Periodic Reporting for period 1 - sc4DMap (Spatiotemporal analysis of mammalian embryonic development at single-cell level)
Reporting period: 2022-09-01 to 2024-06-30
Early mammalian embryonic development relies on precise, spatiotemporal regulation of gene expression at the single-cell level. Despite significant progress in developmental biology, it remains a fundamental challenge to understand how individual cell behaviors govern complex processes like gastrulation and organogenesis in these early stages. A key knowledge gap persists in linking the dynamic movements of cells with their gene expression states throughout development.
Recent advances in two complementary technologies—spatial transcriptomics at the single-cell level and in toto imaging—provide powerful tools to explore this gap. Spatial transcriptomics enables the examination of gene expression in specific locations within tissues, while in toto imaging captures the dynamic behavior of cells as they transition from single-cell stages to fully formed organisms. However, these techniques have not yet been combined at scale in more complex mammalian systems, a limitation that has hindered a more comprehensive understanding of early developmental processes.
This project aims to address this challenge by integrating spatial transcriptomics with in toto imaging to investigate early mammalian development. For the first time, real-time tracking of cellular dynamics will be linked with spatiotemporal gene expression profiles across the developing embryo, offering unprecedented insights into how cells move, interact, and regulate their own gene expression. This integrative approach will allow us to map cellular behaviors and their associated gene expression patterns, ultimately revealing the mechanisms that determine cell fate.
The core objective is to develop a novel framework for the integration of these two technologies, resulting in the first cellular-resolution, four-dimensional gene expression map of mammalian embryo development. This map will reflect both cellular dynamics and gene expression at the single-cell level, providing a foundational resource for future studies. In the longer term, this work will set the stage for the broader integration of multi-omics approaches, which have been increasingly explored in single-cell studies but remain underdeveloped in dynamic, multi-cellular contexts.
By advancing our understanding of early embryonic development, this project will provide valuable insights into fundamental biological processes and offer new perspectives on how cell fate is determined in mammalian systems.
Recent advances in two complementary technologies—spatial transcriptomics at the single-cell level and in toto imaging—provide powerful tools to explore this gap. Spatial transcriptomics enables the examination of gene expression in specific locations within tissues, while in toto imaging captures the dynamic behavior of cells as they transition from single-cell stages to fully formed organisms. However, these techniques have not yet been combined at scale in more complex mammalian systems, a limitation that has hindered a more comprehensive understanding of early developmental processes.
This project aims to address this challenge by integrating spatial transcriptomics with in toto imaging to investigate early mammalian development. For the first time, real-time tracking of cellular dynamics will be linked with spatiotemporal gene expression profiles across the developing embryo, offering unprecedented insights into how cells move, interact, and regulate their own gene expression. This integrative approach will allow us to map cellular behaviors and their associated gene expression patterns, ultimately revealing the mechanisms that determine cell fate.
The core objective is to develop a novel framework for the integration of these two technologies, resulting in the first cellular-resolution, four-dimensional gene expression map of mammalian embryo development. This map will reflect both cellular dynamics and gene expression at the single-cell level, providing a foundational resource for future studies. In the longer term, this work will set the stage for the broader integration of multi-omics approaches, which have been increasingly explored in single-cell studies but remain underdeveloped in dynamic, multi-cellular contexts.
By advancing our understanding of early embryonic development, this project will provide valuable insights into fundamental biological processes and offer new perspectives on how cell fate is determined in mammalian systems.
Within the framework of my fellowship-funded project, significant progress has been made in advancing the understanding of spatial-temporal variability in morphogenetic events during mammalian embryo and organ development. We have developed a novel platform that integrates cutting-edge imaging techniques with deep learning, allowing us to uncover the intrinsic dynamics of embryonic and organ development at the single-cell level. A manuscript titled “Learning Spatial and Temporal Representations from Real-Time 3D Developing Embryos and Organs” is currently in preparation for submission.
Key activities completed include:
1. Development of a 3D live image processing framework: We successfully created an advanced framework that converts complex real-time 3D volumes of developing embryos and organs into 2D images while preserving their spatial and temporal relationships. This transformation streamlines the analysis of large-scale, high-resolution data, enabling more effective interpretation of the underlying developmental patterns.
2. Training and evaluation of deep learning models: Utilizing state-of-the-art deep learning architectures, we trained models to extract and learn from the spatial and temporal features within these 2D representations. The models were fine-tuned to recognize key developmental features across various stages, facilitating precise identification and comparison of morphogenetic changes.
3. Comparative analysis of independent embryos: Our framework enabled the first comparative analysis of real-time 3D mammalian embryos at the single-cell level, allowing for the matching of developmental features across different embryos. This analysis revealed distinct developmental patterns at various stages within individual mammalian embryos compared to model organism embryos, as well as spatial and temporal correlations across multiple embryos.
4. Application to the developing heart: As a proof of concept, we extended our framework to study 3D live heart development, showcasing its versatility across different organ systems. The framework successfully quantified spatio-temporal variability in heart morphogenesis, providing insights relevant to a broader range of developmental biology research.
The main achievements of the project thus far include:
1. The development of a comprehensive framework based on deep learning to process and learn spatial and temporal patterns from 3D live images, leading to novel discoveries in embryonic and organ development.
2. The creation of a robust, data-driven platform for real-time 3D analysis of embryos and organs, which lays the groundwork for more advanced morphogenetic studies and establishes a foundation for the integration of multiple imaging modalities - one of the major goals of this project.
These achievements provide a solid foundation for further exploration in developmental biology and hold significant potential for improving early detection of abnormalities in embryo and organ development, with promising applications extending beyond this project.
Key activities completed include:
1. Development of a 3D live image processing framework: We successfully created an advanced framework that converts complex real-time 3D volumes of developing embryos and organs into 2D images while preserving their spatial and temporal relationships. This transformation streamlines the analysis of large-scale, high-resolution data, enabling more effective interpretation of the underlying developmental patterns.
2. Training and evaluation of deep learning models: Utilizing state-of-the-art deep learning architectures, we trained models to extract and learn from the spatial and temporal features within these 2D representations. The models were fine-tuned to recognize key developmental features across various stages, facilitating precise identification and comparison of morphogenetic changes.
3. Comparative analysis of independent embryos: Our framework enabled the first comparative analysis of real-time 3D mammalian embryos at the single-cell level, allowing for the matching of developmental features across different embryos. This analysis revealed distinct developmental patterns at various stages within individual mammalian embryos compared to model organism embryos, as well as spatial and temporal correlations across multiple embryos.
4. Application to the developing heart: As a proof of concept, we extended our framework to study 3D live heart development, showcasing its versatility across different organ systems. The framework successfully quantified spatio-temporal variability in heart morphogenesis, providing insights relevant to a broader range of developmental biology research.
The main achievements of the project thus far include:
1. The development of a comprehensive framework based on deep learning to process and learn spatial and temporal patterns from 3D live images, leading to novel discoveries in embryonic and organ development.
2. The creation of a robust, data-driven platform for real-time 3D analysis of embryos and organs, which lays the groundwork for more advanced morphogenetic studies and establishes a foundation for the integration of multiple imaging modalities - one of the major goals of this project.
These achievements provide a solid foundation for further exploration in developmental biology and hold significant potential for improving early detection of abnormalities in embryo and organ development, with promising applications extending beyond this project.
In the pursuit of understanding mammalian development, imaging has become an indispensable tool for observing the intricate dynamics of cellular behaviors during embryogenesis and organogenesis. The recent advent of long-term, in toto imaging techniques has revolutionized our capacity to capture dynamic cellular processes at the single-cell level. However, the vast and unique datasets generated by these techniques present significant computational challenges.
To address these challenges, our project has yielded several key results and insights that push the boundaries of current methodologies in advancing our understanding of mammalian embryonic and organ development:
1. Development of Deep Learning-Based Real-Time 3D Imaging Process Architecture
We developed a novel computational architecture that integrates a pretrained Swin Transformer with a Siamese network. This architecture is specifically tailored to analyze extensive real-time 3D bioimaging data, facilitating the learning of spatial and temporal patterns that surpass human capabilities for manual analysis. Our model has demonstrated adaptability across varying dataset sizes, effectively processing both large and small datasets. This capability allows for the precise identification of nuanced, single-cell-level developmental changes, significantly enhancing our analytical proficiency in studying embryogenesis and organogenesis.
2. Establishment of a Robust, Data-Driven Platform for Real-Time 3D Analysis
We established a comprehensive platform that quantifies the spatial-temporal variability of morphogenetic events. This platform enables comparative analysis of real-time 3D mammalian embryos and organs at the single-cell level, providing new insights into both normal and abnormal processes in mammalian embryogenesis and organogenesis.
3. Facilitation of Early Detection of Developmental Abnormalities
By comparing normal and abnormal developmental processes, the methodologies and tools developed through this project hold significant potential for the early detection of diseases related to embryonic and organ development. This capability could lead to impactful advancements in clinical diagnostics and practices within the field of regenerative medicine.
4. Integration of Multi-Modal Imaging and Advancement of Multi-Omics Integration
Looking ahead, we will continue refining our architecture to effectively bridge gaps between various imaging modalities, particularly spatial transcriptomics and real-time imaging data. This integration will allow for simultaneous tracking of cellular dynamics alongside spatiotemporal gene expression profiles, providing a more comprehensive understanding of developmental biology. Our work lays the groundwork for future advancements in multi-omics integration, an area that has been increasingly explored but remains largely confined to single-cell contexts.
Conclusion
Beyond these specific results and impacts, our findings underscore the necessity for cross-national, interdisciplinary research driven by AI and big data. This emphasis on collaboration is crucial for leveraging the full potential of emerging technologies and methodologies in developmental biology and beyond.
To address these challenges, our project has yielded several key results and insights that push the boundaries of current methodologies in advancing our understanding of mammalian embryonic and organ development:
1. Development of Deep Learning-Based Real-Time 3D Imaging Process Architecture
We developed a novel computational architecture that integrates a pretrained Swin Transformer with a Siamese network. This architecture is specifically tailored to analyze extensive real-time 3D bioimaging data, facilitating the learning of spatial and temporal patterns that surpass human capabilities for manual analysis. Our model has demonstrated adaptability across varying dataset sizes, effectively processing both large and small datasets. This capability allows for the precise identification of nuanced, single-cell-level developmental changes, significantly enhancing our analytical proficiency in studying embryogenesis and organogenesis.
2. Establishment of a Robust, Data-Driven Platform for Real-Time 3D Analysis
We established a comprehensive platform that quantifies the spatial-temporal variability of morphogenetic events. This platform enables comparative analysis of real-time 3D mammalian embryos and organs at the single-cell level, providing new insights into both normal and abnormal processes in mammalian embryogenesis and organogenesis.
3. Facilitation of Early Detection of Developmental Abnormalities
By comparing normal and abnormal developmental processes, the methodologies and tools developed through this project hold significant potential for the early detection of diseases related to embryonic and organ development. This capability could lead to impactful advancements in clinical diagnostics and practices within the field of regenerative medicine.
4. Integration of Multi-Modal Imaging and Advancement of Multi-Omics Integration
Looking ahead, we will continue refining our architecture to effectively bridge gaps between various imaging modalities, particularly spatial transcriptomics and real-time imaging data. This integration will allow for simultaneous tracking of cellular dynamics alongside spatiotemporal gene expression profiles, providing a more comprehensive understanding of developmental biology. Our work lays the groundwork for future advancements in multi-omics integration, an area that has been increasingly explored but remains largely confined to single-cell contexts.
Conclusion
Beyond these specific results and impacts, our findings underscore the necessity for cross-national, interdisciplinary research driven by AI and big data. This emphasis on collaboration is crucial for leveraging the full potential of emerging technologies and methodologies in developmental biology and beyond.