Current approaches to fate mapping and lineage tracing using image data

Development ◽  
2021 ◽  
Vol 148 (18) ◽  
Author(s):  
Steffen Wolf ◽  
Yinan Wan ◽  
Katie McDole
Keyword(s):  
Developmental Biology ◽  
Light Microscopy ◽  
Cell Tracking ◽  
Image Data ◽  
Lineage Tracing ◽  
Imaging Data ◽  
Cell Lineages ◽  
Ongoing Effort ◽  
Complex Image ◽  
Software Methodologies

ABSTRACT Visualizing, tracking and reconstructing cell lineages in developing embryos has been an ongoing effort for well over a century. Recent advances in light microscopy, labelling strategies and computational methods to analyse complex image datasets have enabled detailed investigations into the fates of cells. Combined with powerful new advances in genomics and single-cell transcriptomics, the field of developmental biology is able to describe the formation of the embryo like never before. In this Review, we discuss some of the different strategies and applications to lineage tracing in live-imaging data and outline software methodologies that can be applied to various cell-tracking challenges.

scholarly journals Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.

2015 ◽  
Author(s):  
Patrick Smadbeck ◽  
Michael P.H. Stumpf
Keyword(s):  
Developmental Biology ◽  
Population Genetics ◽  
Large Scale ◽  
Cell Tracking ◽  
Temporal Dynamics ◽  
Growth Models ◽  
Tissue Growth ◽  
Lineage Tracing ◽  
Space And Time ◽  
Spatio Temporal

Development is a process that needs to tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny in both space and time. A generic feature of developing (as well as homeostatic) tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end fairly quickly. This has spurned the interest also of computational and theoretical biologists/physicists who have developed a range of modelling -- perhaps most notably are the agent-based modelling (ABM) --- approaches. These can become computationally prohibitively expensive but seem to capture some of the features observed in experiments. Here we develop a complementary perspective that allows us to understand the dynamics leading to the formation of a tissue (or colony of cells). Borrowing from the rich population genetics literature we develop genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We apply this approach to tissues that grow under confined conditions --- as would, for example, be appropriate for the neural crest --- and unbounded growth --- illustrative of the behaviour of 2D tumours or bacterial colonies. The classical coalescent model from population genetics is readily adapted to capture tissue genealogies for different models of tissue growth and development. We show that simple but universal scaling relationships allow us to establish relationships between the coalescent and different fractal growth models that have been extensively studied in many different contexts, including developmental biology. Using our genealogical perspective we are able to study the statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.

scholarly journals Primed Track, high-fidelity lineage tracing in mouse pre-implantation embryos using primed conversion of photoconvertible proteins

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Maaike Welling ◽  
Manuel Alexander Mohr ◽  
Aaron Ponti ◽  
Lluc Rullan Sabater ◽  
Andrea Boni ◽  
...  
Keyword(s):  
Cell Fate ◽  
Cell Tracking ◽  
Fluorescent Proteins ◽  
Time Windows ◽  
Lineage Tracing ◽  
Fluorescence Labeling ◽  
High Fidelity ◽  
Imaging Data ◽  
Cell Stage ◽  
Cell Fate Decisions

Accurate lineage reconstruction of mammalian pre-implantation development is essential for inferring the earliest cell fate decisions. Lineage tracing using global fluorescence labeling techniques is complicated by increasing cell density and rapid embryo rotation, which hampers automatic alignment and accurate cell tracking of obtained four-dimensional imaging data sets. Here, we exploit the advantageous properties of primed convertible fluorescent proteins (pr-pcFPs) to simultaneously visualize the global green and the photoconverted red population in order to minimize tracking uncertainties over prolonged time windows. Confined primed conversion of H2B-pr-mEosFP-labeled nuclei combined with light-sheet imaging greatly facilitates segmentation, classification, and tracking of individual nuclei from the 4-cell stage up to the blastocyst. Using green and red labels as fiducial markers, we computationally correct for rotational and translational drift, reduce overall data size, and accomplish high-fidelity lineage tracing even for increased imaging time intervals – addressing major concerns in the field of volumetric embryo imaging.

scholarly journals High fidelity lineage tracing in mouse pre-implantation embryos using primed conversion of photoconvertible proteins

2017 ◽  
Author(s):  
Maaike Welling ◽  
Manuel Alexander Mohr ◽  
Aaron Ponti ◽  
Lluc Rullan Sabater ◽  
Andrea Boni ◽  
...  
Keyword(s):  
Cell Fate ◽  
Cell Tracking ◽  
Fluorescent Proteins ◽  
Time Windows ◽  
Light Sheet ◽  
Lineage Tracing ◽  
High Fidelity ◽  
Imaging Data ◽  
Cell Stage ◽  
Cell Fate Decisions

AbstractAccurate lineage reconstruction of mammalian pre-implantation development is essential for inferring the earliest cell fate decisions of mammalian development. Lineage tracing using global labeling techniques is complicated by increasing cell density and rapid embryo rotation, impeding automatic alignment and rendering accurate cell tracking of obtained four-dimensional imaging data sets highly challenging. Here, we exploit the advantageous properties of primed convertible fluorescent proteins (pr-pcFPs) to simultaneously visualize the global green and the photoconverted red population to minimize tracking uncertainties over prolonged time windows. Confined primed conversion of H2B-pr-mEosFP labeled nuclei combined with light-sheet imaging greatly facilitates segmentation, classification, and tracking of individual nuclei from the 4-cell stage up to the blastocyst. Using green and red labels as fiducial markers, we computationally correct for rotational and translational drift and accomplish high fidelity lineage tracing combined with a reduced data size – addressing majors concerns in the field of volumetric embryo imaging.

scholarly journals Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues

2016 ◽  
Vol 13 (117) ◽  
pp. 20160112 ◽  
Author(s):  
Patrick Smadbeck ◽  
Michael P. H. Stumpf
Keyword(s):  
Developmental Biology ◽  
Large Scale ◽  
Cell Tracking ◽  
Temporal Dynamics ◽  
Growth Models ◽  
Lineage Tracing ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Spatio Temporal ◽  
Large Scale Simulations

Development is a process that needs to be tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny. A generic feature of developing and homeostatic tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end quickly. Computational and theoretical biologists/physicists have, in response, developed a range of modelling approaches, most notably agent-based modelling. These models seem to capture features observed in experiments, but can also become computationally expensive. Here, we develop complementary genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We show that with both bounded and unbounded growth simple, but universal scaling relationships allow us to connect coalescent theory with the fractal growth models extensively used in developmental biology. Using our genealogical perspective, it is possible to study bulk statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.

scholarly journals ESR white paper: blockchain and medical imaging

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
◽  
Elmar Kotter ◽  
Luis Marti-Bonmati ◽  
Adrian P. Brady ◽  
Nandita M. Desouza
Keyword(s):  
Medical Imaging ◽  
Image Data ◽  
Distributed Database ◽  
Relevant Information ◽  
White Paper ◽  
Imaging Data ◽  
Data Set ◽  
Blockchain Technology ◽  
Potential Applications ◽  
Basic Technology

AbstractBlockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different “authors” including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging.

An efficient approach to converting three-dimensional image data into highly accurate computational models

2008 ◽  
Vol 366 (1878) ◽  
pp. 3155-3173 ◽  
Author(s):  
P.G Young ◽  
T.B.H Beresford-West ◽  
S.R.L Coward ◽  
B Notarberardino ◽  
B Walker ◽  
...  
Keyword(s):  
Mesh Generation ◽  
Computational Models ◽  
Three Dimensional ◽  
Image Data ◽  
Model Material ◽  
Imaging Data ◽  
Material Inhomogeneity ◽  
Dimensional Image ◽  
Wide Range ◽  
Impact Modelling

Image-based meshing is opening up exciting new possibilities for the application of computational continuum mechanics methods (finite-element and computational fluid dynamics) to a wide range of biomechanical and biomedical problems that were previously intractable owing to the difficulty in obtaining suitably realistic models. Innovative surface and volume mesh generation techniques have recently been developed, which convert three-dimensional imaging data, as obtained from magnetic resonance imaging, computed tomography, micro-CT and ultrasound, for example, directly into meshes suitable for use in physics-based simulations. These techniques have several key advantages, including the ability to robustly generate meshes for topologies of arbitrary complexity (such as bioscaffolds or composite micro-architectures) and with any number of constituent materials (multi-part modelling), providing meshes in which the geometric accuracy of mesh domains is only dependent on the image accuracy (image-based accuracy) and the ability for certain problems to model material inhomogeneity by assigning the properties based on image signal strength. Commonly used mesh generation techniques will be compared with the proposed enhanced volumetric marching cubes (EVoMaCs) approach and some issues specific to simulations based on three-dimensional image data will be discussed. A number of case studies will be presented to illustrate how these techniques can be used effectively across a wide range of problems from characterization of micro-scaffolds through to head impact modelling.

Machine learning-assisted imaging analysis of a human epiblast model

2021 ◽  
Author(s):  
Agnes M Resto Irizarry ◽  
Sajedeh Nasr Esfahani ◽  
Yi Zheng ◽  
Robin Zhexuan Yan ◽  
Patrick Kinnunen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cells ◽  
Cell Tracking ◽  
Complex Structure ◽  
Cyst Formation ◽  
System Level ◽  
Imaging Data ◽  
Cell Level ◽  
Human Blastocyst ◽  
Cell Cell

Abstract The human embryo is a complex structure that emerges and develops as a result of cell-level decisions guided by both intrinsic genetic programs and cell–cell interactions. Given limited accessibility and associated ethical constraints of human embryonic tissue samples, researchers have turned to the use of human stem cells to generate embryo models to study specific embryogenic developmental steps. However, to study complex self-organizing developmental events using embryo models, there is a need for computational and imaging tools for detailed characterization of cell-level dynamics at the single cell level. In this work, we obtained live cell imaging data from a human pluripotent stem cell (hPSC)-based epiblast model that can recapitulate the lumenal epiblast cyst formation soon after implantation of the human blastocyst. By processing imaging data with a Python pipeline that incorporates both cell tracking and event recognition with the use of a CNN-LSTM machine learning model, we obtained detailed temporal information of changes in cell state and neighborhood during the dynamic growth and morphogenesis of lumenal hPSC cysts. The use of this tool combined with reporter lines for cell types of interest will drive future mechanistic studies of hPSC fate specification in embryo models and will advance our understanding of how cell-level decisions lead to global organization and emergent phenomena. Insight, innovation, integration: Human pluripotent stem cells (hPSCs) have been successfully used to model and understand cellular events that take place during human embryogenesis. Understanding how cell–cell and cell–environment interactions guide cell actions within a hPSC-based embryo model is a key step in elucidating the mechanisms driving system-level embryonic patterning and growth. In this work, we present a robust video analysis pipeline that incorporates the use of machine learning methods to fully characterize the process of hPSC self-organization into lumenal cysts to mimic the lumenal epiblast cyst formation soon after implantation of the human blastocyst. This pipeline will be a useful tool for understanding cellular mechanisms underlying key embryogenic events in embryo models.

L2MXception: An Improved Xception Network for Classification of Peach diseases 

2020 ◽  
Author(s):  
Na Yao ◽  
Fuchuan Ni ◽  
Ziyan Wang ◽  
Jun Luo ◽  
Wing-Kin Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Identification ◽  
Image Data ◽  
Yield Reduction ◽  
Disease Prediction ◽  
Imaging Data ◽  
Regularization Term ◽  
Detection And Identification ◽  
L2 Norm

Abstract Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

Analyzing Gene Expression Imaging Data in Developmental Biology

2013 ◽  
pp. 327-351
Author(s):  
Liangxiu Han ◽  
Jano van Hemert ◽  
Ian Overton ◽  
Paolo Besana ◽  
Richard Baldock
Keyword(s):  
Gene Expression ◽  
Developmental Biology ◽  
Imaging Data
Sign in / Sign up

Export Citation Format

Share Document