Experimental Biology | Mahyar's world 🌏

Spatiotemporal patterns in the embryonic heart

Mon, 25 Sep 2023 00:00:00 +0000

Authors: Nazanin Ebrahimi, Mahyar Osanlouy, Chris Bradley, Fabiana Kubke, Dane Gerneke, Peter Hunter
Publication: iScience. (July 2022)
Dataset: Mendeley Data

Overview

This study presents an innovative hybrid experimental-computational pipeline to investigate the spatiotemporal growth patterns underlying cardiac C-looping in embryonic chick hearts. C-looping is a critical phase in heart development where the straight heart tube transforms into a C-shaped structure, setting the stage for proper chamber formation. Abnormalities during this process are linked to congenital heart defects. The work combines multi-scale imaging, deep learning-based cell segmentation, and biomechanical modeling to bridge cellular dynamics with tissue-level deformations, offering new insights into the mechanisms driving heart morphogenesis.

Key Contributions

First 3D multi-scale dataset of C-looping hearts, integrating cell-to-organism level data from confocal microscopy and micro-CT.
Fully automated 3D myocardial cell segmentation using a custom convolutional neural network (CNN), achieving a Dice coefficient of 0.91 ± 0.1.
Finite Element (FE) biomechanical models capturing spatiotemporal heart geometry changes across four developmental timepoints.
Variance-driven analysis revealing how inter-cellular space (ICS) and cellular heterogeneity contribute to tissue growth.
Open-source pipeline for integrating cellular features with tissue-level kinematics, enabling future studies on cardiac morphogenesis.

Methodology

Experimental Workflow

Multi-modal imaging:
- Confocal microscopy: Whole-mount staining (WGA, NCAM-1, DAPI) provided 3D cell-resolution images of myocardial cells.
- Micro-CT scanning: Sub-micron resolution imaging contextualized heart geometry within the entire embryo.
- Optical clearing: Enabled high-resolution imaging while preserving 3D architecture.
Temporal staging: Embryos were ordered developmentally using anatomical landmarks and dorsal detachment metrics.

Computational Workflow

3D Deep Learning for Cell Segmentation:
- A 3D U-Net architecture was trained to segment individual myocardial cells from confocal stacks.
- Achieved 91% Dice similarity compared to manual segmentation, reducing processing time from 8 hours to seconds per 100 cells.
- Addressed memory constraints via a traceable slicing-merging algorithm for large-scale image processing.
Finite Element Modeling:
- Anatomically accurate FE meshes were fitted to heart geometries using OpenCMISS.
- RMS fitting error < 3 μm enabled precise representation of tissue deformation.
Spatiotemporal Analysis:
- Cellular features (volume, anisotropy, orientation) were mapped onto FE meshes.
- General Linear Model (GLM) linked tissue growth to cellular dynamics (cell number, ICS volume, anisotropy).

Results

Differential Growth Patterns:
- Ventral regions showed ~2x higher tissue growth than dorsal regions, driven by increased cell proliferation and ICS expansion.
- Outer curvature cells exhibited circumferential alignment, while inner curvature cells remained isotropic.
Role of Inter-Cellular Space (ICS):
- ICS accounted for 30–45% of tissue volume changes, highlighting its role in accommodating growth.
- Ventral ICS volume increased by 62% during bending phases.
Variance as a Developmental Signal:
- Ring-shaped variance patterns in cell volume/anisotropy emerged around the outer curvature, suggesting mechanical feedback loops.
- High-variance regions correlated with zones of rapid tissue remodeling.
Tissue-Cell Dynamics:
- GLM analysis explained 70% of tissue growth variance, with cell number (β = 0.48, p < 0.001) and ICS (β = 0.32, p < 0.01) as key predictors.
- Cell orientation aligned with tissue deformation vectors during rotation (r > 0.8) but not bending phases.

Implications

Mechanistic Insights: Challenges the “differential growth hypothesis” by showing ICS and cellular heterogeneity are equally critical to looping.
Toolkit for Developmental Biology: The pipeline is extensible to other organs/species, enabling studies on how cellular noise shapes tissue patterning.
Clinical Relevance: Provides a framework to explore how genetic mutations disrupt growth coordination, informing congenital heart defect research.
AI/ML Impact: Demonstrates the power of deep learning in automating large-scale 3D biological image analysis.

The SPARC DRC

Wed, 20 Sep 2023 00:00:00 +0000

Authors: Mahyar Osanlouy, Anita Bandrowski, Bernard de Bono, David Brooks, Antonino M. Cassarà, Richard Christie, Nazanin Ebrahimi, Tom Gillespie, Jeffrey S. Grethe, Leonardo A. Guercio, Maci Heal, Mabelle Lin, Niels Kuster, Maryann E. Martone, Esra Neufeld, David P. Nickerson, Elias G. Soltani, Susan Tappan, Joost B. Wagenaar, Katie Zhuang, Peter J. Hunter
Publication: Front. Physiol. (June 2021)
Website: SPARC Science
Interactive 3D Models & Data: MAP Web App

Overview

The NIH Common Fund’s Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is an ambitious initiative aimed at revolutionizing our understanding of the autonomic nervous system (ANS) and its role in organ function. As the lead developer for the Mapping Core (MAP-Core) of the SPARC Data and Resource Center (DRC), I collaborated with an interdisciplinary team to create a comprehensive framework for curating, visualizing, and analyzing ANS data across different species.

The SPARC DRC serves as the technological foundation for the SPARC program, providing researchers worldwide with access to standardized experimental data, computational models, and visualization tools through a unified web portal (https://sparc.science). Our work addresses a critical gap in biomedical research: the need for standardized, reproducible, and interoperable data and computational resources to advance our understanding of neural control of organ function and develop effective neuromodulation therapies.

Key Contributions

Our team developed several groundbreaking innovations that form the technological foundation of the SPARC program:

1. FAIR-Compliant Data Management Platform

We implemented comprehensive data standards and workflows to ensure all SPARC data adheres to FAIR principles (Findable, Accessible, Interoperable, and Reusable). This includes standardized metadata schemas, dataset organization structures, and persistent identifiers for long-term data discoverability.

2. Knowledge Management System

We created a sophisticated knowledge graph integrating anatomical relationships, neural connectivity, and semantic annotations from multiple species. This system enables powerful cross-species comparisons and semantic searches that were previously impossible with traditional database approaches.

3. Multi-Scale Anatomical Mapping Framework

One of our most significant innovations is the development of 3D material coordinate systems (“scaffolds”) for organs and bodies. These scaffolds provide a revolutionary solution to a complex problem: how to map and compare data from organs that undergo substantial deformation (e.g., beating hearts, inflating lungs) across different experimental conditions and species.

4. Computational Modeling Platform

We developed o²S²PARC (“open, online simulations for SPARC”), an online computational environment that enables researchers to analyze data, develop models, and simulate neuromodulation scenarios using cloud computing resources.

Methodology

Developing Standardized Data Workflows

We established comprehensive curation and data standards to ensure consistency across all SPARC datasets. Key aspects include:

Implementation of the SPARC Dataset Structure (SDS) for consistent file organization
Development of the Minimal Information Standard (MIS) for semantic metadata
Integration with community ontologies and specialized annotation tools
Creation of SODA (Software for Organizing Data Automatically) to assist researchers in preparing data submissions

Creating Neural Connectivity Maps

To represent the complex connectivity of the ANS across different species, we:

Utilized the ApiNATOMY toolkit to create topological and semantic models of neural pathways
Generated interactive flatmap diagrams that visualize neural connectivity in 2D for multiple species
Linked these maps to the SPARC Knowledge Graph for data integration

3D Scaffold Framework for Anatomical Mapping

A cornerstone of our innovation is the development of the 3D scaffold system. This methodology addresses the fundamental challenge of mapping data from organs that undergo significant deformation. Our approach includes:

1. Material Coordinate System Development

We developed Scaffold-Maker, specialized CAD software that creates 3D material coordinate systems for body organs. These scaffolds use finite element meshes with smooth interpolation to represent complex anatomical geometries.

2. Cross-Species Anatomical Representation

We designed the scaffolds to accommodate topological differences between species, allowing for meaningful cross-species comparisons despite anatomical variations. For example, we created heart scaffolds that accommodate different numbers of pulmonary veins in humans (4), pigs (2), and rats (3).

3. Deformation-Invariant Mapping

Our material coordinate system maintains consistent references to tissue locations regardless of organ deformation. This allows data to be mapped and compared across different physiological states (e.g., cardiac contraction phases).

4. Integration with Whole-Body Models

We developed methods to embed organ scaffolds within whole-body coordinate systems, facilitating multi-scale data integration from cellular to organism levels.

Results

Our work has produced several tangible outcomes that demonstrate the power of the SPARC DRC infrastructure:

Comprehensive ANS Data Resource

The SPARC Portal now serves as the world’s most comprehensive resource for standardized ANS data, with:

Curated datasets from multiple species and organ systems
Rich semantic annotations linking experimental data to anatomical locations
Standardized protocols and metadata ensuring reproducibility

Species-Specific Anatomical Scaffolds

We have successfully created detailed 3D scaffolds for key organs across multiple species:

Heart scaffolds for human, pig, and rat with species-specific topologies
Colon scaffolds capturing species variations in haustra and taeniae coli structures
Whole-body scaffolds with embedded organ systems
Bladder, stomach, and lung scaffolds for integrating diverse experimental data

Neural Mapping Demonstrations

We have demonstrated successful registration of neural data to our scaffold systems:

Mapping of intrinsic cardiac neurons (ICNs) from rat hearts to a standardized cardiac scaffold
Integration of data from multiple specimens into “integrative” scaffolds
Preservation of spatial relationships across deformed organ states

Computational Modeling Environment

The o²S²PARC platform now provides:

Online access to sophisticated computational models
Tools for simulating neuromodulation effects
Reproducible workflows for data analysis
Collaborative environments for model development

Implications

The SPARC DRC infrastructure we’ve developed has significant implications for both basic science and clinical applications:

Advancing Basic Science

Our work enables new approaches to understanding the ANS by:

Facilitating cross-species comparisons of neural circuitry
Providing standardized reference systems for integrating multi-scale data
Creating a foundation for quantitative, data-driven neural mapping
Enabling reproducible computational analyses

Clinical Applications

The infrastructure directly supports the development of bioelectronic medicine by:

Providing detailed anatomical maps for targeting neuromodulation
Enabling simulation of device effects on neural activity
Supporting the optimization of stimulation parameters
Facilitating translation between animal models and human applications

Beyond SPARC

The methodologies and tools we’ve developed have applications beyond the ANS:

The scaffold concept can be applied to any deformable biological system
Our FAIR data management approaches set standards for other large-scale initiatives
The o²S²PARC computational platform can support diverse modeling applications

Conclusion

The SPARC DRC represents a paradigm shift in how we approach the study of the autonomic nervous system. By creating standardized frameworks for data organization, anatomical mapping, and computational modeling, we’ve laid the groundwork for a new era of quantitative, integrative neuroscience research that can directly inform the development of bioelectronic therapies. Our 3D scaffold methodology, in particular, solves the fundamental challenge of mapping biological data in deformable systems, enabling unprecedented integration of experimental data across scales, species, and physiological states.