Stem Cell Reports

Quantitative (Phospho)Proteomic Profiling of Human Pluripotency and Neural Specification

This record contains raw data related to article "PBX1-directed stem cell transcriptional program drives tumor progression in myeloproliferative neoplasm" PBX1 regulates the balance between self-renewal and differentiation of hematopoietic stem cells and maintains proto-oncogenic transcriptional pathways in early progenitors. Its increased expression was found in myeloproliferative neoplasm (MPN) patients bearing the JAK2V617F mutation. To investigate if PBX1 contributes to MPN, and to explore its potential as therapeutic target, we generated the JP mouse strain, in which the human JAK2 mutation is induced in the absence of PBX1. Typical MPN features, such as thrombocythemia and granulocytosis, did not develop without PBX1, while erythrocytosis, initially displayed by JP mice, gradually resolved over time; splenic myeloid metaplasia and in vitro cytokine independent growth were absent upon PBX1 inactivation. The aberrant transcriptome in stem/progenitor cells from the MPN model was reverted by the absence of PBX1, demonstrating that PBX1 controls part of the molecular pathways deregulated by the JAK2V617F mutation. Modulation of the PBX1-driven transcriptional program might represent a novel therapeutic approach.

INTRODUCTION This document contains information on how to run GARMEN to simulate peri-gastrulation patterns. GARMEN represents an integration of a gene regulatory network (GRN) embedded in an agent-based model (ABM), which, in turn, is linked with a Reaction Diffusion (RD) model. The files made available here simulate a micropatterned human pluripotent stem cell colony of radius 465 µm (colony size based on in vitro measurements, number of colonies measured = 10, refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). Patterns in the simulations emerge as a result of dynamic interplay between the GRNs and signal gradients via agent (Kaul and Ventikos, 2015, https://doi.org/10.1093/bib/bbt077) activity. Model predictions were validated in vitro via signal-based and transcriptomic perturbations (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). GARMEN MODULES FLAME: The virtual nucleus and virtual cell The virtual nucleus and virtual cell modules were simulated using the Flexible Large-scale Agent-based Modelling Environment (FLAME). Visit www.flame.ac.uk for details regarding installation and usage. To run a model in FLAME, one needs to create an XMML scheme describing the structure of the model files (this is the uploaded GARMEN.xml file). Functions or rule-sets that govern agent interactions are written in C (this is the uploaded final_functions.c file). Lastly, an XMML file containing the initial conditions is required to run the model (the 0.xml file). FLAME Architecture FLAME works by parsing a model XMML definition into simulation program code via the xparser (xparser version used in Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004 has been uploaded). Subsequently, the message board library (libmboard) is required to compile the model (libmboard version used in this study has been provided here). The libmboard MUST be in the same folder as the model. FLAME Contents The following files/folders have been provided: - Xparser (v 0.16.2): To parse the model (in our case the GARMEN.xml file), go in the xparser folder and use the following command line xparser> xparser ../model/GARMEN.xml - Libmboard (v 0.2.1): Copy this folder in the model folder. The model will NOT compile, if the libmboard folder is not in the same location as the model - The model files: They contain GARMEN.xml This file contains the model structure. It describes the environment that includes the width of the simulation environment, two user-defined datatypes ‘gen_net’ and ‘chem_sensing’, and the reference to the agent functions file. User-defined Datatypes: The datatype ‘gen_net’ enabled the incorporation of the GRN in each agent, whereas ‘chem_sensing’ enabled the agents to sense signal concentration around them. Whereas gen_net allowed us to define the name of the genes (OCT4, SOX2, etc) and their activation status (OFF, LOW, or HIGH), chem_sensing allowed us to define the signal (BMP4, WNT3, etc) and their concentrations. X-machines: FLAME models agents as communicating X-machines. Here, we defined only one agent (or x-machine): hpsc (human pluripotent stem cell) that had characteristics like id, type (pluripotent, ectodermal, etc), gene activation status (gen_net), signal concentration (chem_sensing), spatial coordinates, number of neighbours (monolayer and edge bonds), and cell cycle. This file also lists the various functions these agents will perform as well as the messages they can exchange with each other. final_functions.c This file details the agent functions that were specified in the .xml file. It contains the source code for how agent functions will be implemented. Refer to annotations in the .c file for more details. Folder ‘its’ containing the initial file 0.xml This initial file contains information regarding the number of agents at the starting point of the simulation and their id, type, and spatial coordinates, etc. The spatial coordinates of the agents were set randomly. Multiple .txt files containing concentrations of BMP4, NOGGIN, WNT3, and DKK at various time points (12 h, 24 h, 36 h, and 48 h) These files contain the spatial concentrations of BMP4 & NOGGIN (12 h) and WNT3 & DKK (24 h, 36 h, 48 h) that were computed in COMSOL. When the model was simulated for the first time, these files were generated in COMSOL after every 30 iterations (equivalent to 12 h of physical time) based on the agent phenotype and position by freezing the agent-based model (refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004, for details). However, we are providing these files so that the user can execute the code fully. These .txt files MUST stay in the same folder as the model. Other files In this folder, we are providing WNT3/DKK concentration profiles equivalent to 12 h when differentiation is induced via WNT3 alone (i.e. the BMP4− simulation). This file (12h_wnt3_dkk_data.txt) MUST stay in the same folder as the model when simulating patterning as triggered by WNT3 (in which case do not use the .txt files specified in the preceding bullet point). Relevant Command Lines To parse the GARMEN.xml file: Go to the xparser folder (wherever you decide to store it), xparser> xparser ../model/GARMEN.xml To compile the files: Go to the model folder, model_folder> make To simulate the model: Stay in the model folder, model_folder> main 180 its\0.xml This command will simulate the model for 180 iterations (equivalent to 72 hours of physical time). This includes the first 60 iterations where agents are undergoing proliferation (equivalent to 24 hours pre-differentiation) followed by 120 iterations capturing differentiation and pattern-emergence (equivalent to 48 hours following induction of differentiation). All output files will be stored in the folder entitled ‘its’. Additional requirements You will need to install MinGW and change the Environment Settings to add MinGW to the path. Refer to www.flame.ac.uk/docs/install.html for instructions. COMSOL: The virtual microenvironment We simulated the signal gradients via the RD paradigm using Gierer-Meinhardt reaction-kinetics (Gierer and Meinhardt, 1972, https://doi.org/10.1007/BF00289234) in COMSOL Multiphysics v5.2a (COMSOL, MA, USA). We used the Coefficient Form PDEs module to simulate the RD equations. In the file, entitled the-virtual-microenvironment-nonDimensional.mph, Analytics 1, 2, 5, and 6 represent the non-dimensional equations that were solved. Refer to Equation 9 in Supplemental Experimental Procedures (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). D_NOG, D_BMP, D_Wnt, and D_Dkk represent the effective diffusivities of NOGGIN, BMP4, WNT3, and DKK, respectively. They were quantified such that D_BMP:D_NOG = 0.4 and D_Wnt:D_Dkk = 0.36. Refer to Supplemental Experimental Procedures (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004) for details. We constructed the non-dimensional virtual twin of a single well of a microtiter plate containing multiple human pluripotent stem cell colonies. We used tetrahedral meshes of size 20 µm inside the cellular colonies and 30 µm outside to discretise the geometry. Initial BMP4, NOGGIN, and DKK concentrations were set randomly with a uniform distribution. The initial concentration of WNT3 was tied to that of BMP4 (for the cases where differentiation was initiated via BMP4). We employed zero-flux boundary conditions. Following simulations, we exported data from COMSOL, assimilated it in a .txt format, and fed it to the agent-based model (relevant files have been uploaded). Data flow between the ABM and COMSOL modules was manual. For details, refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004. COMSOL Files We are providing the file entitled the-virtual-microenvironment-nonDimensional.mph that contains modules to simulate BMP4-NOGGIN and WNT3-DKK dynamics. Model steady state is achieved equivalent to 12 h of physical time. The geometry created is non-dimensional. SUPPORT Contact Himanshu Kaul at himanshu.kaul@leicester.ac.uk for queries related to this model FLAME related queries should be directed to flame-dev@googlegroups.com

INTRODUCTION This document contains information on how to run GARMEN to simulate peri-gastrulation patterns. GARMEN represents an integration of a gene regulatory network (GRN) embedded in an agent-based model (ABM), which, in turn, is linked with a Reaction Diffusion (RD) model. The files made available here simulate a micropatterned human pluripotent stem cell colony of radius 465 µm (colony size based on in vitro measurements, number of colonies measured = 10, refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). Patterns in the simulations emerge as a result of dynamic interplay between the GRNs and signal gradients via agent (Kaul and Ventikos, 2015, https://doi.org/10.1093/bib/bbt077) activity. Model predictions were validated in vitro via signal-based and transcriptomic perturbations (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). GARMEN MODULES FLAME: The virtual nucleus and virtual cell The virtual nucleus and virtual cell modules were simulated using the Flexible Large-scale Agent-based Modelling Environment (FLAME). Visit www.flame.ac.uk for details regarding installation and usage. To run a model in FLAME, one needs to create an XMML scheme describing the structure of the model files (this is the uploaded GARMEN.xml file). Functions or rule-sets that govern agent interactions are written in C (this is the uploaded final_functions.c file). Lastly, an XMML file containing the initial conditions is required to run the model (the 0.xml file). FLAME Architecture FLAME works by parsing a model XMML definition into simulation program code via the xparser (xparser version used in Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004 has been uploaded). Subsequently, the message board library (libmboard) is required to compile the model (libmboard version used in this study has been provided here). The libmboard MUST be in the same folder as the model. FLAME Contents The following files/folders have been provided: - Xparser (v 0.16.2): To parse the model (in our case the GARMEN.xml file), go in the xparser folder and use the following command line xparser> xparser ../model/GARMEN.xml - Libmboard (v 0.2.1): Copy this folder in the model folder. The model will NOT compile, if the libmboard folder is not in the same location as the model - The model files: They contain GARMEN.xml This file contains the model structure. It describes the environment that includes the width of the simulation environment, two user-defined datatypes ‘gen_net’ and ‘chem_sensing’, and the reference to the agent functions file. User-defined Datatypes: The datatype ‘gen_net’ enabled the incorporation of the GRN in each agent, whereas ‘chem_sensing’ enabled the agents to sense signal concentration around them. Whereas gen_net allowed us to define the name of the genes (OCT4, SOX2, etc) and their activation status (OFF, LOW, or HIGH), chem_sensing allowed us to define the signal (BMP4, WNT3, etc) and their concentrations. X-machines: FLAME models agents as communicating X-machines. Here, we defined only one agent (or x-machine): hpsc (human pluripotent stem cell) that had characteristics like id, type (pluripotent, ectodermal, etc), gene activation status (gen_net), signal concentration (chem_sensing), spatial coordinates, number of neighbours (monolayer and edge bonds), and cell cycle. This file also lists the various functions these agents will perform as well as the messages they can exchange with each other. final_functions.c This file details the agent functions that were specified in the .xml file. It contains the source code for how agent functions will be implemented. Refer to annotations in the .c file for more details. Folder ‘its’ containing the initial file 0.xml This initial file contains information regarding the number of agents at the starting point of the simulation and their id, type, and spatial coordinates, etc. The spatial coordinates of the agents were set randomly. Multiple .txt files containing concentrations of BMP4, NOGGIN, WNT3, and DKK at various time points (12 h, 24 h, 36 h, and 48 h) These files contain the spatial concentrations of BMP4 & NOGGIN (12 h) and WNT3 & DKK (24 h, 36 h, 48 h) that were computed in COMSOL. When the model was simulated for the first time, these files were generated in COMSOL after every 30 iterations (equivalent to 12 h of physical time) based on the agent phenotype and position by freezing the agent-based model (refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004, for details). However, we are providing these files so that the user can execute the code fully. These .txt files MUST stay in the same folder as the model. Other files In this folder, we are providing WNT3/DKK concentration profiles equivalent to 12 h when differentiation is induced via WNT3 alone (i.e. the BMP4− simulation). This file (12h_wnt3_dkk_data.txt) MUST stay in the same folder as the model when simulating patterning as triggered by WNT3 (in which case do not use the .txt files specified in the preceding bullet point). Relevant Command Lines To parse the GARMEN.xml file: Go to the xparser folder (wherever you decide to store it), xparser> xparser ../model/GARMEN.xml To compile the files: Go to the model folder, model_folder> make To simulate the model: Stay in the model folder, model_folder> main 180 its\0.xml This command will simulate the model for 180 iterations (equivalent to 72 hours of physical time). This includes the first 60 iterations where agents are undergoing proliferation (equivalent to 24 hours pre-differentiation) followed by 120 iterations capturing differentiation and pattern-emergence (equivalent to 48 hours following induction of differentiation). All output files will be stored in the folder entitled ‘its’. Additional requirements You will need to install MinGW and change the Environment Settings to add MinGW to the path. Refer to www.flame.ac.uk/docs/install.html for instructions. COMSOL: The virtual microenvironment We simulated the signal gradients via the RD paradigm using Gierer-Meinhardt reaction-kinetics (Gierer and Meinhardt, 1972, https://doi.org/10.1007/BF00289234) in COMSOL Multiphysics v5.2a (COMSOL, MA, USA). We used the Coefficient Form PDEs module to simulate the RD equations. In the file, entitled the-virtual-microenvironment-nonDimensional.mph, Analytics 1, 2, 5, and 6 represent the non-dimensional equations that were solved. Refer to Equation 9 in Supplemental Experimental Procedures (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004). D_NOG, D_BMP, D_Wnt, and D_Dkk represent the effective diffusivities of NOGGIN, BMP4, WNT3, and DKK, respectively. They were quantified such that D_BMP:D_NOG = 0.4 and D_Wnt:D_Dkk = 0.36. Refer to Supplemental Experimental Procedures (Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004) for details. We constructed the non-dimensional virtual twin of a single well of a microtiter plate containing multiple human pluripotent stem cell colonies. We used tetrahedral meshes of size 20 µm inside the cellular colonies and 30 µm outside to discretise the geometry. Initial BMP4, NOGGIN, and DKK concentrations were set randomly with a uniform distribution. The initial concentration of WNT3 was tied to that of BMP4 (for the cases where differentiation was initiated via BMP4). We employed zero-flux boundary conditions. Following simulations, we exported data from COMSOL, assimilated it in a .txt format, and fed it to the agent-based model (relevant files have been uploaded). Data flow between the ABM and COMSOL modules was manual. For details, refer to Kaul et al., 2022, https://doi.org/10.1016/j.stemcr.2022.10.004. COMSOL Files We are providing the file entitled the-virtual-microenvironment-nonDimensional.mph that contains modules to simulate BMP4-NOGGIN and WNT3-DKK dynamics. Model steady state is achieved equivalent to 12 h of physical time. The geometry created is non-dimensional. SUPPORT Contact Himanshu Kaul at himanshu.kaul@leicester.ac.uk for queries related to this model FLAME related queries should be directed to flame-dev@googlegroups.com

Deletion of caudal/cdx genes alters hox gene expression and causes defects in posterior tissues and hematopoiesis. Yet, the defects in hox gene expression only partially explain these phenotypes. To gain deeper insight into Cdx4 function, we performed ChIP-seq combined with gene expression profiling in zebrafish, and identified the transcription factor spalt-like 4 (sall4) as a Cdx4 target. ChIP-seq revealed that Sall4 bound to its own gene locus and the cdx4 locus. Expression profiling showed that Cdx4 and Sall4 co-regulate genes such as hox, scl, and lmo2 that initiate hematopoiesis. Combined cdx4/sall4 gene knock-down impairs erythropoiesis, and overexpression of the Cdx4 and Sall4 target genes scl and lmo2 together rescued the erythroid program. These findings suggest that auto- and cross- regulation of Cdx4 and Sall4 establish a stable molecular circuit in mesoderm that facilitates the activation of the blood-specific program as development proceeds. ChIP-seq was performed against Cdx4, Sall4, H3K27ac, and H3K4me3 in bud-stage zebrafish embryos. Input material was sequenced as controls.