""" Multicellular TFM (intestinal organoids) ============================================== This example evaluates contractile forces of an individual intestinal organoid embedded in 1.2mg/ml collagen imaged with confocal reflection microscopy. The relaxed stacks were recorded after Triton x-100 treatment. We evaluate the contraction between ~23h in collagen compared to the state after drug relaxation (indicated at the end of the video). This example can also be evaluated with the graphical user interface. .. figure:: ../images/examples/organoid_tfm/organoid.gif :scale: 50% :align: center """ import saenopy # sphinx_gallery_thumbnail_path = '../../saenopy/img/thumbnails/StainedOrganoid_icon.png' # %% # Downloading the example data files # ---------------------------------- # An individual intestinal organoid is recorded at one positions (Pos07) in collagen 1.2 mg/ml. # Images are recorded in confocal reflection channel (ch00) after ~23h in collagen (t50) and after drug relaxation # approx. 2 hours later (t6). # The lower time index t6 is due of the start of a new image series and refers to the image AFTER relaxation. # The stack has 52 z positions (z00-z51). T # # :: # # 4_OrganoidTFM # ├── Pos007_S001_t50_z00_ch00.tif # ├── Pos007_S001_t50_z01_ch00.tif # ├── Pos007_S001_t50_z02_ch00.tif # ├── ... # ├── Pos007_S001_t6_z00_ch00.tif # ├── Pos007_S001_t6_z01_ch00.tif # ├── Pos007_S001_t6_z02_ch00.tif # └── ... # # download the data saenopy.load_example("OrganoidTFM") # %% # Loading the Stacks # ------------------ # # Saenopy is very flexible in loading stacks from any filename structure. # Here we do not have multiple positions, so we do not need to use and asterisk * for batch processing. # We do not have multiple channels, so we do not need a channel placeholder. # We replace the number of the z slice "z00" with a z placeholder "z{z}" to indicate that this number refers to the # z slice. We do the same for the deformed state and for the reference stack. # load the relaxed and the contracted stack # {z} is the placeholder for the z stack # {c} is the placeholder for the channels # {t} is the placeholder for the time points results = saenopy.get_stacks( '4_OrganoidTFM/Pos007_S001_t50_z{z}_ch00.tif', reference_stack='4_OrganoidTFM/Pos007_S001_t6_z{z}_ch00.tif', output_path='4_OrganoidTFM/example_output', voxel_size=[1.444, 1.444, 1.976]) # %% # Detecting the Deformations # -------------------------- # Saenopy uses 3D Particle Image Velocimetry (PIV) with the following parameters # to calculate matrix deformations between the deformed and relaxed state. # # # +------------------+-------+ # | Piv Parameter | Value | # +==================+=======+ # | element_size | 30 | # +------------------+-------+ # | window_size | 40 | # +------------------+-------+ # | signal_to_noise | 1.3 | # +------------------+-------+ # | drift_correction | True | # +------------------+-------+ # # define the parameters for the piv deformation detection piv_parameters = {'element_size': 30.0, 'window_size': 40.0, 'signal_to_noise': 1.3, 'drift_correction': True} # iterate over all the results objects for result in results: # set the parameters result.piv_parameters = piv_parameters # get count count = len(result.stacks) if result.stack_reference is None: count -= 1 # iterate over all stack pairs for i in range(count): # get two consecutive stacks if result.stack_reference is None: stack1, stack2 = result.stacks[i], result.stacks[i + 1] # or reference stack and one from the list else: stack1, stack2 = result.stack_reference, result.stacks[i] # and calculate the displacement between them result.mesh_piv[i] = saenopy.get_displacements_from_stacks(stack1, stack2, piv_parameters["window_size"], piv_parameters["element_size"], piv_parameters["signal_to_noise"], piv_parameters["drift_correction"]) # save the displacements result.save() # %% # Generating the Finite Element Mesh # ---------------------------------- # Interpolate the found deformations onto a new mesh which will be used for the regularisation. # In this case, we are experimental limited (due to the size, strength and spatial expansion of the organoid and our objective) and can not image the # complete matrix deformation field around the organoid. To obtain higher accuracy for a cropped deformation field, # we perform the force reconstruction in a volume with increased z-height. # # +------------------+------------------+ # | Mesh Parameter | Value | # +==================+==================+ # | element_size | 30 | # +------------------+------------------+ # | mesh_size | (738, 738, 738) | # +------------------+------------------+ # | reference_stack | 'first' | # +------------------+------------------+ # # define the parameters to generate the solver mesh and interpolate the piv mesh onto it mesh_parameters = {'reference_stack': 'first', 'element_size': 30, 'mesh_size': (738, 738, 738)} # iterate over all the results objects for result in results: # correct for the reference state displacement_list = saenopy.subtract_reference_state(result.mesh_piv, mesh_parameters["reference_stack"]) # set the parameters result.mesh_parameters = mesh_parameters # iterate over all stack pairs for i in range(len(result.mesh_piv)): # and create the interpolated solver mesh result.solvers[i] = saenopy.interpolate_mesh(result.mesh_piv[i], displacement_list[i], mesh_parameters) # save the meshes result.save() # %% # Calculating the Forces # ---------------------- # Define the material model and run the regularisation to fit the measured deformations and get the forces. Here we define a low convergence criterion # and let the algorithm run for the maximal amount of steps that we define to 1400. Afterwards, we can check the regularisation_results in the console or # in the graphical user interface. # # +--------------------+---------+ # | Material Parameter | Value | # +====================+=========+ # | k | 6062 | # +--------------------+---------+ # | d_0 | 0.0025 | # +--------------------+---------+ # | lambda_s | 0.0804 | # +--------------------+---------+ # | d_s | 0.034 | # +--------------------+---------+ # # +--------------------------+---------+ # | Regularisation Parameter | Value | # +==========================+=========+ # | alpha | 10**10 | # +--------------------------+---------+ # | step_size | 0.33 | # +--------------------------+---------+ # | max_iterations | 1400 | # +--------------------------+---------+ # | rel_conv_crit | 1e-7 | # +--------------------------+---------+ # # define the parameters to generate the solver mesh and interpolate the piv mesh onto it material_parameters = {'k': 6062.0, 'd_0': 0.0025, 'lambda_s': 0.0804, 'd_s': 0.034} solve_parameters = {'alpha': 10**10, 'step_size': 0.33, 'max_iterations': 1400, 'rel_conv_crit': 1e-7} # iterate over all the results objects for result in results: result.material_parameters = material_parameters result.solve_parameters = solve_parameters for M in result.solvers: # set the material model M.set_material_model(saenopy.materials.SemiAffineFiberMaterial( material_parameters["k"], material_parameters["d_0"], material_parameters["lambda_s"], material_parameters["d_s"], )) # find the regularized force solution M.solve_regularized(alpha=solve_parameters["alpha"], step_size=solve_parameters["step_size"], max_iterations=solve_parameters["max_iterations"], rel_conv_crit=solve_parameters["rel_conv_crit"], verbose=True) # save the forces result.save() # %% # Display Results # ---------------------- # # ToDo # # The reconstructed force field (right) generates a reconstructed deformation field (middle) # that recapitulates the measured matrix deformation field (left). The overall cell contractility is # calculated as all forcecomponents pointing to the force epicenter. #