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Quantitative analysis of tissue deformation dynamics reveals three characteristic growth modes and globally aligned anisotropic tissue deformation during chick limb development.

Morishita Y, Kuroiwa A, Suzuki T - Development (2015)

Bottom Line: We also found anisotropic tissue deformation along the proximal-distal axis.Morphogenetic simulation and experimental studies suggested that this directional tissue elongation, and not local growth, has the greatest impact on limb shaping.Our study marks a pivotal point for multi-scale system understanding in vertebrate development.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Developmental Morphogeometry, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan RIKEN Center for Developmental Biology, Kobe 650-0047, Japan yoshihiro.morishita@riken.jp suzuki.takayuki@j.mbox.nagoya-u.ac.jp.

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Estimation of tissue deformation dynamics for chick hindlimb development from dye-marking data. (A) Dynamic changes in chick hindlimb morphology from stage 20 to stage 30. (B) Between 20 and 50 markers were randomly injected into the mesenchyme (right) at a depth of ∼100 µm from the dorsal ectoderm (left). (C) From positional data on markers in the AP-PD plane (green circles), the 2D deformation map of the dorsal half of a limb bud was estimated for each 12-h time interval (magenta lattice). The number of markers for estimating the map for each time interval was: n=140 (stage 20-22), n=203 (stage 22-23), n=224 (stage 23-24), n=288 (stage 24-25), n=231 (stage 25-27), n=290 (stage 27-28), n=306 (stage 28-29), n=321 (stage 29-30). Scale bars: 1000 µm. (D) Spatial pattern of D-V thickness was measured with an OPT scanner for each developmental stage (every 12 h). (E) Although not all data were necessarily on the frontal plane (ΠD) on which the 2D-deformation map is estimated, the distance between the labeled plane (green) and ΠD (gray) is short, enabling us to estimate precisely the map by using the data from markers after projecting them to ΠD. (F) The goodness of estimation can be evaluated as the prediction error for the estimated maps (cross-validation). (G) Prediction errors of estimated maps along the P-D and A-P axes for different developmental stages. (H) The growth rate along the D-V axis at each point Xp can be approximately calculated as h(ϕ2D(Xp))/H(Xp).
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DEV109728F2: Estimation of tissue deformation dynamics for chick hindlimb development from dye-marking data. (A) Dynamic changes in chick hindlimb morphology from stage 20 to stage 30. (B) Between 20 and 50 markers were randomly injected into the mesenchyme (right) at a depth of ∼100 µm from the dorsal ectoderm (left). (C) From positional data on markers in the AP-PD plane (green circles), the 2D deformation map of the dorsal half of a limb bud was estimated for each 12-h time interval (magenta lattice). The number of markers for estimating the map for each time interval was: n=140 (stage 20-22), n=203 (stage 22-23), n=224 (stage 23-24), n=288 (stage 24-25), n=231 (stage 25-27), n=290 (stage 27-28), n=306 (stage 28-29), n=321 (stage 29-30). Scale bars: 1000 µm. (D) Spatial pattern of D-V thickness was measured with an OPT scanner for each developmental stage (every 12 h). (E) Although not all data were necessarily on the frontal plane (ΠD) on which the 2D-deformation map is estimated, the distance between the labeled plane (green) and ΠD (gray) is short, enabling us to estimate precisely the map by using the data from markers after projecting them to ΠD. (F) The goodness of estimation can be evaluated as the prediction error for the estimated maps (cross-validation). (G) Prediction errors of estimated maps along the P-D and A-P axes for different developmental stages. (H) The growth rate along the D-V axis at each point Xp can be approximately calculated as h(ϕ2D(Xp))/H(Xp).

Mentions: We recently developed a novel method that combines snapshot lineage tracing with Bayesian statistical estimation to construct whole-organ deformation maps from limited space-time point data (Morishita and Suzuki, 2014). In this method, a regular lattice enveloping the target organ before deformation is considered, and how the lattice deforms over a given time interval is estimated. Except for positional data for landmarks before and after deformation, all that is required is the biologically plausible assumption that deformation of tissue occurs smoothly. As shown below, applying the method to data for chick limb development, we here estimated the deformation map with high precision and analyzed the deformation dynamics (Figs 2, 3 and 7).Fig. 2.


Quantitative analysis of tissue deformation dynamics reveals three characteristic growth modes and globally aligned anisotropic tissue deformation during chick limb development.

Morishita Y, Kuroiwa A, Suzuki T - Development (2015)

Estimation of tissue deformation dynamics for chick hindlimb development from dye-marking data. (A) Dynamic changes in chick hindlimb morphology from stage 20 to stage 30. (B) Between 20 and 50 markers were randomly injected into the mesenchyme (right) at a depth of ∼100 µm from the dorsal ectoderm (left). (C) From positional data on markers in the AP-PD plane (green circles), the 2D deformation map of the dorsal half of a limb bud was estimated for each 12-h time interval (magenta lattice). The number of markers for estimating the map for each time interval was: n=140 (stage 20-22), n=203 (stage 22-23), n=224 (stage 23-24), n=288 (stage 24-25), n=231 (stage 25-27), n=290 (stage 27-28), n=306 (stage 28-29), n=321 (stage 29-30). Scale bars: 1000 µm. (D) Spatial pattern of D-V thickness was measured with an OPT scanner for each developmental stage (every 12 h). (E) Although not all data were necessarily on the frontal plane (ΠD) on which the 2D-deformation map is estimated, the distance between the labeled plane (green) and ΠD (gray) is short, enabling us to estimate precisely the map by using the data from markers after projecting them to ΠD. (F) The goodness of estimation can be evaluated as the prediction error for the estimated maps (cross-validation). (G) Prediction errors of estimated maps along the P-D and A-P axes for different developmental stages. (H) The growth rate along the D-V axis at each point Xp can be approximately calculated as h(ϕ2D(Xp))/H(Xp).
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4419272&req=5

DEV109728F2: Estimation of tissue deformation dynamics for chick hindlimb development from dye-marking data. (A) Dynamic changes in chick hindlimb morphology from stage 20 to stage 30. (B) Between 20 and 50 markers were randomly injected into the mesenchyme (right) at a depth of ∼100 µm from the dorsal ectoderm (left). (C) From positional data on markers in the AP-PD plane (green circles), the 2D deformation map of the dorsal half of a limb bud was estimated for each 12-h time interval (magenta lattice). The number of markers for estimating the map for each time interval was: n=140 (stage 20-22), n=203 (stage 22-23), n=224 (stage 23-24), n=288 (stage 24-25), n=231 (stage 25-27), n=290 (stage 27-28), n=306 (stage 28-29), n=321 (stage 29-30). Scale bars: 1000 µm. (D) Spatial pattern of D-V thickness was measured with an OPT scanner for each developmental stage (every 12 h). (E) Although not all data were necessarily on the frontal plane (ΠD) on which the 2D-deformation map is estimated, the distance between the labeled plane (green) and ΠD (gray) is short, enabling us to estimate precisely the map by using the data from markers after projecting them to ΠD. (F) The goodness of estimation can be evaluated as the prediction error for the estimated maps (cross-validation). (G) Prediction errors of estimated maps along the P-D and A-P axes for different developmental stages. (H) The growth rate along the D-V axis at each point Xp can be approximately calculated as h(ϕ2D(Xp))/H(Xp).
Mentions: We recently developed a novel method that combines snapshot lineage tracing with Bayesian statistical estimation to construct whole-organ deformation maps from limited space-time point data (Morishita and Suzuki, 2014). In this method, a regular lattice enveloping the target organ before deformation is considered, and how the lattice deforms over a given time interval is estimated. Except for positional data for landmarks before and after deformation, all that is required is the biologically plausible assumption that deformation of tissue occurs smoothly. As shown below, applying the method to data for chick limb development, we here estimated the deformation map with high precision and analyzed the deformation dynamics (Figs 2, 3 and 7).Fig. 2.

Bottom Line: We also found anisotropic tissue deformation along the proximal-distal axis.Morphogenetic simulation and experimental studies suggested that this directional tissue elongation, and not local growth, has the greatest impact on limb shaping.Our study marks a pivotal point for multi-scale system understanding in vertebrate development.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Developmental Morphogeometry, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan RIKEN Center for Developmental Biology, Kobe 650-0047, Japan yoshihiro.morishita@riken.jp suzuki.takayuki@j.mbox.nagoya-u.ac.jp.

Show MeSH
Related in: MedlinePlus