µMatch: 3D shape correspondence for biological image data

Modern microscopy technologies allow imaging biological objects in 3D over a wide range of spatial and temporal scales, opening the way for a quantitative assessment of morphology. However, establishing a correspondence between objects to be compared, a first necessary step of most shape analysis workflows, remains challenging for soft-tissue objects without striking features allowing them to be landmarked. To address this issue, we introduce the μMatch 3D shape correspondence pipeline. μMatch implements a state-of-the-art correspondence algorithm initially developed for computer graphics and packages it in a streamlined pipeline including tools to carry out all steps from input data pre-processing to classical shape analysis routines. Importantly, μMatch does not require any landmarks on the object surface and establishes correspondence in a fully automated manner. Our open-source method is implemented in Python and can be used to process collections of objects described as triangular meshes. We quantitatively assess the validity of μMatch relying on a well-known benchmark dataset and further demonstrate its reliability by reproducing published results previously obtained through manual landmarking

7.胃管アレルギーに於ける腸粘膜スメアーの研究(第二報)(第415回千葉医学会例会,第63回日本小児科学会千葉地方会総会)

Extended representative data set of lysosomal positioning. (a) Schematic representation of the feature “MAX Contour Position” used to quantify lysosomal positioning. (b) Representative LAMP1 immunofluorescence images for different ranges of the feature “LAMP1 MAX Contour Position” in HeLa cells treated as in Fig. 6. (JPG 3911 kb

A dynamic and expandable digital 3D-atlas maker for monitoring the temporal changes in tissue growth during hindbrain morphogenesis

Reconstruction of prototypic three-dimensional (3D) atlases at the scale of whole tissues or organs requires specific methods to be developed. We have established a digital 3D-atlas maker (DAMAKER) and built a digital 3D-atlas to monitor the changes in the growth of the neuronal differentiation domain in the zebrafish hindbrain upon time. DAMAKER integrates spatial and temporal data of cell populations, neuronal differentiation and brain morphogenesis, through in vivo imaging techniques paired with image analyses and segmentation tools. First, we generated a 3D-reference from several imaged hindbrains and segmented them using a trainable tool; these were aligned using rigid registration, revealing distribution of neuronal differentiation growth patterns along the axes. Second, we quantified the dynamic growth of the neuronal differentiation domain by in vivo neuronal birthdating experiments. We generated digital neuronal birthdating 3D-maps and revealed that the temporal order of neuronal differentiation prefigured the spatial distribution of neurons in the tissue, with an inner-outer differentiation gradient. Last, we applied it to specific differentiated neuronal populations such as glutamatergic and GABAergic neurons, as proof-of-concept that the digital birthdating 3D-maps could be used as a proxy to infer neuronal birthdate. As this protocol uses open-access tools and algorithms, it can be shared for standardized, accessible, tissue-wide cell population atlas construction.This work was funded by grants PGC2018-095663-B-I00 and RED2018-102553-T to CP and PGC2018-101643-B-I00 to FU, from Spanish MCIN/AEI (DOI: 10.13039/501100011033) and Fondo Europeo de Desarrollo Regional (FEDER). Department of Medicine and Life Sciences UPF is a Unidad de Excelencia María de Maeztu funded by the Spanish MCIN/AEI (DOI: 10.13039/501100011033), Ref CEX2018-000792-M. CP is a recipient of ICREA Academia award (Generalitat de Catalunya)

Agent-Based Modeling of Mitochondria Links Sub-Cellular Dynamics to Cellular Homeostasis and Heterogeneity.

Mitochondria are semi-autonomous organelles that supply energy for cellular biochemistry through oxidative phosphorylation. Within a cell, hundreds of mobile mitochondria undergo fusion and fission events to form a dynamic network. These morphological and mobility dynamics are essential for maintaining mitochondrial functional homeostasis, and alterations both impact and reflect cellular stress states. Mitochondrial homeostasis is further dependent on production (biogenesis) and the removal of damaged mitochondria by selective autophagy (mitophagy). While mitochondrial function, dynamics, biogenesis and mitophagy are highly-integrated processes, it is not fully understood how systemic control in the cell is established to maintain homeostasis, or respond to bioenergetic demands. Here we used agent-based modeling (ABM) to integrate molecular and imaging knowledge sets, and simulate population dynamics of mitochondria and their response to environmental energy demand. Using high-dimensional parameter searches we integrated experimentally-measured rates of mitochondrial biogenesis and mitophagy, and using sensitivity analysis we identified parameter influences on population homeostasis. By studying the dynamics of cellular subpopulations with distinct mitochondrial masses, our approach uncovered system properties of mitochondrial populations: (1) mitochondrial fusion and fission activities rapidly establish mitochondrial sub-population homeostasis, and total cellular levels of mitochondria alter fusion and fission activities and subpopulation distributions; (2) restricting the directionality of mitochondrial mobility does not alter morphology subpopulation distributions, but increases network transmission dynamics; and (3) maintaining mitochondrial mass homeostasis and responding to bioenergetic stress requires the integration of mitochondrial dynamics with the cellular bioenergetic state. Finally, (4) our model suggests sources of, and stress conditions amplifying, cell-to-cell variability of mitochondrial morphology and energetic stress states. Overall, our modeling approach integrates biochemical and imaging knowledge, and presents a novel open-modeling approach to investigate how spatial and temporal mitochondrial dynamics contribute to functional homeostasis, and how subcellular organelle heterogeneity contributes to the emergence of cell heterogeneity

4D reconstruction of murine developmental trajectories using spherical harmonics

Normal organogenesis cannot be recapitulated in vitro for mammalian organs, unlike in species including Drosophila and zebrafish. Available 3D data in the form of ex vivo images only provide discrete snapshots of the development of an organ morphology. Here, we propose a computer-based approach to recreate its continuous evolution in time and space from a set of 3D volumetric images. Our method is based on the remapping of shape data into the space of the coefficients of a spherical harmonics expansion where a smooth interpolation over time is simpler. We tested our approach on mouse limb buds and embryonic hearts. A key advantage of this method is that the resulting 4D trajectory can take advantage of all the available data while also being able to interpolate well through time intervals for which there are little or no data. This allows for a quantitative, data-driven 4D description of mouse limb morphogenesis

Impact of mitochondrial directionality on mitochondrial mass subpopulation distributions.

<p>(A) Mitochondrial movements are indicated by projecting 25 images, one every 100 time steps, representing a total of ~40 minutes of a simulation. Time points are indicated according to color-code. Line graphs display mean values and standard deviation (shaded regions) of 100 simulations for mitochondrial population with free movement. The initial mass value was fixed to 300 and the angle ϑ₁ ϵ [0°, 360°]. The plot represents the evolution of three mitochondrial subpopulations, small mass (orange line), medium mass (blue line) and large mass (green line), normalized to the total mass at time point 0. Small graphs underneath show the evolution of the first derivative of mitochondria with large mass (green line). The simulations were performed for a total time of 2 hours. (B) Same as (A) but with restricted movement, ϑ₁ ϵ [0°, 10°].</p

Interaction between mitochondrial health and cellular energetic state, based on the energetic stress.

<p>(A) Evolution of mitochondrial mass (grey lines) over time subjected to four different damage signals (black line). Line graphs display mean value and standard deviation (shaded regions) of 100 simulations for an initial total mitochondrial mass of 300 normalized to the total mass at time point 0. Parameter values obtained from the optimization were used, in particular, fusion probability = fission probability = 50%, fusion frequency = fission frequency = 5 minutes, biogenesis probability = 22.1%, biogenesis frequency = 28.9 minutes, receptor threshold = 12 minutes, damage threshold = 12.4 minutes, degradation frequency = 5.7 minutes and damage probabilities: 0%, 10%, 30% and 50%. Simulations were performed for a total of 12 hours. Blue region indicates the area in which biogenesis is dominant while red region indicates the area in which degradation is dominant. (B) Schematic representation of the link between the number of healthy and damaged mitochondria with the energetic stress (E_S) of the cell. (C) Schematic representation of the connection between energetic (red line) stress and fission (dark green line) and biogenesis (orange line) probabilities. (D) Line graphs display mean value and standard deviation (shaded regions) of 100 simulations of probabilities of fusion (dark green line), fission (light green line), biogenesis (orange line) and damage signal (black line). Initial values assigned to the model: fusion probability = fission probability = 50%, fusion frequency = fission frequency = 5 minutes, biogenesis probability = 22.1%, biogenesis frequency = 28.9 minutes, receptor threshold = 12 minutes, damage threshold = 12.4 minutes, degradation frequency = 5.7 minutes. All the simulations were performed for a total time of 24 hours. (E) Line graphs display mean value and standard deviation (shaded area) of 100 simulations for an initial mitochondrial mass of 300 of the total mitochondrial mass (grey line) and three mitochondria subpopulations: small mass (orange line), medium mass (blue line) and big mass (green line) subjected to five different damage signals (10% and 20%) every two hours for one hour. The plot represents the evolution of the total mass and of three mitochondrial subpopulations normalized to the total mass at time point 0. Initial values assigned to the model: fusion probability = fission probability = 50%, fusion frequency = fission frequency = 5 minutes, biogenesis probability = 22.1%, biogenesis frequency = 28.9 minutes, receptor threshold = 12 minutes, damage threshold = 12.4 minutes, degradation frequency = 5.7 minutes. All the simulations were performed for a total time of 24 hours. (F) Line graphs display mean value and standard deviation (shaded regions) of 100 simulations of bioenergetics stress (red line). Initial values assigned to the model: fusion probability = fission probability = 50%, fusion frequency = fission frequency = 5 minutes, biogenesis probability = 22.1%, biogenesis frequency = 28.9 minutes, receptor threshold = 12 minutes, damage threshold = 12.4 minutes, degradation frequency = 5.7 minutes. All the simulations were performed for a total time of 24 hours. (G) Same as (D) but with mitochondria subjected to five different damage signals (40%) every two hours for one hour. (H) Same as (E) but with mitochondria subjected to five different damage signals (40%) every two hours for one hour. (I) Same as (F) but with mitochondria subjected to five different damage signals (40%) every two hours for one hour.</p

Sensitivity analysis of the transmission dynamics model.

<p>(A) Effect of parameters perturbation on transmission dynamic. Bar graph represents Partial Rank Correlation Coefficient values for each parameter-output response pairing resulting from a Latin-hypercube sampling of 500 parameter value sets (parameter ranges are described in section “Transmission dynamics of mitochondrial network connectivity”). For each parameter set the model was performed over 6 hours. (B) Results of the eFast method used to partition variance in simulation results between parameters. Solid Bars: sensitivity index (Si)–the fraction of output variance explained by the value assigned to that parameter when parameter of interest; Dashed Bars: total sensitivity index (STi)–the variance caused by higher-order non-linear effects between that parameter and the other explored (includes value of Si). Error bars are standard error over four resample curves. Parameter values sets were generated using the sinusoidal sampling approach. 65 parameters were obtained from each of the 4 resampling curves, producing 2080 parameter value sets (260 per parameter).</p