any given time. (A) LTin Domain, representing states and state transitions underlying the biological events of LTin cell behavior. (B) LTin Platform, representing the translation from LTin biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation. (C) Table of assumptions adopted for modeling LTin cell behavior.
Fig. S8. State diagram of LTi cells. This diagram provides a description of LTi states in which this cell type might be found at any given time. (A) LTi Domain, representing states and state transitions underlying the biological events of LTi cell behavior. (B) LTi Platform, representing the translation from LTi biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation. (C) Table of assumptions adopted for modeling LTi cell behavior.
Fig. S9. State diagram of LTo cells. This diagram provides a description of LTo states in which this cell type might be found at any given time. (A) LTo Domain, representing states and state transitions underlying the biological events of LTo cell behavior. (B) LTo Platform, representing the translation from LTo biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation. (C) Table of assumptions adopted for modeling LTo cell behavior.
Fig. S10. State diagram of non-LTo RET ligand producer cells. This diagram provides a description of non-LTo RET ligand producer cell states in which this cell type might be found at any given time. (A) Non-LTo RET ligand producer cell Domain, representing states and states transitions underlying biological events of non-LTo RET ligand producer cell behavior. (B) Non-LTo RET ligand producer cell Platform, representing the translation from non-LTo RET ligand producer cell biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation. (C) Table of assumptions adopted for modeling non-LTo RET ligand producer cell behavior.
Fig. S11. Parameter descriptions. This figure provides a description of the parameters that have been included in the agent-based model.
Fig. S12. Activity diagram of trans RET signaling events leading to PP formation. To test the hypotheses that provision of RET co-receptors in trans is sufficient to activate RETdependent lymphoid structure formation (as opposed to in cis signaling, fig. S6), we built an agent-based model of enteric cell movement and interactions. We investigated, using a stochastic, individual cell-based model, how the effect of RET signaling in trans led to PP triggering, as observed in explant cultures. This diagram provides a description of the cell types and steps used to create the model. We assumed that hematopoietic single cells (LTin or LTi) acted as random entities, which initially migrated into the gut and then moved on an enteric two-dimensional matrix. We considered LTo cells as stationary entities that may express RET ligands, RET co-receptors, or both, and we also included other cells that do not have an LTo potential but are capable of producing RET ligands and RET co-receptors. Cell movements were simulated using a stochastic cell motility algorithm. Activation of RET signaling in LTin cells requires contact of the latter with GFL-expressing LTo cells in the vicinity of cells expressing RET co-receptors.
Fig. S13. State diagram of LTin cells when RET signaling in trans is used. This diagram provides a description of the LTin states in which this cell type might be found at any given time. (A) LTin Domain, representing states and state transitions underlying the biological events of LTin cell behavior. (B) LTin Platform, representing the translation from LTin biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation.
Fig. S14. State diagram of LTi cells when RET signaling in trans is used. This diagram provides a description of the LTi states in which this cell type might be found at any given time. (A) LTi Domain, representing states and state transitions underlying the biological events of LTi cell behavior. (B) LTi Platform, representing the translation from LTi biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation.
Fig. S15. State diagram of LTo cells when RET signaling in trans is used. This diagram provides a description of the LTo states in which this cell type might be found at any given time. (A) LTo Domain, representing states and state transitions underlying the biological events of LTo cell behavior. (B) LTo Platform, representing the translation from LTo biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation.
Fig. S16. State diagram of non-LTo RET ligand producer cells for in trans RET signaling. This diagram provides a description of the non-LTo RET ligand producer cell states in which this cell type might be found at any given time. (A) Non-LTo RET ligand producer cell Domain, representing states and state transitions underlying the biological events of non-LTo RET ligand producer cell behavior. (B) Non-LTo RET ligand producer cell Platform, representing the translation from non-LTo RET ligand producer cell biological events to an agent-based model for this cell type. The platform model translates biological understanding into a specification of how agents and transitions will be encoded in the simulation.
Simulation code Zip file contains: (i) A .jar file, which is the executable simulation code; and (ii) instructions on how to run the .jar file and license guidelines. Movie descriptions Movie S1. Motility of hematopoietic GFP+ cells in explanted gut cultures with ARTNsoaked beads. This movie shows a 60-min time lapse sequence of an E15.5 gut explant culture incubated with ARTN-impregnated agarose beads for 24 hours, when LTin cells aggregate in the vicinity of the beads. Thus, time-lapse images were taken for 60 min between hour 24 and 25 of incubation. LTin cells were not aggregated at incubation time point 0 hours, and did not aggregate in the BSA condition at 24 hours as shown in Fig.1A, Fig. 1 C to E, and Fig. 3A. Movie S2. Motility of hematopoietic GFP+ cells in explanted gut cultures with BSA-soaked beads. This movie shows a 60-min time lapse sequence of an E15.5 gut explant culture incubated with BSA-impregnated agarose beads for 24 hours. Movie S3. Motility of hematopoietic cells in silico using RET signaling in cis. This movie shows a simulation from in silico E14.5 to E17.5 LTin cells in red and LTi cells in green. Movie S4. Motility of hematopoietic cells in silico using RET signaling in trans. This movie shows a simulation from in silico E14.5 to E17.5 LTin cells in red and LTi cells in green.
